Allogeneic hematopoietic stem cell transplantation  remains the only curative treatment for patients with myeloid malignancies such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). However, relapse and graft-versus-host disease (GvHD) still affect the survival of patients who receive allo-HSCT, and more appropriate therapeutic strategies should be applied at all stages of transplantation to prevent these adverse events. The use of epigenetics agents, such as hypomethylating agents (HMAs), has been explored to decrease the risk of relapse by epigenetic modulation, which is especially effective among AML patients with poor mutations in epigenetic regulators. Furthermore, epigenetic agents have also been regarded as prophylactic methods for GvHD management without abrogating graft versus leukemia (GvL) effects. Therefore, the combination of epigenetic therapy and HSCT may optimize the transplantation process and prevent treatment failure. Existing studies have investigated the feasibility and effectiveness of using HMAs in the pretransplant, transplant and posttransplant stages among MDS and AML patients. This review examines the application of HMAs as a bridge treatment to reduce the tumor burden and the determine appropriate dose during allo-HSCT. Within this review, we also examine the efficacy and safety of HMAs alone or HMA-based strategies in posttransplant settings for MDS and AML. Finally, we provide an overview of other epigenetic candidates, which have been discussed in the nontransplant setting. Allogeneic hematopoietic stem cell transplantation  is the only curative treatment for most patients with MDS or AML , 2. HoweMinimal residual disease (MRD) before transplantation significantly increases the probability of relapse after transplantation, which affects patient outcomes . Thus, sThe safety and efficacy of HMAs has been confirmed by several studies and included in the guideline for HSCT-based treatment of AML . One ret+ may benefit from bridging treatment (- (11 months vs. 5 months), and the rate of MRD+ to MRD- conversion was higher with oral azacitidine  negative patients, HSCT should be conducted as soon as possible. It is currently believed that a shorter disease duration pre-HSCT is associated with improved OS and disease-free survival (DFS) and decreased treatment-related mortality (TRM) , 25. Patreatment –28. For reatment . Sungwooreatment . Furtherreatment –33. It hcitidine . With th2 every 12 hours×4, 150 mg/m2 every 12 hours×4, 200 mg/m2 every 12 hours×4) combined with a myeloablative condition regimen (2 on day -9 and 50 mg/m2 on day -8) with short-term and low-dose DAC (15 mg/m2/day on days -10 to -8) on the prognosis of patients  seemed to be a better tolerated dose for DAC maintenance in the post-HSCT setting. CC-486, the oral formulation of AZA, is more likely to be associated with better OS and lower GvHD risk  maintenance combined with recombinant human granulocyte colony-stimulating factor (rhG-CSF) after allo-HSCT in AML or MDS patients  was feasible in AML patients, especially in low-risk AML patients. Low-dose DAC (15 mg/m2×3 days) plus venetoclax (BCL-2i) has also shown promising efficacy in maintenance therapy after HSCT  from China evaluated the efficacy of low-dose DAC  , but alsein NOXA . GO/HMA ein NOXA . HMAs inein NOXA .Venetoclax (VEN), a selective inhibitor of the antiapoptotic protein Bcl-2, has been demonstrated to have the potential to sensitize AML cells to HMAs . Recent There is now substantial evidence showing that patients who are molecular relapsed/hematological relapse with lower blast count or a longer interval from HSCT to relapse might be associated with better survival when HMAs-based therapies are used in relapsed AML or MDS , 70, 97.It has been shown that disease stage at the time of transplantation is one of the most important factors that influences outcome after allogeneic HSCT , 98, andAs a condition regimen, there are relatively few studies on the application of HMAs during transplantation. Patients may benefit more from conditioning regimens using HMAs, especially at a lower dose. Two regimens (5 days and 10 days) of low-dose commonly used therapies show fair efficacy. Compared with the 10-day regimen, a low incidence of infection was reported in the 5-day regimen, which calls attention to infection screening and antibiotic prophylaxis in the 10-day regimen.Relapse after allo-HSCT for AML or MDS is the main cause of treatment failure and is associated with a very poor prognosis and a short survival . Reductiin vitro. When combined with other therapies, HMAs could potentially reverse some epigenetic silencing genes like MAGE-1 and WT-1 to exert an antitumor effect  To iin vivo, which has been used in myeloid neoplasia and has shown promising efficacy in elderly patients with AML , Natural Science Foundation of Chongqing Innovation Group Science Program (No. cstc2021jcyj-cxttX0001) and Clinical Medical Research Project of Army Medical University .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background and Objectives: The COVID-19 pandemic has emerged as a major threat to nurses’ health. This study aimed to investigate the factors affecting nurses’ health promotion behaviors during the coronavirus disease 2019 (COVID-19) pandemic. Material and Methods: The participants were clinical nurses who had direct contact with patients at a university hospital in G province, Korea. Data were collected from March 16 to April 16, 2021, and the final analysis included data from 162 nurses. The general and lifestyle characteristics of the participants were analyzed using descriptive statistics, and the effect on health promotion behavior was analyzed using multiple regression with SPSS/WIN 21.0. Results: The results showed that the factors influencing nurses’ health promotion behavior during the COVID-19 pandemic were social support , self-efficacy , being married , having good health , and not skipping meals . The explanatory power of the variables was 51.4%. Therefore, health promotion programs to promote social support and self-efficacy are needed to improve nurses’ health promotion behaviors during the COVID-19 pandemic. Conclusions: These results indicate that the development of additional management strategies for health promotion among nurses during the COVID-19 pandemic is necessary. It is necessary to prepare organizational policies and manage self-care to improve nurses’ irregular eating habits during the ongoing pandemic. The coronavirus disease 2019 (COVID-19) is a new infectious disease that was first reported in Hubei Province, China, in December 2019 . Thus, tHealth care workers are responsible for attending to suspected and confirmed cases of COVID-19 . Nurses,The information–motivation–behavioral skills (IMB) model is widely used to understand and promote health-related behavior . The IMBMotivation is another determinant of health promotion behavior and can be divided into personal and social motivation ,12. PersIn the IMB model, behavioral skills refer to the confidence that an individual can successfully perform the behavior required to produce a specific outcome with self-efficacy . Even inMeanwhile, the long-term impact of COVID-19 can lead to chronic stress, and nurses caring for COVID-19 patients may experience traumatic events . The fre(1)Identify knowledge of COVID-19, attitude toward COVID-19 infection, social support, self-efficacy, secondary traumatic stress, and health promotion behavior of nurses.(2)Identify the differences in health promotion behaviors according to the general and lifestyle characteristics of nurses.(3)Identify the correlation between nurses’ knowledge of COVID-19, attitude toward COVID-19 infection, social support, self-efficacy, secondary traumatic stress, and health promotion behavior.(4)Identify the factors affecting health promotion progress among nurses.Based on these assumptions, this study aimed to provide basic data to improve nurses’ health promotion behavior by applying the IMB model to identify factors affecting their health promotion behavior during the COVID-19 pandemic. The detailed purposes of this study are as follows:This descriptive correlational study investigated the factors affecting nurses’ health promotion behaviors.The participants were clinical nurses working at a tertiary university hospital in G City, Korea. The hospital serves as a medical center for the entire western Gyeongsangnam-do region of Korea, with numerous general hospitals clustered around it. The hospital has approximately 900 beds, and the staff comprised 313 doctors and 954 nurses at the time of this study. Data were collected from March 16 to April 16, 2021, in compliance with the social distancing measures and quarantine guidelines of Korea. The criteria for the selection of participants for this study were as follows: (1) nurses working at a tertiary general hospital, (2) nurses ranked below the head nurse, and (3) nurses who had been working in the hospital for 12 months. Only those who read the guidelines and agreed to participate in this study took part. The minimum sample size, including six predictors, was 146 nurses, based on a significance level (α) of 0.05, a power (1-β) of 0.95, and an effect size of 0.15 for a two-sided multiple linear regression analysis using G*power 3.1.7 . Considering a 20% dropout rate, 175 copies were distributed and 164 copies were collected. The final analysis used data from 162 copies, excluding two with incomplete answers.The measurement tools were self-report questionnaires on general characteristics, knowledge of COVID-19, attitude toward COVID-19 infection, social support, self-efficacy, secondary traumatic stress, and health promotion behavior.Sociodemographic characteristics  and lifestyle characteristics  were collected using a structured questionnaire.Knowledge about COVID-19 was measured by modifying and supplementing the prevention guidelines of the tool developed by Yoon . ExampleAttitude toward COVID-19 infection refers to the feeling or perception with regard to COVID-19. This was measured using a tool developed by Park , which wSocial support refers to positive resources that an individual can derive from interpersonal relationships. Social support was measured using a tool adapted and modified by Yang  based onSelf-efficacy refers to the belief that an individual can successfully perform the behavior required to produce a certain outcome. Self-efficacy was measured using a tool modified and supplemented by Jung , based oSecondary traumatic stress is a measure of the impact on an individual who has indirectly experienced trauma. Secondary traumatic stress was measured using the Korean version of the secondary traumatic stress scale adapted and modified by Park , based oHealth promotion behavior refers to a positive approach toward health for maintaining or enhancing the optimal well-being of an individual or group, self-actualization, and individual self-esteem. Health promotion behavior was measured using the tool developed by Kim et al. . It const-test and one-way analysis of variance. Post hoc testing was performed using Scheffé’s test. Variables affecting health promotion behavior were examined using Pearson’s correlation coefficient and multiple regression analysis.The general and lifestyle characteristics of the participants were analyzed using descriptive statistics with SPSS/WIN 21.0 . The differences in health promotion behavior according to general and lifestyle characteristics were confirmed by an independent This study was conducted after obtaining approval from the Institutional Review Board of