Background: Continuous passive motion (CPM) is frequently used during rehabilitation following total knee arthroplasty (TKA). Low-load resistance training (LLRT) using continuous active motion (CAM) devices is a promising alternative. We investigated the effectiveness of CPM compared to LLRT using the affected leg (CAMuni) and both legs (CAMbi) in the early post-operative rehabilitation. Hypotheses: (I) LLRT (CAMuni and CAMbi) is superior to CPM, (II) additional training of the unaffected leg (CAMbi) is more effective than unilateral training (CAMuni).Materials and Methods: Eighty-five TKA patients were randomly assigned to three groups, respectively: (i) unilateral CPM of the operated leg; (ii) unilateral CAM of the operated leg (CAMuni); (iii) bilateral alternating CAM (CAMbi). Patients were assessed 1 day before TKA (pre-test), 1 day before discharge (post-test), and 3 months post-operatively (follow-up). Primary outcome: active knee flexion range of motion (ROMFlex). Secondary outcomes: active knee extension ROM (ROMExt), swelling, pain, C-reactive protein, quality of life (Qol), physical activity, timed-up-and-go performance, stair-climbing performance, quadriceps muscle strength. Analyses of covariances were performed (modified intention-to-treat and per-protocol).Results: Hypothesis I: Primary outcome: CAMbi resulted in a higher ROMFlex of 9.0°  and 6.3°  compared to CPM at post-test and follow-up, respectively. Secondary outcomes: At post-test, C-reactive protein was lower in both CAM groups compared with CPM. Knee pain was lower in CAMuni compared to CPM. Improved ROMExt, reduced swelling, better stair-climbing and timed-up-and-go performance were observed for CAMbi compared to CPM. At follow-up, both CAM groups reported higher Qol and CAMbi showed a better timed-up-and-go performance. Hypothesis II: Primary outcome: CAMbi resulted in a higher knee ROMFlex of 6.5°  compared to CAMuni at post-test. Secondary outcomes: At post-test, improved ROMExt, reduced swelling, and better timed-up-and-go performance were observed in CAMbi compared to CAMuni.Conclusions: Additional LLRT of the unaffected leg (CAMbi) seems to be more effective for recovery of function than training of the affected leg only (CAMuni), which may be mediated by positive transfer effects from the unaffected to the affected limb (cross education) and/or preserved neuromuscular function of the trained, unaffected leg.Trial Registration:ClinicalTrials.gov Identifier: NCT02062138. Knee osteoarthrosis and total knee arthroplasty (TKA) are accompanied by modulations of sensory feedback due to damage to joint afferents and removal of joint structures as well as knee joint swelling, pain, inflammation, and joint laxity, which leads to structural and functional changes in the nervous system and muscle . These iThe early restoration of knee joint range of motion (ROM) and physical function are the major objectives after TKA. Continuous passive motion (CPM) is often used during post-operative rehabilitation to improve ROM. However, its effects on physical function of TKA patients are controversially discussed, as a Cochrane review by Harvey et al. concluded that a CPM treatment has no clinically important effects on ROM, pain, function, and quality of life .Low-load resistance training using controlled active motion (CAM) devices may be a promising alternative to CPM treatment. Devries et al. have shown that resistance training with low intensity  during a 2-week period of step-reduction  enhanced muscle anabolic sensitivity in older men. This result indicates that low-load resistance training is an appropriate strategy to preserve muscle mass and function during a phase of reduced physical activity that also affects TKA patients in the early post-operative phase . HoweverThe CAMbi intervention aimed to optimize CAM treatment by the attempt to benefit from the positive effects associated with the use of the unaffected leg during rehabilitation after TKA. The phenomenon behind this approach is known as cross education. Cross education is defined as the performance gain  in the untrained homologous muscle group after unilateral motor training, which has been shown for a wide range of motor tasks. Different changes within the nervous, muscle, and endocrine systems can explain the processes underlying the transfer of effects from training of the unaffected to the affected leg –12. PatiWe hypothesized that (I) progressive low-load resistance training (CAMuni and CAMbi treatments) would increase maximal active knee flexion ROM (primary outcome), maximal active knee extension ROM, physical activity, quality of life, timed-up-and-go performance, stair-climbing performance as well as quadriceps muscle strength and reduce knee joint swelling, knee pain, and inflammation  to a greater extent than standard CPM treatment. Furthermore, we assumed that (II) positive transfer effects from the unaffected to the affected limb (cross education) during the CAMbi treatment would further benefit early rehabilitation after TKA as compared to CPM and CAMuni treatment.ClinicalTrials.gov (Identifier: NCT02062138) and followed the Consort Guidelines.A total of 85 patients scheduled for primary TKA due to clinical and radiological diagnosed severe knee osteoarthrosis were included in this dual-center, three-armed, parallel-group, randomized, active-controlled, double-blinded  clinical superiority study . The study was registered at This study was conducted at the Department of Orthopedics  and the Department of Traumatology, Orthopedics and Hand Surgery .−2. Patients with total knee or hip endoprosthesis on the contralateral side were excluded if the surgery was performed within the preceding year. Additional exclusion criteria were: Mini-Mental State Examination score <25, musculoskeletal and neurological disorders that limit physical function, metabolic bone disease, a surgery planned within the next 12 months, and pain or functional restrictions, which would prevent patients from taking part in examinations. Written informed consent was obtained from all patients before participation.Patients were identified as suitable for the study if they were between 50 and 80 years old and had a body mass index (BMI) of <40 kg·mAll participants underwent the same standard surgical procedure involving inserting a non-constrained bicondylar surface replacement system . The surgery was performed by six orthopedic surgeons with an identical surgical approach (Payr's approach). The implants were non-constrained bicondylar surface replacement systems consisting of cemented metallic femoral and tibial components and ultra-high molecular weight polyethylene liners. Smoothening of the lateral patella facet, denervation and soft-tissue balancing were carried out until perfect positioning of the implant components was achieved with respect to biomechanical aspects. Both femoral and tibial components were fixed using PMMA cement .In the early post-operative phase, all participants received a pain-adapted medical analgesia including piritramide (7.5–15.0 mg), metamizole (500.0 mg), and ibuprofen (600.0 mg). Instead of piritramide, the patients in hospital 2 were treated with oxycodone (5.0–10.0 mg). Epidural analgesia or femoral nerve block was prescribed when considered necessary. Patients were discharged from hospital if they were sufficiently mobile  and medically stable.Eligible patients were randomly assigned to one of the three treatment groups using a permuted block randomization by computer-generated tables of random numbers . Participants were sequentially allocated to the treatments in the order in which they were recruited. After the enrolled patients completed all baseline measurements, intervention assignment were ascertained using sealed, opaque envelopes with consecutive numbering. The investigator who opened the envelopes and carried out the implementation of assignments was not involved in the generation and allocation concealment.The investigator and outcome assessor were blinded to the intervention. Participants were unaware of the treatment allocation at pre-test. Due to the nature of the intervention, participants and physiotherapists were not blinded during the intervention, at post-test, and follow-up.The participants were randomly allocated to one of three treatment groups:continuous passive motion (CPM) unilateral operated leg continuous active motion unilateral operated leg (CAMuni)continuous active motion bilateral alternating (CAMbi).CPM and CAM interventions were conducted from the second to the ninth post-operative day during hospital stay. Maximal knee flexion and extension ROM was gradually increased according to the patient's tolerance and pain.Treatment group I received three CPM interventions per day for 30 min each, using a Kinetec® OptimaTM S3 . The foot of the operated leg was fixed to the device with a belt and the knee joint was passively moved through a controlled ROM  to maximal tolerated knee flexion) at highest adjustable speed. Participants were instructed not to resist the motion of the device or actively support it.The participants of treatment group II (CAMuni) and group III (CAMbi) received a low-load resistance training of the operated leg three times daily for ~30 min with a CAMO®ped device  . The CAMThe leg was fixed to the device with belts at the ankle joint, shinbone, and midfoot. Thus, evasive movements during movement execution were reduced. Both groups performed active knee extensions and flexions through a controlled ROM  to maximal tolerated knee flexion) at self-selected speed. The CAM-treatment was carried out under the supervision of a therapist in order to ensure correct movement execution. The CAM device has four resistance levels (0 = lowest resistance level). The warm-up protocol consisted of one set of 20 repetitions at resistance levels 0 to 2, respectively. Afterwards, five sets at resistance level 3 were performed, each until volitional exhaustion. Between the sets, there was a rest interval of 3 min. The cool down included 20 repetitions at resistance level 0.iMVT). The %AEMGiMVT of rectus femoris and vastus lateralis was 10.0 and 32.7% during knee extension and 13.3 and 8.4% during knee flexion, respectively, classifying the CAM treatment as a low-load resistance training.The resistance levels of the CAMO®ped device are not individually adjustable. To estimate exercise intensity during CAM treatment, myoelectric activity of vastus lateralis and rectus femoris of the operated leg was recorded. However, the regular post-test procedure was very extensive and we did not want to impose this additional effort on all study participants. Therefore, muscle activity was measured in only three patients to obtain some information on exercise intensity during CAM treatment. A detailed description of EMG measurement was given previously . The patIn addition to CPM or CAM intervention, all patients participated in standardized in-hospital physiotherapy performed by physiotherapists once daily for 25 to 30 min (except Sundays) from the first post-operative day until discharge from hospital. The intensity of physiotherapeutic exercises was gradually increased, depending on the pain and tolerance of the patient. All patients underwent immediate full weight-bearing mobilization (four-point gait with two crutches) from the second post-operative day. The detailed description of the standardized in-hospital physiotherapy is provided in the Supplementary File 1 –S3.After discharge from the hospital, patients were treated in an outpatient or inpatient rehabilitation center for 3 weeks. Patients taking part in outpatient rehabilitation stayed at home and attended daily physiotherapy in the nearest rehabilitation center. The inpatient care represents a German peculiarity compared with international standards and was carried out in a rehabilitation clinic. Inpatient and outpatient rehabilitation programmes consisted of daily (except weekends) physiotherapy , gait training, aqua exercise, bicycle ergometer training, CPM, medical training therapy, manual lymphatic drainage, training courses for patients, physical therapy (incl. sling exercises and training), and traction treatment.The patients were examined with comprehensive clinical, functional, and strength measurements at three points in time over a period of 3 months:1 day before TKA (pre-test)9 days post-operatively (post-test)3 months after TKA (follow-up).The primary outcome measure was the active knee flexion ROM. Secondary outcome measures included clinical parameters , quality of life (SF-36), physical activity (number of steps and sit-to-stand-transitions), and functional outcomes . A detailed overview of the experimental design is provided in ROM of active knee flexion (primary outcome) and active knee extension were assessed using a commercially available digital long-arm goniometer  . During In order to ensure the formal intra-rater reliability within the same session (intra-session), the intraclass correlation