This series includes 26 adults with a median duration of 39 days from onset of symptoms to death (ranging 9–108 days), 92% of them subjected to immunomodulatory therapy, and an infant patient. We detected gRNA in the lung of all but one patient, including those with longer disease duration. SgRNA was detected in 11 out of 17 patients (64.7%) with illness duration up to 6 weeks and in 3 out of 9 patients (33.3%) with more than 6 weeks of disease progression. Viral protein was detected using immunohistochemistry and viral mRNA was detected using in situ hybridization in 3 out of 4 adult patients with illness duration of <2 weeks, but in none of the 23 adult patients with an illness duration of >2 weeks. A remarkable result was the detection of viral protein, gRNA and sgRNA in the lung cells of the pediatric patient after 95 days of illness. Additional pulmonary infections included: 9 acute bronchopneumonia, 2 aspergillosis, 2 cytomegalovirus, and 1 BK virus infection. These results suggest that in severe COVID-19, SARS-CoV-2 could persist for longer periods than expected, especially in immunocompromised populations, contributing to the persistence of chronic lung lesions. Additional infections contribute to the fatal course of the disease.The exact role of viral replication in patients with severe COVID-19 has not been extensively studied, and it has only been possible to demonstrate the presence of replicative virus for more than 3 months in a few cases using different techniques. Our objective was to study the presence of RNA SARS-CoV-2 in autopsy samples of patients who died from COVID-19 long after the onset of symptoms. Secondary superimposed pulmonary infections present in these patients were also studied. We present an autopsy series of 27 COVID-19 patients with long disease duration, where pulmonary and extrapulmonary samples were obtained. In addition to histopathological analysis, viral genomic RNA (gRNA) and viral subgenomic RNA (sgRNA) were detected using RT-PCR and  The iniion ISH; . Severalion ISH; , but theion ISH; . Most auIn addition to SARS-CoV-2 infection, patients with severe COVID-19, especially the immunocompromised and those with longer ICU stays are prone to develop other pulmonary infections , includiHere, we study the presence of RNA SARS-CoV-2 after long periods of illness, using a collection of post-mortem specimens from lungs and extra-respiratory organs. We detected genomic SARS-CoV-2 RNA (gRNA) and subgenomic RNA (sgRNA) in 27 patients with a median time to death of 39 days. In addition, we evaluated the presence of other pulmonary infections in these patients with severe COVID-19.n = 26). The autopsies corresponded to patients with severe respiratory disease and were requested by the medical staff according to clinical interest and represented about 3% of all COVID-19 deaths during this period. Consequently, the series does not represent the complete spectrum of causes of death attributable to COVID-19. All autopsies were performed under the consent of the patients’ relatives and carried out following safety protocols, in a negative pressure autopsy room, using personal protection equipment, as previously reported . It included all consecutive autopsies of COVID-19 patients performed at Hospital Universitario Ramón y Cajal  from April 2020 to March 2021  lesions was performed according to In addition to hematoxylin and eosin stain, PAS and Grocott stains were performed in suspicious cases to highlight fungal infections. Immunohistochemistry for Herpesvirus , CMV  and polyomavirus BK  were performed on a Dako Omnis platform and the staining was visualized with the EnVision system FLEX/HRP . These cases were selected by morphological features in lung samples suspicious of those infections.SARS-CoV-2 detection was done in post-mortem swabs taken from the nasopharynx (NPS), lungs (LS) (the two superior lobes), heart, liver, and right kidney. In addition, testing was done in post-mortem formalin-fixed paraffin-embedded (FFPE) tissue from the lungs , heart, liver and kidney in all patients, and large intestine in eight patients. RT-PCR was utilized for detection of genomic SARS-CoV-2 RNA (gRNA) and subgenomic viral RNA (sgRNA) in all patients.For gRNA detection, swab samples were sent on the same day to the Microbiology Department for the detection of genomic SARS-CoV-2 RNA. RNA extraction and RT-PCR amplification were performed within 3 h after reception in the laboratory. RNA extraction was performed using MagmaxTM Core Nucleic Acid Purification Kit  and gRNA SARS-CoV-2 was detected using TaqmanTM 2019 nCoV assay . Samples with a cycle threshold (Ct) lower than 40 were considered positive.For FFPE samples, RNA was extracted from 10 sections of 5 μm obtained from paraffin blocks using RecoverAll Total Nucleic Acid Isolation Kit (Invitrogen), following the manufacturer’s instructions. RNA quantity was measured fluorometrically with Qubit RNA high-sensitivity assay kit . gRNA SARS-CoV-2 was detected in the same way as the swab samples, using TaqmanTM 2019 nCoV assay . Samples with a Ct lower than 40 were considered positive.sgRNA was detected using RT-PCR. Retrotranscription was performed with High-Capacity cDNA Reverse Transcription Kits (Thermo Fisher Scientific), following the manufacturer’s instructions. Specific primers were designed between the leader region and the nucleocapsid protein (N) gene . The PCR was performed at 54° annealing temperature for 45 cycles. PCR products were run with High Sensitivity D1000 ScreenTape kit on the Tape Station 2200 . Confirmation of positive fragments was performed using Sanger sequencing.As internal quality controls, RNAseP or MS2 were used. Only those PCR assays yielding a positive amplification for internal controls were validated following the manufacture’s guidelines.In each patient, the FFPE RT-PCR positive lung sample with the lowest Ct value was also analyzed with IHC and ISH.For SARS-CoV-2 immunohistochemistry, we used the monoclonal antibody  against the spike protein of SARS-Cov/SARS-CoV-2 in 1:100 dilution on 3 μm slides obtained from paraffin blocks. Antigen retrieval was performed with 10 mM citrate buffer (pH 6.0). The staining was visualized with the EnVision system FLEX/HRP .® SARS-CoV-2 probes for the SARS-CoV-2 S gene encoding the spike protein  on a Leica Bond III automated stainer , according to the manufacturer’s instructions. Briefly, 4-μm formalin-fixed and paraffin-embedded tissue sections were pre-treated with heat and protease prior to hybridization. Tissue sections were hybridized separately with the target probe to detect infected cells and with the positive and negative control probes. Specific staining signals were identified as brown, punctate dots present in the cytoplasm.RNA ISH for SARS-CoV-2 was performed using the RNAscopeWe performed a transmission electron microscopic examination of the FFPE tissue from the lung of the pediatric patient. Thin sections were stained with uranyl acetate and lead citrate and were examined with an EM-10 Zeiss microscope.This series included 26 adults and 1 pediatric patient. The clinical data of the adult patients are shown in Histopathological analysis revealed severe diffuse alveolar damage (DAD) in all but one patient, in whom only occasional areas of hyaline membranes were found. The predominant lung pattern was DAD in fibroproliferative stage, but exudative or fibrotic lesions were also present in different proportions in each patient .Aspergillus flavus using PCR . The fungal structures of the other case were not identified by PCR nor culture. We observed intranuclear and intracytoplasmic inclusions in respiratory epithelial cells, suggestive of CMV infections, in 2 patients (7.7%). Specific immunohistochemistry for CMV was performed and rendered positive results . Moreover, we observed the presence of hyphae within areas of mixed inflammatory infiltrates in two patients. These hyphae were visible under the PAS and Grocott techniques  and were results . We did Other pathological findings in the lungs, hearts, kidneys, livers, and bone marrows are enumerated in This series also included an 8-year-old boy with primary immunodeficiency (PI). This patient was subjected to a haploidentical transplant due to a severe PI characterized by an almost total absence of naïve T cells and T cell receptor excision circles (TRECs). A next generation sequencing study did not identify any pathogenic mutations in the genes related to the main severe combined immunodeficiencies. The patient was waiting for a comparative genomic hybridization study to evaluate the possibility of a 22q11 related syndrome. During his stay in the hospital for the diagnosis of his primary disease, SARS-CoV2 was identified in a NPS during a febrile episode with little respiratory symptoms. The patient recovered from the clinical symptoms but the NPS stayed positive. He developed acute respiratory insufficiency 95 days after the initial diagnosis of COVID-19 and eventually died. In the autopsy, the main pulmonary finding was an extensive bilateral pulmonary hemorrhage. Moreover, some bronchial epithelial cells showed amphophilic nuclear inclusions that were positive for Polyomavirus BK immunohistochemistry. Electron microscopic examination revealed multiple intranuclear viral particles arranged in paracrystalline arrays, approximately 40 nm in diameter, with no envelope, typical of Polyomavirus BK .All patients tested positive for SARS-CoV-2 in NPS during the development of their disease. Ct values are shown in SARS-CoV-2 gRNA was detected in at least one autopsy sample in all patients . The numPositive post-mortem NPS were obtained in 17 of 23 (73.9%) patients and positive LS were obtained in 15 of 23 (65.2%) patients. No positive results were obtained in swab samples from the heart, kidney, and liver in all patients except one, who showed a positive result in the heart swab.p = 0.038 and p = 0.024, respectively). No statistically significant differences were observed in the Ct values obtained from post-mortem swabs and FFPE positive samples. Ct values from FFPE samples showed a statistically significant positive correlation with illness duration   than in post-mortem NPS (73.9%) and LS (65.2%) (= 0.011) , indicatIn adult patients, we detected sgRNA in 11 of 17 (64.7%) patients with illness duration up to 6 weeks. Moreover, sgRNA was also detected in 2 of 9 (22.2%) adult patients with more than 6 weeks of disease progression .Viral protein – using IHC –, and viral mRNA – using ISH – were detected in 3 out of 4 adult patients with illness duration of less than 14 days, but in none of the 22 patients with an illness duration between 15 and 103 days. Signals were observed in areas with hyaline membranes, in the cytoplasm of pneumocytes, some of them desquamated into the alveolar lumen, and more infrequently in the respiratory epithelium of bronchi or bronchioles. Staining of intraalveolar cells, suggestive of macrophages, was also observed. Staining of endothelial cells was inconclusive in our samples.We detected viral protein using IHC, viral gRNA using ISH and RT-PCR, and sgRNA in the lung of the pediatric patient after 95 days of the onset of symptoms . We did To the best of our knowledge, we report the autopsy series with the longest illness duration in which viral detection has been performed in post-mortem lung samples. The availability of post-mortem specimens has provided a comprehensive insight into the pathophysiology of COVID-19. Several