G-University Hospital in G-Province . Informed consent was obtained from the head of the participating hospital after explaining the purpose and procedures of this study. The participants who completed the questionnaires received small gifts.t-test was performed to verify the differences in health promotion behaviors according to participant characteristics. The general characteristics that showed significant differences in health promotion behavior were marital status  and health status .A total of 162 participated in this study. The mean and standard deviation of health promotion behavior scores are shown in p = 0.043), number of meals skipped , and stress management .p < 0.001), self-efficacy , and secondary traumatic stress  were significantly correlated with health promotion behaviors.A multiple regression analysis was conducted to examine the factors affecting nurses’ health promotion behavior during the COVID-19 pandemic. p < 0.001) and self-efficacy . In other words, higher social support and self-efficacy were correlated with better health promotion behavior.According to the analysis, the variables affecting health promotion behavior were social support  also found that the social support of colleagues was the driving force that allowed these workers to continue their work . FrontliAfter social support, self-efficacy had the greatest influence on health promotion behavior, and the higher the self-efficacy, the better the health promotion behavior. Studies on nurses  and nursAttitude toward COVID-19 infection was significantly correlated with health promotion behavior but was not an explanatory factor. A study of college students  reportedThis study expanded the IMB model’s conceptual framework to set the COVID-19 pandemic as a specific scenario and added secondary traumatic stress, a psychological factor, among the personal characteristic factors. However, although this study expected high secondary traumatic stress and a negative impact on health promotion behavior among nurses due to the risks of isolation and direct infections due to COVID-19, secondary traumatic stress was not a statistically significant factor. Although secondary traumatic stress was not statistically significant, the COVID-19 pandemic is still ongoing. Nurses may have an increased chance of developing secondary traumatic stress because they experience burnout due to excessive workload and perceive high stress due to direct contact with COVID-19 patients . These fAfter investigating the general characteristics influencing the health promotion behavior of participants, married people showed better health promotion behavior than those who were single. This finding is consistent with that of a study on the health promotion behavior of general hospital nurses . A studyIn addition, healthier nurses showed better health promotion behaviors than those who were unhealthy. A study on nurses working in shifts  also repFinally, nurses who did not skip meals showed better health promotion behaviors. A study on college students  reportedIn summary, the most influential factors affecting nurses’ health promotion behaviors during the COVID-19 pandemic were social support, self-efficacy, and eating habits. Therefore, programs to promote social support and self-efficacy and measures to compensate for irregular eating habits are necessary to improve nurses’ health promotion behavior.Despite its various strengths, this study has several limitations. The participants in this study were limited to nurses from one university hospital; hence, the results may not be applicable to all nurses. Follow-up studies should include more nurses working at other hospitals. Second, further research should validate the effects of developing and implementing health promotion programs, including the improvement of self-efficacy and support from colleagues, which were the factors influencing health promotion behavior in this study. Third, the explanatory power between secondary traumatic stress and health promotion behaviors was not significant in this study. In-depth research should be conducted on the influence of individual characteristics or psychological factors on health promotion behavior in the future. Fourth, this study applied the COVID-19 pandemic as a specific situation in the IMB model, but it failed to verify whether knowledge about COVID-19 and attitude toward COVID-19 influenced health promotion behavior through self-efficacy as a medium. Therefore, further research is needed.This study investigated the factors affecting the health promotion behavior of nurses who have influenced patients during the COVID-19 pandemic through their roles as educators and caregivers. The results showed that social support, self-efficacy, marital status, perceived good health, and regular or correct eating patterns were the influential variables. In addition to caring for patients, frontline nurses attending to COVID-19 patients should maintain self-care. Therefore, hospitals should develop health promotion programs to increase the social support and self-efficacy of nurses based on the results of this study. It is also necessary to motivate nurses to develop a healthy lifestyle during the ongoing pandemic.


In the ongoing COVID-19 pandemic, an increased incidence of ROCM was noted in India among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multicentric, hospital-based, unmatched case-control study across India.We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, co-morbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with a p-value for crude OR of less than 0·20 were considered for the regression model.Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors  for post-COVID ROCM among the hospitalised were age 45–59 years , having diabetes mellitus , elevated plasma glucose , steroid use  and frequent nasal washing . Among those never hospitalised, age ≥ 60 years , having diabetes mellitus , elevated plasma glucose , steroid use , and cloth facemask use  were associated with increased risk of post-COVID ROCM.Hyperglycemia, irrespective of having diabetes mellitus and steroid use, was associated with an increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID. Mucormycosis is a rare opportunistic angio-invasive fungal infection associated with high morbidity and mortality. Several clinical presentations of mucormycosis have been described depending on the site of involvement, the most common being rhino-orbito-cerebral mucormycosis (ROCM). Globally, reported incidence (per million population) ranges from 0·005 to 1·7 in the pre-Coronavirus disease 2019 (COVID-19) era, and in India, the incidence is 140, about 80 times higher than reported in developed countries [Rapid progression of Mucormycosis makes its early diagnosis and treatment critical, considering that a delay of even six days doubles 30-day fatality from 32% to 66% , 5. A hiIn the context of the ongoing COVID-19 pandemic in India, reports highlighted the increased incidence of ROCM, otherwise a rare entity –8. The rWe designed and conducted an unmatched case-control study in 20 tertiary care hospitals from the public and private sectors providing management for COVID-19 and ROCM across India during June and July 2021.Those testing positive for Severe Acute Respiratory Syndrome (SARS)-Coronavirus (CoV)-2 through rRT-PCR/TrueNat/ GeneXpert/Rapid Antigen Test.Individuals diagnosed with ROCM clinically and/or based on diagnostic nasal endoscopy and/or contrast-enhanced MRI/CT scan with laboratory confirmation on direct microscopy  or culture or histopathology or molecular diagnostic methods  and tested positive for COVID-19 any time between 12 weeks to 1 day before ROCM diagnosis.Patients who had PC-ROCM and received hospitalised COVID-19 care, irrespective of the duration of hospitalization.Patients who had the negative history of ROCM symptoms and received hospitalised COVID-19 care, irrespective of the duration of hospitalization.Patients who had PC-ROCM and received COVID-19 care at home/COVID care centre only.Patients who had a negative history of ROCM symptoms and received COVID-19 care at home/COVID care centre only.Sample size for case-control study among ever-hospitalised for COVID-19. We needed 256 cases and controls each based on assumptions that 68% of the hospitalised COVID-19 patients were treated with steroids [steroids , odds raSample size for case-control study among never-hospitalised for COVID-19. We needed 108 cases and 216 controls for the assumptions that 15% of the never-hospitalised COVID-19 patients were treated with steroids, expected OR of at least three for steroid use, alpha error of 5%, power of 80%, the ratio of cases: controls to be 1:2 and 25% non-response.https://www.openepi.com/SampleSize/SSCC.htm).We used OpenEpi Online calculator version 3·01 for sample size calculation . We selected controls through simple random sampling from the list of patients who tested positive for COVID-19 on the same date as cases through a custom-built web tool [https://www.project-redcap.org/).We developed case record forms (CRFs) to gather data on exposure (a) from the time of diagnosis of COVID-19 until discharge from the hospital/home care and (b) from the time of discharge from the hospital for COVID/19 until the development of PC-ROCM. Trained site investigators interviewed the cases in the hospital and abstracted treatment and diagnostic information from hospital records. For controls, data was collected through telephone interviews, and relevant clinical information was abstracted from hospital records/prescriptions. The filled-in CRFs were shared online with the I-MUCOR study group for data entry into Research Electronic Data Capture (REDCap) software (We used descriptive statistics (percentages), means (standard deviation), median (inter-quartile range) to describe cases and controls. We converted steroid dose into prednisolone equivalents  and usedWe used logistic regression to calculate crude and adjusted odds ratios (AOR) with 95% CI. For each of the ever- and never-hospitalised groups we selected covariates with a p-value for crude OR of less 0·20 for consideration in the multiple regression model [http://www.dagitty.net/) [Using these variables, we constructed directed acyclic graphs (DAG) separately for the never- and ever-hospitalized using DAGitty software (ty.net/)  to visuaty.net/) . We compThe Institutional Human Ethics Committee of the lead institutions of I-MUCOR and all the study hospitals approved the study procedures. The treating physicians of the study hospitals recruited study participants after explaining the study procedures and obtaining written informed consent directly from the cases and through digital modes from the controls.We included 267 cases and 256 controls. More cases than controls (48% vs. 35%) were 45–59 years old. The majority of cases and controls were men (67% and 62%), resided in a city or town (63% vs. 72%), had completed secondary and graduate education (68% vs. 75%), and lived in pucca (solid and permanent structure) house (93% vs. 97%).A significantly higher proportion of cases had a known history of diabetes mellitus (71% vs. 33%) than controls. Participants with diabetes mellitus were five times more likely to have PC-ROCM . A substantial number of non-diabetic cases and controls had elevated plasma glucose levels . Hence, it necessitated the inclusion of plasma glucose as an independent variable in the multiple regression model. Elevated plasma glucose was six times more likely to be associated with PC-ROCM after adjusting for diabetes mellitus and steroid use  . No doseWe