coefficient (ICC) and coefficient of variation (CV) for maximal active knee flexion and active knee extension ROM were calculated for 11 gonarthrosis patients. The results showed high absolute  and relative (ICC's = 0.99) intra-rater reliability.A visual analog scale was used to assess acute knee pain after TKA implantation . The parKnee joint circumference was assessed using an ordinary tape measure . During the examination, the patient was in a supine position with the knee joint in full extension. The measurement of circumference was performed 1 cm above the superior border of the patella .Intra-rater reliability within the same session (intra-session) of the measurement was determined for 11 patients with gonarthrosis. The results demonstrated a high absolute (CV = 5.89%) and relative (ICC = 0.99) intra-rater reliability, which is in line with other studies reporting high intra- and inter-session reliability for circumferential measurements in TKA patients  .TM interface program (version 7.1.18).Physical activity of the patients was recorded with an activity detection system  . The incTM device with the ActiGraph device. The authors documented a high validity of the activPALTM for step count  Health Survey (SF-36) questionnaire is one of the most frequently used questionnaires for the assessment of the subjective state of health or health-related quality of life . The scoMobility of the patients was assessed with the timed-up-and go-test . The patThe stair-climbing-test is a clinical physical performance measure for estimating postural control and strength of the lower extremities . The patThe measurement of iMVT was performed on a custom-made knee extension dynamometer , 29. TheThe patients were instructed to act as forcefully as possible. An investigator verbally motivated the patients and checked that the contraction was performed without any visible countermovement or pretension. At least three to five familiarization trials were carried out. The patient was familiar with the test procedure if the CV of successive iMVTs was below 5% (CV = standard deviation/mean ×100). The mean value of the three test trials was used as measure of iMVT.The force signal was captured with a KM40 force sensor , preamplified  and recorded with a sampling rate of 3 kHz with the Telemyo 2400T G2 EMG telemetry system. The signals were filtered using MATLAB : third-order Butterworth IIR low-pass filter (25 Hz). Torque was calculated by multiplying the length of the lever arm with the force.Prior to the study, the intra-rater reliability within the same session (intra-session) of iMVT measurement was assessed in 20 healthy age-homogeneous volunteers (age: 62.1 ± 6.2 years) and 20 patients with knee osteoarthrosis (age: 66.7 ± 8.8 years). The results showed a high relative intra-rater reliability (ICC's = 0.99) and high absolute intra-rater reliability  in both groups.The level of CRP serves inter alia as a biomarker for inflammation and periprosthetic joint infection . Venous f = 0.40) with a two-sided significance of 0.050 and a power of 0.80 to estimate sample size. According to this, a total of 66 patients (22 patients in each group) were required for the trial. A recruitment period of 24 months was assumed for the enrolment of patients.Active knee joint ROM was chosen as primary outcome variable as the ROM is a primary indicator for a successful TKA and is required for the performance of ADL , 33. A rn = 66), i.e., patients who were randomized but never received any treatment were excluded from the analyses  included all randomized patients according to their original treatment allocation who started the treatment .In addition, a sensitivity analysis for each outcome was performed on a per-protocol (PP) basis to test the robustness of the main analysis, i.e., only those patients were included who completed the treatment originally allocated and participated in pre-, post-test, and follow-up  including all three groups . Holm-Sidak post-hoc tests were conducted to determine differences between groups. Statistical values of the ANCOVA  were calculated from the log-transformed (Lg10) or reverse-transformed data if data was not normally distributed.Data were checked for normal distribution using the Kolmogorov-Smirnov-Test. In the mITT analysis, multiple imputations (10 imputed data sets) were used to account for missing data using the Markov Chain Monte Carlo method. Differences between the groups were tested for significance using Fisher's exact test, unpaired Student's p2), Cohen's f , and Cohen's d were calculated as measures of effect size. The effect size Cohen's f was used for ANCOVA and interpreted using the following classification: f = 0.10 small effect, f = 0.25 medium effect, f = 0.40 large effect. Furthermore, Cohen's d effect size was used to determine the statistical relevance of mean differences between two groups (effect size for post-hoc comparisons) with 0.50 to 0.79 indicating a medium effect and 0.80 or higher a large effect. Pooled multiple imputation data are presented as covariate-adjusted mean values (adjusted standard deviation) together with the adjusted mean difference  in the tables and figures . All other analyses were performed using SPSS statistical package 22.0 .Sample size and effect sizes were calculated with the statistical software package GFurthermore, intra-rater reliability was calculated using an Excel spreadsheet developed by Hopkins . The CV Patient recruitment was stopped when the planned sample size was reached. Eighty-five patients underwent randomization. However, 19 patients were excluded from the full analysis because the treatment was not applied. Thus, 66 patients (22 in each group) received at least one intervention and were included in the mITT analysis. Complete information on the reasons for non-participation in treatment that led to exclusions after randomization is provided in the CONSORT flow diagram . The rean = 60).In each group, 20 of 22 patients completed the 3-month follow-up (drop-out rate 9.1% per group). Only patients who completed the originally assigned treatment and participated in the pre-, post-test, and follow-up were included in the PP analysis  and statistical significance  for post-hoc comparisons between groups are provided in The CAMbi group revealed less interventions for the operated leg compared to the CPM group  and CAMuni group  because a few patients of the CAMbi group refused to participate in individual interventions due to the high training volume .The results showed clinically relevant differences between CAMbi and CPM treatment in the primary outcome. The data analyses revealed medium effects for a 9.0° (+11.2%) and a 6.3° (+6.1%) greater active knee flexion ROM in the CAMbi compared to the CPM group at post- and follow-up-tests, respectively.No relevant between-group differences were found for knee flexion ROM between CAMuni vs. CPM treatment at post- and follow-up-tests .At post-test, large effects were found for reduced knee pain (−52.1%), larger number of sit-to-stand-transitions (+42.1%), and a lower CRP value (−42.5%) in favor of CAMuni treatment as compared to CPM treatment.At follow-up-test, patients of the CAMuni treatment reported a higher quality of life , resulting from a better physical  and mental health  following CAMuni as compared to CPM treatment.At post-test, medium effect sizes were found for stair-climbing performance and timed-up-and-go performance when comparing CAMbi with CPM treatment. The patients of the CAMbi goups needed less time to climb stairs (−21.8%) and to perform the timed-up-and-go test (−16.2%) than participants of the CPM treatment. Moreover, analyses revealed medium effects for a shorter hospital stay (−11.0%), improved knee extension ROM (+31.7%), reduced swelling (−2.5%), and a lower CRP value (−34.0%) in favor of the CAMbi treatment.At follow-up-test, medium effects were observed for a better timed-up-and-go performance (+12.1%), higher quality of life , and better physical health  in the CAMbi treatment compared to the CPM treatment.The results are presented in The comparison of CAMbi vs. CAMuni treatment showed a practically relevant higher knee flexion ROM of 6.5° for CAMbi compared to the CAMuni treatment at post-test , but not at follow-up-test .At post-test, medium effect sizes were found for improved active knee extension ROM (+28.9%), reduced swelling (-2.6%) and better timed-up-and-go performance (+18.6%) following CAMbi compared to CAMuni treatment.In contrast, patients of the CAMbi treatment performed fewer sit-to-stand-transitions than CAMuni group patients in the post-test  and follow-up-test . Furthermore, patients of the CAMbi treatment reported to have more knee pain  at the post-test. This effect was reversed in the follow-up-test, i.e., patients of the CAMbi treatment had less pain compared to participants of the CAMuni treatment .The results are presented in There was a consistency between the results of primary analysis  and sensitivity analysis  for the primary outcome and most secondary outcomes at post- and follow-up-test [see Supplementary File 2 –S7].d = 0.81; p = 0.013) compared to the PP analysis .Results of both analyses differed considerably for the comparison of stair-climbing performance between CPM and CAMbi treatment at post-test. The mITT approach showed large treatment effects  between CPM and CAMbi treatment. A similar trend was observed for the comparison of active knee extension ROM between CPM and CAMbi treatment  and between CAMbi and CAMuni treatment . Furthermore, the mITT analysis was not robust to the sensitivity analysis regarding the quality of life outcomes when comparing CPM with CAMbi. The SF-36 score  and the subscales mental health  and physical health  showed larger treatment effects when applying the mITT approach compared to PP.At follow-up, the mITT approach tended to underestimate the effect for the comparison of Timed-up-and-go performance  with CAMuni treatment (affected leg 3 ×30 min/day for 8 days), and CAMbi treatment  in the early post-operative rehabilitation following TKA.We hypothesized that (I) voluntary muscle activation of the operated leg during the CAM treatments is more effective for restoring function than standard CPM treatment, and (II) positive cross education effects from the unaffected to the affected leg during CAMbi treatment would further promote rehabilitation after TKA.For the primary outcome variable, the first hypothesis can only be confirmed partially. No relevant difference between CAMuni and CPM treatment was observed, while CAMbi treatment was superior to CPM treatment in improving active knee flexion ROM. An increase in knee flexion ROM of more than 5° is required to justify the added time and costs for a motion treatment after TKA  and reported less knee pain during hospital stay. The inflammatory marker CRP was lower in both CAM groups compared with CPM. Further differences were found for the comparison of CAMbi with CPM. We observed an improved active knee extension ROM, reduced knee joint swelling, a shorter hospital stay, and differences in functional measures  in favor of CAMbi as compared to CPM treatment at post-test. Three months after TKA, participants of the CAMbi group showed a better timed-up-and-go performance compared to the CPM group. Furthermore, patients of both CAM groups reported a higher quality of life (SF-36 score).Patients of the CPM group do not actively participate in rehabilitation because the knee joint is passively mobilized. We assumed that low-load resistance training of the affected leg (CAM treatments) may enhance muscle anabolic sensitivity and preserve muscle mass and function during hospital stay . HoweverVoluntary muscle activation during the CAM treatments may have stimulated pain modulating processes resulting in an exercise-induced hypoalgesia . HoweverPeak CRP levels after TKA are usually present at the second and third post-operative day and reduce abruptly if there are no post-operative complications . Long-teIt is likely that the CAM treatments have stimulated motivational processes and the ability to overcome stressful situations that may arise from disease and surgery. An increased self-efficacy and mood of patients involved in CAM treatments may have contributed to a better quality of life 3 months after surgery . HoweverThe current study is the first to investigate the effectiveness of a CAMbi treatment in patients after TKA. According to the hypothesis, we observed that CAMbi treatment is more effective than CAMuni treatment in improving active knee flexion ROM. We found a clinically relevant higher flexion ROM of 6.5° in patients of the CAMbi group at post-test.The results of the secondary outcomes further indicate that the early rehabilitation process following TKA may primarily benefit from the CAMbi intervention. Besides the positive effect of CAMbi treatment on the primary outcome active knee flexion ROM, we observed improved active knee extension ROM, decreased knee joint swelling, and