Furthermore, we show the patterning results of nanoporous crossbar arrays on colorless polyimide (CPI) by a repeated nTP process. We expect that this combined nanopatterning method of EPIL and nTP processes will be extendable to the fabrication of various nanodevices with complex circuits based on micro/nanostructures.High-resolution nanotransfer printing (nTP) technologies have attracted a tremendous amount of attention due to their excellent patternability, high productivity, and cost-effectiveness. However, there is still a need to develop low-cost mold manufacturing methods, because most nTP techniques generally require the use of patterned molds fabricated by high-cost lithography technology. Here, we introduce a novel nTP strategy that uses imprinted metal molds to serve as an alternative to a Si stamp in the transfer printing process. We present a method by which to fabricate rigid surface-patterned metallic molds  based on the process of direct extreme-pressure imprint lithography (EPIL). We also demonstrate the nanoscale pattern formation of functional materials, in this case Au, TiO High-resolution nanofabrication methods are essential for the realization of well-arranged nanostructures with specific functionalities applicable to various advanced future device systems, such as lithium-ion batteries (LIBs) ,2,3,4, hTo overcome these issues, several alternative lithography techniques, such as extreme ultraviolet (EUV) lithography ,27, nanoHerein, we introduce a new concept of the nTP process that uses surface-patterned metal master molds by extreme-pressure imprint lithography (EPIL). EPIL enables precise nanogeometries on a metallic substrate through the nanoscopic plastic deformation of the metal surface by a high load or high pressure using a Si stamp. The approach of fabricating numerous metal molds using a Si stamp can bring long-term economic feasibility in terms of the initial mold-based nTP process, which involves infinitely repeated replication steps. We also show how to fabricate various metal molds with nanopatterns by controlling the pressure based on the EPIL method. In addition, we explain how to obtain the replicated polymer patterns from the imprinted metal mold using an adhesive PI tape. Moreover, we demonstrate the reliable nanoscale pattern formation of functional materials on various surfaces via the T-nTP process.4, working pressure: 7 mTorr, power: 250 W). After the removal of the residual PR areas from the dry-etched Si substrate using an RIE system, the nanopatterned Si mold with nanoscale line structures was finally obtained. All chip-level Si stamps used in this study were used by cutting an 8-inch wafer fabricated by photolithography. Zn  and Al  substrates were directly pressed at extreme pressures of 6 and 6.5 tons, respectively, using a Si stamp fabricated by conventional photolithography. A Ni  substrate was imprinted at approximately 13 tons.To create the surface nanopatterns on metallic materials, extreme-pressure imprint lithography, which can directly generate multiscale periodic micro/nanoscale pattern structures, was experimentally employed. A Si stamp fabricated by photolithography with a width/space of 250 nm was mounted onto the RAM head of press equipment capable of injecting a high load onto metal substrates. The Si mold with a depth of 250 to 350 nm used for the EPIL process was produced by conventional KrF photolithography and the reactive ion etching (RIE) process. Spin-coated positive photoresist  with a thickness of ~400 nm on the 8-inch Si wafer was exposed using a KrF scanner . Then, exposed PR was developed using a developer solution . The remaining PR patterns were used as an etch mask to produce the Si pattern with plasma treatment by means of RIE (gas: CFVtol:Vace:Vhep = 4:4:2) to yield a 4 wt%. The solubility parameters  of PMMA, toluene, and acetone are 19.0, 18.3, and 19.7, respectively. Toluene and acetone are good solvents for dissolving solid PMMA. To impart a hydrophobic state on the surface of the imprinted metal substrates, the PDMS solution dissolved in toluene yielding a 2.5 wt% was spin-cast for 23 s at a speed of 5000 rpm. This step was followed by annealing at 150 °C for 2 h using a vacuum oven . After the spin-casting of the PMMA solution on the hydrophobic surface of the imprinted metal mold, the PMMA film was attached and detached with mild pressure (~25 kPa) using a PI adhesive tape . A heptane (δ = 15.3 MPa1/2), which has a poor solubility parameter for brush-treated PDMS material, can help spin-coated PMMA film to separate well from the PDMS-coated metal molds. In the replication process, a laminator system that can provide uniform pressure on the replication area was used.During the replication step, solid poly(methyl methacrylate) (PMMA) particle with a molecular weight of 100 kg/mol  and liquid hydroxyl-terminated polydimethylsiloxane (PDMS-OH) with a molecular weight of 5 kg/mol  were used. PMMA granules were dissolved in a mixed solvent of toluene , acetone , and heptane  , on PMMA replica pattern were contacted on the target substrate and passed between heated rolls. The deposition processes were implemented by physical vapor deposition (PVD) using a direct-current (DC) and radio-frequency (RF) sputtering system . The working pressure of the main chamber was constantly maintained at 5 × 10−3 torr during the PVD process (applied power: 200 W). An adhesive PI tape was manually removed from the target substrate after the bonded substrate-patterning medium-PI tape passed over the rolls.To transfer the nanoscale pattern structures, we utilized a heat-rolling-press system . This system has four rolls composed of elastic silicone rubber that can effectively provide both uniform pressure and heat. The speed of the rolls is controllable from 200 to 1500 mm/min. The rolling speed and temperature for pattern transfer printing in this study were set to 1000 mm/min and 150 °C. Deposited functional materials, in this case Au, TiOThe nanoscale pattern structures were observed using a field emission scanning electron microscope  at an acceleration voltage of 10 kV and a working distance of 5–8 mm.The process sequence for the formation of the nanoscale pattern structure using an imprinted metal mold is schematically depicted in To produce the master molds with metallic materials, the reliable top-down hard lithography of the EPIL process was employed. Several imprinted metal molds, which can be produced using one Si stamp, serve as a viable alternative with economic value to a Si mold during the T-nTP process for the fabrication of high-resolution nanostructures. 2/Si substrate. TiO2 and GST nanostructures were also successfully printed on the surfaces of polished metal and glass substrates, respectively. 2 nanoporous crossbar on a flexible and transparent colorless polyimide (CPI) film, indicating stable attachments even after the substrate was bent. These results clearly demonstrate that the generation of nanoscale patterns on diverse surfaces and materials can be realized via the versatile T-nTP method when using surface-patterned metal molds imprinted by EPIL.On the basis of these results for the combined method of EPIL and nTP, the possibility of applications in device systems that require the formation of high-resolution nanostructures on the surface of various substrates was confirmed. For the low-cost combined patterning method using an imprinted metal mold to become highly competitive compared to conventional lithographic technologies, such as photolithography, nanoimprint lithography, and inkjet printing, further research on shape control of the surfaces of metal molds and selective mirror polishing of surface-patterned metal substrates should be needed. Moreover, the development of a high-throughput automatic equipment system for the combined patterning technique will improve the reliability of the findings presented in this study.2, and GST, on various surfaces including SiO2/Si, polished metal, and slippery glass via the T-nTP process. Furthermore, we undertook the pattern generation of a TiO2 nanoporous crossbar structure on CPI film by the multiple T-nTP technique. We expect that this approach can be applied to other emerging device fabrication processes that involve complex functional nanostructures.In summary, a novel nanopatterning method that combines the EPIL and nTP processes to obtain well-ordered functional nanostructures was proposed. We fabricated various metallic nanomolds, in this case Zn, Al, and Ni, with periodic line structures from a Si stamp using the EPIL process without the use of an imprint resist or heat, laser, UV light, or electrical sources. The surface-patterned metal substrates were utilized as a master mold that can replace the Si stamps typically used during pattern transfer printing processes. We successfully replicated polymer patterns from the imprinted metal molds, and obtained well-ordered nanoscale line patterns with different line widths using PMMA replica patterns. We also demonstrated nanoscale pattern formation with functional materials, specifically, Au, TiO