autopsy series including 4 to 64 patients (median 16 patients) with a median duration of illness ranging from 5.7 to 38.5 days (median 14.9 days) have been published . In thisWe detected sgRNA in 14 of 26 (53.8%) adult patients, including two who died after 49 and 57 days of disease. These results are in accordance with the observations of We did not detect SARS-Cov-2 mRNA in any organ but the lung in patients with more than 2 weeks of evolution, and no other organs presented lesions related with SARS-Cov-2 infection but the lungs. These facts support the role of persistent lung viral shedding in lung pathology. Moreover, it might suggest a rapid and short extrapulmonary spread of SARS-CoV-2 in the acute phase of the disease, which is generally cleared without triggering any explosive local inflammatory response in other organs .Persistent RNA viral shedding could be related to host immunity in severe COVID-19 cases, which is supported by the results observed in our pediatric patient with severe primary immunodeficiency. This patient is similar to a few previously reported living patients with secondary immunodeficiency in whom ongoing viral replication was demonstrated for more than 100 days . In our The immunocompromised populations, including immunosenescent elderly patients or patients subjected to immunosuppression treatment – which most of the patients in this series were , were prPatients hospitalized with COVID-19 have a high risk of secondary infections due to intensive care unit (ICU) admission, prolonged mechanical ventilation, and severe lymphopenia . In our It has been suggested that COVID-19–associated pulmonary aspergillosis (CAPA) is a contributing factor to mortality in patients with severe COVID-19 . In our Corticosteroid use is a known risk factor for CMV reactivation and disease. In our series, 2 patients (7.69%) showed CMV lung infection. Niitsu et al investigated the frequency and characteristics of CMV infection in 26 critically ill patients with COVID-19 who required mechanical ventilation for more than 1 week . They foIn the pediatric patient in this series, we demonstrated the presence of Polyomavirus BK inclusions in epithelial cells using IHC and ultrastructural studies. Pulmonary lesions produced by Polyomavirus BK seem to be very infrequent . As far Our study has several limitations. The number of patients is not very high. Moreover, preanalytical issues related with FFPE samples could have negatively affected RNA detection. Finally, we could not test virus infectivity in any sample due to the lack of appropriate biosecure facilities. Despite these limitations, our results suggest a role of viral prolonged presence in the persistence of severe lung lesions in COVID-19 patients and of host immunity in the clearance of viral infection, given that immunocompromised patients are more susceptible to prolonged viral infections. Moreover, pulmonary co-infections may have an impact in the prognosis of these patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee, Hospital Universitario Ramón y Cajal. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.JP, JG, and BP-M contributed to the conception of the study. TC-C, LM-G, ASn, and MR contributed to the sample processing. MA-R, NM-D-C, IR-C, RP, ASi, RR, and JR-P contributed to the data collection. DP, JP, JG, BP-M, TC-C, and LM-G contributed to the data analysis and interpretation. JP, JG, and IC-B drafted the manuscript. All authors revised the manuscript for intellectual content and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Driving a three-wheel car is an emerging job opportunity in most parts of developing countries. Drivers are at risk for developing low back pain (LBP). However, very little is known about the association between ergonomics factors and LBP among three-wheel drivers.  This study was aimed to identify ergonomic risk factors of LBP among three-wheel drivers. A community-based cross-sectional study on 396 participants was conducted in the Jimma city on all selected public three-wheel drivers in March, 2020. The data were collected using a standard questionnaire adapted from the Nordic Musculoskeletal questionnaire, anthropometric measurements, and observation checklist. The collected data were entered into Epi Data and exported to SPSS version 21.0. Logistic regression analysis was used for analysis based on the p value less than 0.05, 95% C.I. Results. Among 422 planned respondents, 396 (93.8%) have given the complete response. The mean age of the study population was 27.94 (±5.45). One hundred four (26.26%) out of 396 participants had experienced low back pain in the last 12 months. Driving in sitting upright position OR = 0.32 (95% CI = 0.12–0.86), steer wheel handling OR = 3.02 (95% CI = 1.58–5.77), not holding extra passengers OR = 0.35 , rest breaks, and brand of the three-wheel vehicles were significantly associated with LBP.  Nearly more than one-fourth of three-wheel drivers in our study had LBP. The finding implies a significant number of three-wheel drivers are at risk of developing reduced well-being. An appropriate health visit, lifestyle modification, and adequate policy should be established in the study area. Work-related low back pain (LBP) is one of the most typical musculoskeletal disorders which affect work performance and the general well-being –3. LBP oOccupational health has been a major cause of worry in the driving profession particularly among three-wheel drivers , 7. BajaGlobally, 37% of LBP is due to the occupation , and itsAccording to research in the field of ergonomics, prolonged sitting, whole-body vibration, the ergonomic mismatch between anthropometric measurements of the drivers, physical environment, lifting or carrying heavy objects, and prolonged uncomfortable postures while driving are risk factors for LBP , 18. TheThis community-based cross-sectional study was conducted in the Jimma city, Oromia regional state, southwest Ethiopia, from March 01 to March 31, 2020. Jimma city is one of the oldest cities in Ethiopia, and it is located 352 km southwest from the capital Addis Ababa. Based on data from 2016 of the town administration, it has a total population of 195,443. The city has 17 kebeles and two woredas. There are a total of 2000 public three wheel cars in the city that act aspublic transport within 12 site stations, and most of the main roads of the city are covered by asphalt.All four hundred twenty-two selected public three-wheel drivers were our study population. Full-time three-wheel drivers, with at least six months driving experience, on-duty during data collection time, and willing to participate were included in the study. However, individuals who have known LBP prior to entrance into the profession or having a low back pain of traumatic origin were excluded from the study.The sample size was estimated using a sample size formula for estimating a single population proportion with a margin of error of 5%, confidence interval of 95%, and by considering a 50% proportion of low back pain among three-wheel drivers because there are no previous data on this study subject. After adding 10% for the nonresponse rate, our final sample size was 422.The list of three-wheel cars' plate numbers was taken from the city administration with respective terminal sites. Then, three-wheel car plates were randomly selected by computer-generated simple random sampling method using ran between approaches on an excel sheet. Proportional allocation of the total sample size to all terminal's sites  was done. A total of 422 three-wheel drivers who fulfil inclusion criteria were interviewed. For observational purposes, a nonparticipatory observational approach was used. Finally, the data were collected using an interviewer-administered questionnaire and observation checklist by trained professionals under consideration of WHO prevention for COVID-19 global pandemic such as mask on, social distancing, and utilization of personal protective methods  more likely to develop low back pain than respondents who were holding passengers beside their seat. Respondents who had a taken a break between driving for less than 15 minutes and 15 minutes and more were less likely to develop LBP than respondents who had not a break between driving OR = 0.45  and OR = 0.31 , respectively. Respondents who were driving brand 2 and brand 4 of the three-wheel car were more likely to develop lower back pain than respondents who were driving brand 5 OR = 10.19  and OR = 9.37 , respectively .This study investigated the association between work-related ergonomic risk factors and LBP among three-wheel drivers in the Jimma city, southwest Ethiopia. The finding showed that 26.26% of the three-wheel drivers experienced LBP in the last 12 months. This may indicate driving three-wheel car subjects the individual to the tightness of the muscle beneath the coastal edge and lower gluteal pleat , 5. OtheThe current study identified that uncomfortable sitting postures, steering wheel handling, number of passengers beside the driver, rest breaks between driving, and brand of the three-wheel car as significant risk factors for experiencing LBP among three-wheel drivers. These findings are in line with reports from prior studies , 19–21. This finding shows that drivers who reported comfortable seat posture were 68 times less likely to develop low back pain than respondents who had an uncomfortable sitting posture. This finding is similar to studies conducted in Malaysia  and EthiFurthermore, respondents who complained of uncomfortable steer wheel handling were 3 times more likely to develop LBP than respondents who did not report uncomfortable steer wheel handling. This is comparable with a study conducted in Egypt  in whichThe main strengths of this study are that we used a nonparticipatory observation checklist of records as confirmatory for self-reported ergonomic risk factors for LBP. Moreover, this study gives attention to the occupation-related health problem of three-wheel drivers which was neglected by the health care workers and researchers. In addition, the data collection was performed under consideration of WHO prevention for COVID-19 global pandemic.However, the limitations of this study include, like any observational study, the analysis might only identify the associations between exposure to risk factors and LBP but not fix the causality. The twelve months work-related lower back pain prevalence may be under or overestimated due to recall bias. Therefore, the recall bias might be another weakness of the study. Thus, for future, we recommend a longitudinal study for better outcome in all dimensions of pain assessment. This study was conducted in only one part of southwest of Ethiopia which may not show a more comprehensive result representing three-wheel drivers working in Ethiopia.This finding shows that nearly more than one-fourth of three-wheel drivers of the Jimma city had low back pain. Some ergonomic risk factors such as uncomfortable sitting postures, steer wheel handling, number of passengers beside the driver, rest breaks between driving, and brand of the three-wheel car are significantly associated with LBP among three-wheel drivers. The finding implies a significant number of three-wheel drivers are at risk of developing reduced well-being. An appropriate health visit, lifestyle modification, and adequate policy brief that addresses this high burden occupation-related problem should be established in the study area.