better timed-up-and-go performance compared to CAMuni at post-test. It has been shown that increased swelling is related to functional impairments , thus, bHowever, contrary to the hypothesis, patients of the CAMbi treatment had more knee pain compared to CAMuni group patients at post-test. Three months after TKA, this effect was reversed, i.e., patients of the CAMbi group had less knee pain. The lower long-term knee pain found in the CAMbi group might be of higher relevance for the patients ADL and physical activity behavior.Furthermore, CAMbi group patients were physically less active  during hospitalization compared to patients of the CAMuni group, which might be related to the higher total training volume (see limitations section).We assumed that patients in the CAMbi group could benefit from transfer effects from the additional training of the unaffected leg to the affected leg , 44, 45.Conversely, recent studies by Zult et al. showed that patients  who participated in a cross education intervention after ACL reconstruction  experienced (i) increased limb asymmetry (9–10%) 5 and 12 weeks after surgery and (ii) reduced voluntary activation of the knee extensors of the reconstructed leg (−6%) 12 weeks after ACL reconstruction compared with the control group , 46. FurTwelve weeks after distal radius fracture, Magnus et al. found improved handgrip strength 47%) and wrist flexion/extension ROM (25%) in women older than 50 years after a cross education intervention . However, the authors found no differences in strength and ROM at 9 and 26 weeks after surgery . A recen% and wriTaken together, the mentioned studies are heterogeneous in terms of patient population  and the study design  and are therefore only partially comparable to our findings for TKA patients.Adaptations to cross education include functional and structural changes within the neuromuscular system , 11. FirSecond, cross education can prevent muscle atrophy in the untrained leg, which might be mediated by an altered balance between muscle protein synthesis and breakdown , 49.However, the modulation of sensory feedback due to knee joint swelling, pain, inflammation, joint laxity, damage to joint afferents, and removal of joint structures as a result of knee osteoarthrosis and TKA  may haveCross education usually occurs with the presence of a training effect in the trained leg. However, episodic muscle disuse as a result of reduced physical activity after surgery may have induced a higher strength loss in the untrained, unaffected leg of the CAMuni group compared to the trained, unaffected leg of the CAMbi group. Physiological consequences of reduced physical activity  contribute to reductions in muscle mass and strength, impaired insulin sensitivity, and an increase in systematic inflammation [for a review see Oikawaet al. ]. Thus, CAMbi group patients received an additional daily intervention for the unaffected leg and may have been more fatigued than CAMuni group patients. Some patients of the CAMbi group actually refused to participate in individual interventions due to the high training volume, resulting in a lower number of interventions for the operated leg compared to the CPM group (−14.0%) and CAMuni group (−9.2%). The higher training volume may also be related to the lower physical activity level  of the CAMbi group patients compared to CAMuni during hospitalization.In the present study, the average number of cross education interventions (training sessions of the unaffected leg) was 6.5. It remains to be discussed if this number is sufficient to induce cross education effects. A recently published study by Brass et el. analyzed the time-course of handgrip force after a ‘traditional' cross education protocol  and a daily cross education treatment  . SignifiiMVT of around 33% in the vastus lateralis muscle during knee extensions. Thus, the CAM treatment of the affected leg can be defined as low-load resistance training  by the presence of an instructor. Due to the limited personnel capacities and the independent applicability of the CPM machine, it was not possible to supervise the CPM treatment. Further research is necessary to control for such supervisor effects, for example by investigating the effects of low-load resistance training with autonomic CAM devices or by supervising CPM treatment in the same way as necessary for CAM treatment.It has been shown that trait and state characteristics of subjects are related to performance measures. For example, state fatigue can modulate endurance and dynamic balance performance , 54. Furpost-hoc. The reasons for post-randomization exclusion of patients were not related to the treatment. These exclusions can thus be justified as unlikely to bias the results.We decided to conduct a mITT analysis, meaning that 19 patients who were randomized but never received any treatment were excluded from the analyses. These modifications were applied to the data Our findings support the implementation of unilateral (CAMuni) and bilateral alternating (CAMbi) low-load resistance training for restoring function in early rehabilitation programs after TKA. The positive effects of both CAM interventions compared to CPM may be due to (i) preserved neuromuscular function of the trained, affected leg, (ii) the anti-inflammatory effect of resistance training, and/or (iii) increased self-efficacy.Furthermore, the CAMbi treatment proved to be more effective for recovery than CAMuni. Possible mechanisms mediating the positive effects of CAMbi compared to CAMuni include (i) positive transfer effects from the unaffected to the affected limb (cross education effect) and/or (ii) preserved neuromuscular function of the trained, unaffected leg.Taken together, aside from knee osteoarthrosis and TKA itself, reduced physical activity during early rehabilitation leads to structural and functional changes within the nervous, muscle, and endocrine systems. Since the greatest loss of physical function occurs in the first month following TKA, low-load resistance training of the affected leg (CAMuni) and especially of both legs (CAMbi) seems to be a promising and viable therapeutic approach to restore and preserve function during early rehabilitation after TKA.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the University Medicine Rostock. The patients/participants provided their written informed consent to participate in this study.RJ: conceptualization, methodology, software, formal analysis, investigation, data curation, original draft preparation, and review and editing. TS: conceptualization, methodology, formal analysis, investigation, data curation, and review and editing. MB: conceptualization, methodology, formal analysis, investigation, data curation, review and editing, and funding acquisition. FF: methodology, software, formal analysis, and review and editing. PB: methodology and review and editing. RB: methodology, software, funding acquisition, and review and editing. WM and RS: methodology, funding acquisition, and review and editing. AM-M: conceptualization, methodology, software, formal analysis, investigation, data curation, original draft preparation, funding acquisition, and review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Understanding the relationships between pre-existing conditions and complications of COVID-19 infection is critical to identifying which patients will develop severe disease. Here, we leverage ~1.1 million clinical notes from 1803 hospitalized COVID-19 patients and deep neural network models to characterize associations between 21 pre-existing conditions and the development of 20 complications  of COVID-19 infection throughout the course of infection . Pleural effusion was the most frequent complication of early COVID-19 infection  followed by cardiac arrhythmia . Notably, hypertension was the most significant risk factor associated with 10 different complications including acute respiratory distress syndrome, cardiac arrhythmia, and anemia. The onset of new complications after 30 days is rare and most commonly involves pleural effusion . Lastly, comparing the rates of complications with a propensity-matched COVID-negative hospitalized population confirmed the importance of hypertension as a risk factor for early-onset complications. Overall, the associations between pre-COVID conditions and COVID-associated complications presented here may form the basis for the development of risk assessment scores to guide clinical care pathways. There are already several emerging reports of risk factors of severe disease as well as lingering long-term effects such as fatigue, myalgia, and renal complications2. However, there is an incomplete understanding of the relationship between pre-existing comorbidities and post-COVID complications.The COVID-19 pandemic remains an ongoing public health crisis3. We have previously benchmarked different natural language processing models and transformer architectures4 and developed BERT-based models to curate unstructured clinical data from EHRs to uncover associations with COVID-19 infection5.Automated curation of clinical notes affords the ability to rapidly perform epidemiologic studies from unstructured text found in electronic health records (EHRs). Previous efforts have leveraged various models for natural language processing to extract information regarding diagnoses, treatments, and clinical courses from unstructured data4. While the structured EHR fields such as ICD codes are modestly informative, the true context of comorbidities and complications is buried in the millions of unstructured patient notes. In this study, we have leveraged ‘augmented curation’ of EHR notes in COVID-19 patients4 to map the relationships between complications, comorbidities, and outcomes in the hospitalized COVID-19 patients and non-COVID-19 hospitalized matched controls.Longitudinal multi-center patient data in EHRs of over 20,000 COVID-19 patients  from the Mayo Clinic  and associated health systems provide a unique opportunity to understand the relationship between comorbidities and COVID-19 complications1803 patients were hospitalized with a diagnosis of COVID-19 between March 12, 2020, and September 15, 2020. Using the date of the first positive SARS-CoV-2 PCR test, we analyzed the clinical notes of each patient in their pre-COVID-19 vs. the post-COVID-19 phase Fig. . Using dIn Table The main objective of our analysis was to identify associations between comorbidities and short-term (up to 30 days post-infection) and long-term (31–90 days post-infection) complications of COVID-19 infection. Here, we observe the majority of complications occur within the first month post-infection Fig. .p-value: 2.9e−64) as the most significant risk factor followed by other cardiovascular chronic diseases , anemia , and chronic kidney disease , as the most significant predictors of clinical complication in early COVID-19 infection.We identify multiple comorbidities that are associated with significantly higher rates of any complications in the early onset time period (days 0–30 post-PCR diagnosis). From this analysis, we validate that many of the CDC-reported risk factors for severe COVID-19 illness are associated with increased rates of early-onset COVID complications across multiple organ systems Table . Among tp-value: 2.4e−22)  within the first months Fig. . The pri22) Fig. . While t22) Fig.  and 4.p-value: 4.2e−8). The other most significantly associated baseline comorbidities include anemia  and chronic kidney disease   Fig. . In the p-value: 2.7e−19)  Fig. . Hypertep-value: 2.9e−3) and other cardiovascular risk factors   and immunosuppression  are also uncovered as significant risk factors. The cardiovascular complications examined in this study occur most frequently in days 0–30 post-infection  Fig. . It is pp-value: 1.2e−7), and chronic kidney disease  , Fig.  and is a−6) Fig.  and 4.p-value: 3.9e−5), hypertension , and coronary artery disease  , which is most associated in our cohort with heart failure  and heart failure . Obesity appeared significantly associated with an increased risk of early-onset AKI . Type 2 diabetes , anemia  and COPD  before infection were also associated with increased risk of respiratory complications. As discussed previously, late-onset complications were rare and not statistically associated with COVID-19 between the two cohorts.In order to take into account the base rate of complications from in a hospitalized population, we generated a control population of COVID-negative patients that have been propensity matched on a number of clinical parameters including demographic covariates  and comorbidities (the same set of 20 comorbidities). Using the same deep language models as described above, we extracted the 20 risk factors for COVID-19 severe illness reported by the CDC and the 18 COVID-associated complications in order to analyze their association among COVID-negative patients. As shown in Table In the present study we have set out to understand the relationship