included 116 cases and 231 controls. The proportion aged 45–59 years and ≥60 years was significantly higher among the cases (46% and 22%) as compared to the controls (19% and 8%). The majority of the cases than the controls were men (66% vs. 58%), had completed secondary and graduate-level education (66% vs. 83%), and lived in a pucca house (85% vs. 97%). More cases were village residents (43%) than the controls (22%).Cases were more likely to be ≥45 years of age. Cases were seven times more likely to have had diabetes mellitus  than controls after adjusting for age. After adjusting for steroid usage, those with elevated plasma glucose levels were 14 times more likely to have RC-ROCM . Steroid usage was associated with PC-ROCM .Cases had lower odds of using oxygen concentrators/humidifiers than the controls  after adjusting for age, diabetes mellitus, and hypertension. The odds of use of steam inhalation was also lower among the cases than in the controls . Nasal washing, other medications such as remdesivir, ivermectin, iron/zinc supplementation, and medications from the traditional medical system were not associated with PC- ROCM.The results of this pan-India case-control study indicates that older age, hyperglycemia, diabetes mellitus, and steroid usage were associated with PC- ROCM, irrespective of hospitalization for COVID-19. Frequent nasal wash and use of oxygen from cylinders were additional risk factors among those ever-hospitalised. In the never-hospitalised group, individuals reporting the use of oxygen through concentrators/humidifiers and practice of steam inhalation were at a lower risk for PC-ROCM.per se [Hyperglycemia as an independent risk factor for PC-ROCM, irrespective of hospitalization for COVID-19, merits attention. Published reports indicate that COVID-19 is associated with hyperglycemia in general. Hyperglycemia or new-onset diabetes mellitus has been observed with COVID-19  and is hper se  and sterper se , 24. TheDiabetes mellitus as a risk factor for ROCM is well documented during the pre-COVID era , 25. In In this study steroid usage was associated with PC-ROCM independent of hospitalisation, irrespective of cumulative dose. We documented substantial steroid usage among all COVID-19 patients and more so among the never-hospitalised group indicating irrational and unmonitored use. Poor adherence to the guidelines on steroid use for COVID-19 has been reported widely in India –32. HypeSimilar to the widely reported use of steroids, antibiotic overuse and consequent secondary bacterial infections were documented , 31. AntIn India, people use nasal irrigation with warm saline water at home  for symptom relief based on the traditional Indian systems of medicine. Among the ever-hospitalised group, frequent nasal wash (many times a day) was associated with PC-ROCM despite being reported by a small subset. It is possible that repeated nasal washing depletes the commensal organisms of the nasal mucosa and thus allowing the fungus to thrive uninterrupted . Poor maOxygen requirement during COVID-19 reflects the severity of pneumonia caused by SARS-CoV-2. During the pandemic’s peak, receipt of oxygen and its type depended entirely on the availability of hospital beds. Acute shortage of hospital beds with oxygen support could have been the reason for higher case fatality among severe COVID-19. Hence, it is likely that our participants could have had mild to moderate COVID-19, and such survival bias might have obscured any reported association with oxygen requirement. Among those who received oxygen, we documented a higher likelihood of PC-ROCM among those receiving through oxygen cylinder, albeit based on small sample size. Oxygen cylinders were used when the health care system was overwhelmed over and above that which could be met by piped oxygen supply. We hypothesize that a breach in the infection control practices during these periods could have contributed to the higher risk for PC-ROCM. During the same period, increased use of industrial oxygen for medical purposes was also implicated in the rise of mucormycosis among hospitalised patients . This isThe practise of steam inhalation and the use of oxygen concentrators/humidifiers seemed to reduce the likelihood of PC-ROCM among those isolated at home or in non-hospital settings in our study. A large proportion of study participants reported the practice of steam inhalation. The Indian Ministry of AYUSH  recommended steam inhalation once a day for symptomatic relief during COVID-19 . We did We did a multicentric study with representation from all regions of India to strengthen the generalisability of our findings. Internal validity of the study was ensured by systematic training and monitoring of the site investigators and assessment of risk separately in ever- and never-hospitalized groups. We used an unmatched design since matching may complicate the enrolment of the study participants. Instead, we identified confounders (through DAG) and estimated adjusted measures of association. We analysed the role of an exhaustive set of factors linked to PC-ROCM, such as plasma glucose, steroid dose, and behavioural factors, that were hitherto not commonly examined. Statistical analysis of risk factors was based on an a-priori causal framework guided by directed acyclic graphs for the relationship between PC-ROCM and other variables. Thus each of the associations was adjusted separately for the relevant confounders only, which prevented over adjustment by covariates that are not confounders that would have resulted in potentially biased effect estimates due to induced confounding.Our study had a few limitations. Firstly, the selection of cases could suffer from survival bias as those who survived ROCM were more likely to be recruited. This could bias our estimates in either direction. To increase representativeness and reduce selection bias, the study included individuals from both private and public health facilities, as well as two groups of people participants , to also include the persons who did not seek medical care for COVID-19. Secondly, although recruited from within the same hospital, many of the cases and controls in the ever-hospitalised group had received COVID-19 treatment elsewhere before their hospitalization for ROCM management. Hence, we could have missed certain risk factors from earlier hospitalization. However, we included only those individuals wherever complete information was available. In addition, misclassification could have occurred for certain factors such as plasma glycaemic status and steroid usage among the ever-hospitalised group. However, that is likely to be non-differential, leading to the association being biased towards the null. Despite this, we could calculate the odds ratios with reasonable precision. Thirdly, among the never-hospitalised group, recall bias and unverifiable level of practices leading to misclassification of key risk factors could have resulted in biased estimates in either direction. Fourthly, we could not directly measure the association of PC-ROCM with the severity of COVID-19 on account of a lack of uniformity in severity grading and irregular documentation across the multiple hospitals where cases and controls were managed. Finally, we could not incorporate factors like HbA1C and BMI, due to substantial missing values.We concluded that in COVID-19, hyperglycaemia irrespective of pre-existing diabetes mellitus and high steroid usage, was associated with the occurrence of PC-ROCM regardless of hospitalisation for COVID-19. To reduce the risk of PC-ROCM, we recommended adherence to protocols for rational use of steroids and systematic monitoring of plasma glucose levels notwithstanding the diabetic status.S1 Table(DOCX)Click here for additional data file.


Does prediction of suicide risk improve when combining face-to-face screening with electronic health record–based machine learning models?In this cohort study of 120 398 adult patient encounters, an ensemble learning approach combined suicide risk predictions from the Columbia Suicide Severity Rating Scale and a real-time machine learning model. Combined models outperformed either model alone for risks of suicide attempt and suicidal ideation across a variety of time periods.These findings suggest that health care systems should attempt to leverage the independent, complementary strengths of traditional clinician assessment and automated machine learning to improve suicide risk detection. This cohort study evaluates the respective and combined abilities of a real-time machine learning model and the Columbia Suicide Severity Rating Scale to predict suicide attempt and suicidal ideation among adults. Understanding the differences and potential synergies between traditional clinician assessment and automated machine learning might enable more accurate and useful suicide risk detection.To evaluate the respective and combined abilities of a real-time machine learning model and the Columbia Suicide Severity Rating Scale (C-SSRS) to predict suicide attempt (SA) and suicidal ideation (SI).This cohort study included encounters with adult patients (aged ≥18 years) at a major academic medical center. The C-SSRS was administered during routine care, and a Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) prediction was generated in the electronic health record. Encounters took place in the inpatient, ambulatory surgical, and emergency department settings. Data were collected from June 2019 to September 2020.International Classification of Diseases codes, occurring within various time periods after an index visit. We evaluated the retrospective validity of the C-SSRS, VSAIL, and ensemble models combining both. Discrimination metrics included area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPR), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Primary outcomes were the incidence of SA and SI, encoded as The cohort included 120 398 unique index visits for 83 394 patients . Within 30 days of an index visit, the combined models had higher AUROC  than both the VSAIL  and C-SSRS  models. In the highest risk-decile, ensemble methods had PPV of 1.3% to 1.4% for SA and 8.3% to 8.7% for SI and sensitivity of 77.6% to 79.5% for SA and 67.4% to 70.1% for SI, outperforming VSAIL  and C-SSRS .In this study, suicide risk prediction was optimal when leveraging both in-person screening  and historical EHR data . To improve suicide risk classification, prediction systems could combine pretrained machine learning with structured clinician assessment without needing to retrain the original model. In a civilian population, a model incorporating longitudinal EHR data16 predicted future suicidal behavior in both inpatient and outpatient visits with good sensitivity (33%-45%) and specificity (90%-95%). The Mental Health Research Network used data across 7 health systems, including the 9-item Patient Health Questionnaire (PHQ-9),17 to predict suicide attempt and death within 90 days, with the top 5% of risk scores accounting for 43% to 48% of suicide attempts. In a pediatric ED, a model combining EHR data and brief suicide screening (Ask Suicide Questionnaire [ASQ])36 outperformed screening alone in the prediction of subsequent suicide-related visits.Predictive models might augment face-to-face screening and leverage electronic health record (EHR) data toward the automated, early detection of individuals at risk of suicide. Kessler et al18 However, it remains to be established whether these alternative methods are “competing, complementary, or merely duplicative.”18 Vanderbilt University Medical Center (VUMC) has implemented universal screening in the ED with the C-SSRS since 2018. In parallel, our team has deployed the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model, which has generated real-time suicide risk predictions across the enterprise since June 2019. In this study, we evaluated the ability of the C-SSRS alone to predict suicide attempt (SA) and suicidal ideation (SI) and compared this performance to our real-time suicide risk prediction model. Finally, we analyzed the performance of our risk model combined with the C-SSRS to evaluate whether synergistic effects improve performance.Integrating clinical and statistical risk predictions might provide more effective suicide risk detection.20 We extracted C-SSRS response data from all screened patients from the Clarity Database in the EPIC Systems EHR. At each index visit in which the C-SSRS was administered, we also extracted the corresponding VSAIL risk scores generated at the start of each encounter. We included patient encounters in the inpatient , ambulatory surgical, and ED settings. The institutional review board at VUMC approved the analyses needed in this study. Because of the impracticality of obtaining patient consent in this EHR-based study, analyses were approved with waiver of consent. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines.35We studied an observational cohort of adult patients (aged ≥18 years) at VUMC from June 2019 to September 2020, extracted from the Vanderbilt Research Derivative, a clinical research repository.International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) stored in our EHR. To avoid circularity when using the C-SSRS to predict future SI and SA, we defined these outcomes exclusively through ICD codes without any reference to clinical screening. In eTable 1 in the 21The primary outcomes in this study were SA and SI, defined as separate events by coded self-injurious thoughts and behaviors (SITBs) occurring within 7, 30, 60, 90, and 180 days after the discharge date of each documented visit during the time period. SITBs were extracted from encounter diagnosis documentation encoded as 22 The timing of screening during clinical workflows varied considerably between sites and according to competing clinical priorities. Screening was performed during triage for ED encounters and during every shift for patients admitted to the psychiatric hospital. In outpatient clinical settings, screening was performed sporadically as dictated by clinical need . Registered nurses typically administered the C-SSRS to patients face to face, eg, at triage in the ED.VUMC implemented universal screening using the C-SSRS in June 2018 in line with Joint Commission mandates.The brief version of the C-SSRS consists of 6 cascading questions related to suicidal thoughts and behaviors. The 6 components of the brief C-SSRS implemented at VUMC were: question (Q) 1 (wish to be dead), Q 2 , Q 3 , Q 4 , Q 5 , and Q 6 . All screened patients were asked Qs 1, 2, and 6. Qs 3 to 5 were only asked if patients answered yes to Q 2.13Each question was asked with respect to occurrences within the past month or since the last assessment. Since there are many permutations of answers, C-SSRS scores were aggregated into an ordered series of risk groups determined by the VUMC safety board: green, indicating no reported ideation or behavior; yellow, wish to be dead, suicidal thoughts without method or plan, or suicidal behavior more than 1 year ago; orange, suicidal thoughts with method but without plan or suicidal behavior between 3 months and 1 year ago; and red, suicidal intent with or without plan or suicidal behavior within 3 months. If patients scored yellow, orange, or red, interventions were recommended by automated clinician-focused advisories in the patient medical record. These C-SSRS risk classifications used at VUMC do not correspond directly to the scoring methods validated by Posner et al.23 The model uses historical EHR data, including the following features: demographic data , diagnostic codes, medication data, past health care utilization, and area deprivation index by patient zip code. Full model training and validation details have been published elsewhere.23The VSAIL model was initially trained using the random forest algorithm on a heterogenous mix of adult VUMC patients that included 3250 manually validated SA cases and a set of 12 695 controls with no history of SA.24 Although the VSAIL model was initially trained to predict SA and SI at 30 days, it has been validated for SA and SI across a variety of time periods .25 Therefore, for each index visit, we used the associated VSAIL score as the predicted risk of SA and SI occurring within each corresponding time interval. A comprehensive evaluation is provided for 30-day outcomes based on previous selection of this prediction target.Since its deployment in 2019 to our production EHR environment (Epic Systems Corporation), the VSAIL model has silently calculated predictions of suicide risk at the start of routine clinical visits. Clinical decision support prompted by VSAIL has been designed in parallel but was not active during the period of this research.The data were transformed to the encounter level, with each row representing a unique patient visit. Only the responses associated with the index visit were considered, and a patient’s historical screening responses were ignored. Patient responses to the 6 C-SSRS components were represented as binary features with yes represented as 1 and 0 otherwise. An unstructured text field containing screener comments was also transformed to a binary feature in which nonnull values were represented as 1 and null values as 0. Using these covariate features, we fit separate logistic regression models for SA and SI within each time period. We calculated 95% CIs for the odds ratio (OR) of each covariate and tested the statistical significance of the observed effect sizes.To assess the predictive validity of the C-SSRS triage system used at VUMC to estimate risk of SA and SI, we developed a rule-based model that classified patients scoring red as high-risk (predicted class 1) and all others as low-risk (predicted class 0). To optimally compare and combine face-to-face screening assessments with VSAIL predictions, we also trained a logistic regression model using the C-SSRS component features and predicted suicide risk at each index visit in the cohort. We generated out-of-sample predictions for each observation by using 5-fold cross-validation and combining the hold out set predictions from each fold. Based on an initial performance comparison and the continuous output of the VSAIL model, we used the similarly formatted C-SSRS regression model predictions, rather than the binary output of the C-SSRS rule-based model, to develop ensemble models.34 We used the mean and the maximum of these 2 predictions to test simple aggregations. We also calculated a weighted average equal to the C-SSRS regression for medium- to high-risk categories , and the VSAIL prediction for low-risk categories . To accommodate safety concerns with real-time deployment, the weighted average favored the C-SSRS for patients with active suicidal thoughts and behaviors, while favoring historical EHR data otherwise. Finally, we trained a lasso regression model with 5-fold cross-validation that used both the C-SSRS regression and VSAIL predictions as input features and produced a final risk prediction. To evaluate the lasso model, we combined hold out set predictions from each cross-validation fold.Since the VSAIL model was already implemented in production and calculated in real-time, we avoided retraining in favor of ensemble methods that combined the C-SSRS regression and VSAIL predictions. This scenario is an example of late fusion, which combines the results of models trained separately into a single model.26 Instead, we focused our primary evaluation on PPV and sensitivity at various prediction cutoffs to provide more informative metrics for clinical decision-making and risk detection with rare outcomes. To assess the added value of ensemble models, we calculated the integrated discrimination improvement (IDI), a measure of the improvement in integrated sensitivity and specificity.43We assessed standard discrimination performance metrics: area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPR), sensitivity, specificity, positive predictive value (PPV), and negative predictive (NPV). Since AUROC can be problematic in classification problems with high case imbalance, it was used only as a preliminary comparison between models.Statistical analyses were conducted using Python version 3.8.2 (Python Software Foundation). Hypotheses tests were 2-sided and specified at a significance level of .05.Our cohort included 120 398 unique index-visits for 83 394 patients . SA was documented in 84 (0.07%), 205 (0.17%), 272 (0.23%), 356 (0.30%), and 514 (0.43%) cases at 7, 30, 60, 90, and 180 days, respectively. SI was documented in 614 (0.51%), 1486 (1.23%), 2036 (1.69%), 2433 (2.02%), 3126 (2.60%) cases at 7, 30, 60, 90, and 180 days, respectively .For SA and SI across all time intervals, positive responses to Q 1, Q 2, and Q 6 had the largest effect sizes and were most frequently statistically significant . For SA Across all time intervals, the logistic regression model trained with C-SSRS question-response features outperformed the C-SSRS triage system in terms of AUROC and AUPR. The combination of the C-SSRS regression and VSAIL models outperformed either alone in terms of AUROC and AUPR for both SA and SI at all time intervals. For example, within 30 days of an index visit, the combined models had an AUROC for SA of 0.874 to 0.887 and for SI of 0.869 to 0.879, while the VSAIL model had an AUROC for SA of 0.729 and for SI of 0.773 and the C-SSRS regression model had an AUROC for SA of 0.823 and for SI of 0.777. The weighted average had the highest AUROC among the ensemble models, while the unweighted average generally performed the best in terms of AUPR. The maximum prediction was equivalent to the C-SSRS regression and therefore disregarded in further evaluation. Both C-SSRS–based predictions outperformed the VSAIL model, except in terms of AUROC for longer-term SI. The VSAIL model showed equivalent or higher AUROC as time increased, while the AUROC for C-SSRS predictions decreased over time At lower-risk cutoffs for outcomes occurring within 30 days, the VSAIL model had higher sensitivity and specificity than the C-SSRS regression model. At higher-risk thresholds, the C-SSRS regression model offered substantially higher PPV than the VSAIL model and continued to identify the majority of cases up until the 95th percentile. The ensemble models had higher sensitivity, specificity, and PPV than both the C-SSRS regression and VSAIL models across all risk thresholds, except for the top 1%, where the best ensemble model was equivalent to the C-SSRS regression. In the top 10% of SA risk, ensemble models had higher sensitivity  and PPV  than the C-SSRS regression  and VSAIL models . The red C-SSRS triage tier identified most SA cases (53.7%) with a PPV (3.8%) that rivaled the C-SSRS regression and ensemble models at the 99th risk percentile . Full diFor SA occurring within 30 days, VSAIL had a higher AUPR (0.235) than the C-SSRS regression model (0.029) among psychiatric ED encounters. In the ED and inpatient settings, the C-SSRS regression had AUPR scores of 0.019 and 0.031, respectively, while the VSAIL model had AUPR values of 0.007 and 0.011, respectively. The VSAIL model performed better among White patients than patients with other