Studies regarding effectiveness of anakinra and tocilizumab treatments in coronavirus disease 2019 (COVID-19) have contradictory results. Furthermore, there is scarce comparative data regarding superiority of any agent. To further elucidate any superiority between these two agents, we retrospectively investigated and compared outcomes in hospitalized COVID-19 patients of our inpatient cohort who received anakinra or tocilizumab.This study was designed as a single-center, retrospective, cross-sectional cohort study. Hospitalized patients with confirmed diagnosis of COVID-19 who had Brescia-COVID respiratory severity scale score ≥3 and hyperinflammation (defined as elevation of C reactive protein ≥50 g/L or ferritin ≥700 ng/mL) and received anakinra or tocilizumab in addition to standard care were enrolled in the study. Length of hospital stay after initiation of antiinflammatory treatment, need for mechanical ventilation, need for intensive care unit admission, mortality were set as primary outcomes and compared between tocilizumab and anakinra recipients after propensity score matching.One hundred and six patients were placed in each group after propensity score matching. In the anakinra group, relative risk reduction for intensive care unit admission was 50% when compared to the tocilizumab group and the number needed to treat to avert an intensive care unit admission was 3 . In terms of mortality, a 52% relative risk reduction was observed with anakinra treatment and the number needed to treat to avert an intensive care unit admission was 8 . Significantly more patients were observed to receive glucocorticoids in the anakinra group.Anakinra administration in severe COVID-19 patients was significantly associated with better survival and greater clinical improvement compared to the tocilizumab administration in our study. Increased rate of glucocorticoid use in the anakinra group might have contributed to better outcomes. Coronavirus disease 2019 (COVID-19) has a wide spectrum of clinical presentations with disease course varying from asymptomatic infection to severe pneumonia causing death [Cytokine storm has been revealed to correlate with disease severity in hospitalized COVID-19 patients and defined as a prognostic factor for worse outcomes –5. ThereInterleukin-1 and IL-6 are proinflammatory cytokines which have been demonstrated to be major contributors to development of hyperinflammatory response in COVID-19. Interleukin-1a is one of the initial cytokines released in COVID-19, inducing IL-6, TNF-α, granulocyte-macrophage colony stimulating factor (GM-CSF), and IL-17 expression after binding to its receptor [Tocilizumab is a recombinant humanized monoclonal antibody directed against soluble and membrane-bound IL-6 receptors, and anakinra is a recombinant human IL-1 receptor antagonist. Effectiveness of both tocilizumab and anakinra on outcomes has been reported alongside contradictory results; nevertheless, both agents have been used in management of COVID-19 during the pandemic –19. In aTo further elucidate any superiority between these two agents, here in this study, we retrospectively investigated and compared outcomes in hospitalized COVID-19 patients of our inpatient cohort who received anakinra or tocilizumab treatments.This is a single-center, retrospective, cross-sectional study. Ethical approval of the study was obtained from the Ethics Committee of Ankara City Hospital. Hospitalized adult COVID-19 patients between April 1 and December 31, 2020, from Ankara City Hospital Internal Medicine, Infectious Diseases and Clinical Microbiology, Critical Care clinics were retrospectively investigated. Among these, patients with confirmed diagnosis of COVID-19 (positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction test with compatible findings in chest X-ray or computed tomography) who had Brescia-COVID respiratory severity scale (BCRSS) score ≥3 and hyperinflammation (defined as elevation of C-reactive protein (CRP) ≥50 mg/L or ferritin ≥700 ng/mL) and received anakinra or tocilizumab treatments in addition to standard care were enrolled in the study . PatientIn our center during the pandemic, hospitalization, treatment, management, and discharge decisions of the patients were made according to the national guidelines prepared by Turkish Health MinistryDemographic, clinical, laboratory, imaging, treatment, and outcome data were collected using a standardized form. All data were saved by the same physician (OK). Patients were grouped into two as anakinra recipients and tocilizumab recipients. Length of hospital stay after initiation of antiinflammatory treatment, need for mechanical ventilation, need for intensive care unit (ICU) admission, and mortality were set as primary outcomes and compared between groups after propensity score (PPS) matching.2 test was used and the outcomes were expressed as number and percentages. Relative risk (RR) values and their 95% confidence interval (CI) were calculated through crosstabs. p-values below 0.05 were considered statistically significant. We calculated the propensity between two groups on the basis number of days from symptom onset to administration of anticytokine treatment used in the logistic-regression analysis. We matched each patient in the tocilizumab group to the patient in the anakinra group (1:1) with the closest propensity score, using a greedy nearest neighbor matching without replacement within 0.001 caliper widths.Data were analyzed using SPSS v. 22.0 software except for propensity score matching which was done with the statistical software R . Shapiro–Wilk’s test was used to determine the distribution of the data. The distribution of continuous data was expressed as mean ± standard deviation. Continuous variables that did not conform to normal distribution were expressed as median and interquartile range (IQR) values. Continuous variable was compared by using either Student’s t-test or the Mann–Whitney U test according to normality. For categorical variables, χA total of 360 patients were enrolled in the study . Demographics, comorbidities, COVID-19 symptoms, and days from symptom onset to administration of anticytokine treatment are given in Baseline and last laboratory parameters of both groups before and after PPS matching are given in Initial median (IQR) anakinra dose was found to be 600 (200) mg and median (IQR) duration of treatment was 7 (4) days before and after PPS matching. Median (IQR) tocilizumab dose for first administration was 400 (200) mg and median (IQR) duration of treatment was 2 (1) days.Primary outcomes are presented in Rates of favipiravir, hydroxychloroquine, and glucocorticoids are presented in After propensity score matching, in the intention-to-treat (ITT) population, intensive care unit admission developed in 35 of 106 patients (33%) who received anakinra and in 70 of 106 patients (66%) who received tocilizumab . The relative risk reduction with anakinra was 50% and the number needed to treat (NNT) to avert an intensive care unit admission was found to be 3 . After propensity score matching, in the ITT population, mortality developed in 12 of 106 patients (11.3%) who received anakinra and in 25 of 106 patients (23.6%) who received tocilizumab . The relative risk reduction with anakinra was 52% and the NNT to avert a mortality was found to be 8 .In this retrospective cohort study, we evaluated the outcomes of tocilizumab and anakinra treatments in severe (BCRSS ≥ 3) COVID-19 patients. Both tocilizumab and anakinra groups were similar in terms of age, sex, and comorbidity. After PPS matching based on days from symptom onset to initiation of anticytokine treatment, rates of mortality and admission to ICU were significantly lower in the anakinra group. Rate of additional glucocorticoid use was significantly higher in the anakinra group.COVID-19 generally causes mild illness; however, a substantial number of patients suffer from severe respiratory distress leading to death . SARS-CoImmune dysregulation in COVID-19 is reported to be mediated by IL-6 and in animal models low IL-6 concentrations were associated with less severe acute lung injury . InterleInterleukin-1 also seems to be an important cytokine in pathogenesis of COVID-19–related cytokine storm. Increased IL-1α and IL-1β expression was revealed in severe COVID-19 patients prior to development of respiratory distress . FurtherThere is scarce comparative data in the literature regarding superiority of anakinra and tocilizumab to each other in management of COVID-19. In a recently published retrospective study with comparison of IL-1 inhibition (anakinra) and IL-6 inhibition (tocilizumab or sarilumab) strategies, IL-1 inhibition but not IL-6 inhibition was reported to reduce mortality, respiratory insufficiency, and hyperinflammation significantly in hospitalized COVID-19 patients . In our In management of autoinflammatory conditions, anakinra was generally administered with a dose of 100 mg/day subcutaneously . SeveralThe RECOVERY group demonstrated effects of dexamethasone on mortality in COVID-19 and glucocorticoids have been used widespread ever since in treatment of COVID-19 . In our There are several limitations in our study. Firstly, this was a retrospective observational study, which might aggravate the risk of bias and causal inferences cannot be drawn because of inherent known and unknown confounders. Secondly, there was a possibility of sampling bias since we obtained data from a convenience sample. A major limitation was that the use of glucocorticoids, even after PPS matching, was more frequent in the anakinra group; therefore, it cannot be differentiated whether better outcomes in the anakinra group was a result of anakinra use or anakinra and glucocorticoid combination. Additionally, the frequency of cough, dyspnea, and baseline levels of CRP, fibrinogen, and D-dimer were higher in the tocilizumab group, so we cannot elude the possibility of higher disease severity in the tocilizumab group. Finally, adverse effects were not evaluated for neither of the agents.In conclusion, in our study, survival was better and clinical improvement was greater in severe COVID-19 patients who received anakinra when compared to those who received tocilizumab. Glucocorticoid use was significantly more frequent in the anakinra group, which may be indicative of better outcomes with anakinra and glucocorticoid combination. Furthermore, there was a chance that disease severity was higher in the tocilizumab recipients. There are several factors which may interfere with effectiveness of anticytokine treatments in COVID-19 such as dosing and timing of initiation. Future studies are needed to optimize anticytokine treatment strategies in COVID-19.