between baseline comorbidities and clinical complications over the course of COVID-19 infection. Here, we leverage natural language processing of unstructured patient notes from 1803 patients hospitalized with COVID-19 in the Mayo Clinic health system.12 including hypertension, chronic kidney disease, type 2 diabetes, cardiovascular disease, and malignancy. In general, these studies have examined risk of severe COVID-19 infection but have not examined the relationship between baseline comorbidities and risk of specific complications. Furthermore, our study has leveraged augmented curation to accelerate the mapping of the comorbidities and COVID-associated complications.While it stands to reason that individuals with poorer health status and multiple underlying comorbidities will experience worse outcomes during COVID-19 infection, our study reveals that not all risk factors are created equal and are associated with different complications. Previous studies have begun to uncover numerous factors associated with increased risk of more severe COVID-19 infection8. Specifically, our data suggest that a recent history of hypertension is the strongest predictor of ARDS, the most significant and life-threatening complication of COVID-19, among hospitalized COVID-19 patients, similar to previous observations10. We further observed anemia, chronic kidney disease, immunosuppression, coronary artery disease and hyperglycemia to be associated with increased rates of ARDS. Our analysis also uncovered unexpected associations, including associations between history of cancer and immunosuppression with heart failure following COVID-19 infection.In our analysis, we observe that hypertension is the single most significant risk factor among all examined complications with exception of deep vein thrombosis. This is consistent with previous studies, where patients with baseline hypertension have been reported to have higher risk of more severe COVID-19 diseaseOur data also highlight the temporal relationship between baseline health status and complications throughout COVID-19 infection. For example, cancer, obesity, and obstructive sleep apnea are associated with higher rates of short-term complications (days 0–30 post-PCR test), but not with late-onset complications. While many comorbidities are chronic , others are amenable to short-term intervention, suggesting that tight control of modifiable risk factors might limit the risk of complication due to COVID-19 infection. For example, controlling hypertension, smoking cessation, treating anemia, and having tight glycemic control might reduce the rate of cardiovascular complications in the early stages of COVID-19.13. While our analyses are limited to hospitalized COVID-19 patients, which biases our data towards patients with more severe disease, this enables improved risk stratification for patients most likely to develop serious complications. We have further explored the rate of clinical complications in a control population of hospitalized COVID-negative patients to establish baseline complication rates within a hospitalized population.Many of the comorbidities examined likely influence the development of complications, even in the absence of COVID-19 infection. For example, we do not observe new-onset pleural effusion among patients with pre-existing liver disease. It is possible that this is related to previous incidence of pleural effusion among patients with liver disease14.At present, our analysis does not account for the co-dependent relationships between comorbidities or between complications. In many cases, individual patients likely have multiple complications, which can obscure the interpretation of data, particularly at later time points where we observe fewer events. Another limitation of this study is that we employ a relatively broad inclusion criteria , so more focused observational studies with stricter eligibility criteria would be required to obtain robust conclusions about a particular patient subpopulation. One potential data limitation may be that the comorbidities of the patient population are not fully captured by the EHR database, because the patients may have received care at other institutions prior to receiving treatment at the Mayo Clinic. However, we expect that most of the major comorbidities for these patients will be captured because all of these patients received hospital care for COVID-19 through Mayo Clinic Health Systems. Additionally, it is possible that many of the late-stage complications arise directly from baseline comorbidities rather than a direct result of COVID-19 infection. This study can be leveraged for the development of controlled trials to identify appropriate prophylactic or therapeutic interventions for high-risk COVID-19 patients, particularly among hospitalized patients. Future analyses will focus on creation of a multivariate model to enable risk prediction of post-COVID complicationsGiven the richness and complexity of information present in the clinical notes, there are multiple promising avenues for future research to develop the natural language processing (NLP) models further. For example, Bayesian modeling can be used to aggregate the sentence-level BERT sentiment predictions into robust patient-level sentiment predictions. Although the sentence-level accuracy of the BERT model is currently 98.0% (see the “Methods” section), there is potential for improvement if we aggregate all of the sentences from each patient to determine the predictions. Interpreting potentially conflicting sentences in a single clinical note or in a set of clinical notes for a patient is a challenge when applying NLP methods to the unstructured text in the EHR, and is an important area for future study. For example, sentence-level models may be validated by expert determination at the document-level and patient-level and compared against models which incorporate additional contextual information. Furthermore, NLP methods may be developed to determine other health indicators from the clinical notes such as disease severity and quality of life.15, and mapping risk factors to complications in long-COVID patients is a particularly interesting area for follow-up research16. Since this study was restricted to patients in a single academic medical center, studies in other medical centers would be valuable to validate the clinical findings and see how the results generalize to different patient populations . One of the major contributions of this work is the development of the NLP methodology to make exploration of comorbidities and complications in the unstructured clinical record more efficient. We hope that this research can pave the way for future observational studies leveraging the rich diversity of clinical phenotypes in unstructured notes to explore a broad range of scientific questions.There are also numerous promising clinical directions for future research. For example, we may discover novel predictors of COVID-19 complications by exploring comorbidities which are not identified as risk factors of severe COVID-19 disease by the CDC. There have been initial studies of patients with long-term complications from COVID-19 (aka “long-COVID”)www.mayo.edu/research/institutional-review-board/overview.This retrospective research was conducted under IRB 20-003278: “Study of COVID-19 patient characteristics with augmented curation of EHRs to inform strategic and operational decisions”. The study was deemed exempt by the Mayo Clinic Institutional Review Board and waived from consent. For further information regarding the Mayo Clinic Institutional Review Board (IRB) policy, and its institutional commitment, membership requirements, review of research, informed consent, recruitment, vulnerable population protection, biologics, and confidentiality policy, please refer to 17 was performed in order to control for potential confounding factors when comparing the COVID-positive and COVID-negative cohorts. First, propensity scores for each of the patients in the two cohorts were computed by fitting a logistic regression model (using scikit-learn v0.20.3 in python 3.6.8) as a function of the covariates, including demographic variables  and comorbidities. Next, patients from the COVID-positive and COVID-negative cohorts were matched using a 1:1 matching ratio and a heuristic caliper of 0.1 × pooled standard deviation18. Prior to matching, there were 1803 patients in the COVID-positive cohort, and there were 94,953 patients in the COVID-negative cohort. Matched controls were found for all COVID-positive patients, giving us a matched COVID-negative cohort of 1803 patients. An overview of the clinical characteristics of the study population is provided in Tables This was an observational study of 1803 hospitalized COVID-19 positive patients (positive PCR for SARS-CoV-2) in the Mayo Clinic electronic health record (EHR) database from March 12, 2020 to September 15, 2020. The sample size was determined based upon the number of patients in the Mayo Clinic EHR database at the time of the study in order to maximize the power of the downstream statistical tests. Patients who declined to give research authorization were excluded from the study. No patients were excluded on the basis of age, sex, ethnicity, or other clinical parameters. In order to compare rates of comorbidities and complications against a baseline, a control cohort of 94,953 COVID-19 negative hospitalized patients in the Mayo Clinic EHR database during the same time period was considered. Propensity score matching19, including: anemia, asthma, BMI between 25–30 (overweight), BMI between 30–40 (obese), BMI ≥ 40 (severe obesity), cancer, cardiomyopathy, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD), heart failure (HF), hyperglycemia, hypertension, immunosuppressant medication usage, liver disease, neurologic conditions, obstructive sleep apnea (OSA), smoker (former or current), steroid medication usage, type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D). We also note that bone marrow transplant, HIV/AIDS, pediatric conditions, pregnancy, sickle cell disease, solid organ transplant, and thalassemia were also considered, but were not included in the analysis because fewer than 20 patients had each of these comorbidities.For comorbidities, we considered 21 risk factors for COVID-19 severe illness reported by the CDC20 and analyzed in follow-up observational studies21, including: acute respiratory distress syndrome/acute lung injury (ARDS/ALI), acute kidney injury (AKI), anemia, cardiac arrest, cardiac arrhythmias, chronic fatigue syndrome, disseminated intravascular coagulation (DIC), heart failure, hyperglycemia, hypertension, myocardial infarction (MI), pleural effusion, pulmonary embolism (PE), respiratory failure, sepsis, septic shock, stroke/cerebrovascular incident, venous thromboembolism/deep vein thrombosis (VTE/DVT), delirium/encephalopathy, and numbness.For complications, we considered 20 COVID-associated complications collected by the Society for Critical Care Medicine (SCCM) Viral Infection and Respiratory Illness Universal Study (VIRUS) data registryAn augmented curation approach was used to classify the sentiment of phenotypes mentioned in the clinical notes across the Mayo Clinic healthcare system. The Mayo Clinic EHR system relies upon Epic for electronic management of health records which is used by providers across its campuses. For this analysis, we considered a database of 2.0 million clinical notes covering all of the unstructured text written, typed, or dictated into the Epic system during the course of clinical care for the study population and propensity-matched controls, including but not limited to: progress notes, discharge summaries, inpatient notes, outpatient notes, and telephone call transcripts. This corresponds to an average of 567 notes per patient for the study population and the propensity-matched cohort which each includes 1803 patients. We note that radiology reports and pathology reports were not included in this dataset. In Supplementary Fig. 4. The model was previously developed to classify the sentiment of general phenotypes4 and thrombotic event phenotypes5 in the encounter notes of COVID-19 patients. The categories of this classification model for each comorbidity and complication include: Yes (confirmed diagnosis), No (ruled out a diagnosis), Maybe (possibility of disease), and Other . This model was trained using nearly 250 different phenotypes and 18,490 sentences and achieves 93.6% overall accuracy and over 95% precision and recall for Yes/No sentiment classification4. For this study, the phenotypes of interest included a specific list of comorbidities and complications along with their synonyms. The comorbidities list consists of asthma, cancer, chronic kidney disease, chronic obstructive pulmonary disease, obesity, obstructive sleep apnea, type 1 diabetes mellitus, and type 2 diabetes mellitus. The complications list includes acute respiratory distress syndrome/acute