Coronavirus disease 2019 (COVID-19) has affected all age groups worldwide, but older adults have been affected greatly with an increased risk of severe illness and mortality. Nepal is struggling with the COVID-19 pandemic. The normal life of older adults, one of the vulnerable populations to COVID-19 infection, has been primarily impacted. The current evidence shows that the COVID-19 virus strains are deadly, and non-compliance to standard protocols can have serious consequences, increasing fear among older adults. This study assessed the perceived fear of COVID-19 and associated factors among older adults in eastern Nepal.A cross-sectional study was conducted between July and September 2020 among 847 older adults (≥60 years) residing in three districts of eastern Nepal. Perceived fear of COVID-19 was measured using the seven-item Fear of COVID-19 Scale (FCV-19S). Multivariate logistic regression identified the factors associated with COVID-19 fear.The mean score of the FCV-19S was 18.1 (SD = 5.2), and a sizeable proportion of older adults, ranging between 12%-34%, agreed with the seven items of the fear scale. Increasing age, Dalit ethnicity, remoteness to the health facility, and being concerned or overwhelmed with the COVID-19 were associated with greater fear of COVID-19. In contrast, preexisting health conditions were inversely associated with fear.Greater fear of the COVID-19 among the older adults in eastern Nepal suggests that during unprecedented times such as the current pandemic, the psychological needs of older adults should be prioritized. Establishing and integrating community-level mental health support as a part of the COVID-19 preparedness and response plan might help to combat COVID-19 fear among them. As of June 13, 2021, over 175 million confirmed cases and over 3.7 million deaths are reported worldwide from SARS-CoV-2 infection (COVID-19) .The life of older adults has been disrupted in most parts of the world, and anxiety is common among people to accept the increasing cruelty of COVID-19. Although most people seem to be physically, mentally, socio-economically impacted by the pandemic, the impact may be greater for certain groups such as the older adults who are at an elevated risk of severe illness and mortality ; the COVThe Senior Citizen Act of Nepal defines individuals aged ≥60 years as senior citizens or older adults . The aveThere is scarce data on fear and its predictors among older adults in south Asian countries, mainly from resource-limited countries like Nepal. To the best of our knowledge, this is the first study ever been conducted to assess the fear of COVID-19 and its associated factors among the older adults at the community settings in Nepal amid the COVID-19 pandemic.Morang, Pachthar, and Terathum) of Province 1 in eastern Nepal. Morang district is located in the plains, while Pachthar and Terathum in a hilly region of Nepal. The sample size of 847 was calculated considering the following parameters: unknown prevalence  =  50%, CI  =  95.0%, sampling error  =  5.0%, design effect  =  2, and non-response rate  =  5.0%. Multi-stage cluster sampling recruited older adults . In the first stage, three districts from Province 1 were selected randomly. Then, one urban and one rural municipality in each district were selected randomly. Subsequently, from each municipality, three wards  were randomly selected. A proportionate simple random sampling technique was used to recruit older adults from each ward in the final step. Representatives from the municipalities provided the list of eligible older adults in the selected wards. Eligibility criteria included age ≥60 years, Nepali nationals with a minimum of one year of residence in the community. The exclusion criteria included older adults residing in nursing care, having any mental conditions, hearing disability, or inability to communicate.A cross-sectional study was conducted between July and September 2020 among the older adults (≥60 years) residing in three districts  developed and validated by Ahorus et al. among the general Iranian population . Older aThe internal consistency or the reliability of the scale among Nepali older adults was acceptable . Confirmatory factor analysis (CFA) was conducted to validate the scale among study participants where the standardized root mean square residual (SRMR) close to 0.06, and the Goodness of Fit Index (GFI), Normed Fit Index (NFI) and Comparative Fit Index (CFI) close to 0.90 were considered to be acceptable . In the COVID-19 ” and “how concerned are you because of this COVID-19 ”.Independent variables included age group, gender, marital status, family type, ethnicity, education, occupation, urban/rural residence, walking distance to the nearest health facility, any preexisting chronic health problems, any current medications, financial hardships with health care access, the need of additional care from family members/caregivers, recipient of social security allowance, source of COVID-19 information, and feeling overwhelmed and concerned because of COVID-19 pandemic. These co-variates definitions and measurements are described in our previous work , 22. ForThe English version of the questionnaires was first translated to Nepali and then back-translated to English by three researchers to ensure the contents’ consistency. Piloting the questionnaires among a small sample (n = 10) of older adults helped refine the final version by considering their remarks from the older adults. Following piloting, minor editorial issues were resolved, such as “COVID-19” was replaced with the term coronavirus throughout, and the question that aimed to capture proximity to the health facility was clarified by specifying “walking distance to the nearest health facility.”This study received ethics approval (Ref# 150/2020) from the ethics board of Nepal Health Research Council (NHRC), Kathmandu, Nepal. All study participants provided informed written consent. For participants unable to read and write, proxy written consent was obtained from their close guardians. Participation was voluntary, and older adults did not receive any financial compensation. After completing the interview, community health care providers delivered 10 minutes of counseling services on dealing with COVID-19 was delivered to all participants.Fig 2) shows the older adults’ agreement with the seven items of FCV-19S. A generalized estimating equation, adjusting the sampling design, was used to examine the factors associated with the COVID 19 fear. We assessed the multicollinearity of covariates using Variance Inflation Factors (VIFs). The VIFs for all covariates that were included in the logistic regression analysis were less than 2.0. The variables with p<0.2 in the bivariate analyses were included in the final multivariate model. STATA 15 and JASP 0.13.1 were used for data analyses.Mean (SD) and frequency (%) is used to depict participants’ characteristics. Independent t-tests and ANOVA evaluated the mean differences in the FCV-19S score by participants’ characteristics. The bar graph , had preexisting conditions (64%) or used medications (51%), and had financial hardships to access health care (55%) (Table 1).Table 1). Older adults’ agreement on the seven items of FCV-19S is shown in Fig 2, whereby 34.6%, 24.1%, 14.3%, 28.0%, 31.6%, 12.2%, and 14.0% participants agreed being afraid, uncomfortable, clamminess, fearful of losing life, being nervous or anxious, and experienced sleeplessness and palpitation due to COVID-19, respectively. Mean differences in fear of COVID-19 were noted for several independent variables (p<0.05), including age category, marital status, ethnicity, education, occupation, residence, distance to the nearest health facility, preexisting health conditions, medications use, financial hardships with health care, needing additional care from family, receiving social security allowance, and being concerned or overwhelmed with the COVID-19 pandemic (Table 1).The mean score of the FCV-19S was 18.1 (SD 5.2) . Likewise, a dose-response relationship was observed between perceived COVID-19 fear and the proximity or remoteness to the nearest health facility, measured in terms of approximate walking time to the facility; those at the most remoteness  had the highest fear coefficient  compared to those at the proximity . Older adults with preexisting health conditions were less fearful of COVID-19 than those without any conditions . Moreover, those who were concerned about COVID-19 [ and ] were more fearful than those who were not concerned at all. Likewise, older adults overwhelmed with the COVID-19 pandemic were more fearful than their counterparts who were not overwhelmed with the COVID-19 pandemic .Table 2).In the adjusted model, the coefficient for the variable’s marital status, education level, occupation, residence, current medications use, financial hardships with health care, needing additional care from family, and receiving social security allowance lost statistical significance after adjusting for covariates  on the seven items of FCV-19S. Although the literature on fear of COVID-19 among older adults lacks from Nepal, experts opined that COVID-19 has heavily affected older adults’ mental health as they are more susceptible to fear and anxiety problems during the pandemic , 24. WhiIn the current study, increased age was associated with greater fear of COVID-19, which was plausible given that the impact of COVID-19 in terms of hospitalizations, ICU admissions, and fatality rate, is higher among older adults , 6, 31. Another notable finding from this study was the greater perceived fear among Dalits  compared to the Brahmin/Chhetris or so-called upper caste. Notably, the Dalit ethnic group was historically considered “untouchables” until the recent past and is still disadvantaged in terms of opportunities. The role of social determinants of health and their linkage to poor health outcomes among minorities is well documented . AlthougThis study found that being distant from health facilities  was associated with greater fear of COVID-19. The possible explanation could be that most local healthcare facilities have halted their services due to fear of COVID-19 transmission . MoreoveOur study identified that older adults overwhelmed and concerned about the effect of the COVID-19 were more fearful than those who were indifferent to it. This is anticipated as fear of COVID-19 cripples when people become more concerned about its lethal outcomes, triggering psychological distress . Poor heThe current study did not find the association between sources of information, such as social media, newspapers, and COVID-19 fear. The possible explanation could be that older adults with limited literacy or no formal education are less likely to follow pandemic-related news, information, or social media. In contrast to current findings, emerging evidence has shown that mass media, including social media, plays a pivotal role in disseminating information on COVID-19 infection and death rates, making people more concerned, resulting in adverse psychological effects , 44.Our finding contrasts with another study that reported that older adults with preexisting conditions were more concerned about contracting COVID-19 . This coOne of the strengths of this study is that it is the first of its kind to assess the level of COVID-19 related fear among older adults in Nepal. Another strength was that this study used the Nepali version of the survey instrument validated with ten target participants to check if the target population correctly chose the included questions’ responses. Moreover, questions framed in a positive or neutral light helped solicit truthful answers from the study participants. These strategies may have reduced the social-desirability bias in this study. Despite these strategies, we acknowledge that social-desirability bias could have occurred as we relied on the information provided by participants. As with other studies, this study does have some limitations. First, the study was conducted in three districts of Province 1, limiting the generalizability to other provinces and/or settings of Nepal. Second, it is worthy of mentioning that the status of fear might have altered after COVID-19 cases continue to increase in Nepal. Lastly, the FCV-19S has not been validated in Nepal yet; therefore, the stability of the scale over time must be examined.The greater fear of the COVID-19 pandemic among the older adults suggests that during unprecedented times such as the current pandemic, due care of the psychological needs of the older adults in eastern Nepal need to be taken. Thus, addressing the mental health needs of older adults should be one of the priorities of pandemic management. In this overwhelming condition, social connections are extremely important for maintaining older adults’ physical and mental wellbeing. Therefore, it is important to engage family members and friends through phone calls or video chats to cultivate social connections and promote healthy relationships with others. Thus, fostering meaningful connections and creating a sense of belonging will enhance motivation and reduce the fear and risk of COVID-19. Moreover, community health workers for the same community can be trained virtually on a wellbeing support package to provide psychological support following World Health Organization’s COVID-19 safety protocols that may improve older adults’ psychological wellbeing.S1 Dataset(SAV)Click here for additional data file.