racial or ethnic identities (AUPR: 0.107 vs 0.024), while the disparity was smaller for the C-SSRS regression (AUPR: 0.036 vs 0.024).P < .001) and 0.510 between the lasso and VSAIL models (P < .001). For SI at 30 days, the IDI was 0.024 between the lasso and C-SSRS regression models (P < .001) and 0.462 between the lasso and VSAIL models (P < .001).For SA at 30 days, the IDI was 0.053 between the lasso and C-SSRS regression models  had at least 1 prior visit with a diagnostic code for SA or SI, compared with 55 (75.3%) predicted by VSAIL alone. Cases identified by VSAIL alone, compared with the C-SSRS alone, were disproportionately male (61 [83.6%] vs 144 [60.8%]) and had a higher median (IQR) number of total visits (126 [78-302] vs 77 [21-137]) and years of EHR data (17.14 [3.97-26.85] vs 12.00 [2.05-19.54]).30 We found this improvement  to be clinically significant, although the costs and benefits of our ensemble approach will vary greatly between health care sites.We analyzed the predictive validity of the C-SSRS and evaluated the utility of combining face-to-face screening with an automated suicide risk prediction model. The primary finding was that the combination of the C-SSRS and VSAIL models outperformed either alone in the prediction of SA and SI at all time intervals. By leveraging the complementary strengths of historical EHR data and face-to-face screening, ensemble learning improved discrimination at various risk thresholds. In the highest risk decile for SA at 30 days, only the ensemble models surpassed thresholds (for PPV and sensitivity) required for suicide prediction models to deliver health economic benefit.27 Performance of the VSAIL model may have suffered because some observations in the analysis did not have extensive historical clinical data. Ensemble methods seemed to mitigate the respective weaknesses of both data sources by exploiting their diversity while also synthesizing their independent, complementary strengths.While the sensitivity of the C-SSRS regression decreased over time and increased for the VSAIL model, ensemble models showed consistent performance across time. There was a distinct difference in the predictive time scale of the C-SSRS, which records dynamic, short-term indicators of a patient’s suicidal thoughts and behaviors, and the EHR-based model, which learns a patient’s underlying risk level by incorporating longitudinal clinical features. At the 50th risk percentile cutoff, the VSAIL model had higher sensitivity and PPV than the C-SSRS regression model for both SA and SI. At the 95th and 99th percentile cutoffs, the C-SSRS regression performed notably better. Ensemble models might have benefited from the relative strengths of the VSAIL and C-SSRS regression models at lower and higher risk thresholds, respectively. The C-SSRS predictions might have been limited by the commonality of patients denying SI despite being at high risk of SA and death.17 found suicide risk screening (patient self-report via the PHQ-9) to be an important predictive feature when included alongside other clinical variables in initial EHR model training. Our work builds on this by demonstrating that structured clinician assessment  can be combined with existing risk prediction models for improved performance, even when screening data are not available at the time of EHR model training. Through our novel ensemble approach, we isolated and quantified the additive benefit of face-to-face screening when incorporated with EHR-based predictions. By also analyzing the discordance and potential synergies between these 2 existing risk prediction methods, we make novel contributions toward the reconciliation of statistical and clinical risk prediction, as outlined by Simon et al.18Simon et al33 showed poor predictive validity for screening instruments such as the C-SSRS. However, these instruments continue to be used clinically, and measuring their local predictive validity remains worthwhile. We found that the C-SSRS performs well locally, but its combination with automated scalable risk modeling performs better than either alone. Rapid assessments, such as the brief version of the C-SSRS used here or the ASQ, are likely preferable to longer assessments like the full C-SSRS .37A meta-analysis by Franklin et al41 An important benefit of clinical screening is that it creates an opportunity for patient-physician dialogue that can lead to individualized treatment interventions. Although EHR-based machine learning can be automated at scale, developing, validating, and implementing predictive models requires a substantial initial resource investment. Ethical and legal issues around privacy, data usage, and accountability also hinder the adoption of machine learning in health care.42Clinical screening and EHR-based models have strengths and weaknesses beyond their predictive performance. In-person screening requires time, mental health resources (which are often limited), training on standardized assessments , support from health care administrators, and workflow modifications.29 Although this has been proposed elsewhere, our results empirically support using an EHR-based model as an initial detection mechanism that prompts further in-person screening.19 At the 50th risk percentile cutoff, the VSAIL model would have identified 18% to 35% more individuals with SA (25%-42% for ensemble models) that were not detected by C-SSRS triage. However, the higher PPV offered by the C-SSRS will likely be necessary to confidently recommend preventive care for individuals with high risk. Using EHR-based machine learning and face-to-face screening in this hierarchical series seems to leverage their complementary nature in a way that augments, rather than replaces, clinician-centered care.Our findings highlight specific ways that face-to-face screening and EHR-based models might be used in tandem to overcome the challenges of predicting rare phenomena like SA and death. The low PPV currently offered by suicide risk models limits their potential utility in clinical practice, as falsely classifying patients as high-risk for suicide might worsen stigma and unnecessary interventions.31 Our results suggest that EHR-based models should incorporate available in-person screening data to improve sensitivity and PPV . For the majority of health care systems implementing face-to-face screening alone, incorporating EHR-based models can improve sensitivity at lower risk thresholds, provide continuous output for more specific decision cutoffs, and identify cases typically overlooked by clinician assessment .Alternatively, it might be more natural in some settings  to use a series implementation that applies statistical prediction to patients screening negative. The C-SSRS outperformed the VSAIL model , and SA cases identified by VSAIL alone were more likely to have no history of suicidal behavior (24.7% vs 7.2%). In settings where screening is widely administered, statistical prediction might be used secondarily to identify cases without prior suicidal behavior or with low screening risk due to nondisclosure. In-person screening and EHR-based models could also be (and often would be) implemented in parallel and combined with an ensemble model that outputs a final risk prediction and triggers clinical action. This would provide a marginally higher PPV than our C-SSRS triage system but would introduce many significant obstacles associated with using machine learning alone to dictate clinical interventions.23 The C-SSRS is a validated screening tool that predicts suicidal behavior, which likely enabled us to demonstrate notable performance improvements when integrating face-to-face screening and an EHR-based model.13 SA and SI are more prevalent than suicide death, and targeting these outcomes might lead to more clinically useful risk models with acceptable PPV.28A primary strength of this study is the collection of a large sample of EHR data with significant overlapping implementation periods for an automated prediction model and universal screening. Our cohort included a relatively large number of SA cases for an observational EHR-based study, which enabled us to develop and evaluate predictive risk models. This analysis greatly benefited from an existing prediction model developed on-site and supported by multiple validation studies.38 Additional attempts could allow clinicians to heuristically combine EHR-based predictions with in-person screening, in contrast to our ensemble learning approach. To validate the use of automated risk prediction and face-to-face screening in series, one must evaluate how screening methods would perform on the cohort initially identified by statistical risk prediction alone.Immediate extensions of this work include ongoing research using the VSAIL model to prompt suicide risk screening in a pragmatic clinical trial and concurrent efforts to incorporate the C-SSRS response data within our real-time modeling pipeline. Rigorous statistical analysis and replication are needed to further evaluate the discordance between statistical and clinical risk prediction across various patient cohorts. Our work leaves ample opportunity to explore alternate ways of integrating statistical and clinical risk predictions. Although lasso was chosen as a simple ensemble method, more complex algorithms, such as nonnegative least squares (NNLS), might improve performance.39 By defining the outcomes of interest (SA or SI) based on ICD codes associated with follow-up visits at VUMC, we excluded cases occurring outside our health care system. ICD codes may have introduced diagnostic imprecision or excluded incompletely coded cases of suicide .32 The ICD-10-CM codes used to define SA include language of “intentional self-harm”  or “suicide attempt” (T14). A caveat remains that intent to die cannot be gleaned from an ICD-10-CM code for “intentional self-harm” alone. Holistic ascertainment of suicide death was unavailable during the study period and therefore not considered as an outcome The C-SSRS was used to recommend preventive interventions, and this likely confounded the predictive performance of the C-SSRS model, as patients screening positive might have received treatments that prevented future suicidal events.Limitations of this work include its confinement to a single medical center, with reliance on universal screening  and a pretrained model, which may limit generalizability. Although we estimated that screening was administered for 98% of adult ED visits, our cohort excluded a larger set of patient visits (particularly those outside the ED) that did not include the screening. This underestimated the broader applicability of the VSAIL model and may have inflated PPV given the higher case prevalence in this cohort. Since patients with more than 1 visit were included multiple times, discrimination metrics might have been affected by the repeated-observations problem.In this study, ensemble models combining the C-SSRS and an EHR-based machine learning model outperformed either alone in the prediction of SA and SI. Differences across various risk thresholds, time periods, and characteristics of identified cases seem to underly a synergy between clinical and statistical risk prediction. The improvement  from combining in-person screening and historical EHR data was clinically significant, although the costs and benefits of our ensemble approach will vary greatly between health care sites. Further research is needed to compare alternate ways of combining clinical and statistical risk prediction and to analyze the practical implications of implementing them in clinical systems.