lung injury, acute kidney injury, anemia, cardiac arrest, cardiac arrhythmias, disseminated intravascular coagulation, heart failure, hyperglycemia, hypertension, myocardial infarction, pleural effusion, pulmonary embolism, respiratory failure, sepsis, septic shock, stroke/cerebrovascular incident, venous thromboembolism/deep vein thrombosis.A BERT-based neural network was applied to identify phenotypes of interest in the clinical notes of the study populationWe used this model to classify the sentiment for all of the above phenotypes in all of the clinical notes for each patient in the time periods considered in this study. Only sentences containing a phenotype with a positive sentiment (labeled “Yes” by the model) with a confidence of 0.95 or above were deemed positive sentiment associations. In this analysis, repeated sentences for the same patient were ignored. For each patient, in each time period, we consider the phenotype to be present if there are at least three positive mentions in the clinical notes, regardless of the number of negative mentions. As a result, patients who were recorded as negative for a particular phenotype at some time may be classified as positive for the phenotype-based upon the other clinical notes during the time period.4. In order to validate the augmented curation model for the present set of phenotypes of complications/comorbidities, we manually labeled a set of 2404 randomly selected sentences from the clinical notes containing the phenotypes. This validation set of 2404 sentences was derived from ~2000 (0.1%) unique notes in the dataset of 2.0 million notes for the study population and propensity-matched cohort. For each phenotype, the sentences in this validation set were selected by randomly sampling the clinical notes from the 1803 COVID-19-positive patients in the study population. The true positive, true negative, false positive, and false-negative rates are reported in Supplementary Table 5.The model used to curate complications/comorbidities was initially validated, as previously describedA patient was determined to have a clinical phenotype  if the clinical phenotype or synonyms were mentioned (with positive sentiment) within that patients’ EHRs, as described above. For comorbidities, the mention must have occurred within a note at any point in the patient history prior to the patient’s first positive COVID-19 PCR test. Patients were only considered if they had at least one note within the Mayo Clinic EHR system dated before days −31 relative to their first positive PCR test.For the patients included in this study, we stratified the rates of new-onset complications by comorbidities. For each comorbidity, e.g. chronic kidney disease, we compare the rates of “new-onset” complications in cohorts of COVID-19 patients with and without chronic kidney disease. To calculate the rate of a new-onset complication, e.g. acute kidney injury (AKI), the numerator is the number of patients with AKI recorded in the clinical notes (with positive sentiment) during but not prior to the time period. The denominator is the number of patients without AKI recorded in the clinical notes with positive sentiment prior to the time period.To determine the relationship between individual comorbidities/pre-existing conditions and complications of COVID-19 infection, we performed a two-sided Chi-square test to compare the frequency of complications in patients with and without a given co-morbidity. Statistical significance tests were run using the software package scipy v1.5.4 in Python. The software package scikit-learn v0.20.3 in Python was used to train logistic regression models for the propensity score matching analysis.Further information on research design is available in the Supplementary InformationReporting Summary


Urticarial eruptions and angioedema are the most common cutaneous reactions in patients undergoing mRNA COVID-19 vaccinations. The vasoactive peptide bradykinin has long been known to be involved in angioedema and recently also in urticaria. Bradykinin is mainly catabolized by angiotensin-converting enzyme (ACE), which is inhibited by ACE inhibitors, a commonly employed class of antihypertensive drugs. We evaluated the risk of developing urticaria/angioedema after inoculation with the BNT162b2 mRNA COVID-19 vaccine in a population of 3586 health care workers. The influences of ACE inhibitors and selected potential confounding variables  were evaluated by fitting univariate and multivariable Poisson regression models. The overall cumulative incidence of urticaria/angioedema was 1.8% . Symptoms were mild, and no subject consulted a physician. Subjects taking ACE inhibitors had an adjusted three-fold increased risk of urticaria/angioedema . When we restricted the analysis to those aged 50 years or more, the adjusted RR was 3.98 (95% CI: 1.44–11.0). In conclusion, our data indicate that subjects taking ACE inhibitors have an increased risk of urticaria/angioedema after vaccination with the BNT162b2 mRNA COVID-19 vaccine. Symptoms are mild and self-limited; however, they should be considered to adequately advise subjects undergoing vaccination. After vaccination with the BNT162b2 mRNA COVID-19 vaccine, the main adverse events are short-term, mild-to-moderate pain at the injection site, fatigue, and headache ,4,5 and In our hospital , one of the main hospitals in Milan, Lombardy, northwest Italy, the vaccination campaign with the BNT162b2 mRNA COVID-19 vaccine  began on 27 December 2020. As per the vaccine schedule, the interval between the two doses was 21 days.We performed a cohort study to calculate the cumulative incidence (risk) of urticaria/angioedema after completion of BNT162b2 COVID-19 vaccination and the impact of ACE inhibitors on this risk. The cohort included health care workers of the hospital vaccinated on 27 December 2020 or afterwards. One month after the second dose, health care workers were invited to fill in an acute reaction form at the time of blood collection to test for seroconversion by the assessment of anti-Spike-1 antibody values [The study was approved by the hospital’s ethics committee  and carried out in conformity with the 2013 revision of the Declaration of Helsinki. All subjects gave their written consent to participate in the study.The cohort database included the following occupations: physicians, nurses, midwives, health care assistants, health care technicians , and clerical/administrative workers. Since it is a teaching hospital linked with the Università degli Studi di Milano, we also included students and residents in medicine and other health-related disciplines.Our hospital coordinated three large vaccination centers in Milan . Therefore, for subjects vaccinated within these three centers, the complete datasets containing demographics, dates of vaccination, and type of vaccine were directly available to us. For subjects vaccinated elsewhere, we completed information on vaccinations by linking the subjects in the cohort with a publicly available regional vaccination database. We used a deterministic linkage using the individual fiscal code as a unique identifier.Allplex™ SARS-CoV-2 assay  on the CFX96  and the SARS-CoV-2 AMP Kit  on Alinity  in line with manufacturer’s instructions. The Allplex™ SARS-CoV-2 assay detects the SARS-CoV-2 N gene, E gene, and RdRp and S genes together. Cycle threshold (Ct) values were recorded for each gene and then averaged. Mean Ct values of less than 40 were interpreted as positive. The SARS-CoV-2 AMP Kit detects SARS-CoV-2 RdRp and N genes together; positive samples were those with Ct < 42.Data on previous SARS-CoV-2 infections were extracted from the laboratory databases, which contained results of real-time reverse transcriptase polymerase chain reaction (RT-PCR) tests performed on nasopharyngeal swabs (NPSs). Two different assays were used to detect SARS-CoV-2 RNA, At the time of vaccination (either the first or second dose) each subject signed an informed consent form and filled in a short standardized questionnaire to investigate health status in the days before vaccination and history of allergy to vaccines and other substances as well as history of previous serious diseases and related therapies . The questionnaire was designed following a standard national format designed by the Ministry of Health. Data were stored in a dedicated Microsoft Access database.A short structured questionnaire was designed and used to collect information about all types of reactions that occurred after vaccination schedule completion. As mentioned above, this questionnaire was administered at the time of blood collection to test for seroconversion by the assessment of anti-Spike-1 antibody values, about one month after the second vaccination dose. Data on acute reactions were stored in a dedicated Microsoft Access database.We merged and performed data editing of all the databases described above. We included in the analysis all cohort subjects satisfying the following criteria: vaccination with BNT162b2 (two doses); having filled in the vaccination form at the first dose and therefore having a record in the vaccination form database, which contains information on ACE inhibitor medication and on previous allergies; presentation for blood drawing and filling in the acute reaction questionnaire one month after the second dose.In the overall cohort, we calculated the cumulative incidence (risk) of urticaria/angioedema and its 95% confidence interval (CI). Then, we examined the influence of ACE inhibitors and selected potential confounding variables, including sex, age class , previous SARS-CoV-2 infection, and allergy history), by fitting univariate and multivariable Poisson regression models with robust variance to calculate adjusted risk ratios (RRs) and 95% confidence intervals (CIs) ,29. AnalBetween 18 February and 9 June 2021, 3586 health care workers filled in the acute reaction form at the time of blood drawing (one month after the second dose). There were 63 health care workers in therapy with ACE inhibitors, with similar distributions of sex and previous SARS-CoV-2 infection to the group not taking ACE inhibitors. Health care workers in therapy with ACE inhibitors were older (mean age 55.3 vs. 42.9 years), with almost 80% aged 50 years or more, and reported a more frequent history of allergy (33.3% vs. 20.5%) .The overall cumulative incidence of urticaria/angioedema was 1.8% . In univariate analyses, risk of urticaria/angioedema was higher in females and in subjects aged 45–49 years, was moderately increased in subjects with previous SARS-CoV-2 infection, and was strongly increased in subjects with allergy history and in those taking ACE inhibitors . These fIn all subjects taking ACE inhibitors who reported urticaria/angioedema, urticaria/angioedema was defined as “light”. In all cases but one, urticaria/angioedema lasted less than one day and was not treated; only in one female, urticaria/angioedema lasted three days and required therapy with anti-histamine drugs. No one consulted a physician, went to an emergency department, or required hospitalization.After considering potential confounding factors such as age, sex, previous SARS-CoV-2 infection and history of allergy, we found that the risk of developing urticaria/angioedema after vaccination with BNT162b2 in subjects taking ACE inhibitors was three times higher than that of subjects not taking ACE inhibitors, and four times higher when looking at subjects aged 50 years or more. Urticaria/angioedema reactions are generally minor and self-limited, and, according to several authors ,22. IndeTo the best of our knowledge, our study is the first investigating the association between ACE inhibitors and urticaria/angioedema after COVID-19 vaccination with an mRNA vaccine. The main limitations of the study were as follows: (1) not all health care workers responded to the invitation to perform a serology test; although this fact reduced the sample size, selection bias in reporting acute effects after vaccination is implausible, because participation was not associated with the development of adverse effects; (2) due to time constraints, we were not able to collect data on acute reactions after the first dose of vaccine; (3) our analysis was based on subjects’ self-reports and not on clinical diagnosis. Notwithstanding these limitations, we found an increased risk of urticarial/angioedema in persons taking ACE inhibitors which has a plausible physiopathological mechanism. Future studies on larger populations are needed to confirm and better characterize our findings.In conclusion, in health care workers, urticaria/angioedema reactions are not frequent after vaccinations with the BNT162b2 mRNA COVID-19 vaccine; they are present in less than 2% of cases and increase to more than 6% in subjects taking ACE inhibitors. Although urticaria/angioedema symptoms are mild and self-limited, they should be taken into consideration by physicians in order to adequately advise subjects undergoing vaccination.