With unclear characteristics of post-infection and post-vaccination immunity, the multiple sclerosis community lacks evidence to guide patients on their continued coronavirus disease 2019 (COVID-19) infection risk. As disease modifying treatments all modulate the immune system, we expect their use to alter acquired immunity to COVID-19, but the specific impact of individual treatments is unclear. To address this, we analyzed the patient and COVID-19 specific characteristics associated with post-infection humoral immunity in 58 patients with central nervous system (CNS) demyelinating disorders in the Boston metropolitan area. Univariate analysis of variance was performed using Mann Whitney U test for continuous variables, and Chi Square or Fisher Exact test for nominal variables. Univariate and stepwise multivariate nominal logistic regression identified clinical characteristics associated with COVID-19 specific nucleocapsid IgG antibody formation post-infection. Our cohort demonstrated a 42% post-infection seropositive rate with a significantly higher rate observed with shorter duration between infection and antibody collection and use of natalizumab over no/other treatment. Use of anti-CD20 treatments compared to no/other treatment was associated with a significantly lower rate of seropositivity. However, only shorter duration between infection and antibody collection as well as use of no/other treatment compared to anti-CD20 treatment were found to be independently associated with increased likelihood of post-infection seropositivity. Additionally, we demonstrate durability of antibody response up to 9 months in a small subset of patients. Thus, our data supports that patients with CNS demyelinating disorders regardless of DMT are able to form a measurable antibody response after COVID-19 infection, and that patients on anti-CD20 treatments form less robust immunity after COVID-19 infection. The evolving coronavirus disease 2019 (COVID-19) pandemic and availability of multiple COVID-19 vaccines have made it clear that the best solution to the COVID-19 pandemic is sustained immunity against the virus. Given the large number of patients with central nervous system (CNS) demyelinating disorders that require immunomodulatory or immunosuppressive medications, identifying the forms and duration of post-infection and post-vaccination immunity in specific subpopulations is critical to guide patient care.Several studies have demonstrated both humoral and cellular immunity are formed after COVID-19 infection in >90% of the general population, although the duration remains debated . PatientIn this study, we evaluated the clinical characteristics associated with COVID-19 IgG seropositivity in a cohort of 58 patients with CNS demyelinating disorders and prior COVID-19 infection. We hypothesized that lymphocyte depleting/sequestering agents would decrease seropositivity.https://www.project-redcap.org/) to collect study data. COVID-19 IgG was obtained as part of routine patient care. Repeat measurements were only available for 10 seropositive patients as the number of repeat measurements was determined by patient desire for repeat testing to evaluate durability of antibody response. Qualitative COVID-19 nucleocapsid protein specific IgG antibody testing was performed in house on an Abbott Architect platform . Data included was collected between January 2020 and November 2021, but follow-up data continues to be collected from the current cohort. Comorbidities included were cancer (prior or current), hypertension, cardiac disease (coronary artery disease or heart failure), chronic lung disease , and diabetes mellitus (type 1 or type 2). Labs collected included white blood cell count, absolute lymphocyte count, vitamin D level, and IgG quantification in addition to COVID-19 IgG. A few patients in this cohort were included in our recent publication, but this focused on factors affecting COVID-19 severity and did not evaluate post-infection characteristics such as markers of humoral immunity  for descriptive statistics and graphing and JMP Pro 16 statistical software  for more complex statistics. Lab values, age, CNS demyelinating disease duration, Expanded Disability Status Scale (EDSS), and duration between events (e.g. infection and antibody collection) were treated as continuous variables with the remaining variables considered nominal. As the infection date for patients diagnosed by positive IgG was unknown given no prior COVID-19 related symptoms or positive PCR, infection date was considered the same as antibody collection date for those diagnosed by positive IgG obtained as part of routine care, and any analysis possibly affected by this assumption is highlighted in the results. The rare unknown variable in nominal categories was folded into the lower severity category or unknown (UNK), and the number of unknowns is detailed in the table/figure legends. There was no missing data for continuous variables. Hospitalization status, ICU admission, and method of COVID-19 infection diagnosis was obtained by treating physicians from either patient report or electronic medical record. DMTs were analyzed as individual treatments vs. none/other DMT with the exception of anti-CD20 monoclonal antibodies (i.e. rituximab and ocrelizumab), which were combined given similar mechanism of action. Univariate analysis was with Mann Whitney U test or ANOVA for continuous variables and Chi Square or Fisher Exact test for nominal variables. Univariate nominal logistic regression was run with target value of “positive.” Multivariate nominal logistic regression was run in a stepwise fashion based upon Akaike information criteria and mixed effects modeling. Significance was considered at p > 0.05). There was no significant difference in presence of major comorbidities . The average duration of either NMOSD or MS was 15.6 ± 1.9 years in seropositive group and 13.6 ± 1.9 years in seronegative group (p > 0.05). Most recent EDSS  and vitamin D levels  were also similar.We evaluated the demographic and clinical characteristics of 58 patients with available COVID-19 IgG testing, prior suspected or confirmed COVID-19 infection, and a known diagnosis of MS or NMOSD. We found an overall seropositive rate, or seroprevalence, of 42%. The characteristics of seronegative and seropositive patients are presented in p > 0.05) and were diagnosed by PCR as opposed to symptoms alone . Of note, eight patients who were diagnosed by symptoms alone were in the seronegative group. The majority of these infections occurred early in the pandemic, and testing was either not performed or not available. However, as our prior publication and many others include both suspected and confirmed COVID-19 cases, these patients were included in the dataset (p < 0.01), this is likely biased by the patients diagnosed by antibody as this is no longer significant with their exclusion (p = 0.08). One patient in the seropositive group and three in the seronegative group received steroids within one month of COVID-19 infection although this was not found to impact seroprevalence (p > 0.05). In addition, some patients in each group received COVID-19 vaccination prior to antibody testing. As the vaccines received all target the spike protein, this has no effect on nucleocapsid specific IgG utilized in our study . Although limited by small sample size, there were no significant differences between seronegative and seropositive patients on rituximab or ocrelizumab with regards to average months between COVID-19 infection and antibody collection, months between most recent anti-CD20 infusion and infection or antibody, duration of anti-CD20 treatment, number of infections that were symptomatic, or most recent labs   and anti-CD20 treatment vs. other/none  were found to be independent predictors of seropositivity , anti-CD20 treatment vs. other/none , and natalizumab vs. other/none  were significant contributors in univariate analysis . Howeversitivity .Multiple post-infection COVID-19 antibody measurements were available for ten seropositive patients. Five demonstrated continued antibody positivity for 3–9 months after COVID-19 infection . Two of Herein, we demonstrate a seropositivity rate  of 42% in patients with MS and NMOSD after suspected or confirmed COVID-19 infection. This is similar to most case series and small cohort studies of patients with CNS demyelinating disorders around the world –8. HowevWe found no impact of COVID-19 severity, age, sex, presence of major comoribities, or PCR confirmation of infection similar to other cohorts of MS patients , 18, 19.Although most studies have found little impact of DMT on COVID-19 severity , 21–24, Unexpectedly, natalizumab was associated with a higher seropositivity rate in univariate analyses, although was not an independent risk factor in multivariate regression. In a small Italian study of fifty MS patients on natalizumab, the rate of seropositivity was double the local general population at 16%, but interestingly only three patients reported symptoms suggestive of a prior COVID-19 infection . NatalizAs studies utilizing quantitative COVID-19 serology have demonstrated at least some COVID-19 antibody titer post-infection present in most MS patients after infection, the impact of DMT type is likely on both intensity and durability of antibody response . StudiesThe clinical significance of decreased post-infection antibodies could imply more than impaired humoral immunity. Immunologic studies suggest COVID-19 antibody titers parallels COVID-19 specific cellular immunity –37. PeakOur study is not without limitations. Our analysis is limited by small sample size, especially when querying individual DMTs. Geographic location, even within the United States, also has demonstrated effect on seroprevalence, which we are unable to assess in our Boston-based cohort , 20, 42.In conclusion, we present data from 58 patients with MS or NMOSD and prior COVID-19 infection, showing an overall lower seropositivity rate than observed in the general population that was further decreased by use of anti-CD20 monoclonal antibodies and longer time from infection to antibody collection. Importantly, we demonstrate some humoral immunity with almost all included DMTs, lasting as long as 9 months post-infection. Although humoral immunity is thought to parallel both neutralizing antibody activity and T cell response, we have reservations that this generalization can be made in the MS population given the varied mechanisms of action utilized by DMTs. As the pandemic continues, we hope that further studies will build upon this to further delineate the DMT-specific humoral and cellular immunity both post-infection and post-vaccination to better guide patient care.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Beth Israel Deaconess Medical Center IRB Committee (Protocol# 2020P000343). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.KM performed drafting/revision of manuscript and analysis/interpretation of data. JS designed study, performed drafting/revision of manuscript, and assisted with acquisition of data and data analysis. Remaining authors played a major role in data acquisition. All authors contributed to the article and approved the submitted version.Outside of this work, JS has grant funding from National MS Society, Biogen, and Genentech and has consulted for Biogen, Genentech, Teva, Banner, Sanofi, and Cellgene.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources.To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs.Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronavirus disease 2019 (COVID-19) and then subsequently tested on patients with COVID-19 between January 2020 and December 2020 at a single tertiary care center who had at least one radiograph taken within 48 hours of hospital admission.Patient deterioration was defined as the need for invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, IV vasopressor administration or in-hospital mortality at any time following admission. The EPIC deterioration index was augmented with unstructured data from chest radiographs to predict risk of deterioration. We compared discriminative performance of the models with and without incorporating chest radiographs using area under the receiver operating curve (AUROC), focusing on comparing the fraction and total patients identified as low risk at different negative predictive values (NPV).Data from 6278 hospitalizations were analyzed, including 5562 hospitalizations without COVID-19 (training cohort) and 716 with COVID-19 . At a NPV of 0.95, the best-performing image-augmented deterioration index identified 49 more (9.8%) individuals as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. At a NPV of 0.9, the EPIC image-augmented deterioration index identified 26 more individuals (5.2%) as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission.Augmenting existing deterioration indices with chest radiographs results in better identification of low-risk patients. The model augmentation strategy could be used in the future to incorporate other forms of unstructured data into existing disease models. An essential characteristic of risk models used in medical decision-making is predictive performance, and new strategies should therefore prioritize the inclusion of all available data that could improve this accuracy. However, the vast majority of risk models analyze only structured data from the electronic health record (EHR) when making predictions –7. In coLarge influxes of hospitalized patients during the COVID-19 pandemic caused significant and acute strains on hospital resources around the globe . While iIn this work, we describe a method using machine learning to augment existing deterioration indices with chest radiographs. We apply the approach to predict deterioration of patients hospitalized with COVID19, augmenting two existing EHR-based deterioration indices , 5. WhilThis retrospective cohort study was approved by the Institutional Review Board of the University of Michigan Medical School (HUM00179831: Prediction of complications and outcomes in COVID-19 patients at Michigan Medicine). Given the retrospective nature of the data analysis, the Michigan Medicine IRB waived the requirement for informed consent among study subjects, and data were anonymized prior to analysis.All data analysis were performed using Python.Because of the limited number of COVID-19 patients available for model development and testing, we used COVID-19 PCR negative patients admitted through the emergency department at Michigan Medicine (MM) and required any type of respiratory support during their hospitalization from January 1, 2020 to December 31, 2020 for model training. We used COVID-19 PCR positive patients for validation and testing during the same time period. Patients were included if they required supplemental oxygen during their hospitalization and had at least one chest radiograph performed during the first 48 hours. Patients that experienced clinical deterioration or discharge within four hours of presentation were excluded. Clinical deterioration was defined as death or the need for ICU-level therapies including invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, or vasopressor support. Patients that experienced clinical deterioration prior to their first radiograph being taken were also excluded.The analysis focused on two related clinical problems: (1) identifying low-risk patients unlikely to deteriorate who may be safe for early discharge; (2) identifying high-risk patients likely to deteriorate and need intensive care level therapies. We determined whether patients experienced a clinical deterioration event during their hospitalization using data recorded in the patient’s electronic health records. If a patient experienced two or more deterioration events during the hospitalization, we define the deterioration time to be the time of the first event.In the analysis, we augmented two different existing EHR-based deterioration indices: the Epic Deterioration Index (EDI) and MCURES , 5.The Epic Deterioration Index (EDI) model is a proprietary prediction model used in at least 100 different hospital systems across the United States . This moThe MCURES index is a deterioration index locally developed at the University of Michigan. It makes predictions every four hours from 1 am to 9 pm each day, using 8 curated EHR variables: age, respiratory rate, oxygen saturation (SpO2), O2 flow rate, pulse oximetry type (intermittent or continuous), head of bed position, blood pressure (BP) patient position, pH venous blood gas (VBG) and pCO2 arterial blood gas (ABG).To augment these existing EHR deterioration indices with imaging data, we first trained an image model to predict a patient’s risk of deterioration based on the chest x-ray data alone. Then, we combined the image model outputs and deterioration index outputs as described below and illustrated in To train the image model, we used a deep neural network with a DenseNet-121 architecture  which waModel hyperparameters (e.g. learning rate and training epochs) were selected based on the lowest binary cross entropy loss on the validation data.Using a multi-task learning approach similar to Sriram et al., the image-model was fine-tuned to analyze chest x-rays and predict the likelihood of clinical deterioration over multiple time horizons  on the COVID-19 negative patient population .We combine the image model outputs, deterioration index scores and time-dependent variables to construct vector inputs to our image-augmented model. The image-augmented model consists of a feed-forward neural network with a single hidden layer of five nodes and a single-prediction output. We trained this model with a learning rate of 0.001 with binary cross-entropy loss.Fig 2, the EHR deterioration indices make predictions throughout the hospitalization while the image model makes predictions when chest x-rays are performed. The combined index makes predictions following the time of the chest x-ray, using the deterioration index prediction prior. At subsequent time intervals, the most recent image-model outputs are combined with the EHR risk index from the current interval. Two additional time-related variables are included encoding (i) time since the chest x-ray was performed and (ii) the time since the patient was admitted. The image model output, EHR deterioration index output, and time-related variables are then passed through a fully connected feed-forward neural network to estimate the likelihood of clinical deterioration.To construct the image-augmented deterioration model, the image-model output and deterioration index output were combined with two time-related variables described below. As illustrated in Model training was performed on the COVID-19-negative patient population with acute dyspnea, and the model was validated on a randomly chosen subset of 30% the COVID-19 positive population, with model hyperparameters selected based on validation performance  and the fraction of patients identified as low-risk.For the Epic Deterioration Index, MCURES, and the augmented versions of each model, and the image-model alone, we determined the number of low-risk patients that each model could identify while maintaining NPVs of 95% or 90%. We also determined the fraction of patients that would be identified as low risk at these NPV thresholds if we make predictions at 24, 32, 40 and 48 hours after hospital admission. Empirical 95% confidence intervals (CIs) were calculated by bootstrapping the test set 1000 times.When evaluating how well models could discriminate high-risk patients at risk for deterioration, the primary metric of evaluation was the area under the receiver operator characteristics curve (AUROC). We calculated the AUROC by comparing the risk scores returned by the deterioration index to the binary label of whether a patient deteriorated within the first five days for the entire patient population. The risk score is computed as the maximum of all predicted risk scores prior to the deterioration event or within the first five days.Data from 6278 patient hospitalizations, representing 5063 unique patients, were analyzed. Training data included 5562 hospitalizations among patients without COVID-19; validation and test data included 216 and 500 hospitalizations among patients with COVID-19 . The cohThe median age of the COVID-19 patients was 64.0 (IQR: 53.0–74.0) years; 43.0% were female and 58.6% were white. Demographics between the non-COVID-19 and COVID-19 patients were largely similar with a few exceptions. Patients with COVID-19 were more often Black compared to non-COVID-19 patients (27.4% versus 13.5%) and deteriorate more often compared to our non-COVID-19 training cohort (31.4% vs 17.6%) .The image-augmented EDI model and image-augmented MCURES model consistently maintained a higher negative predictive value than the non-image augmented versions as the fraction of patients considered low risk was increased . Models We also evaluated how many patients each model could identify as low risk from hour 24 through 48 at specific negative predictive values. For this analysis, we analyzed the Epic DI model at a negative predictive value of 90% and the MCURES models at a negative predictive value at 95%. We used a lower threshold for the Epic DI model because it was unable to maintain a negative predictive value above 95%. At a NPV of 0.9, the image-augmented EDI index identified 26 more individuals out of 500 compared to the EDI model as low-risk of deterioration in the first 48 hours of admission. At a NPV and 0.95, the image-augmented MCURES index identified 48 more individuals compared to the MCURES model as low-risk of deterioration in the first 48 hours of admission. At 24, 32, and 40 hours, at a NPV of 0.9, the image-augmented EDI model performs similarly to the EDI model, but outperforms the EDI model at 48 hours . Across Augmentation of the EPIC DI score yielded some improvement in the detection of high-risk patients but there was not meaningfully improvement for the MCURES index. The image-augmented Epic DI score outperformed the Epic DI score alone (0.659 [95% CI: 0.603–0.712] vs. 0.631 [95% CI: 0.571–0.686]) . At a seWe describe a new approach for augmenting EHR-based deterioration models with imaging data. We applied the approach to two EHR-based deterioration indices used for COVID-19, EDI and a locally-developed model called MCURES , 5. WhenAutomated risk indices based on clinical data recorded in the EHR are becoming increasingly common in hospital-based setting, including proprietary risk models  or disease-specific or institution-specific indices (e.g. PICTURE) , 18. To Augmenting an already validated risk index, rather than training a new model has additional benefits. Augmentation, rather than re-training, requires less data because the augmentation model is trained on the deterioration index output (rather than its inputs), resulting in a smaller feature set during training. When using a large variety of different data sources, multimodal models trained from scratch tend to overfit to limited data . By usinWhile our goal was to develop an augmented deterioration index for patients with COVID-19, we trained the augmented models using patients who tested negative for COVID-19. One key limitation of existing machine learning models for the diagnosis and prognosis of COVID-19 is that, as with any emerging disease, a limited amount of data is available for training , 20–22. Augmenting validated risk models could be extended to other types of data . While the combined model does not have to be trained from scratch, some form of additional pre-processing and training is required. For example, text data may need to be first processed through an existing language model, prior to additional training on the joint