This prospective cohort study aimed to assess the effect of prior female SARS-CoV-2 infection on subsequent A total of 451 women who underwent fresh IVF treatment between December 1, 2022 and April 30, 2023 were included from an academic fertility center. Participants were divided into the infected group if they had a prior COVID-19 history before cycle initiation (n = 252), while the control group were those uninfected (n = 199). The primary outcomes were the number of oocytes retrieved and clinical pregnancy rate after fresh embryo transfer. Multivariate linear and logistic regression analyses were conducted to control for potential confounders.The number of oocytes retrieved  and clinical pregnancy rate  were similar between infected and uninfected groups, with a fully adjusted β coefficient of 0  and odds ratio of 0.64 (95% CI: 0.20–2.07), respectively. Consistently, the two groups were comparable in cycle characteristics as well as other laboratory and pregnancy parameters. In both subgroup analyses and restricted cubic splines, different post-infection time intervals to IVF cycle initiation showed no significant associations with treatment outcomes.Prior SARS-CoV-2 infection in females had no adverse influence on subsequent IVF treatment, regardless of the time interval following infection. Our findings provide reassurance for infected women planning for assisted reproduction. Additional prospective cohort studies with larger datasets and longer follow-up are required to confirm the conclusion. Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) .in vitro experiments have proven a cytopathic effect of SARS-CoV-2 on human blastocyst, ranging from focal degradation to global collapse (SARS-CoV-2 gains entry into host cells by attaching its spike (S) protein to angiotensin-converting enzyme 2 (ACE2) receptors on the cell surface , 5, whiccollapse . These fin vitro fertilization (IVF) treatment, the first published cohort with seven women found reduced top-quality embryos after SARS-CoV-2 infection  levels were significantly lower in COVID-19 women of reproductive age compared with matched controls . Nonethenfection  observednfection  demonstrnfection –24. ThesOn December 2022, the Chinese government released “a circular on further optimizing the COVID-19 response”. This shift marked the relaxation of the dynamic zero-COVID policy, and led to a widespread infection. In this context, the aim of the present study was to thoroughly assess the effect of prior SARS-CoV-2 infection on subsequent IVF cycle outcomes in a prospective cohort of Chinese infertile women.This prospective cohort study was conducted at the Center for Reproductive Medicine, Jiangxi Maternal and Child Health Hospital affiliated to Nanchang Medical College. The study received approval from the Reproductive Medicine Ethics Committee of Jiangxi Maternal and Child Health Hospital (No. 2022-10) and adhered to the Declaration of Helsinki. All women provided written informed contents for data collection and anonymous use in scientific research.From December 1, 2022 to April 30, 2023, all infertile women attending our center for fresh IVF treatment were approached for eligibility. Participant were divided into the infected group if they had a prior COVID-19 history before cycle initiation, while the control group were those uninfected. For the study purpose, women with SARS-CoV-2 infection during ovarian stimulation or after embryo transfer were not included. Other exclusion criteria were: 1) age ≥45 years old; 2) history of recurrent pregnancy loss or repeated implantation failure; 3) donor sperm or oocyte cycle; 4) preimplantation genetic testing cycle; and 5) lost to follow-up or missing data in the electronic health record. For women undergoing multiple IVF cycles during the timeframe, only the first cycles were retained for analysis.The diagnosis of COVID-19 was determined by positive rapid SARS-CoV-2 antigen test or real-time RT-PCR assay in nasopharyngeal swabs, while non-infection was confirmed by negative RT-PCR test. In compliance with the tenth edition of the Diagnosis and Treatment Protocol for COVID-19 issued by the National Health Commission of China , we furtControlled ovarian stimulation was performed in a depot gonadotropin-releasing hormone agonist long regimen, a flexible antagonist regimen, or other regimens such as progestin-primed stimulation. The initial gonadotropin dosage was mainly based on female age, body mass index (BMI), and markers of ovarian reserve including AMH, antral follicle count, and basal follicle-stimulating hormone (FSH) level. During stimulation, follicular development was monitored by transvaginal ultrasound and serum hormone measurement, and gonadotropin dosage was further modified. As soon as the largest follicle reached 20 mm or two dominant follicles reached a mean diameter of 18 mm, ovulation was triggered by the administration of 250 μg recombinant human chorionic gonadotropin .After triggering, follicular aspiration was planned 36 to 38 hours later. According to the semen quality and fertilization outcome in previous cycles, conventional IVF and/or intracytoplasmic sperm injection (ICSI) was used to inseminate the retrieved oocytes. Evaluation of pronuclei (PN) was conducted 16–18 hours after insemination. The zygotes were sequentially cultivated in G1-plus medium  to day 3 cleavage-stage embryos, and then switched to G2-plus medium  for blastocyst culture until day 5 or 6. On day 3, those with ≥7 even blastomeres, ≤15% fragmentation, and no multinucleation and vacuoles were defined as good-quality embryos according to the Cummins’ morphological criteria . BlastocAfter oocyte retrieval, 60 mg progesterone  was injected per day to induce endometrial transformation of women undergoing fresh embryo transfer. Depending on the developmental stage, up to two embryos were transferred three or five days later via the direction of transabdominal ultrasound. Daily luteal phase support was provided in a combination of 90 mg progesterone gel vaginally  and 20 mg dydrogesterone orally , and continued until 10 gestational weeks. Other embryos were vitrified for subsequent thawing and transfer.For laboratory results, the oocyte retrieval rate was calculated by dividing the number of oocytes retrieved by the number of follicles ≥14 mm on trigger day. The ICSI mature oocyte rate and normal fertilization rate were determined by dividing the number of metaphase II (MII) and 2PN oocytes by the total number of oocytes, respectively. The cleavage rate was obtained by dividing the number of day 3 cleavage-stage embryos produced from 2PN oocytes by the total number of 2PN oocytes. The good-quality embryo rate was calculated by dividing the number of good-quality embryos on day 3 by the total number of embryos at the cleavage stage. The blastocyst formation rate was calculated by dividing the number of blastocysts by the number of day 3 embryos subjected to extended culture. Finally, the available blastocyst rate was calculated by dividing the number of available blastocysts by the total number of blastocysts on days 5 and 6.Regarding pregnancy outcomes, biochemical pregnancy was defined as a serum β-hCG level of ≥5 mIU/mL at 10–12 days after embryo transfer. Clinical pregnancy was determined as the discovery of at least one gestational sac with or without fetal heart beat at 1 month following transfer. The number of gestational sacs divided by the number of embryos transferred was used to measure the implantation rate.Continuous variables were summarized as means with standard deviations and assessed for normality using the Shapiro-Wilk and Kolmogorov-Smirnov tests. Normally distributed data were compared using Student’s t-test or one-way analysis of variance (ANOVA), while skewed data were analyzed using the Mann-Whitney U-test or Kruskal-Wallis test. Categorical variables were presented as numbers and percentages, and comparisons were made using Pearson’s Chi-square test or Fisher’s exact test when appropriate.Multivariate regression analyses were conducted to assess the independent effect of prior SARS-CoV-2 infection on IVF outcomes. Linear regression models were used to estimate β coefficients and 95% confidence intervals (CIs) for the laboratory outcomes. Adjusted variables included age, BMI, infertility duration, type and diseases, AFC, AMH, basal FSH level, previous IVF attempts, ovarian stimulation regimen, and vaccination status without or with male infection. Logistic regression models were used to calculate odds ratios (ORs) and 95% CIs for pregnancy outcomes. In addition to the aforementioned covariates, we also controlled for the number, stage, and quality of transferred embryo in the adjusted analysis.To evaluate time-associated changes, subgroup analyses were further carried out by dividing infected women according to the interval from infection to IVF treatment: ≤30 days, 31–60 days, and >60 days. Restricted cubic splines were also used to depict the relationship between time interval as a continuous variable and the primary outcomes of retrieved oocyte number and clinical pregnancy rate.Data analyses were performed using SAS version 9.4  and R version 4.0.2 . All tests were 2-sided and P <0.05 was considered as statistically significant.In total, 451 eligible women were included in the analysis, including 252 (55.9%) with a prior COVID-19 history and 199 (44.1%) without. For infected women, the mean time interval to IVF treatment was 63.4 ± 21.9 days. Most were mildly to moderately infected , while a minority were asymptomatic . No participants were lost to follow-up or withdrew from the cohort.Cycle characteristics and laboratory outcomes grouped by the infection status are demonstrated in According to the time interval between SARS-CoV-2 infection and IVF cycle initiation, infected women were subdivided into three categories: ≤30 days, 31–60 days, and >60 days. As observed in The results of our prospective cohort study demonstrated that prior SARS-CoV-2 infection in females had no adverse influence on IVF treatment. Moreover, different time intervals from infection to cycle initiation did not significantly affect either laboratory or pregnancy outcomes.Since ACE2 and TMPRSS2 receptors are expressed in the ovary, it has been speculated that SARS-CoV-2 may be harmful to female reproduction –9, 29–31Emerging cohort studies have made investigations on the clinical effect of these changes. Based on small sample sizes 7 to 80, some earlier studies found lower oocyte number, fewer top-quality embryos and decreased oocyte maturation rate in women after SARS-CoV-2 infection , 19, 37.Endometrium is essential for embryo implantation and was thought to be safe from SARS-CoV-2 entry due to the low levels of ACE2 and TMPRSS2 . HoweverThe ideal time period between infection and the start of fresh IVF treatment was discussed by limited studies, and no agreement has been established yet. A small self-controlled study with seven women showed a significant decrease in the proportion of high-quality embryos after SARS-CoV-2 infection between 8 and 92 days. Therefore, the authors recommended delaying IVF for at least 3 months to allow for the completion of prior folliculogenesis cycle . ContrarThere are some limitations that should be acknowledged. Firstly, the observational design is inherently prone to potential selection bias and confounding variables. For example, we did not classify the variants of SARS-CoV-2 , whose evolving transmissibility and pathogenicity may have different effect on IVF outcomes. Other co-morbidities like diabetes and hypertension were also missed and should be taken into consideration in the future. Secondly, although this study is one of the largest prospective cohorts thus far, the sample size may still be inadequate especially in subgroup analysis. Moreover, the single-center setting may limit the generalizability of our finding. Thirdly, only asymptomatic and mildly to moderately infected women were included, and therefore, the conclusion should be cautiously interpreted for severe COVID-19 cases. We were also unable to further distinguish between mild and moderate infections which rely on computed tomographic scan of the chest . FinallyTo summarize, the current study demonstrated that prior female SARS-CoV-2 infection did not pose measurable adverse effects on subsequent IVF treatment. Moreover, both laboratory and pregnancy outcomes were comparable across different post-infection time intervals. Our finding should provide reassuring information for COVID-19 women of reproductive age. However, larger multicenter prospective cohorts are warranted to validate our conclusion and assess live birth outcome as well as offspring health.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by Reproductive Medicine Ethics Committee of Jiangxi Maternal and Child Health Hospital (No 2022-10). The studies were conducted in accordance with the local legislation and institutional requirements. All women provided written informed contents for data collection and anonymous use in scientific research.JH, JW and QW contributed to the study conception and design. LX, YZ, LT, DX, QS, YH, QX, JC, YJL and XA were responsible for data collection and curation. JH, YXL and LX conducted the statistical analyses. JH and YXL drafted the manuscript. XA, JW and QW supervised the project administration. All authors contributed to the article and approved the submitted version.