Symptoms of subclinical hypomania (SHM) are common in the general population of adolescents and young adults. SHM are most often transient yet might be risk markers of later bipolar disorder. The current study aimed to assess the clinical correlates of SHM at age 11 in the general population, examine the continuity of SHM from age 11-age 16 and explore the clinical precursors of age 16 SHM. As part of the Copenhagen Child Cohort 2000, 1,632 preadolescents participated in the examination of SHM and various clinical correlates at age 11, 893 were re-assessed for SHM at age 16 years. At age 11, SHM, psychotic experiences and depressive symptoms were assessed by semi-structured psychopathological interviews. Furthermore, the participants were diagnostically assessed by the Development and Well-Being Assessment and interviewed about sleep length. At age 16, SHM was assessed by self-report, using the Hypomania Checklist-32. Cannabis use occurring at age 15 or earlier was assessed at age 16. At age 11, SHM was associated with depressive disorders (Relative Risk [RR] = 2.96 [95% CI 1.26–6.96]), interview-based depressive symptoms (RR = 9.22 [5.93–14.34]), neurodevelopmental disorders (RR = 2.94 [1.66–5.20]), psychotic experiences (RR = 4.51 [2.90–7.01]) and insufficient sleep , psychotic experiences , emotional disorders  and cannabis use , after mutual adjustment and adjustment for sex, and sociodemographic factors. In conclusion, age 11 SHM was statistically significantly associated with other types of psychopathology in cross-sectional analyses and showed some continuity with later self-reported SHM at age 16. Particularly early psychotic experiences and cannabis use stood out as independent precursors of self-reported SHM and might constitute important risk markers for the development of future SHM and bipolar disorder. An important potential caveat of the current study includes the self-report assessment of SHM. Mania is characterized by a variety of symptoms including irritability, overactivity, reduced need to sleep and pressured speech . HypomanOther potential risk factors of developing later clinical mania and BD have been examined in the general population. Given that depression is part of BD, unipolar depression (i.e. episodes of depression in the absence of periods with manic/hypomanic symptoms) as a precursor of later BD has been studied rather extensively, and a recent review found that depressive disorder in adolescence is a strong predictor of later BD . AdditioOther domains of psychopathology have also been found to be associated with hypomania. Particularly irritability is a frequent symptom in a broad spectrum of common mental disorders among children and adolescents, e.g. attention deficit hyperactivity disorder (ADHD), depression and autism spectrum disorders . This miMania can be viewed as part of psychosis spectrum psychopathology , 20 and Furthermore, a recent review including community-based studies reported a significant positive correlation between self-reported insomnia and hypomania . HoweverRisk factors of hypomania have mainly been explored in late adolescence and adulthood, and research data on SHM in early adolescence are scarce. In order to possibly prevent later development of hypomania among young adults, it is important to identify the early risk factors of SHM. The current study therefore aimed to describe the clinical correlates of SHM in early adolescence, at age 11 years, and examine preadolescent precursors of SHM at age 16 years.Our main hypothesis was that clinician-assessed SHM at age 11 would predict self-reported SHM at age 16. Moreover, we hypothesized that other putative risk factors of hypomania–including depressive symptoms, emotional disorders, neurodevelopmental disorders, psychotic experiences, reduced duration of sleep and cannabis use would predict self-reported SHM at age 16.The Copenhagen Child Cohort (CCC2000) is a birth cohort comprising 6,090 children born in 16 municipalities in the county of Copenhagen in year 2000. The cohort is representative for the children born in Denmark that year regarding key perinatal and sociodemographic characteristic, except more immigrant families at baseline . For theThe study was approved by the Danish Data Protection Agency , and the Local Committee on Health Research Ethics (protocol 16023242). Participation was voluntary. At the 11-year follow-up, the parents gave oral informed consent to the study participation. The written information given to the participants stated that the participation in the study is voluntary, that by answering the questionnaires consent was given and consent can be withdrawn at any time. Separate written consent was not required by the ethical committee . The parSHM was assessed by semi-structured face-to-face interviews using the Kiddie Schedules of Affective Disorders and Schizophrenia–Present and Lifetime Version (K-SADS-PL) . SHM wasThe interviews were performed by trained clinicians who underwent training in performing Kiddie-SADS interviews and were supervised in conjunction with video-taped joint ratings approximately every two weeks, supervised by the senior author (PJ) .Depressive symptoms and psychotic experiences were also assessed using the semi-structured interview K-SADS-PL . DepressSleep duration was assessed by a structured interview. The interview contained questions regarding both sleep onset, awakening time and a number of sleep problems including difficulties falling asleep, nightmares among others . Only slICD-10 mental disorders at age 11 were assessed by child and adolescent psychiatrists using the Development and Well-Being Assessment (DAWBA). The DAWBA is an extensive psychiatric interview regarding children aged 5–16 years, their parents and teachers encompassing psychopathology broadly. The DAWBA includes structured questions, which cover the operational diagnostic criteria for both DSM-IV diagnoses and ICD-10 diagnoses, and includes open-ended questions in the end of each section . The queEach child was evaluated independently by two child and adolescent psychiatrists (in pairs), and consensus ratings involving the whole group of raters were made in cases of disagreement. The inter-rater reliability was examined for the assessment of diagnoses by three senior child and adolescent psychiatrists, and a good inter-rater reliability was found with a kappa-value of 0.81 .The DAWBA-based ICD-10 diagnoses were divided into two diagnostic groups for the purpose of the current study: emotional disorders  and neurodevelopmental disorders . Depression and ADHD were also considered separately.Cannabis use was assessed by self-report at the 16-year follow-up, using the World Health Organization’s “Alcohol, Smoking and Substance Involvement Screening test” (ASSIST), complemented with questions on age of first use . To examth percentile of sum-scores on the HCL as our measure of SHM, which corresponded to endorsing at least 20 out of 32 symptoms as present. See The Hypomania Checklist 32 (HCL-32) is a non-diagnostic self-reported questionnaire on lifetime hypomania symptoms. The HCL-32 was completed during the face-to-face assessment of the 16-year follow-up. The questionnaire contains 32 symptoms, and the participants had to answer “no” or “yes” (i.e. score 0 or 1) . The HCLFor adjustment purposes and for attrition analyses, we used register-based data from the Integrated database for Labor Market Research to describe sociodemographic characteristics , and datWe used a family adversity index for adjustment purposes. The index comprises 6 variables, each scored 0 (not present) or 1 (present), resulting in index scores ranging from of 0–6. The categories are as follows: 1) both parents born outside of Denmark 2) parents not living together at child’s birth 3) any change in family composition over the period 2000–2010 4) young mother (age< 21 years at childbirth) 5) low education of mother in 2010 (≤ 10 years of primary school) 6) household income within the lowest quartile in 2009–2010. The index has been used in previous CCC2000 studies .The statistical analyses were performed using STATA version 15.1. The statistical tests had a significance level of 5% and were 2 sided.We used chi-square tests for categorical variables to compare the participants with the non-participants still alive at the 11-year follow-up with regard to sociodemographic and perinatal characteristics.For cross-sectional analyses at age 11, presence/non-presence of SHM was used as the dependent variable. The independent variables at age 11 years were emotional disorders , neurodevelopmental disorders , interview-based depressive symptoms, psychotic experiences, insufficient sleep. We used binary regression analyses with a log-link function to calculate the relative risk (RR) of having hypomania.For the longitudinal analyses we also used binary regression with a log-link function to examine the associations between SHM and clinical correlates of SHM at age 11 and presence/non-presence of SHM top 10% score at age 16. This was first done for all putative risk factors individually i.e., SHM, emotional disorders, neurodevelopmental