predictions of the underlying models .We also found that augmentation was more effective for the EDI risk index than the local MCURES model, likely because of the significantly higher performance of the MCURES model. This suggests that augmenting global models with institution specific imaging data may help more than augmenting institution specific models.In addition, our combined models were more useful for identifying low risk patients than for identifying high risk patients. We speculate that this is because chest radiographs may not be as helpful in constructing features for identifying high-risk patients as for identifying low-risk patients. The augmented deterioration indices improve upon the existing EHR-based indices and image model for identifying both low-risk patients for COVID-19 deterioration. This suggests that there does exist complementary information between the two modalities for identifying low-risk patients. Future work to further understand the relative value of various data modalities considered for inclusion in multimodal models is warranted.While our models perform well on our COVID-19 test cohort, there are several limitations to our analysis. While using non-COVID-19 patients provided access to more patients for model training, we might not have identified COVID-19-specific features during the training process . As incrAlthough risk stratification models are used frequently elsewhere in medicine, models may not utilize all sources of readily available data types for patients on which they are applied. For example, in the setting of detecting cancer in pulmonary nodules , validatIn summary, we describe a new approach to augment existing deterioration indices with chest radiograph data. Applied to two existing models to determine risk for COVID-19 patients, this approach more accurately identified a larger fraction of patients as low-risk. We found that the image-augmented EDI was also able to improve upon performance at identifying high-risk patients.S1 FigImage model predictions for COVID19 positive patients within the first 48 hours of admission, shown with exponential weight moving average and 95% CIs. This plot shows the number of patients flagged as low-risk by lowest aggregated prediction and the resulting accuracy for that fraction of patients for the image model alone.(TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Long-term opioid treatment in patients with chronic pain is often ineffective and possibly harmful. These patients are often managed by GPs who are calling for a clear overview of effective opioid reduction strategies for primary care.To evaluate effectiveness of opioid reduction strategies applicable in primary care for patients with chronic pain on long-term opioid treatment.Systematic review of controlled trials and cohort studies performed in primary care from inception date to 15 January 2021.Literature search conducted in EMBASE, MEDLINE, Web of Science, Cochrane Central Register of Controlled Trials, CINAHL, Google Scholar, and PsycINFO. Studies evaluating opioid reduction interventions applicable in primary care among adults on long-term opioid treatment for chronic non-cancer pain were included. Risk of bias was assessed using the Cochrane risk-of-bias tool (version 2) (RoB 2) or the Risk of bias in non-randomized studies — of interventions (ROBINS-I) tool. Narrative synthesis was performed because of clinical heterogeneity in study designs and types of interventions.N = 1717, range n = 35 to n = 985) exploring various opioid reduction strategies. Of these, six studies had high/critical RoB, three moderate RoB, and one low RoB. Three cohort studies: investigating a GP-supervised opioid taper , an integrative pain treatment (moderate ROBINS-I), and group medical visits  demonstrated significant between-group opioid reduction.In total, five randomised controlled trials (RCTs) and five cohort studies were included (Results carefully point in the direction of a GP supervised tapering and multidisciplinary group therapeutic sessions to reduce long-term opioid treatment. However, because of high risk of bias and small sample sizes, no firm conclusions can be made demonstrating the need for more high-quality research. Short-term side effects of opioids include, among others, sedation and respiratory depression.13 GPs have by default become responsible for reducing LTOT because of waiting lists at addiction clinics and pain centres. Yet, qualitative research among GPs report that GPs around the world feel ill equipped to reduce opioid use in these patients.14,15 In other words, GPs playing a pivotal role in the opioid epidemic call for a clear overview of effective opioid reduction strategies that they can safely use in primary care.Key to turning the tide against the opioid epidemic is to reduce LTOT in patients with CNCP. In the US the estimated prevalence of LTOT among patients with CNCP ranges between approximately 1 and 9%.16–18 two of these reviews searched the literature up to 2017, whereas several trials have been published in more recent years.19–24Recently, three systematic reviews were published on effectiveness of opioid reduction strategies in CNCP;In addition, all three reviews looked at all available opioid reduction strategies and, thus, also included strategies that are not applicable in primary care. The present systematic review was specifically aimed at evaluating the most recent evidence on effectiveness of opioid reduction strategies for patients with CNCP on LTOT that are applicable in primary care.25 The review protocol was pre-registered in PROSPERO (CRD42021236399).This systematic review is reported according to the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.16–18 were performed.Searches were carried out in EMBASE, MEDLINE (Ovid), Web of Science Core Collection, Cochrane Central Register of Controlled Trials, CINAHL, Google Scholar, and PsycINFO, from inception date to 15 January 2021 without restriction on language. The complete search strategy is presented in Supplementary Box S1. Backward citation tracking of eligible studies and backward snowballing of recent reviewsStudies evaluating opioid reduction interventions applicable in primary care among adults on LTOT for CNCP were included. The first and second authors (reviewers) independently screened the articles by title and abstract using eligibility criteria presented in Study characteristics were extracted by the first author and checked by the second. Outcome data were extracted independently by both the first and second authors using a standardised extraction form. Data extracted included, among others, intervention characteristics; comparison characteristics; primary outcome measures ; and secondary outcome measures . For cohort studies, outcome data at the last available timepoint when still receiving the intervention were extracted and, for studies that did not report outcome at end-of-intervention, outcome data at the next available timepoint were extracted.26 and Risk of bias in non-randomized studies — of interventions tool (ROBINS-I) for non-randomised studies.27 Disagreements between reviewers were solved by consensus.The first and second authors independently assessed risk of bias of the included studies using the Cochrane risk-of-bias tool (version 2) (RoB 2) for randomised controlled trials28 Effectiveness of interventions is presented as between-group differences and/or (if between-group differences were not available or if the outcome at baseline was significantly different between the groups) as within-group difference. Adjusted analysis correcting for baseline imbalances were presented if these resulted in a different conclusion.Due to substantial clinical heterogeneity in type of interventions and study designs, though a priori planned, meta-analysis was not possible and a narrative synthesis approach was adopted following the Synthesis without meta (SWiM) analysis guideline.19–24,29–32 met the inclusion criteria and were included in this review  patients. Five studies19,21,29,31,32 were randomised controlled trials (RCTs) and five studies20,22,23,24,30 were cohort studies. A variety of opioid reduction methods, from tapering protocols to alternative care strategies, were explored. Supplementary Table S1 includes a summary of studies and patient characteristics included in this systematic review.The 10 included studies comprised 29 had low risk of bias, two RCTs19,32 had some concerns because of non-blinding of research participants and research staff, and the two remaining RCTs21,31 had high risk of bias  and two with a critical risk in bias (n = 84 and n = 41), demonstrated a significant between-group difference in opioid reduction favouring the experimental intervention groups. The interventions researched by these cohort studies varied from multidisciplinary pain treatment,23 group therapeutic sessions,30 to an individually tailored taper plan.20None of the included RCTs demonstrated a significant between-group difference in opioid dose. Three out of the five relatively small cohort studies,A narrative synthesis of the included studies, primary outcomes, and a brief discussion of secondary outcomes is presented below. An overview and thorough explanation of outcomes is presented in n = 985), 1 year of an intervention, Transforming opioid prescribing in primary care (TOPCARE), consisting of nurse care management, electronic registry, academic detailing, and electronic decision tools was compared with 1 year of electronic decision tools only. There was no significant between-group difference in opioid dose at end-of-intervention . An adjusted regression analysis accounting for baseline imbalances demonstrated a statistically significant lower opioid use in the TOPCARE group . Additionally, a non-statistically significant difference in opioid discontinuation among the groups was reported.In this cluster RCT (n = 95), Mindfulness-oriented recovery enhancement (MORE), consisting of 8 weekly 2-hour mindfulness group sessions and daily 15-minute mindfulness practices at home, was compared with an active support group in which the group sessions consisted of discussions on chronic pain and opioid reduction. In both groups there was no explicit call made to reduce opioid treatment. The study did not report on opioid dose at end-of-intervention, but at 1 month after intervention. The between-group mean difference was 110.6 mg MEDD in favour of the intervention . An intention-to-treat analysis using a linear mixed model with an interaction of group and time demonstrated a statistically significant between-group difference (P = 0.006).In this RCT (n = 35), patients were randomised to either meditation-cognitive behavioural therapy, consisting of 2-hour weekly group sessions and encouragement in formal mindfulness, or usual care. At the end-of-intervention period, groups did not significantly differ in opioid dose change  = −34.3 to 45.7). Additionally, a significant between-group change in pain severity was reported in favour of the experimental intervention. There was no significant between-group difference in change in physical function or in perceived stress.In this RCT (n = 35), all included participants were (if needed) switched to sustained-release opioid therapy, after which they were randomised to either a taper-off intervention group or usual care. The intervention consisted of motivational talks and weekly or bi-weekly 10% reduction of opioid dose until discontinuation. Because of a large participant drop-out, outcome assessments were performed at 3.5 months. The mean between-group difference in opioid use was not statistically significant . Additionally, the study reported no significant between-group difference in pain severity.In this RCT (n = 35), participants interested in tapering their opioid dose were randomly assigned to either a taper support intervention or usual care. The intervention