Heterogeneity of the population in relation to infection, COVID-19 vaccination, and host characteristics is likely reflected in the underlying SARS-CoV-2 antibody responses.We measured IgM, IgA, and IgG levels against SARS-CoV-2 spike and nucleocapsid antigens in 1076 adults of a cohort study in Catalonia between June and November 2020 and a second time between May and July 2021. Questionnaire data and electronic health records on vaccination and COVID-19 testing were available in both periods. Data on several lifestyle, health-related, and sociodemographic characteristics were also available.Antibody seroreversion occurred in 35.8% of the 64 participants non-vaccinated and infected almost a year ago and was related to asymptomatic infection, age above 60 years, and smoking. Moreover, the analysis on kinetics revealed that among all responses, IgG RBD, IgA RBD, and IgG S2 decreased less within 1 year after infection. Among vaccinated, 2.1% did not present antibodies at the time of testing and approximately 1% had breakthrough infections post-vaccination. In the post-vaccination era, IgM responses and those against nucleoprotein were much less prevalent. In previously infected individuals, vaccination boosted the immune response and there was a slight but statistically significant increase in responses after a 2nd compared to the 1st dose. Infected vaccinated participants had superior antibody levels across time compared to naïve-vaccinated people. mRNA vaccines and, particularly the Spikevax, induced higher antibodies after 1st and 2nd doses compared to Vaxzevria or Janssen COVID-19 vaccines. In multivariable regression analyses, antibody responses after vaccination were predicted by the type of vaccine, infection age, sex, smoking, and mental and cardiovascular diseases.Our data support that infected people would benefit from vaccination. Results also indicate that hybrid immunity results in superior antibody responses and infection-naïve people would need a booster dose earlier than previously infected people. Mental diseases are associated with less efficient responses to vaccination.The online version contains supplementary material available at 10.1186/s12916-022-02547-2. Natural SARS-CoV-2 infection and vaccination against COVID-19 both contribute to building the population’s immunity. Immune responses are multifaceted but single components that are easy to measure such as antibodies are used in epidemiological studies to answer emerging questions such as what is the percentage of the population lacking antibodies, their characteristics, and the longevity of antibody responses induced after infection and/or vaccination.Most seroepidemiological studies conducted after COVID-19 vaccine administration began have focused on specific groups  . A simulA growing number of studies show the benefit of vaccination in previously infected people although there is still uncertainty on the number of doses required and how this could be modified by vaccine type and time –3. Most The durability of antibody responses after infection and/or vaccination is less well explored within the general population . StudiesIn this study, we are following up for a second time during the pandemic an adult population in Catalonia using multiplex antibody serology against SARS-CoV-2 in blood samples collected 6 months after the start of the vaccine rollout in Spain and incorporated questionnaire data and information from healthcare registries. We aimed to assess the SARS-CoV-2 seroprevalence in vaccinated and unvaccinated people and the durability of antibody responses induced by infection and vaccination and to identify major determinants of antibody responses induced by vaccination.This analysis uses data longitudinally collected during the COVID-19 pandemic among participants of the Genomes for Life (GCAT) cohort in Catalonia. The GCAT cohort includes mainly middle-aged participants who are residents in Catalonia, and their recruitment started in 2014 [E. coli and His tag-purified [Blood samples collected at both time points were processed within 24 h of collection and were analyzed at the ISGlobal Immunology laboratory in Barcelona. The levels [median fluorescence intensity (MFI)] of IgG, IgM, and IgA were assessed by high-throughput multiplex quantitative suspension array technology against a panel of 5 SARS-CoV-2 antigens: the spike full-length protein (S) and the receptor-binding domain (RBD) produced at IDIBAPs  , the subpurified ). In addpurified .10-transformed MFI of 128 pre-pandemic controls. Results were defined as undetermined when the MFI levels for a given isotype-antigen combination were between the positivity threshold and an upper limit at 10 to the mean plus 4.5 standard deviations of the log10-transformed MFIs of pre-pandemic samples, and no other isotype-antigen combination was above the positivity cut-off. We defined overall serostatus, by isotype and by antigen.Assay performance was previously established as 100% specificity and 95.78% sensitivity for seropositivity 14 days after symptom onset . AntigenDuring the course of this study, the vaccines approved for use in Spain were the Comirnaty ); the Spikevax ; the Vaxzevria ; and the Janssen COVID-19 vaccine . The first doses (January 2021) were distributed among some of the most vulnerable groups, including residents and personnel working in retirement homes, front-line healthcare workers, highly dependent people, and seniors aged 80 and older. Subsequently, essential workers and then adults in descending age order were vaccinated. The government restricted vaccination with Vaxzevria to specific age groups  and offered the Comirnaty or the Spikevax vaccine as 2nd dose for those <60 years old. The vaccines were offered on a voluntary basis to all individuals.In this study, we used electronic registries to identify the number of doses, date of administration, and trade names of vaccines for each study participant. We defined participants as non-vaccinated, partially vaccinated (when they had received one dose for those vaccines that have a two-dose regimen and had not acquired infection previously), and fully vaccinated (when they had received two doses for those vaccines that have a two-dose regimen or one dose in those previously infected with SARS-CoV-2 [recommended after 6 months following infection] or one dose for vaccines that have a one-dose regimen).Performance and results on viral detection tests (PCR or antigen test) were either self-reported or identified by the use of the official repository of tests for SARS-CoV-2 of the Epidemiological Surveillance Emergency Service of Catalonia of the Department of Health . We comb10 transformed, due to their skewed distribution. Differences in antibody levels by vaccine type or by vaccination and/or infection were examined using one-way ANOVA and pairwise comparisons were performed using the Tukey post hoc test. Previously infected non-vaccinated individuals were classified for each isotype-antigen combination, as sustainers when the ratio of antibody levels between the two visits ≥1 or decayers when the ratio of antibody levels between the two visits <1 following the previous methodology . In increasers and decayers, linear mixed-effects models with random intercept and random slope were used to evaluate the trend in antibody levels between the two serology time points, we estimated fold change 1 year after infection separately in sustainers and decayers. Generalized additive models were used to explore the shape of the relationship between days since vaccination and antibody levels among vaccinated people stratified by infection, models were done separately for participants with 1 dose or 2 doses at the time of serology. Among vaccinated people, univariable and multivariable stepwise linear regression models were fit to determine the effect of several variables on the antibody levels. We excluded only people vaccinated with the Janssen COVID-19 vaccine because they were few and eligible only for one dose at that time. We evaluated all the determinants in univariable models and also built a final model for each isotype-antigen combination using forward stepwise regression models including all the described determinants considering 0.1 the significance level for addition to the model and 0.2 for removal from the model. We report associations as a fold change with 95% confidence intervals (FC 95% CI) obtained by 10^beta. We considered the following variables as determinants: age (<60 or ≥60 years old), sex , highest attained educational level , area-based socioeconomic status according to the area of residency (in quantiles), current smoking status , daily alcohol consumption , body mass index (BMI) status , SARS-CoV-2 infection, vaccine type, and self-reported information on chronic diseases  including cardiovascular diseases , mental health diseases/addictions , and immune-related diseases . We adjusted all models a priori for time since the last vaccine dose  and the number of doses (one or two). Analysis was repeated by vaccine type. Participants with missing covariates were excluded from the final analysis models. We performed all statistical analyses using Stata/SE .Descriptive analyses of the study population characteristics and comparisons between groups were conducted. In all analyses, we used antibody levels logOverall, 1076 people were included in this analysis, aged 43–72 years old and 59.1% were female. By the time of sample collection , 130 (12.1%) participants were not vaccinated, 267 (24.8%) were partially vaccinated, and 679 (63.1%) were fully vaccinated according to the guidelines in force at that time (details in the “Methods” section). We detected a heterologous prime-boost approach in 11 people (Vaxzevria as a first shot followed by Comirnaty). Among vaccinated participants, the first dose was either Comirnaty (44.4 %) or Vaxzevria (41.9%) and a smaller fraction of people was vaccinated with Spikevax (12%) or Janssen COVID-19 vaccine (1.7%). Median time since the last vaccination was 28 days . The median time between 1st and 2nd doses was 21 days for Comirnaty , 28 days for Spikevax , and 83 days for Vaxzevria . A higher proportion of non-vaccinated participants had a previous positive viral detection test (32.3%) or positive serology in 2020 (49.2%) compared to vaccinated participants (12.5% and 28.2% respectively). Post-vaccination, approximately 1% of the people presented breakthrough infections detected by viral detection tests. Three participants were infected after the first vaccine dose and six after the 2nd vaccine dose  people had been infected incorporating data from serology in 2020 and 2021, registries, and questionnaires , the overall seroprevalence of our study sample increased from 30.8 to 92.6%  had significantly higher levels of IgM, IgA, and IgG against RBD, S, and S2 compared to Vaxzevria or Janssen COVID-19 vaccination Fig. a, b. TheWe examined the effect of time since vaccination on antibody levels against RBD, S, and S2 using cross-sectional data from all vaccinated participants with time since the last vaccination ranging from 1 to 160 days, adjusting for participant’s age and vaccine type and stratified by infection Fig. a, b. PreFigure In stepwise multivariable regression models Fig. , the vacOur analysis, based on a middle-aged adult population followed up twice during the pandemic with measures of multiple antibody responses against SARS-CoV-2 before and after vaccination, identified key aspects in both infection-acquired and vaccine-acquired immunity. First, 35.8% of infected non-vaccinated participants had no longer detectable antibodies over a year after infection. Second, vaccination induced higher responses in previously infected individuals and they had considerably higher antibody levels over time compared to naïve-vaccinated people. Third, clinical phenotype and antibody levels after infection were strongly associated with the antibody responses after vaccination. Fourth, vaccine type was the major modifiable factor of antibody responses after vaccination and Spikevax resulted in the highest responses. Other intrinsic and extrinsic factors also determined the vaccination responses.A very small (7.5%) proportion of our population had no detectable antibodies in the 2021 serological assessment