The health impact of the COVID-19 pandemic largely depends on the ability of the healthcare systems to develop effective and adaptable preparedness and mitigation strategies. A collaborative initiative (BRCCH-EDCTP COVID-19 Initiative) was set up between Lesotho and Zambia early on in the pandemic, to jointly conduct a project to investigate creating access to SARS-CoV-2 screening and testing through community-based COVID-19 case-finding.Two different community case-finding strategies were deployed. In Lesotho, an approach was implemented whereby a community (village) health worker screened community members at their home or during community gatherings for COVID-19 signs and symptoms. All community members who screened positive were then offered SARS-CoV-2 testing. In Zambia, so-called community hubs, staffed by community health care workers, were set up at different locations in the community for people to walk in and get tested for SARS-CoV-2. Hubs changed location from week-to-week and targeted transmission hotspots. All persons visiting the hubs were offered testing for SARS-CoV-2 irrespective of self-reported signs and symptoms of COVID-19 though information was collected on occurrence of these. Testing in both approaches was done using SARS-CoV-2 rapid antigen tests.Setting up testing in the community setting was feasible in both countries. In Lesotho in the village health worker approach, over a period of 46 weeks, 7221 persons were screened, and 49 (11.4%) SARS-COV-2 cases identified among 428 COVID-19 screen positive participants. In the community hubs among 3150 people tested, 166 (5.3%) SARS-CoV-2 cases were identified in a period of 26 weeks. From the community hubs approach, where all seen were offered COVID-19 testing it was learned that people screening positive for COVID-19 signs and symptoms were more likely to test SARS-COV-2 positive, especially those reporting classic COVID-19 symptoms like loss of sense/smell for a short period of time (1–3 days).In conclusion, in this project we learned that implementing COVID-19 screening and testing by lay health workers in the community is possible. Characteristics of the population screened, tested, and identified to have SARS-CoV-2 are described to help guide development of future testing strategies.The online version contains supplementary material available at 10.1186/s12889-023-16306-2. The COVID-19 pandemic has caused a global public health emergency at unprecedented scale . In sub-Screening and testing strategies are part of a comprehensive control package. In several African countries, community screening and testing strategies have been implemented building upon existing community health worker programs , 7. SuchLike other African countries the Lesotho and Zambian health systems were also negatively impacted by the COVID-19 pandemic –9. AccesA collaborative project (BRCCH-EDCTP COVID-19 Initiative) was set up between researchers and Ministries of Health in Lesotho and Zambia to jointly investigate community based approaches for SARS-COV-2 screening and testing. In this paper, we discuss findings from the implementation of two community-based approaches for COVID-19 screening and testing and, characteristics of the population attracted by these two approaches.Using a cross-sectional design, we implemented two different community-based approaches for COVID-19 screening and testing in Zambia and Lesotho. In Lesotho, screening and testing was performed by village healthcare workers (VHW) at people’s home or at community gatherings. In Zambia, screening and testing was conducted at community hubs by community health workers.In Lesotho, the project was implemented in selected peri-urban and rural villages in the Butha-Buthe district. Butha-Buthe is a small district of 1,767 square km located in northern Lesotho with an estimated total population of 118,242 people of which more than 75 per cent are living in rural settings . A totalIn Zambia, the project was conducted in a peri-urban community in the Kabwe district. This community was selected as it was part of an ongoing study to gain rapid evidence on COVID-19 in an African setting. Kabwe district is a middle to high density urban area located about 15 kms north-west of Kabwe town centre in the Central Province of Zambia. This community has been previously characterised as a mixed economy typical of other Zambian peri-urban communities . The totTable The VHW approach was implemented from the 4th of August 2021 to the 15th of June 2022 in Lesotho. In the selected villages, a VHW screened and tested community members at the participant or the VHW home or during community gatherings. VHWs worked in close collaboration with the Butha Buthe DHMT and the village chiefs from whom they received information on households with possible COVID-19 cases. Within villages, they visited those households for screening and testing. Additionally, community members who were sensitised through the village chief could call or walk to the VHW home to receive COVID-19 services when needed. Participants, who had to be at least 18 years of age, were considered screen positive for COVID-19 if they, at the time of the visit/screening, reported a history of close contact with a COVID-19 case and/or at least one of the following symptoms for any duration: cough, fever, pronounced tiredness, shortness of breath, sore throat, muscle or body pain, diarrhoea, loss of taste/smell, weight loss, night sweat, skin rash, or chest pain. We defined close contact with confirmed or probable COVID-19 case as a contact within one meter for more than 15 min or direct physical contact with a probable or confirmed COVID-19 case or direct care for a patient with probable or confirmed COVID-19 disease without using proper personal protective equipment (PPE). All screened positive were offered a COVID-19 test.We relied on community health workers trained as VHW by the Lesotho Ministry of Health (MoH). VHWs were recruited by the project from the MoH pool using specific criteria such as ability to read and write, ability to use an electronic device such as a tablet. All recruited VHWs for the project were trained on study procedures such as basic COVID-19 knowledge, COVID-19 sample collection and processing using rapid diagnostic tests, use of a COVID-19 application developed for data collection and infection prevention and control techniques. A professional nurse supervised all the VHWs during the study period both through remote supervision as well as onsite visits.The professional nurse was employed full-time by the project and received full salary while the VHWs continued with their assignments for the MoH and received a monthly stipend from the project for the additional work. All staff involved in the study received PPE: face mask, gowns, gloves, sanitiser etc.), symptomatic patients were provided with a surgical face mask.A community hub approach was designed and implemented in Zambia to create access to COVID-19 screening and testing in the community. So called community hubs were set up at different locations in the community for community members to walk in and get tested for SARS-CoV-2. From 4th May to 29th October 2021, a total of 4 hubs were operational. Location of hubs was decided in consultation with the Community Advisory Board (CAB) and targeted areas perceived to have higher transmission risk, i.e., a hot spot, community intersection or areas where many people pass or conduct community activities. Hubs changed location on weekly basis though depending on attendance, this period could be shortened or extended. Each hub served an estimated 3,000 to 4,000 people. Community members were sensitised using different community engagement approaches like drama or by loudspeaker to inform them about location of hubs and services available.Services at the hubs targeted individuals aged 15 years and above. Upon arrival at the hub and, after consent taking, all participants were administered a simple questionnaire collecting information on socio-demographics characteristics and clinical information. Participants were also asked about COVID-19 signs and symptoms and whether they had a (known) history of contact with a confirmed COVID-19 case. In Zambia the following COVID-19 signs and symptoms were inquired about: fever , (dry) cough, new episode of shortness of breath, sudden loss of taste and/or smell (anosmia and/or ageusia). Following the questionnaire all participants were offered COVID-19 testing regardless of reporting of signs and symptoms. Besides COVID-19 testing, HIV testing services were also offered at the hubs. In addition, all participants reporting signs and symptoms suggestive for TB were referred for TB testing. All participants identified with COVID-19 and newly diagnosed with HIV or TB were referred for appropriate care taking following national guidelines. Hubs were staffed by a nurse and community health workers who were recruited by the project from the local community, both worked full time in the hubs for the duration of the project. PPE was provided to all study staff members of the hubs, i.e. mask, sanitizer, apron and community members coming for testing were asked to wear masks as per guidelines at the time for public places. Masks were also available at the hubs free of charge.In the VHW approach, the VHW collected a nasopharyngeal swab and processed it using the Standard Q COVID-19 rapid diagnostic test . For invalid results, the test was repeated using a new sample. Both the sample collection and testing process were done at well-ventilated places agreed upon by the study participant and the VHW.In the community hubs, a nasal swab was collected from individuals attending the hubs and tested on the spot for COVID-19 using the Panbio™ Ag-RDT  following the manufacturer’s instructions for use. If the result was invalid, the test was repeated with the remainder of the sample or with a new sample. Sample collection and testing process were done at a well-ventilated location in the hub.Study participants who tested positive and those who tested negative but had symptoms in the VHWs approach were advised on self-isolation. Symptomatic participants with a negative result were followed up within 48 h and retested if they still reported symptoms. In Zambia following national guidance, only those testing positive were advised to self-isolate at home.Data were collected in both countries using electronic data collection tools incorporated into an electronic device (tablet). Due to technical challenges with the application, Lesotho switched from electronic data collection to paper-based data collection during the project. For every participant enrolled, in both countries, the following data were collected: age, sex, history of contact with a confirmed or probable COVID-19 case (Lesotho) or household contact (Zambia), COVID-19 signs and symptoms (including duration thereof), type of swab collected, type of tests done, and test results. In addition, TB status, whether or not the participant had a previous history of TB treatment was obtained. Both countries have a high TB burden and as COVID also affects the lungs we explored TB status as potential risk factor.The data collected was analysed using STATA Version 15 (Zambia) and 16.1 (Lesotho). Demographics of study participants are presented using frequency tables, mean, standard deviation and 95% confidence interval (CI) for normally distributed data or median and interquartile range for skewed data. A p-value of < 0.05 was considered significant.Figure ≥ 55 years while 56/428 (13.1%) were 18–24-year-olds. In the community hubs, more males than females were tested, 1791 (56.9%) male versus 1359 (43.1%) female  were 15–24 years. Age distribution was similar for male and females  reported a history of TB treatment including 7 who were on TB treatment when seen at the hubs.In the two community approaches combined a total of 215 