consisted of one visit with a physician followed by 17 weekly sessions in cognitive behavioural therapy for chronic pain with a physician assistant followed by weekly dose reduction of 10%. Between-group mean difference was 57.91 mg MEDD in favour of the taper support intervention . Adjusted analysis for baseline imbalances reported a non-significant between-group difference in MEDD  and pain severity.In this RCT (n = 294), participants receiving consultations from an integrated pain team, consisting of primary care providers with training in pain management, pain pharmacists, and pain psychologists, were compared with participants receiving usual primary care (moderate ROBINS-I). At 6 months the mean between-group difference in opioid reduction was statistically significant in favour of the experimental intervention .In this cohort study (n = 66), participants enrolled in a medical cannabis programme were compared with participants who had declined the offer to enrol in the programme. The difference in opioid dose was non-statistically significant between groups . The patients in the cannabis programme were significantly more likely to discontinue their opioid treatment.In this cohort study (n = 84), participants attending group medical visits (GMVs) at least twice monthly, in which non-pharmacological, complementary, and alternative therapy were encouraged for the treatment of pain, were matched with participants receiving usual care in the same age decile, with same major diagnosis, same sex, and within 25% in mg MEDD . At end-of-intervention (range 6 months to 2.5 years) a statistically significant mean between-group difference of 53.7 mg MEDD was reported in favour of the intervention . Additionally, a statistically significant difference in between-group discontinuation rate in favour of the intervention was reported. Differences in pain severity and quality of life were only reported for the intervention group, both demonstrating a statistically significant within-group change.In this cohort study (n = 47), battlefield acupuncture (BFA), a unique five-point auricular acupuncture procedure, was compared with usual care in patients on long-term opioid pain contract. The study reported a non-significant between-group mean difference of 18.85 mg MEDD . Of note, both groups increased opioid dose over the course of the study . The study reported no significant difference in pain severity.In this cohort study (n = 41), patients had a conversation with their family GP discussing opioid cessation, after which they could choose either individually tailored opioid tapering by their family GP or further pain treatment at a medical pain clinic . A statistically significant between-group difference in opioid use in favour of the GP-supervised tapering was reported . However, the groups differed significantly in mean opioid dose at baseline with a higher opioid dose in the control group . Within-group difference comparing opioid dose at baseline with opioid dose at end-of-intervention demonstrated a significant reduction in the GP-supervised tapering group  and a non-significant reduction in the control group .In this cohort study (29 (RoB 2 tool) but none of the cohort studies received this grading (ROBIN-S tool). There were some overarching principles explored: five studies19,23,30–32 used psychological interventions as part of the intervention, four studies19,23,30,32 explored the effect of therapeutic group sessions, and three studies20,21,31 looked at opioid tapering. None of the RCTs demonstrated a significant between-group difference in opioid dose. Three out of the five cohort studies20,23,30 demonstrated a significant between-group difference in opioid reduction favouring the experimental intervention groups.A total of five RCTS and five cohort studies were included in this systematic review. Studies were generally small and overall risk of bias was high. One RCT was graded low-risk biasThe results from this systematic review suggest that multidisciplinary GP-supervised and multidisciplinary group-based therapeutic sessions may be effective in reducing and discontinuing LTOT. However, because of high risk of bias and small sample sizes, no strong conclusions can be made, demonstrating the need for further high-quality research in this field.et al19 and Kurita et al,21 these results were not provided. In Kurita et al (2018),21 because of loss of follow-up the extraction timepoint was brought forward, that is, before the end-of-intervention was reached, causing possible bias away from the null. Whereas, in Garland et al (2020),19 the extraction timepoint was 10 weeks after intervention, creating possible bias towards the null.Conclusions of this systematic review are not without limitations. A comparison of study results was proposed by extracting between-group difference at end-of-intervention. However, in two studies, by Garland 33 Moreover, to compare effectiveness equally and objectively among all included studies, 12 studies were excluded as they did not report opioid dose reduction in MEDD. However, though excluded from this review, they might have reported on effective opioid reduction interventions.The generalisability of findings presented in this review to countries outside of the US is limited, since all but one study was performed in the US. It cannot be simply assumed that effectiveness of strategies for patients with CNCP on LTOT is the same all over the world, especially since the opioid crisis in the US has been found to be very different compared with Europe and the rest of the world.Finally, studies were excluded if they were considered not applicable in primary care, which was at the discretion of the first and second reviewers, who based their opinions on the types of strategies that would be applicable in the Netherlands and Denmark. Since primary care services vary from country to country, studies might have been included (or excluded) that could not (or could) have been implemented in the primary care of other countries.19 that was not included in two most recent systematic reviews.17,18 Additionally, four new cohort studies,20,22–24 which had not been included in the most recent systematic review,16 were included. Hence, this review is, to the authors’ knowledge the most up-to-date review discussing intervention effectiveness in studies exploring opioid reduction strategies for patients with CNCP on LTOT applicable in primary care. Moreover, this is the first review that has included only studies on opioid reduction strategies that are applicable in primary care.This systematic review included one RCT16–18 namely that no strong conclusions can be made regarding the benefit of opioid reduction strategies for people with CNCP due to overall high risks of bias and small sample sizes. With five new and recent studies identified,19,20,22–24 the presented review demonstrates a fast growth of studies exploring opioid reduction strategies.Though this review specifically explored reduction strategies applicable in primary care, the overall conclusion is in line with other recent reviews,34–37 can be seen as a step in the right direction. These upcoming studies should build on lessons learned. Future research should include high-quality RCTs, blinded for patients or at least for research personnel, where possible, to reduce risks of bias. In addition, large drop-out rates, demonstrated by some included studies,19,21,30 can be expected and studies should opt for larger sample sizes than strictly needed to secure sufficient statistically powerful end results. In addition, it would be worthwhile for pilot trials to evaluate methods of retaining patients before performing large-scale trials and to investigate, through qualitative evaluations, the reasons for patient drop-out.This review demonstrates the need for more high-quality research on opioid reduction strategies for patients with CNCP on LTOT. The fact that multiple research protocols for future research in this field were identified while scanning abstracts and titles16 Moreover, to increase generalisability of results, studies should be performed in more countries around the world.Moreover, to successfully evaluate a reduction strategy, patient outcomes, such as pain severity and quality of life, should be assessed throughout the study to map the risks and benefits of opioid reduction strategies as these outcome measures are important topics in patient–doctor conversations on reducing opioids.et al,20 might inspire GPs to start with individually tailored opioid taper plans with patients who are receptive to that idea while taking the study’s limitations into consideration. Likewise, study results of Seal et al23 and Mehl-Madrona et al30 carefully point in the direction of multidisciplinary group therapeutic sessions with a role for non-pharmacological pain treatments. High drop-out rates in some studies19,21,30 suggest a need for close monitoring of patients when reducing their opioid treatment. Here, time is of the essence, something that is not always available in general practice. A role for nurse practitioners, as was proposed by Liebschutz et al,29 may be a solution; however, this will have to be investigated in more depth.Considering increasing waiting lists at pain clinics and rehabilitation centres, the positive outcome of Goodman


Is convalescent plasma therapy associated with improved outcomes of hospitalized patients with COVID-19 and hematologic cancer?In this cohort study of 966 patients with hematologic cancer and COVID-19, after adjustment for potential confounding factors, convalescent plasma treatment was associated with a significantly improved 30-day mortality in the 143 individuals who received it. This association remained significant after propensity score matching.These findings suggest a potential survival benefit in the administration of convalescent plasma to patients with hematologic cancers and COVID-19. COVID-19 is a life-threatening illness for many patients. Prior studies have established hematologic cancers as a risk factor associated with particularly poor outcomes from COVID-19. To our knowledge, no studies have established a beneficial role for anti–COVID-19 interventions in this at-risk population. Convalescent plasma therapy may benefit immunocompromised individuals with COVID-19, including those with hematologic cancers.To evaluate the association of convalescent plasma treatment with 30-day mortality in hospitalized adults with hematologic cancers and COVID-19 from a multi-institutional cohort.This retrospective cohort study using data from the COVID-19 and Cancer Consortium registry with propensity score matching evaluated patients with hematologic cancers who were hospitalized for COVID-19. Data were collected between March 17, 2020, and January 21, 2021.Convalescent plasma treatment at any time during hospitalization.The main outcome was 30-day all-cause mortality. Cox proportional hazards regression analysis with adjustment for potential confounders was performed. Hazard ratios (HRs) are reported with 95% CIs. Secondary subgroup analyses were conducted on patients with severe COVID-19 who required mechanical ventilatory support and/or intensive care unit admission.A total of 966 individuals  were evaluated in this study; 143 convalescent plasma recipients were compared with 823 untreated control patients. After adjustment for potential confounding factors, convalescent plasma treatment was associated with improved 30-day mortality . This association remained significant after propensity score matching . Among the 338 patients admitted to the intensive care unit, mortality was significantly lower in convalescent plasma recipients compared with nonrecipients . Among the 227 patients who required mechanical ventilatory support, mortality was significantly lower in convalescent plasma recipients compared with nonrecipients .The findings of this cohort study