and was likely susceptible to COVID-19. These people are a target group for vaccination campaigns. Most of them were not vaccinated people and around half of those reported a previous infection. Studies on vaccine hesitancy highlight that having experienced an infection is one of the reasons for not being vaccinated  or lowerHigher IgA and IgG responses against S antigens were produced among infected vaccinated people compared to their naïve-vaccinated peers. This difference in responses was shown to persist 5 months after vaccination. Bates et al. showed that a boost in humoral immune responses occurs regardless of whether the infection is acquired before or after any vaccination and concerns neutralizing and binding antibodies as well . In our Only a few vaccinated people had no detectable antibodies at the time of sampling and this was related to the vaccine type and the time since vaccination uptake (either too close to the 1st shot of Comirnaty or too far to the 2nd dose of Vaxzevria). Vaccine type among other characteristics was one of the determinants of all antibody responses after vaccination. We confirmed that mRNA vaccination was associated with higher antibody levels compared to other vaccine types . ParticuWe found that individuals 60 years or older had lower antibody levels after vaccination compared to younger participants and increased age has been associated with reduced vaccine responses in a number of studies irrespective of vaccine type , 37. A sSmokers had considerably lower antibody levels after vaccination and infected smokers were more likely to serorevert a year after infection. A recent systematic review indicated that active smoking negatively impacts humoral response to COVID-19 vaccines but there is limited evidence on the underlying pathophysiologic mechanisms . People This study was limited by its use of a sample nested within a cohort mainly of blood donors, which may limit the generalizability of the results. Focus on early time periods after vaccination limits our analysis on the durability of responses induced by vaccines. Testing people at a later phase of the vaccination campaign would probably result in more people fully vaccinated but we were able to show the effect of different combinations of the number of doses and type of vaccine on antibody levels. Another issue is that we measured binding but not neutralizing antibodies, which is arguably a better surrogate of immune protection. IgG levels to S and RBD and neutralization titers are typically highly correlated . AnotherIn summary, in this large cohort study, we compared the antibody responses induced by different COVID-19 vaccines, in people with different histories of exposure to the virus and underlying characteristics. All these differences accounted for non-homologous vaccine responses among individuals. We showed that infection and vaccination resulted in higher and longer-lasting antibody responses compared to vaccination alone, in the long term as well. Importantly, the response to primary infection was strongly related to the response to vaccination, adding to the growing literature on the potency of previous (or first) exposures to shape our immune system. Moreover, in addition to the well-studied determinants of antibody responses after vaccination , we identified other novel determinants . Taken together, our results indicate that vaccination campaigns should be tailored according to vaccines available, previous history of SARS-CoV-2 infection, and characteristics of the population in order to achieve optimal responses and protection as possible across individuals.Additional file 1: Table S1. Characteristics of participants with breakthrough infections, post-vaccination. Table S2. Allocation of vaccinated and non-vaccinated participants with evidence of infection according to the criterion of infection fulfilled. Table S3. Fold change (FC) (95% CI) in antibody levels within one year after infection estimated using two repeated samples among decayers. Estimates are based on linear mixed-effects models. Table S4. Spearman correlations for RBD antigen. All participants, n=1,076. Darker red=stronger association. Table S5. Cross tabulation between serostatus to RBD of Wuhan variant with the RBD of Alpha, Beta Gamma and Delta variants. Table S6. Characteristics of non-responders (seronagetive or with an undetermined status) to vaccination. Table S7. Association  between each determinant with log10 antibody leves in vaccinated people after adjusting each model for time since last vaccination and number of doses. Participants with any vaccination excluding Janssen (n=923). Table S8. P-values for comparisons related to Figs. Figure S1. Dates and density of positive viral detection tests, sampling in 2020  and 2021  and receipt of 1st vaccine dose in the study population . Figure S2. Venn diagram illustrating overlap between sustainer groups of IgA or IgG antibodies against nucleoprotein and spike antigens, among all infected unvaccinated participants (n=64). Figure S3. Differences in IgG antibody responses against RBD between Wuhan, Alpha, Beta, Gamma and Delta variant among vaccinated people. All differences were statistically significant apart from Delta vs Wuhan (p=0.861) and Alpha vs Wuhan (p=0.051). Figure S4. Generalized additive models for associations of days since vaccination with antibody responses to the six isotype-antigen combinations in infected (red) and naïve (blue) participants after first or second dose in people vaccinated by Vaxzevria (a), Comirnaty (b) or Spikevax (c). Fitted lines after adjustment for participant’s age. Plus symbols (+) represent measured responses for a specific participant. Figure S5. Differences in antibody responses by infection and/or vaccination and number of doses in people vaccinated with Comirnaty (a), Spikevarx (b), Vaxzevria (c) of Janssen COVID-19 vaccine (d). Figure S6. Differences in IgM responses by infection and/or vaccination and number of doses. Table S8 presents corresponding p-values.ge (FC) 9% CI in a


In follicular lymphoma (FL), progression of disease ≤24 months (POD24) has emerged as an important prognostic marker for overall survival (OS). We aimed to investigate survival more broadly by timing of progression and treatment in a national population-based setting. We identified 948 stage II-IV indolent FL patients in the Swedish Lymphoma Register diagnosed 2007–2014 who received first-line systemic therapy, followed through 2020. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated by first POD at any time during follow-up using Cox regression. OS was predicted by POD using an illness-death model. During a median follow-up of 6.1 years (IQR: 3.5–8.4), 414 patients experienced POD (44%), of which 270 (65%) occurred ≤24 months. POD was represented by a transformation in 15% of cases. Compared to progression-free patients, POD increased all-cause mortality across treatments, but less so among patients treated with rituximab(R)-single  than R-chemotherapy . The effect of POD was similar following R-CHOP  and BR . The negative impact of POD on survival remained for progressions up to 5 years after R-chemotherapy, but was restricted to 2 years after R-single. After R-chemotherapy, the 5-year OS conditional on POD occurring at 12, 24, and 60 months was 34%, 46%, and 57% respectively, versus 78%, 82%, and 83% if progression-free. To conclude, POD before but also beyond 24 months is associated with worse survival, illustrating the need for individualized management for optimal care of FL patients. Due to the incurability of advanced-stage FL even with modern therapy, and the fact that early therapy has not been shown to prolong survival, many patients with indolent FL are managed with an initial watch-and-wait approach.4 However, some patients require immediate systemic treatment due to rapidly progressing or symptomatic disease. During recent decades, both progression-free and overall survival (PFS/OS) have improved following the introduction of monoclonal anti-CD20 antibody therapy , and improved supportive care.7 Importantly, these results have also translated into improvements in lymphoma-specific survival outside the clinical trial setting, as illustrated in 2 European population-based studies.9 Treatments of choice include R as a single agent or in combination with chemotherapy  or in cases where a fast tumor reduction is needed, R-CHOP .3 Still, some patients do not respond to therapy and suffer from rapidly progressive disease and risk of transformation to diffuse large B-cell lymphoma.10Follicular lymphoma (FL) is the most common indolent lymphoma type, accounting for about 20% of all lymphomas.13 but these are still not sufficient to predict need for treatment and risk of progression, why novel scoring methods are being developed.14 Several studies have found that FL patients in need of initial systemic treatment and subsequent early re-treatment within 2 years of diagnosis (POD24) have a particularly bad prognosis.20 The impact of POD24 on prognosis was initially established for patients treated with R-chemotherapy,22 but has since been demonstrated to predict outcome also among patients managed with immunotherapy only.25 However, the majority of reports on the impact of POD24 have been performed on patients from clinical trials or from single centers, and comprehensive real-world evaluations of the impact of timing of progression are lacking. Also, the proportion of patients that experience POD24 appears to vary by treatment, as does the association of POD24 with risk for transformation.24 For example, lower rates of progression of disease (POD) have been reported for BR-treated patients, but with a concomitantly higher risk of transformation among BR-treated patients who do experience POD24.26 In contrast, the risk of experiencing POD24 appears to be higher among patients managed with immunotherapy only,24 but the impact on survival among these patients is less pronounced.17Factors that determine early disease progression and need for treatment are not yet entirely defined. Currently, patients with FL are risk stratified using FLIPI, FLIPI2, and m7-FLIPI, which take into account clinical and laboratory characteristics and molecular features in the latter,The aim of the current study was to investigate the impact of timing of first progression on survival more broadly both before and beyond the POD24 time point, and by type of first-line systemic treatment in a large Swedish population-based cohort of FL patients diagnosed 2007–2014 and followed through 2020.27 The SLR records detailed data on patient and clinical characteristics at diagnosis, first-line treatment, and progression. For this study, the register data were validated and supplemented with data on progression, transformation, and second-line therapies from a medical chart review. Informed consent was enquired for all patients alive by October 18, 2018, except in 2 Swedish regions where the ethical permission allowed for complementary data collection without active consent . For deceased patients, no informed consent was needed from next-of-kin. The medical chart data collection was initiated in 2018 and finalized in 2020.Patients registered with a first diagnosis of indolent FL between 2007 and 2014 were identified through the Swedish Lymphoma Register (SLR). Discordant and primary cutaneous FL patients were not included. The SLR was initiated in 2000 and has a coverage above 95% compared to the nationwide Swedish Cancer Register.A total of 2079 registered patients were identified as eligible for the study and the medical chart review could be completed for 1844 FL patients (89%). Noncompletion was primarily due to a lack of active consent, missing medical records, and cases found to be discordant (transformed) at diagnosis although registered as indolent. Patients who were managed with watch-and-wait only during the entire follow-up period (n = 412), had grade 3B (n = 72), were stage I or limited stage II and treated with curative intent RT (n = 304), were stage III-IV disease and treated with RT only (n = 42), and patients who experienced a transformation before first-line treatment initiation (n = 66) were excluded. The final study population comprised 948 patients  without R, oral treatments such as