SARS-COV2 cases were identified. In the VHW approach, 49/438 (11.4%) persons tested positive for SARS-CoV-2., This was higher among males than females (36/153 (23.5%) males versus 13/275 (4.7%) females, p < 0.0001). Looking at the age distribution a larger proportion tested positive in the groups up to 44 years compared to those 45 years and older  with the highest proportion testing SARS-COV-2 positive observed in the 35-44-year-old at 14.3%. Patterns observed differed a bit in the community hubs. There, among 3,150 persons tested with Ag-RDT, 166 (5.3%) tested SARS-CoV-2 positive  at 17.3% (143/828) versus 1.0% (23/2322) (p < 0.0001) among those screening negative for COVID-19 . In each approach, people accessed COVID-19 services within their communities at or close to their homes without having to travel long distances.Differences observed in the two approaches could be related to differences in the strategies used for screening and testing. In the VHW approach, VHW visited households of participants with suspected SARS-CoV-2 infection while in the community hubs, community health workers waited for community participants at a designated place in the community. The difference in the type of population attracted by each approach may be explained by the fact that women are more likely to be found around the house because of child-care and household duties as has been found in other studies . In LesoIn other studies, an even higher number of people could be reached, and the yield was higher than what we observed in our study. A study in South Africa , coverinThe difference in SARS-CoV-2 positivity yield among people that were tested may be due to the prevalence of COVID-19 in the population tested. Since the beginning of the pandemic, South Africa reported much higher numbers of COVID-19 cases  than LesWe observed that in Lesotho the proportion of males testing SARS-CoV-2 positive was much higher than that for females (23.5% versus 4.7%), while in Zambia the reverse was observed, with yield of SARS-CoV-2 slightly higher among females than males (6.5% versus 4.3% respectively). Data compiled by sex, gender of the COVID-19 Project , 21. ExiOur work contributed to the evidence base on community-based testing strategies in the African setting. The fact that our study was conducted (partly) in rural African communities with challenges to access to basic health services and where community-based COVID-19 screening and testing are mostly needed is an added value. Community based testing can help scale up SARS-CoV-2 testing and improve case-detection rates in the community as also concluded by Otshudiema and colleagues .Our study has various limitations. Firstly, we implemented this study in a small number of communities including a relatively low number of people. Replicating this study in a larger population and more communities may come with additional challenges that we did not experience during the implementation of our two approaches in Lesotho and Zambia. Secondly, the analysis done in this study was mainly descriptive and both countries implemented a different strategy at a different time. The number of cases identified varied, and testing took place in different epidemic waves and as COVID-19 is a rapid disease no direct comparison could be made.In conclusion, in this project we learned that implementing SARS-COV2 screening and testing by lay health workers in the community is feasible and people do come forward for testing. Characteristics of the population screened, tested, and identified to have SARS-CoV-2 are described to help guide development of future testing strategies. Offering testing in the community is preferable and if resources (staff and/or consumables) are scarce testing could be targeted at those with signs/symptoms of SARS-COV2.Below is the link to the electronic supplementary material.Supplementary Material 1


Sepsis, characterized by organ dysfunction due to presumed or proven infection, has a case-fatality over 20% in severe cases in low-and-middle income countries. Early diagnosis and treatment have proven benefits, prompting our implementation of Smart Triage at Jinja Regional Referral Hospital in Uganda, a program that expedites treatment through a data-driven triage platform. We conducted a cost-effectiveness analysis of Smart Triage to explore its impact on patients and inform multicenter scale up.The parent clinical trial for Smart Triage was pre-post in design, using the proportion of children receiving sepsis treatment within one hour as the primary outcome, a measure linked to mortality benefit in existing literature. We used a decision-analytic model with Monte Carlo simulation to calculate the cost per year-of-life-lost (YLL) averted of Smart Triage from societal, government, and patient perspectives. Healthcare utilization and lost work for seven days post-discharge were translated into costs and productivity losses via secondary linkage data.In 2021 United States dollars, Smart Triage requires an annuitized program cost of only $0.05 per child, but results in $15.32 saved per YLL averted. At a willingness-to-pay threshold of only $3 per YLL averted, well below published cost-effectiveness threshold estimates for Uganda, Smart Triage approaches 100% probability of cost-effectiveness over the baseline manual triage system. This cost-effectiveness was observed from societal, government, and patient perspectives. The cost-effectiveness observed was driven by a reduction in admission that, while explainable by an improved triage mechanism, may also be partially attributable to changes in healthcare utilization influenced by the coronavirus pandemic. However, Smart Triage remains cost-effective in sensitivity analyses introducing a penalty factor of up to 50% in the reduction in admission.Smart Triage’s ability to both save costs and avert YLLs indicates that patients benefit both economically and clinically, while its high probability of cost-effectiveness strongly supports multicenter scale up. Areas for further research include the incorporation of years lived with disability when sepsis disability weights in low-resource settings become available and analyzing budget impact during multicenter scale up.NCT04304235 .The online version contains supplementary material available at 10.1186/s12913-023-09977-5. Sepsis is a syndrome characterized by an inflammatory state triggered by a presumed or proven infection that results in organ dysfunction and/or death . In seveIn high income countries, evidence-based triage strategies aimed at expediting sepsis treatment  involve The Smart Triage program, which was implemented at the pediatric outpatient department at Jinja Regional Referral Hospital (JRRH) from 2020–2021, targeted these barriers in several ways . First, In high-income settings, economic evaluations of quality improvement initiatives aimed at delivering early sepsis care have demonstrated reduced cost and increased benefit over standard of care –21. HoweDetermine the incremental cost effectiveness ratio (ICER) for years-of-life lost (YLL) averted by the Smart Triage program compared to the baseline triage infrastructure at JRRH, using the pre-intervention phase at JRRH as the control.Determine the probability of cost-effectiveness of the Smart Triage program under different willingness-to-pay thresholds, compared to the baseline triage infrastructure at JRRH.Investigate the impact of out-of-pocket costs on cost-effectiveness by secondary analyses separating the government and patient perspectives.The Smart Triage program, and corresponding clinical trial, were implemented in the pediatric outpatient department at JRRH in Uganda, which serves children up to age 18 years presenting at various degrees of illness severity. A concurrent control site where Smart Triage would not be implemented was originally planned, but coronavirus disease (COVID) pandemic-induced delays in site initiation precluded the use of this site as a control for our analysis. Therefore, the clinical trial, and data used for this analysis, is of a pre-post design. The comparator to Smart Triage was a manual triage system used in the pre-implementation phase at JRRH that is based on ETAT, a set up common in many comparable LMIC centers . The preAll presenting patients, except for those with scheduled appointments or procedures, were eligible for enrollment. Primary outcome was defined as the difference, between the pre- and post-implementation phases, in the proportion of children who received a timely sepsis bundle . This choice of outcome is based on benchmarks established by international guidelines , and impWe conducted our analysis using a decision analytic model from a societal perspective, using secondary data from the literature to link health resource utilization (HRU) rates to costs and process outcomes to clinical endpoints , 28. SubKey assumptions of our model include the sustainability of Smart Triage beyond study end date, the generalizability of secondary linkage data used in the model to the Smart Triage population, and the adequacy of our seven-day duration of patient follow-up. The rationale behind these assumptions, and further details on our model, are previously published . Our anaAs the Smart Triage trial was powered for the pre-post difference in proportion of children receiving a timely sepsis bundle and not mortality difference, the estimated improvement in the latter for any observed improvement in the former was calculated through the literature odds ratio for mortality improvement if given a timely sepsis bundle vs. not . YLLs weIn terms of HRU costs, unit costs of care center visits with and without admission were obtained from the Decade of Vaccine Economics project , 34. ThiLastly, program costs were tabulated directly from our trial; costs solely attributable to research were excluded. Program costs were counted from a public payer  perspective, as patients were not charged out-of-pocket fees for Smart Triage due to the reusable nature of program components. All costs were inflated to 2021, the implementation year of Smart Triage, via the gross domestic product (GDP) deflator of the country in which they incurred, then converted to United States dollar (USD) using 2021 average exchange rates. 