suggest a potential survival benefit in the administration of convalescent plasma to patients with hematologic cancers and COVID-19. This cohort study evaluated the association of convalescent plasma treatment with 30-day mortality in hospitalized adults with hematologic cancers and COVID-19. Patients with hematologic cancers represent a distinctive subset of patients with COVID-19 caused by immune deficits associated with both the diseases themselves and their treatments. Hematologic cancers have been consistently associated with increased COVID-19 mortality and other complications.6Since initial reports in late 2019, SARS-CoV-2 has infected more than 100 million people worldwide and caused more than 2 million deaths by early 2021.7 Lymphopenia is known to be associated with more severe COVID-19.8Antibody-based immunity is an important correlate of SARS-CoV-2 recovery and vaccine-associated prevention. Hematologic cancers are associated with defects in humoral and cellular immunity that may contribute to adverse COVID-19 outcomes. Impaired antibody function is a well-described complication of plasma cell neoplasms, chronic lymphocytic leukemia (CLL), and other lymphoid cancers. Treatment of hematologic cancers often exacerbates these immune defects; for example, rituximab targets the pan-B cell marker CD20 and is highly effective therapy for B-cell cancers. However, B-cell depletion can cause lymphopenia and hypogammaglobulinemia and is associated with more severe COVID-19.9 and older patients10 when high titer units were administered early in the disease. A negative prospective randomized trial included only 4 patients with hematologic cancers in the convalescent plasma group.11 In patients with immunodeficiency, case reports have noted exceptional improvements in clinical status after convalescent plasma therapy, even after relatively late infusion.12 Given the absence of definitive prospective trial data in patients with hematologic cancers, we conducted a retrospective cohort study to evaluate the hypothesis that convalescent plasma therapy can correct defects in humoral deficiency and improve outcomes.Antibody therapy using COVID-19 convalescent plasma was associated with a therapeutic benefit in a general patient population13 We analyzed data from hospitalized US adults with a current or past diagnosis of hematologic cancers diagnosed with confirmed or suspected SARS-CoV-2 infection in 2020 and reported from March 17, 2020, to January 21, 2021 (full list of contributors is in the eAppendix in STROBE) reporting guideline.The COVID-19 and Cancer Consortium (CCC19) is an international consortium aimed at understanding the clinical impact of COVID-19 in patients with cancer through a Vanderbilt University Institutional Review Board–exempted comprehensive registry. The methods for CCC19 have been described and published previously.14 Individual propensities for receipt of convalescent plasma treatment were estimated using a multivariable probit regression model with baseline covariate adjustment using covariates that were determined a priori based on published literature and clinical importance: age, sex, race and ethnic groups, hematologic cancer type, cancer status, cancer treatment timing, ECOG performance status, obesity, presence of type 2 diabetes, hypertension, renal comorbidities, pulmonary comorbidities, receipt of cytotoxic chemotherapy within 3 months of COVID-19 diagnosis, and trimester of diagnosis . For matching, the nearest-neighbor method with a 1:1 ratio (treated units to control units) and 0.2 SD of the distance measure was applied to estimate the mean treatment effect.15 Marginal HRs along with 95% CIs based on cluster-robust SEs are reported. Kaplan-Meier survival curves were generated to compare survival probabilities using log-rank and stratified log-rank tests between convalescent plasma recipients and nonrecipients for unmatched and matched samples, respectively. We conducted several sensitivity analyses to explore the robustness of the findings for the primary hypothesis against the model specifications, such as varying the caliper size by ±0.1 and changing the matching order from the default maximum distance first to random order with different seeds. Exploratory subgroup analyses were conducted to determine whether patients with more severe illness  had differential outcome by convalescent plasma exposure.We calculated bivariate frequencies to examine the associations among the baseline characteristics and receipt of convalescent plasma. The primary end point was death within 30 days of COVID-19 diagnosis. Living patients had their data censored at 30 days from diagnosis. Crude and adjusted hazard ratios (HRs) and 95% CIs to estimate the association between convalescent plasma use and 30-day all-cause mortality were calculated using Cox proportional hazards regression models. The primary analysis used propensity score matching to help account for the nonrandomized treatment administration of convalescent plasma.P values are 2-sided, with α < .05 considered to be statistically significant. Statistical analyses were performed using R software, version 4.0.3 with packages MatchIt and Survival .We interpreted findings based on the 95% CIs for the estimated measures of association. Reported 9/L, multiply by 0.001]) at presentation; this proportion increased to 91 (79.1%) in patients who had received anti-CD20 antibodies. Propensity score matching was successful, with good balance achieved between the exposed and nonexposed groups  had a primary or secondary hematologic cancer, with lymphoid cancers being the most common. After eligibility criteria were applied  and the propensity score–matched comparison  , 223 (23.1%) deaths occurred within 30 days of COVID-19 diagnosis . The cruP = .03) . MultiplP = .03) .17 Patients with hematologic cancers may have immunodeficiencies from patient factors (including age), disease factors, and treatment factors. For example, in a single-center cohort of patients with CLL who had documented symptomatic COVID-19, 7 of 21 (33%) did not develop detectable anti–SARS-CoV-2 antibodies, notably lower than the 100% seroconversion rate observed in a noncancer population.19 A larger study20 recently found lower rates of seroconversion in patients with hematologic cancers, patients who received anti-CD20 antibodies, and hematopoietic transplant recipients. Several small studies23 have found improvement in clinical course after administration of convalescent plasma to patients with cancer, primarily hematologic cancers. Clinical improvement in COVID-19 symptoms within 48 hours of convalescent plasma transfusion was also reported in 16 of 17 patients with B-cell lymphopenia and prolonged COVID-19, 15 of whom had received anti-CD20 therapy in the 3 to 6 months before symptom onset.23This cohort study adds to the accumulating evidence supporting the efficacy of convalescent plasma treatment in patients with primary or secondary immunodeficiency, including those subjected to profound immunosuppression in the setting of hematopoietic stem cell transplantation.26 On this basis, interventional trials of convalescent plasma treatment for patients with COVID-19 are ongoing; to our knowledge, only one of these, COVID19-Convalescent Plasma for Treating Patients With Active Symptomatic COVID 19 Infection (FALP-COVID),27 is specifically recruiting patients with cancer. Despite this notable absence of prospective clinical trials specifically for patients with cancer, there was widespread availability of convalescent plasma through the Expanded Access Program (EAP) and the subsequent US Food and Drug Administration Emergency Use Authorization (EUA). The EAP was open to more than 2800 acute care facilities in the US and territories.28 Presumably, most patients in this report received treatment through the EAP, EUA, or local non–cancer-specific clinical trials.There is historical evidence of the efficacy of passive antibody therapy for infectious diseases when given early in the disease before the development of endogenous antibody responses, including in severe acute respiratory infections.23 reduction in secondary bacterial and fungal infections, neutralization of inflammatory cytokines that may otherwise promote a hyperinflammatory immune phenotype,29 and temporizing until the native immune system generates additional humoral and cell-mediated responses in the recovery phase after myelosuppressive or lymphodepleting anticancer therapy.Lymphopenia was common in the study population, especially in patients with recent anti-CD20 treatment, as would be expected. We are unable to ascertain rates of hypogammaglobulinemia because this was not a routinely collected variable. The exact mechanism by which convalescent plasma may have mediated improved outcomes in the treated patients is likely multifactorial and could include reduction in viral load via enhanced clearance,32 It is reassuring that the rates of thromboses are low in both recipients and nonrecipients and the rates of acute kidney injury are similar. Although low, the rate of congestive heart failure in the convalescent plasma recipients is higher than in the matched control patients, and this finding bears additional scrutiny in larger cohorts.The current study is the largest such series reported to date, to our knowledge. Because of the multi-institutional nature of the data with more than 70 contributing institutions ; this possibility is partially addressed through adjustment for cancer status. In addition, fewer patients in the convalescent plasma–therapy group had disseminated disease at cancer diagnosis. Differential access to convalescent plasma because of health care system or socioeconomic factors, similar to what we previously observed for the investigational drug remdesivir, cannot be excluded.34 Although multi-institutional diversity is a strength of our study, it is also likely that heterogeneity in how stressed or overloaded a hospital was when the patient with COVID-19 was treated, as well as differences in academic and community settings, could have added additional potential confounding. It is possible that the findings in the first 30 days would not persist into later periods, which would require a more extended follow-up. Therefore, as with any observational study, causality cannot be inferred from these findings, but rather these findings can be viewed as contributing to the accumulating evidence regarding survival benefit with convalescent plasma treatment in patients with COVID-19 illness. Prospective randomized trials evaluating convalescent plasma in patients with hematologic cancers with attention to administration timing and consideration of repeated dosing are recommended.This study has limitations, including its retrospective nature and unmeasured variables, such as the exact timing of convalescent plasma administration with respect to the date of COVID-19 diagnosis, the antibody titers and levels in the plasma that was administered, and whether repeat dosing was used. Although timing information is valuable, the feasibility of creating and maintaining a large, primarily voluntary, registry effort has necessitated study design decisions that would minimize the data entry burden for respondents; temporality is particularly burdensome and is only collected for very limited events . As with many pharmacoepidemiological studies, immortal time bias is possible for both the time to convalescent plasma exposure in the treatment group and time from COVID-19 diagnosis to hospitalization in both recipients and nonrecipients.This cohort study found that convalescent plasma therapy was associated with a survival benefit in patients with hematologic cancers and COVID-19. If this finding should hold up in prospective clinical trials, convalescent plasma would be, to our knowledge, the first COVID-19 intervention with a survival benefit in this high-risk population.