chlorambucil, trophosphamide or lenalidomide, and patients with missing information on type of systemic treatment.Patients were classified as treated first-line with either R-chemotherapy (R-chemo), R-single, or other treatments. The R-chemo group included R-CHOP, BR, R-CVP , and R-FC . Standard dose of bendamustine in Sweden during the study period was 90 mg/mPOD was defined as either progressive disease as best response to first-line therapy, initial response but later relapse/progression as indication for second-line therapy, or transformation. Patients not (yet) fulfilling any of these criteria were considered progression-free (PF). Date of POD was subsequently defined as date of first-line evaluation, second-line treatment initiation date, or date of first transformation (after first-line treatment). POD24 was defined as fulfilling any of the above criteria within 24 months after start of first-line treatment.Frequencies and proportions of demographical, clinical, and follow-up characteristics were calculated by type of first-line treatment. Chi-square tests were used to test difference in distribution between categorical variables. Among patients with POD, the proportion of patients where POD (and POD24) was represented by a transformation was calculated by first-line treatment. Follow-up ended at date of death due to any cause, or end of study period, whichever came first. End of study period depended on when the data collection was performed, and varied between December 31, 2018, and December 31, 2020. Follow-up was restricted to the first 10 years following diagnosis. The POD rate was predicted by first-line treatment type from a univariable flexible parametric survival model allowing for non-proportional effects, with 4 degrees of freedom for both the baseline rate and the time-dependent effect.28 with three states: diagnosed and treated for FL and still PF and alive , progressed and alive (POD state), and deceased (DEAD state) . Transition rates from the initial state to the POD or DEAD state, and from POD to DEAD, were modeled using flexible parametric models. Time since initiation of first-line treatment was used as the underlying time scale, and the transition from POD to DEAD was additionally adjusted for time of POD . We separately assessed POD24 since previous studies have focused on this time point. Follow-up started at date of progression among POD24 patients, and at 24 months after treatment initiation among patients who were PF as in previous studies.31 In the PF patient group, patients dying (n = 74) or being censored due to the end of study period (n = 6) within 24 months were excluded from this analysis (n = 80). OS was estimated using the Kaplan–Meier method by POD24 for the different treatment groups. Last, we compared the lymphoma-specific and other-cause mortality between progressed and nonprogressed patients, using similar models as for the all-cause mortality analyses.The prognostic impact of POD was analyzed in 2 ways. To estimate the relative effect of POD on all-cause mortality by first-line treatment, Cox regression models were fitted treating POD as a time-varying exposure. Both univariable (adjusted for time since first-line treatment) and multivariable models  were fitted. Interaction effects between POD and treatments were tested using Wald tests. To further estimate the prognostic value of progression as a function of time of POD/PF since first-line treatment on the absolute risk scale, and hence, quantify the impact of timing of POD on survival, the 5-year OS conditional on either being PF or having experienced POD at different time points during follow-up was estimated. This was done using an illness-death modelIn the final cohort of 948 patients, 519 (55%) were treated with R-chemo, 273 (29%) with R-single, and 156 (16%) with other treatments . Patients treated with R-single had a lower proportion of high-risk FLIPI (35%) compared to patients treated with R-chemo (57%) and other treatments (39%) . Increasing age, male sex, high-risk FLIPI, and poor performance status were associated with increased all-cause mortality (Suppl. Table S1).The median time to primary treatment initiation was 47 days (interquartile range [IQR]: 22–133), and 205 (22%) patients started their treatment after 6 months or more. Median age at diagnosis was 66 (IQR: 58–73), 64 (55–71), and 71 (62–79) years for patients treated with R-chemo, R-single, and other treatments, respectively (Table P < 0.001). Within the R-chemo group, 110 (36%) and 44 (29%) patients treated with R-CHOP and BR, respectively, experienced POD. The rate of POD was highest during the first 2 to 3 years following first-line treatment  was 6.1 years (IQR: 3.5–8.4), during which 414 (44%) patients experienced POD Table . The pro) and 44 9% patien) and 44 9% patienadj = 8.17, 95% CI: 6.09-10.94). For patients treated with R-single, the effect of POD was significantly smaller   as among patients treated with BR  (P = 0.7020). Similar results were seen in the analysis of POD24, but with a significantly stronger effect of POD24 in BR compared to R-CHOP treated patients: patients with POD24 after BR experienced a 17-fold increased mortality compared with PF patients, whereas among R-CHOP treated patients it was 6-fold increased . When investigating lymphoma-specific and other-cause mortality among progressed and nonprogressed patients, patients treated with R-chemo had a 42 times higher lymphoma-specific mortality rate compared with similar patients staying PF, and a 2.4 times higher mortality due to other causes (Suppl. Table S2). Patients who progressed following R-single had a highly increased rate of lymphoma-specific death, but not of other causes.Patients experiencing POD after R-chemo had an 8-fold increased all-cause mortality rate following POD compared to PF patients  compared to previous studies,24 which may reflect our population-based setting where high-risk patients are more often selected for R-single. In contrast to other studies that have shown a lower proportion of POD24 after treatment with BR,18 we observe a similar proportion of POD (and POD24) in R-CHOP and BR-treated patients. Regardless of first-line treatment strategy, POD was associated with poor prognosis on both the relative and absolute survival scale. While patients treated with R-single had higher proportion of POD, its impact on survival was less dramatic compared to immunochemotherapy-treated patients who experienced POD. This is in line with previous findings on POD24 in patients treated with immunotherapy only24 and may be explained both by more favorable disease, and high efficacy of second-line treatment after initial management with immunotherapy only.To the best of our knowledge, this is the first population-based study of POD and its timing in relation to first-line treatment and survival. Most previous studies have focused on POD24, and have been performed on selected patients included in clinical trials, with lower median age and more favorable baseline characteristics than in our population-based study.23 demonstrated a 5-year OS of 74% among patients treated with immunotherapy who experienced POD24 and 90% among those who did not, compared to 73% and 85% (respectively) in our study. However, our real-world data showed slightly worse survival among patients with POD within 24 months after R-chemo compared to previous studies.17 In part, this likely also reflects our population-based setting with national guidelines for indolent FL in Sweden recommend R-single also to high-risk patients. Thus, the patients who are selected to receive R-chemo in Sweden may represent an even more pronounced high-risk disease group, which could contribute to the worse survival rates observed. Complementary analyses of cause of death confirmed a strong association between POD and lymphoma-specific death, but also indicated increased mortality due to other causes among immunochemotherapy-treated patients.For patients managed with R-single, we show similar OS as those reported for patients treated in clinical trials with immunotherapy alone. For example, Lansigan et al18 compared with most other studies where approximately 20% of POD24 patients represented transformed disease.24 In our study, only 62 patients (15%) out of all progressed patients had a morphologically verified transformation, and this did not significantly differ between patients treated with R-CHOP, BR, or R-single. When including also clinically suspected transformation the total percentage was 17%, which is still lower than in previous reports. Thus, the inferior outcome among patients who experience POD after immunochemotherapy in our study is not explained by an increased risk of transformation, as was the case of POD24 in BR-treated patients in British Columbia.18The association between POD24 and transformation has varied between studies, with almost 76% of patients who experienced POD24 after BR treatment having transformed disease in one study,The main strength of this study is the population-based setting with unselected patients in combination with a detailed medical chart review, providing comprehensive data and few missing values. Another strength lies in our choice of statistical approach to analyze the prognostic value of timing of POD. Previous publications have reported POD24, which has laid the ground for research in prognostic markers with its ease of implementation and interpretation. However, POD24 is prone to some issues and limitations. First, the choice of time point can be considered more or less arbitrary, and dichotomizing an exposure implicitly results in loss of information. Moreover, patients progressing even shortly after the 24-month mark will contribute as PF, and patients without progression who die before 24 months will not enter the analysis, resulting in a loss of data. Last, the time scale has a different meaning for PF and progressed, as all patients who are PF at 24 months start their follow-up then, whereas progressed patients may start follow-up shortly after diagnosis. We believe that the prognostic impact of POD is even better studied when progression at different time points are accounted for, all patients are included, and the time scale has the same meaning for all. Regarding limitations, as is inevitable in observational studies, our study comes with an inherent bias due to selection to treatment. Although we have adjusted for several baseline characteristics affecting treatment selection the possibility of residual confounding remains.32 as well as the rapid development of novel agents such as CAR-T cell therapy and bispecific antibodies in FL,34 augment the need for improved individual risk assessment and treatment choice. The here reported findings on how the impact of progression on OS varies by timing of POD and by treatment constitutes important factors for this assessment. In future research, it is crucial to identify prognostic and predictive markers, not only for patients with POD24 but also for patients progressing after this time point.The recently reported use of response-adapted therapy in FLPOD up to five years after first-line systemic treatment has strong prognostic value for patients treated with immunochemotherapy, but less so for patients treated with R-single. In addition to the important finding of POD24 as a marker of subsequent poor survival in previous landmark studies, we should keep in mind that the strength of the association between POD and survival changes continuously over time both before and beyond the 24-month time point. This insight will allow us to improve the clinical prognostic value of POD to support decisions on second-line treatments and inclusion in trials of new therapies. Future studies should aim to determine factors that predict progression at different time points and evaluate whether risk of progression can be reduced through alternative initial management.https://etikprovningsmyndigheten.se).The data underlying this study are available at the National Board of Health and Welfare, Sweden, and Statistics Sweden for investigators with the appropriate approvals, but restrictions apply. However, data can be made available from the authors upon reasonable request for meta-analyses, and with the appropriate approvals of the Swedish Ethical Review Authority .