2021 was chosen as that was the implementation year of Smart Triage.Our analysis from the government perspective includes costs of care not borne out-of-pocket by patients . Our analysis from the patient perspective includes out-of-pocket costs directly related to healthcare , costs indirectly related to having a hospitalized child (e.g. transportation), and productivity losses (lost wages). Our analysis from the societal perspective combines these two perspectives to create a wholistic picture of Smart Triage’s cost-effectiveness.Bivariate analyses were performed using the chi squared test for discrete outcomes and student t test for continuous outcomes. Fischer exact test was used in place of chi-squared test when the number of events were less than five. The incremental cost-effectiveness ratio (ICER) is represented by: (post–pre difference in cost)/(post–pre difference in YLL). The ICER was calculated by multiplying out the decision analytic model after filling in probabilities for each branch point, and summing the corresponding total costs and YLL for each terminal node . Monte CNext, the probability of cost-effectiveness under varying thresholds for willingness to pay per YLL averted were plotted on a cost-effectiveness acceptability curve (CEAC). The CEAC was generated by modelling increasing monetary amounts a payer would be willing to pay to avert one YLL; these amounts correspond to lines with increasing slopes on the cost-effectiveness plane. The proportion of simulated ICERs that fall below each line on the cost-effectiveness plane corresponds to the probability of cost-effectiveness at that willingness to pay threshold . TherefoFor the above analyses, annuitization and discounting rates of 3% per annum were used for costs and YLL, in congruence with a systematic review of other LMIC economic evaluations , assuminAs mentioned above, the CEAC plots the probability of cost-effectiveness of Smart Triage over the baseline triage infrastructure at increasing willingness to pay thresholds. A cost-effectiveness threshold of a country is its chosen willingness to pay threshold to adopt an intervention if, at or below that threshold, an intervention has a high probability of cost-effectiveness over the comparator. However, most LMICs, including Uganda, do not have an formally established cost-effectiveness threshold. Therefore, for our analysis, we selected a threshold based on projected health opportunity costs (HOC) of Uganda (threshold = $174.12 in 2021 USD). As many cost-effectiveness analyses have historically used multiples of GDP per capita ($883.89 in 2021 USD for Uganda), as recommended by the World Health Organization (WHO), we have also stated our results in the context of this standard . See “DiWe performed one-way deterministic sensitivity analyses using different annuitization rates , and by removing the assumption that Smart Triage would continue past the study end date. The upper limit of 5% was chosen in congruence with variations in economic evaluation guidelines in global health , stemminSince the cost-effectiveness of Smart Triage was driven mainly by a reduction in admissions rather than an improvement in receiving a timely sepsis bundle, we performed a post-hoc sensitivity analysis whereby the absolute reduction in admission was further reduced in magnitude by 25%, 50%, and 75%. The rationale for this sensitivity analysis is that a portion of the reduction in admission rate may be driven by other factors independent from the efficacy of Smart Triage, including changing HRU behaviors throughout the COVID pandemic.The sex and age distribution of children were similar between the pre- and post-intervention phases, with the vast majority (over 85%) being children under 5 years. Malnutrition rates and HIV positivity were evenly distributed between phases. The rate of HIV positivity was low, but more than a third of patients have never been tested. Among admitted patients, greater than 90% were admitted for suspected infection. Diagnoses for non-admitted patients were not gathered, due to constraints in trial human resources. Approximately 10% of patients received a triage category of “emergency”, while the remaining 90% were split between “priority” and “non-urgent.” Of note, 41 patients were missing triage priority data due to technical difficulties. Data recovery was unfortunately not possible despite the best efforts of the research team. However, triage priority was not an input variable to the economic model and therefore does not affect our analysis and key findings. Lastly, greater than 90% of patients resided outside of the Jinja Central municipality, thereby necessitating some form of transportation to JRRH. We achieved a high rate of follow-up, with only 1.8% and 1.4% of patients lost to follow-up in the two phases respectively (Table p = 0.016]). In the pre-implementation phase, 0.7% (95% CI 0.4 to 1.3) of children received a timely sepsis bundle, compared with 0.7% (95% CI 0.4 to 1.2) of children in the post-implementation phase, (p > 0.99). Among admitted patients, we found in-hospital mortality rates of 2.9% (95% 1.5 to 5.5) and 1.1% (95% CI 0.22 to 3.4) during the pre- and post-implementation phases respectively (p = 0.13). Finally, follow-up completed 7 days after return home showed a 7-day mortality rate of 0.2% (95% CI 0.0 to 0.6) and 0.1% (95% CI 0.0 to 0.4) in the pre- and post-implementation phases respectively (p = 0.798) , shared consumables (e.g. reusable radiofrequency patient tracking identification bands), or wages (e.g. one-time technologist fee for set-up). Details are available in Additional file p < 0.001). There were also reductions in seeking care  and readmissions at any health facilities  after leaving JRRH. The mean length of stay for primary admission and readmission for both phases were 4 days , average days of work missed are shown in Table From the societal perspective, Smart Triage resulted in an incremental cost of -$1321.86 per 1000 children (95% CI -4735.48 to 444.73) and an incremental YLL averted of 86.3 years per 1000 children (95% CI 37.7 to 152.3), corresponding to an average ICER of -$15.32 per YLL averted ($15.32 saved per YLL averted). On the cost-effectiveness plane, 91.9% of the Monte Carlo simulations fall within the southeast quadrant, corresponding to both cost-savings and improved clinical outcome, while the remaining 8.1% of simulations fall within the northeast quadrant, corresponding to improved clinical outcome at an added cost  and an incremental YLL averted of 86.3 years per 1000 children (95% CI 37.2 to 153.1), corresponding to an average ICER of $0.06 per YLL averted. 52.7% of the Monte Carlo simulations result in both cost-savings and improved clinical outcome, while the remaining 47.3% result in improved clinical outcome at an added cost  and a YLL averted of 86.4 years per 1000 children (37.0 to 151.8), corresponding to an average ICER of -$15.27 per YLL averted ($15.27 saved per YLL averted). 93.9% of the Monte Carlo simulations result in both cost-savings and improved clinical outcome, while the remaining 6.1% result in improved clinical outcome at an added cost  to clinical outcomes (YLL-averted). This technique results in a more comprehensive cost-effectiveness estimate to inform policy and allows for an estimate involving clinical endpoints such as YLL-averted despite our parent trial being powered for timely sepsis bundle, a process outcome. A previous economic evaluation of an ETAT-based pediatric triage system in Kenya, the ETAT + , also showed cost-effectiveness. However, this analysis utilized only program costs and the primary outcome of the parent cluster randomized trial . As thisAnother strength of our analysis is our novel use of an HOC-based willingness to pay threshold to define cost-effectiveness. Historically, cost-effectiveness analyses have used multiples of GDP per capita as defined by the WHO. While easy to understand, use of GDP per capita does not take into account an intervention’s cost-effectiveness relative to other existing interventions in a country , 52. HowLastly, our analysis includes post-discharge follow-up data in healthcare utilization, clinical outcome, and productivity loss. These data add comprehensiveness to our analysis, in contrast to existing literature that largely focuses on the inpatient period , 14, 22.An important limitation of this analysis is that we did not achieve an improvement in the primary target outcome – proportion of children receiving a timely sepsis bundle. Instead, the cost effectiveness observed was driven by other factors, in particular a reduction in admission rate, that may have been confounded by factors such as COVID-related shifts in HRU behavior. The Smart Triage trial was originally designed as a difference in differences study, with a control site able to account for changes unrelated to Smart Triage implementation at JRRH . UnfortuDespite potential confounding, a reduction in admission due to Smart Triage is entirely plausible. For example, bringing sicker patients to the front of the assessment queue may have given these patients priority for consideration of hospital admission, a limited resource in LMIC settings. Simultaneously, a low-risk classification by Smart Triage’s data-driven algorithm may have added confidence for clinicians to treat patients with mild to moderate symptoms as outpatients, whereas prior to Smart Triage, a conservative recommendation of admission may have been offered to these patients. While the observed reduction in admissions that drove cost-effectiveness is likely multi-factorial in etiology, our post-hoc sensitivity analysis reassuringly showed that the cost-effectiveness of Smart Triage under the HOC-based threshold is preserved when a penalty factor of up to 50% is applied to the reduction in admission rate.An additional limitation to our analysis lies in our inability to model years lived with disability due to the lack of long-term quality-of-life data and corresponding disability weights in sepsis survivors in the LMIC context. Extrapolation from HIC data would be inaccurate given the vast differences in social support systems and standards of care between HICs and LMICs. However, our model may be rerun to incorporate such data when available, to calculate incremental cost per DALY averted of Smart Triage. This re-analysis would be informative, as the HOC-based threshold is based on DALYs averted and not YLLs averted. Mathematically speaking, as DALYs are calculated by summing YLLs and years lived with disability, some YLLs averted by Smart Triage may be offset by years lived with disability. This offset would potentially lower the denominator for ICER, increasing the ICER and diluting cost-effectiveness. Nonetheless, our observation of cost-effectiveness well before the HOC-based threshold provides ample room to maintain cost-effectiveness even if years lived with disability were incorporated, despite this potential dilution.A third limitation lies in our inability to count opportunity costs unrelated to missed wages, such as the death of livestock or spoilage of unattended crops that may have been used to generate income . The varFinally, scaling up of interventions must consider costs of scale up as well as budget impact , which aSmart Triage reached a high probability of cost-effectiveness as defined by an HOC-based cost-effectiveness threshold, which assesses proposed interventions based on the country-specific per dollar impact on healthcare outcomes. This cost-effectiveness provides strong rationale for multicenter scale up. Also, Smart Triage benefits patients both clinically and economically, in terms of YLLs averted and cost savings respectively. Our analysis represents the first application of economic modelling methods to evaluate the cost-effectiveness of a pediatric sepsis triage intervention, providing a more comprehensive analysis than those based on only program costs and surrogate outcomes. Smart Triage’s cost-effectiveness was primarily driven by a reduction in admissions, which may be confounded by changes in healthcare utilization induced by factors such as the COVID pandemic. Reassuringly, sensitivity analyses using a penalty factor of up to 50% for reduction in admissions show persistent cost-effectiveness. Areas for future research include confirmation of our results in a post-pandemic “new normal” era, calculation of cost per DALY averted using long-term disability data when available, and modelling of budget impact based on scale up costs measured through a larger multicenter trial. To this end, we are actively pursuing expansion into additional sites in collaboration with local agencies.Additional file 1: Supplementary table 1. Program costs of Smart Triage in 2021 United States Dollars. Costs marked with * were originally incurred in 2021 Ugandan Shillings and were converted into USD by the average conversion rate in 2021 as per the World Bank (3587.075 Ugandan Shillings to 1 USD). Abbreviations: USD = United States Dollar, JRRH = Jinja Regional Referral Hospital.Additional file 2: Supplementary table 2. Detailed Monte Carlo simulation results from societal, government, and patient perspectives. Costs in 2021 USD. Abbreviations: YLL averted = years of life lost averted, ICER = incremental cost effectiveness ratio, USD = United States dollars.Additional file 3:Supplementary table 3. Details of Monte Carlo simulations for sensitivity analyses. Abbreviations: USD = United States dollars, YLL averted = years of life lost averted, ICER = incremental cost-effectiveness ratio.


Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors.st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults.In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7–0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6).This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC. Can we predict those who are at risk of newly incident post-acute sequelae of SARS-CoV-2 infection (PASC) using EHR data and machine learning predictive models? What are the factors associated with newly incident PASC conditions?In this retrospective cohort study of 35,275  adults with SARS-CoV-2 infection and 326,126  adults without SARS-CoV-2 infection from INSIGHT (OneFlorida+), we found that several incident PASC conditions  were associated with severe acute SARS-CoV-2 infection and older age, and these conditions were better predicted based on information collected before baseline and during the acute phase. However, a variety of PASC conditions were moderately predictable  or less predictable .These findings suggest that different PASC conditions exhibited heterogeneous predictability and association patterns and using machine learning-based predictive models and EHR datasets could help in identifying patients who were at risk of developing incident PASC conditions and managing the heterogeneous PASC conditions. Recently there have been several retrospective cohort analyses identifying potential PASC using real-world patient data. However, research on the predictability of PASC and their associated risk factors is still limited, and mixed results have been reported. Such predictive modeling research can help patients and healthcare professionals to recognize the risk of PASC early and inform effective actions. Several studies found older age, higher severities in the acute phase of SARS-CoV-2 infection6, and pre-existing conditions  may be associated with a higher risk of developing PASC.12 By contrast, some studies also reported that baseline clinical characteristics or demographics were not associated with PASC.10 Two main challenges may explain these seemingly conflicting findings: 1) Prior studies have typically been conducted using patient cohorts with small sample sizes including only a few hundred13 or thousand8 patients, limiting the significance and generalizability of conclusions derived; and 2) PASC conditions are highly heterogeneous16, thus their predictabilities and associated risk factors could be heterogeneous as well.The global COVID-19 pandemic starting in late 2019 has led to more than 557 million infections and 6.4 million deaths as of July 14, 2022.17, namely INSIGHT18, covering patients in the New York City (NYC) area, and OneFlorida +19, including patients from Florida. The INSIGHT and OneFlorida + were used as primary analyses and validation respectively. A wide range of PASC conditions were selected based on our previous findings using a rigorous data-driven analysis pipeline14 and other existing evidence or clinical knowledge  cohorts from the PCORnet clinical research networks (CRN)18 and the OneFlorida + CRN19. The INSIGHT CRN contained longitudinal clinical data of approximately 12 million patients in the New York City metropolitan area, and the OneFlorida + CRN contained the EHR data of nearly 15 million patients from Florida and selected cities in Georgia and Alabama. The use of the INSIGHT data was approved by the Institutional Review Board (IRB) of Weill Cornell Medicine following NIH protocol 21–10-95–380 with protocol title: Adult PCORnet-PASC Response to the Proposed Revised Milestones for the PASC EHR/ORWD Teams (RECOVER). The use of the OneFlorida + data for this study was approved under the University of Florida IRB number IRB202001831.This study used two large-scale de-identified real-world EHR datasets from the INSIGHT Clinical Research Network (CRN)14 Any incident condition was defined in the SARS-CoV-2 infected patients who had the condition from 31 days to 180 days after but not having the condition three years to seven days before their acute infection. See Supplementary Table 1 for the detailed code list.We examined a list of potential PASC conditions as outcomes, including depressive disorders, anxiety disorder, general PASC symptoms and signs with ICD codes U099/B948, fever, malaise and fatigue, dizziness, malnutrition, fluid disorders, diabetes mellitus, edema, pressure ulcers, hair loss, paresthesia, dermatitis, chronic obstructive pulmonary disease (COPD), atelectasis, pulmonary fibrosis, dyspnea, acute pharyngitis, acute bronchitis, dementia, myopathies, cerebral ischemia, encephalopathy, cognitive problems, sleep disorders, headache, muscle weakness, fibromyalgia, joint pain, acute kidney failure, cystitis, genitourinary problems, constipation, gastroparesis, abdominal pain, gastroesophageal reflux disease (GERD), heart failure, hypotension, pulmonary embolism, thromboembolism, abnormal heartbeat, chest pain, and anemia. We compiled this list based on both our previous study and evidence from other literature.We included adult patients aged 20 years or older with at least one SARS-CoV-2 polymerase chain reaction (PCR) or antigen laboratory test from March 1st, 2020, to November 30th, 2021. We further required at least one diagnosis code within three years to seven days before the index date (referred to as the baseline period), and at least one diagnosis code from 31 days to 180 days after the index date (referred to as the post-acute phase or follow-up period), to ensure that patients were connected to the healthcare system and were being observed during the study period. We followed each patient from 31 days after his/her index date until the day of the first target outcome, documented death, the latest date of any documented records in the database, 180 days after the baseline, or the end of our observational window , whichever came first. Two exposure groups were the SARS-CoV-2 infected group and the non-infected group. The SARS-CoV-2 infected group included patients with a positive SARS-CoV-2 PCR or antigen laboratory test. The index date of the infected group was defined as the date of the first documented positive PCR or antigen test. The non-infected group included patients whose SARS-CoV-2 PCR or Antigen tests were all negative throughout the entire study period with no documented COVID-19-related diagnoses at any time. The index date for patients in the non-infected group was defined as the date of the first negative PCR or antigen test. The association study and predictive modeling were conducted on the infected group. The non-infected group was used to rule out background associations that were not specific to PASC. The patient inclusion and exclusion cascades were illustrated in 20 Larger ADI values indicate mode socioeconomically deprived status. Missing ADI value was imputed with median ADI per site. The ADI is a ranking from 1 to 100 with 1 and 100 representing the lowest and the highest level of disadvantage, respectively. We grouped ADI into five categories and set the ADI category 1–20 as the reference group. Baseline healthcare utilization up to three years before the index date was measured according to their care setting. For each inpatient, outpatient, and emergency department encounter, we categorized each setting into 0 visit (reference group), 1 or 2 visits, and 3 or more visits, respectively. We also considered the infection periods, which were grouped into March 2020 – June 2020, July 2020 – October 2020, November 2020 - February 2021, March 2021 – June 2021, and July 2021 – November 2021. We set the first wave of the pandemic from March 2020 to June 2020 in INSIGHT as the reference group. Of note, the third wave from July 2021 – November 2021 period was dominated by the Delta variant. Body mass index (BMI) was grouped according to the WHO classification, BMI < 18.5 as underweight, BMI 18.5–24.9 as normal weight (reference), BMI 25–29.9 as overweight, BMI > = 30 obese, and set missing value as a separate group. Smoking status was also considered, and categorized into never (reference), current, former, and missing. There is a significant missingness regarding smoking status (90.2% in COVID-positive patients and 49.8% in patients with at least one PASC) and we grouped these patients into the missing category.We collected clinical features in the baseline period (3 years to 1 week before lab-confirmed SARS-CoV-2 infection) and the severity of acute infection (1 week before to 30 days after lab-confirmed SARS-CoV-2 infection). Age was categorized into 20–39 years, 40–54 years, 55–64 years, 65–74 years, and 75 years and older groups. We set 55–64 as the reference group. Gender was grouped into female and male (reference). Only three patients in INSIGHT were identified as other/missing gender who were excluded. The race was categorized into Asian, Black or African American, White (reference), other or missing. Ethnicity was grouped into Hispanic, not Hispanic (reference), and other/missing. We used the national-level area deprivation index (ADI) to capture the socioeconomic disadvantage of patients’ residential neighborhood.A wide range of baseline clinical comorbidities were collected, based on a revised list of the Elixhauser comorbidities, conditions recommended by our clinician group, and related medications. Patients were ascertained as having a condition if they had at least two corresponding diagnoses documented during the baseline period. We also counted the number of pre-existing conditions and grouped them into no comorbidity (reference), 1, 2, 3, 4, and 5 or more. A detailed list of these pre-existing conditions and defined reference categories were summarized in Extended Data Table 1.(Step I) We built a separate multivariate Cox proportional hazard model on the EHR of patients who were infected by SARS-CoV-2 to assess associations of covariates and time to the first incident event or censoring in the follow-up period (31–180 days after COVID-19 confirmation). The censoring event is defined as the earliest event of documented death, loss of follow-up in the database, 180 days after the baseline, or the end of our observational window . Fully adjusted hazard ratios (aHR) of each covariate and target PASC event were estimated.(Step II) We built another multivariate Cox proportional hazard model on the EHR of all patients regardless of their SARS-CoV-2 infection status. The model inputs include two parts. One is the set of covariates. The other is the set of interaction terms defined as the product of each covariate and SARS-CoV-2 infection status (1 for SAR-CoV-2 infected patients and 0 for non-infected control patients) on the outcome condition. In this way, the coefficient of a particular covariate captured its association with the outcome condition for patients who were not infected by SARS-CoV-2, and the coefficient of its corresponding interaction term captured the “quantitative modifications” of such association for patients who were infected by SARS-CoV-2. Fully adjusted hazard ratios of each covariate and interaction term were estimated on both infected and non-infected patients.For each potential PASC condition, we performed statistical analysis on its association with various covariates including the following steps.The adjusted hazard ratio (aHR) estimated from the infected patients in Step I were greater than 1;The p-value of the above aHR was smaller than 0.000562, which was corrected by the Bonferroni method for multiple testing;The aHR of the interaction term of the corresponding covariate should also be greater than 1 in Step II.We identified a covariate to be a potential risk factor of a particular PASC condition if it satisfied the following three criteria:To build predictive models for each PASC condition, we examined different machine learning models in both survival analysis and binary classification settings. For the survival analysis setting, we used a multivariate Cox proportional hazard model with L2 norm regularization to predict the time to the outcome event. For the binary classification setting, the occurrence of the target event in the follow-up period was labeled as 1 and 0 otherwise. We used logistic regression with L2 norm regularization, gradient boosting machine with random forest base learner, and deep feed-forward neural network. For each of the abovementioned models, the best model was selected by grid search  a predefined hyper-parameter space through repeated cross-validation . The concordance index (C-index) and the area under the receiver operating characteristic curve (AUROC) were used to evaluate survival prediction performance and binary prediction performance respectively. Both two measures range from 0 to 1 with 0.5 indicating random guess and 1 indicating perfect prediction. The 95% confidence