disorders, psychotic experiences, reduced duration of sleep and depressive symptoms at age 11 years and cannabis use at age 15 years or prior. The diagnosis of ADHD and depressive disorder at age 11 years were not viewed individually in the longitudinal analyses due to too small cell sizes, in order to adhere to the ethical guidelines described previously.Subsequently, all risk factors, besides interview-based depressive symptoms, were included in a multivariate binary regression model for mutual adjustment. Interview-based depressive symptoms were not included because they tapped into the same symptoms as depressive disorders within the emotional disorder variable. The Variance Inflation Factor  value was calculated for the multivariate binary regression in order to check for multicollinearity. All the above crude estimates were reported, and subsequently the models were adjusted for sex and the family adversity index.Lastly, sensitivities analyses were conducted. The associations between age 11 variables and subclinical hypomania at age 16 were also calculated when viewing age 16 hypomania on a continuous scale. This was done for both the univariate and multivariate analyses by use of linear regression. Because the data on hypomania at age 16 were not normally distributed, we used a non-parametric boot-strapping method with 5000 repetitions to estimate 95% bias corrected and accelerated bootstrap confidence intervals.Further sensitivity analyses were conducted: two multivariate analyses were conducted without including SHM and emotional disorders respectively. Thus, the sensitivity analyses included only one of the two opposite poles (depression and hypomania) of bipolar disorder in the same analysis to avoid over-adjustment for markers of bipolar disorder.As described in the method section, attrition analysis for the cross-sectional study population at age 11 years has been reported previously, showing lower frequencies of psychosocial and perinatal adversities among participants compared to non-participants . For theAmong the 1,632 adolescents assessed at age 11 years, 82 (5.0%) had experienced at least one subclinical lifetime SHM symptom according to the semi-structured interviews. A total of 91 adolescents out of 893 adolescents (10.2%) had SHM (a-priori cut-off set at 10%) at the follow-up at age 16 years. See In multivariate analyses, after mutual adjustment and adjustment for sociodemographic adversities, the risk ratios for all the symptoms and diagnoses were largely unchanged see .The mean Variance Inflation Factor value  for the multivariate analysis was 1.09 which indicates that the level of multicollinearity is acceptable.The results found for the sensitivity analyses that view the outcome of SHM at age 16 as the number reported symptoms at age 16 on a continuous scale, can be seen in Moreover, the two sensitivity analyses of the multivariate analyses, without SHM and emotional disorders, respectively, did not markedly change the estimates see .Any SHM symptom was clinician-rated in 5% of the children at age 11 years. As hypothesized, most of the examined clinical correlates at age 11, i.e. interview-based depressive symptoms, depressive disorders, neurodevelopmental disorders including ADHD, psychotic experiences, and insufficient sleep, were associated with SHM, except for the broader emotional disorders variable which mostly comprised anxiety disorders. As hypothesized, SHM at age 11 years was associated with self-reported SHM at age 16 years, also after adjustment for other putative risk factors, sex and socioeconomic factors. Furthermore, the multivariate analyses showed that particularly early psychotic experiences and cannabis use were robust risk factors of self-reported SHM at age 16 years.Strengths of the study include the large study population, the comprehensive assessments of psychopathology in preadolescence, at age 11 years, and follow-up in early adolescence at age 16 years. We examined putative risk factors of SHM at a developmentally important age in preadolescence and early adolescence. Although the incidence rates of affective disorders, in particular depressive disorders, increase during adolescence, the early and mid-adolescence has so far been scarcely described in relation to the clinical correlates and precursors of hypomania.th percentile cut-off on the HCL-32 as our SHM outcome at age 16 years. An official cut-off on the HCL has not been developed, and our cut-off is arguably arbitrary. We chose the top 10th percentile cut-off, because we expected that it would optimize the chance of identifying individuals who were likely to have had impairing mania/hypomania, without being too inclusive. Lastly, the HCL-32 includes question concerning both lifetime and present subclinical hypomania symptoms. This might have resulted in increased continuity of SHM. However, we expected that most of the adolescents would report recent symptoms whereas it was highly unlikely that symptoms experienced only in childhood would be reported only when answering the HCL-32 at age 16. Secondly, cannabis use was measured retrospectively at the 16-year follow-up, whereas the other putative risk factors were measured at the 11-year follow-up. This may have led to recall-bias and additionally caused a stronger association between cannabis use and SHM at age 16 years. Thirdly, the participants had been exposed to less psychosocial adversities compared to the non-participants. This selection might have affected the range of scores on clinical characteristics and thereby possibly attenuated the associations between the risk factors and the outcome of hypomania.The results should also be interpreted in the light of several limitations. Most importantly, SHM at age 16 years, was measured by self-report. Although the HCL-32 has been rather extensively used, the use of self-reported hypomania symptoms in the general population has been criticized for misclassification, i.e. including normal phenomena/behaviours as being hypomania, or simply tapping in to more general affective dysregulation not specific to hypomania/mania , 39. AltSHM at age 11 years were associated with all the clinical correlates investigated at age 11 years besides a broad measure of emotional disorders. Particularly strong associations were found between SHM on one hand and interview-based depressive symptoms and depressive disorders on the other hand, in line with meta-analytic evidence , and as In the longitudinal analyses, SHM at age 11 years was a precursor of self-reported SHM at age 16, also in the final adjusted models. This coincides with previous research on SHM in childhood, which has shown some stability throughout adolescence , which mBoth psychotic experiences and cannabis use showed to be strongly associated with SHM in cross section as well as in the longitudinal analyses, in line with findings from other studies , 30, 49.The symptom reduced sleep is a part of hypomanic symptomatology, yet insufficient sleep was only associated with SHM in the cross-sectional analyses, and not in the longitudinal analyses. Insufficient sleep  could be caused by many different factors, including gaming, use of social media, parental neglect regarding the supervision of bedtime etc., and most sleep patterns are probably fluctuating over the course of follow-up. In line with our findings, an association between sleep problems and hypomania in cross-section in a recent meta-analysis of community based studies .Finally, in the sensitivity analyses the SHM at age 16 years were measured on a continuous scale. Although the overall findings from the main longitudinal analyses were replicated, the associations were somewhat less statistically robust. This might indicate that the self-reporting of low numbers of SHM symptoms at age 16 can be considered as a normal phenomenon, whereas scores at the extreme high end are more likely to be considered as potentially pathological and associated with other worrying psychopathology and behaviors.In conclusion, the current study adds to the existing evidence that, SHM shows some continuity from preadolescence to adolescence. Additionally, psychotic experiences in preadolescence and cannabis use were strong predictors of later self-reported SHM, whereas the association between preadolescent emotional problems and disorders were less convincingly associated with SHM at age 16 years, and neurodevelopmental disorders were not. Thereby our findings indicate that early precursors can be identified and perhaps point to a window of opportunity for targeted prevention and early interventions, most obviously targeting early use of cannabis. The findings also underline that psychotic experiences in childhood are important to consider when screening for a wide array of later mental health problems. All longitudinal associations found in the current study should be considered in the light of SHM being self-reported with low face-validity at age 16. Our findings emphasize the need of a holistic and transdiagnostic approach when identifying the risk factors for later development of mental disorders .S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.