interval of the final performance was estimated by 1000-times bootstrapping performance on each of the testing datasets in repeated cross-validation.The stratified analysis was conducted by stratifying patients by their severity in the acute infection phase  and then performing statistical analysis within each stratum. The noninfected control patients were also stratified according to the hospitalized or non-hospitalized during the 1 week before to 30 days after their index date, to capture background associations within each subgroup population.To get robust conclusions, we conducted the following sensitivity analyses. For association analysis, we also used a univariate Cox model for each covariate adjusted for age, sex, and acute severity. We further tested the impact of lifting Step II of the statistical analysis on the identified risk associations.For the predictive modeling, we also tested a different feature engineering method, which used the first 3-digits of ICD-10 codes and medication at the ingredient level to test to what extent PASC can be predicted in a data-driven manner. We selected 1,593 ICD-10 diagnosis codes, 2,309 drugs and 1,698 ICD-10 diagnosis codes, and 4,366 drugs from the INSIGHT dataset and OneFlorida + data, respectively. These ICD-10 diagnosis codes and medications were selected to construct the input feature vectors of the prediction model based on the significant difference  between patients with positive and negative PASC conditions results. After the feature selection process, the selected ICD-10 diagnosis codes, medication, and collected baseline covariates were constructed to represent every PASC condition.We also tested different machine learning predictive models in both the survival analysis setting and binary classification setting to validate the predictability of each PASC potentially impacted by different models. For the survival analysis setting, we tested Cox proportional model with L2-norm regularization. For the binary classification setting, we investigated three machine-learning models with different complexity. The first one is the regularized logistic regression. We adopted the L2-norm penalty and searched the inverse of regularization strength from 10^−3 to 10^3 with 0.5 as the sampling step size. The second one is the gradient boosting machine with a random forest as the base learner. We searched hyperparameters from maximum depth , max number of leaves in one tree , and a minimal number of data in one leaf (30). The third one is the deep forward neural network. We used ReLU (Rectified Linear Unit) activation function for the hidden layer, and search the hidden layers , , , , , ), and learning rate . For each of the above-mentioned models, the best model was selected by grid search of the corresponding hyperparameter space through repeated cross-validation . In the repeated cross-validation process, we set one of the folds as the test set and the rest of the data as the training set. The C-index and the area under the receiver operating characteristic curve (AUROC) were used to measure the predictive performance in the survival setting and binary classification setting, respectively.To get a generalizable conclusion, we further replicated the abovementioned association analyses and predictive analyses to the OneFlorida + cohort. The cohort selection and modeling strategies were the same as our primary analyses on the INSIGHT cohort.We developed our primary results on the INSIGHT cohort and used the OneFlorida + cohort as a validation cohort. Both cohorts were collected from patients who has at least one PCR/antigen test for SARS-CoV-2 infection from March 2020 to November 2021, and the inclusion-exclusion cascade was provided in 21 We compiled a broad list of potential PASC conditions in terms of Clinical Classifications Software Refined (CCSR) categories22 based on our previous findings14 and evidence from other literature.4 Here we studied incident PASC conditions ascertained from 31 days to 180 days after the start of the acute SARS-CoV-2 infection date, denoted as the index date, but not existed one week to three years prior to the index date (See Method for the approach on how the list was compiled and Supplementary Table 1 for the detailed information).The current definition of PASC in the RECOVER protocols is ongoing, relapsing, new symptoms, or other health effects occurring four or more weeks after the acute phase of SARS-CoV-2 infection.21, healthcare utilization history, body mass index, the period of infection, comorbidities, and the care settings in acute phase including hospitalization and ICU admission. For each of the categorical covariates, we defined its reference group the same as prior studies for acute SARS-CoV-2 infection 23 with a 95% confidence interval was shown in We built a list of 89 covariates that are potentially associated with PASC based on a revised list of Elixhauser comorbidities, recommendations of our RECOVER clinician team, and the severity of acute infection of SARS-CoV-2. These covariates included basic demographics , social-economic status in terms of Area Deprivation Index (ADI)Furthermore, we analyzed the associations between the covariates and the risk of developing any incident condition from our list. The unadjusted hazard ratio (HR) and fully adjusted hazard ratio (aHR) for each covariate. A covariate was identified as a potential risk factor for developing a particular condition if it satisfied the following three criteria: (1) the corresponding aHR of the covariate with respect to the target condition is larger than 1 when compared with the reference group Method . The assIncreased severity of the acute SARS-CoV-2 infection (according to the care settings) was associated with a higher risk of being diagnosed with new incident conditions in the post-acute period. Overall, a higher risk of getting any incident diagnosis was observed in patients who were hospitalized during the acute phase (1.29 (1.24–1.33)) or in ICU (1.40 (1.32–1.49)) compared to patients who were not hospitalized during the acute phase . Patients aged 75 or older showed an increased risk of being diagnosed with a wide range of potential PASC conditions in the post-acute infection phase, including dementia (5.8-fold higher), COPD (2.2-fold), cerebral ischemia (2.1-fold), malnutrition (1.8-fold), pressure ulcer (1.8-fold), anemia (1.6-fold), cognitive problems (1.6-fold) compared to patients were 55–64 years old (as reference). Patients with 65 to 74 years old showed an increased risk of being diagnosed with dementia (2.7-fold), heart failure (1.6-fold), and diabetes mellitus (1.6-fold) compared to reference patients. By contrast, younger patients aged 20–39 years old exhibited an increased risk of getting milder potential PASC conditions including acute pharyngitis (1.7-fold), headache (1.4-fold), and anxiety disorder (1.4-fold) than patients in the reference group.Female patients exhibited a 4.3- and 1.3-fold increased risk of being diagnosed with incident hair loss and anxiety disorder in the post-acute infection period compared to male patients. Black patients exhibited a 1.9-fold increased risk of being diagnosed with incident diabetes mellitus than white patients.2) or obese (BMI ≥ 30 kg/m2) were at higher risk of being diagnosed with certain potential PASC conditions than those with normal BMI (BMI from 18.5 to 24.9 kg/m2). Specifically, underweight patients were at a 1.6-fold-increased risk of being diagnosed with heart failure, and diabetes mellitus, and a 1.4-fold-increased risk of being diagnosed with malnutrition than patients with normal BMI. Obese patients showed a 1.8-fold-increased risk of being diagnosed with diabetes mellitus and a 1.3-fold-increased risk of being diagnosed with a sleep disorder.Patients who were underweight , chest pain (1.9-fold), abdominal pain (1.7-fold), dyspnea (1.6-fold), as well as being diagnosed with general PASC symptoms and signs with the U099/B948 ICD codes (5-fold) in the post-acute infection period compared to patients got infected during March 2020 to June 2020 (the 1st wave) as the reference period.We observed that patients who got infected from July 2021 to November 2021, which was dominated by the Delta variant of SARS-CoV-2As shown in We further conducted stratified analyses to examine the associations between baseline factors and incident potential PASC conditions according to the care settings in the acute phase . The same criteria on adjusted hazard ratio and statistical significance as we used in We have examined the impact of the criterion on requiring the identified association to be with a higher risk in SARS-CoV-2 infected patients compared to non-infected patients. Extended Data Fig. 1 depicted the identified associations after we lifted this requirement, i.e., we only require these associations to satisfy the adjusted hazard ratio and statistical significance constraints. From the figure, we observed that more associations have been identified compared to We also tested to what extent the predictability of incident potential PASC conditions is affected by different machine learning models. We investigated a range of machine learning models with different complexities, including regularized logistic regression models, gradient boosting machines, and feed-forward deep neural networks. As shown in Extended Data Fig. 2, we observed little difference across the performance of these different models, and the heterogeneous predictability patterns were still observed, i.e., conditions that were difficult to predict in Lastly, we studied if different feature engineering can impact the prediction results of different PASC conditions. Instead of using pre-defined baseline comorbidities, we leveraged a data-driven approach by using the first three digits of ICD-10 codes of all diagnoses and all medications in RxNorm codes at the ingredient level in the baseline period to predict PASC. We reported the predictive performance of different machine learning models using this large set of features in Extended Data Fig. 3, which does not show big differences compared to the performance in Extended Data Fig. 2 or To assess the generalizability of our findings, we replicated our analyses on the OneFlorida + cohort as an independent validation. The OneFlorida + cohort included 22,341 adult patients with lab-confirmed SARS-CoV-2 infection and 177,010 non-infected as control patients  to be larger than 1 and statistically significant. We further required the estimated aHR value to be larger in patients who were infected by SARS-CoV-2 compared to the non-infected patients, in this way associations that may not be attributed to COVID-19 can be filtered. There were several strengths of our study. First, we studied a comprehensive set of associations between 89 factors and 44 incident PASC conditions in two large EHR cohorts. To our knowledge, this is one of the largest studies on predictive modeling and risk factor analysis for PASC. Second, we derived our primary results from INSIGHT and did a validation study on OneFlorida+, which validated the generalizability of our findings. Third, we have tested the prediction performance of different machine learning models on both a narrow and broad list of covariates, which further validates the robustness of our conclusions. Finally, we ruled out potential background associations by requiring the adjusted hazard ratio value of the identified association estimated from the patients who were infected by SARS-CoV-2 to be larger than the value estimated from patients who are not infected by SARS-CoV-2.Our study had several limitations. Our analysis was based on EHR data, which would miss the information from patients who did not visit the hospitals within the CRNs. We only considered newly incident individual conditions in the post-acute period but did not explore conditions that were prolonged, worsened, or relapsed before and after COVID-19 infection, as well as condition clusters or subphenotypes. Vaccine information was not incorporated in our study due to its incompleteness in the EHR records, and we are working on adding other data sources  to make the information more robust. In addition, our analyses did not cover the recent Omicron wave due to the availability of the data.In conclusion, we used two large-scale clinical research networks, INSIGHT and OneFlorida + to identify risk factors associated with newly incident PASC conditions and to develop predictive models to identify those who are at risk of these conditions. Our results highlight that several predictive PASC diagnoses are associated with severity in the acute phase. However, less predictable PASC diagnoses represent an ongoing challenge that may not respond to other measures that decrease the severity of acute COVID.