ObjectivesThe primary goal of our study is to evaluate the mortality rate in inpatient recipients of multivessel percutaneous coronary intervention (MVPCI) and to evaluate the demographic risk factors and medical complications that increase the risk of in-hospital mortality.MethodsWe conducted a cross-sectional study using the Nationwide Inpatient Sample  and included 127,145 inpatients who received MVPCI as a primary procedure in United States' hospitals. We used a multivariable logistic regression model adjusted for demographic confounders to measure the odds ratio (OR) of association of medical complications and in-hospital mortality risk in MVPCI recipients.ResultsThe in-hospital mortality rate was 2% in MVPCI recipients and was seen majorly in older-age adults  and males (61%). Even though the prevalence of mortality among females was comparatively low, yet in the regression model, they were at a higher risk for in-hospital mortality than males . While comparing ethnicities, in-hospital mortality was prevalent in whites (79%) followed by blacks (9%) and Hispanics (7.5%). Patients who developed cardiogenic shock were at higher odds of in-hospital mortality  followed by respiratory failure  and ventricular fibrillation .ConclusionAccelerated use of MVPCI made it important to study in-hospital mortality risk factors allowing us to devise strategies to improve the utilization and improve the quality of life of these at-risk patients. Despite its effectiveness and comparatively lower mortality profile, aggressive usage of MVPCI is restricted due to the periprocedural complications and morbidity profile of the patients. Annually, over 790,000 acute myocardial infarctions (AMI) occur in the United States (US), and the number one cause of death remains coronary artery disease . PercutaMultivessel percutaneous coronary intervention (MVPCI) procedures are done predominantly in the elderly population. Males undergo MVPCI procedures twice compared to females and this predominance is likely because of a lower prevalence of coronary diseases among women of similar age . Racial Patients undergoing MVPCI have baseline comorbidities, most notably hypertension and diabetes. However, the percentage of MVPCI utilization in diabetic patients decreased by 40.5% from 2006 to 2012 . The draHowever, like any medical intervention, PCI does pose certain risks of morbidity and mortality. Early complications of MVPCI are stent thrombosis, bleeding, coronary dissection, and renal failure. Late complications are complications of coronary heart disease. The Cleveland Clinic institutional PCI registry report found that 2% of mortality post-PCI occurred within the first 30 days. Of these deaths, 58% died of cardiac and 42% of non-cardiac causes. Interestingly, less than half (42%) of 30-day deaths were attributed to PCI-related complications . HoweverSo, we conducted a cross-sectional inpatient study to evaluate the mortality rate in patients hospitalized and managed with MVPCI and to evaluate the demographic predictors and medical conditions that increase the risk of in-hospital mortality in MVPCI recipients.Study sampleWe conducted a cross-sectional study using the Nationwide Inpatient Sample (NIS) from January 1 to December 31, 2016. We were not required to take the institutional review board's permission for this study as the NIS is a de-identified publicly available dataset and protects any health-related patient information . We inclVariablesDemographic variables included were age at admission , sex , and race/ethnicity . Comorbid medical complications included in the study are cardiogenic shock, ventricular fibrillation (VFib), respiratory failure, intracranial hemorrhage (ICH), periprocedural stroke, acute kidney injury (AKI), and acute gastrointestinal (GI) bleeding. Our primary hospital outcome is in-hospital mortality, which is all-cause mortality in the NIS .Statistical analysisWe compared the distributions of demographic characteristics, and comorbid medical complications by in-hospital mortality using descriptive statistics and Pearson’s chi-square test. Next, we used a logistic regression model adjusted for demographic confounders to measure the odds ratio (OR) of association of comorbid medical complications and in-hospital mortality risk in inpatients undergoing MVPCI. All analyses were conducted using SPSS Statistics v. 26.0  and statistical significance was set at a two-sided P-value <0.05.We included 127,145 medical inpatients who underwent MVPCI as a primary procedure during hospitalization; the in-hospital mortality rate was 1.88%. A higher proportion of these inpatients who died during hospitalization were older-age adults above 64 years of age (74.4%) and males (60.8%). While comparing ethnicities, in-hospital mortality was most prevalent in whites (79.0%), subsequently blacks (8.8%), Hispanics (7.5%), and other races (4.6%). The most prevalent comorbid medical complications among patients who died during hospitalization were a respiratory failure (60%), followed by AKI (53.2%), cardiogenic shock (49.9%), and VFib (37.3%) as shown in Table Even though the prevalence of in-hospital mortality among females was comparatively low, females were at a higher risk of mortality than males . Compared to the white population, other races/ethnicities had a lower and non-significant association with in-hospital mortality.There existed a statistically significant association between certain risk factors and in-hospital mortality. Patients who developed cardiogenic shock were at higher odds of mortality , followed by respiratory failure , acute gastrointestinal (GI) bleeding , VFib , and AKI  as shown in Table In our comprehensive analysis study of 127,145 inpatient recipients of MVPCI, we determined demographic predictors  and medical risk factors that increase the risk of in-hospital mortality. Several papers have already shown the mortality in MVPCI is comparable or even lower than that of post-PCI mortality. Our study further analyzes the potential benefits of MVPCI by assessing in-hospital mortality in the demographic and comorbidity subcategories.Whites had the highest in-hospital mortality rate in our study inpatients. An observational study in 2019 evaluated race and gender disparities in MVPCI outcomes and resource utilization. Two important conclusions that they made were that the black population had the highest prevalence of comorbidities and Asian women had the highest risk of in-hospital mortality, with rates of in-hospital morbidities increasing yearly with the largest increment in the Hispanic population . ContrarIn-hospital mortality was significantly higher in females by 24% compared to males. Females tend to have a higher mean low-density lipoprotein (LDL) than men because of physiologically higher estrogen levels, wider use of hormonal contraceptives, and hormone replacement therapy contributing to a higher comorbid profile . Other fAdvanced age is a universal predictor of death in cardiovascular diseases and procedures . The extA recent meta-analysis confirms that multivessel disease is common in ST-elevated myocardial infarction (STEMI) patients . TheoretOur study shows that VFib was an important risk factor for mortality, owing to the prothrombotic and proinflammatory nature of an acute disease like STEMI . Global Periprocedural stroke is clinically significant and a disabling risk factor for mortality due to micro-embolisms leading to anoxic brain injury. Increased contrast load and multiple ischemic insults due to shock led to renal failure as well. Our study confirms that GI bleed was one of the significant and probably preventable causes of mortality following MVPCI. Pre-existing peptic ulcer disease (PUD), increased vagal stimulation following shock causing ulcers that bleed off, and concomitant usage of antiplatelet therapy could be risk factors besides physiological stress to GI mucosa .There are certain limitations associated with the study conducted. Firstly, this is a cross-sectional study and thus it is hard to establish causal relationships for in-hospital mortality risk, and it is reported all-cause in the NIS. Furthermore, the database does not contain in-depth patient-level clinical information, and in this study, the relevant data were extracted using diagnostic codes, which may have caused the under-reporting of comorbidities. Yet, there are some strengths of this study. NIS database is a large inpatient population data that lead to uniform patient records and provide a population-based perception of mortality associated with systematic and temporal factors. Further, the information is coded independently by the individual physicians, which protects it from reporting bias.Accelerated use of MVPCI made it important to study in-hospital mortality risk factors allowing us to devise strategies to improve the utilization and improve the quality of life of these at-risk patients. The highest in-hospital mortality following MVPCI was among females and whites. MVPCI inpatients with medical complications had a higher risk of mortality, with cardiogenic shock posing the highest risk ( increased by nine times), followed by respiratory failure (increased by six times), and VFib (increased by 3.5 times). Despite its effectiveness and comparatively lower mortality profile, aggressive usage of MVPCI is restricted due to the periprocedural complications and morbidity profile of the patients.


Sufficient uptake of the COVID-19 vaccine is key to slowing the spread of the coronavirus among the most vulnerable in society, including individuals experiencing homelessness. However, COVID-19 vaccination rates among the Veteran homeless population are currently unknown. This study examines the COVID-19 vaccination rate among homeless Veterans who receive care at the U.S. Department of Veterans Affairs (VA), and the factors that are associated with vaccine uptake. Using VA administrative and clinical data, bivariate and multivariate analyses were conducted to identify the sociodemographic, health-related, and healthcare and housing services utilization factors that influenced COVID-19 vaccine uptake during the first eight months of the vaccine rollout (December 2020–August 2021). Of the 83,528 Veterans experiencing homelessness included in the study, 45.8% were vaccinated for COVID-19. Non-white, older Veterans (65+), females, those who received the seasonal flu vaccine, and Veterans with multiple comorbidities and mental health conditions were more likely to be vaccinated. There was a strong association between COVID-19 vaccination and Veterans who utilized VA healthcare and housing services. VA healthcare and homeless service providers are particularly well-positioned to provide trusted information and overcome access barriers for homeless Veterans to receive the COVID-19 vaccine. Infectious disease outbreaks often disproportionately impact those with underlying health conditions and individuals experiencing poverty, marginalization, and discrimination ,2. AmidsLong-term control of the pandemic largely depends on the sufficient uptake of the COVID-19 vaccine. The U.S. Centers for Disease Control and Prevention (CDC) issued guidelines advising that homeless persons residing in congregate facilities be among the first groups to be vaccinated , and ensCOVID-19 inoculation rates among the homeless population in the U.S., however, are currently unknown, as many state immunization registries do not track housing status ,16, althThis study examines the COVID-19 vaccination rates among homeless Veterans receiving care at the VA nationwide, as well as identifies the key sociodemographic, health-related, and healthcare and housing services utilization factors that influence vaccine uptake. By identifying these characteristics, the VA and other entities that provide care to homeless persons can effectively tailor health messaging for those individuals who may be vaccine hesitant.n = 83,528) in the one year prior to the COVID-19 vaccine rollout (14 December 2019–13 December 2020). The data for this study were obtained from the Veterans Health Administration (VHA) Corporate Data Warehouse (CDW), which is a national repository of data from VHA clinical and administrative systems. The study period was the first eight months of the vaccination rollout period (14 December 2020 through 1 August 2021).We conducted an observational cohort analysis of all Veterans who had an International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) homelessness diagnosis of Z59.0  during the eight-month study period. COVID-19 vaccinations were identified through Current Procedural Terminology (CPT) codes as specified by the U.S. Centers for Medicare and Medicaid Services (CMS) guide on “Coding for COVID-19 Vaccine Shots” , as wellBivariate analyses were used to descriptively compare homeless Veterans who received at least one dose of the COVID-19 vaccine to homeless Veterans who did not receive any dose of the COVID-19 vaccine during the eight-month vaccine rollout period. We used tests for the differences in categorical variables guided by the conceptual framework  and emergency department ; having any hospitalizations (yes/no); having received an influenza vaccine during the 2019–2020 flu season (yes/no) (which could have been obtained within or outside the VA), and congregate living status in a VA Community Living Center, nursing home, or Domiciliary, which provide treatment and housing to geriatric Veterans and/or those with physical or mental disabilities or substance abuse disorders ,39. In ap values were 2-sided and considered statistically significant at less than 0.05. All analyses were computed using Stata 15.1 .Lastly, we used multivariable logistic regression models to further explore which variables from n = 38,269) were vaccinated during the study period. Among the study population, 45.8%  and overall Veteran populations (51.5%). To our knowledge, this is the largest study of homeless persons’ uptake of the COVID-19 vaccine to date.Vaccine hesitancy is often linked to issues such as confidence, complacency, and convenience ,43,44. AThis study also found that Veterans who were enrolled in VA housing or supportive services, particularly GPD transitional housing programs, were dramatically more likely to be vaccinated. These programs provide a vital safety net for Veterans who lack basic resources, particularly during disasters which often disproportionately affects this population and disrupts access to care and services ,52. Gin Contrary to earlier trends of racial disparities to COVID-19 vaccine equity and acceptance, African American and Hispanic Veterans experiencing homelessness were more likely to be vaccinated, which supports the results of a study by the Kaiser Family Foundation that white Americans were more likely to refuse the vaccine . These fDespite the devastating impact of COVID-19 on the Veteran community, with over 15,000 known deaths , COVID-1The sample size of 83,528 homeless Veterans is greater than what was previously reported by HUD  . HoweverImproving vaccination rates will alleviate transmission concerns in homeless housing programs and mitigate the health effects of COVID-19 for those who are at higher risk for severe illness. As the coronavirus’s highly transmissible Delta variant has become the most prevalent strain in the U.S., and is almost exclusively affecting people who have not been vaccinated , it is i