To determine, in patients with coronavirus disease 2019 (COVID-19) infection, the associations of pulmonary embolism (PE) with mortality and risk factors for PE as well as the therapeutic benefit of anticoagulant prophylaxis. Embase, PubMed, Cochrane controlled trials register, and Web of Science databases were searched from inception to October 10, 2020. We included all published trials on PE in patients diagnosed with COVID-19 with eligibility of the trials assessed following the PRISMA guidelines. Sixteen clinical trials with 5826 patients were eligible. There were significant associations of PE with the male gender , mechanical ventilation , intensive care unit admission , circulating D-dimer [mean difference (MD) = 5.04 µg/mL, 95% CI 3.67–6.42) and CRP  concentrations without significant correlation between PE and mortality  as well as other parameters or comorbidities. After omitting one trial with strict patient selection criteria for anticoagulant prophylaxis, significant prophylactic benefit was noted . Our findings identified the risk factors associated with PE in COVID-19 patients and supported the therapeutic benefit of anticoagulant prophylaxis against PE in this patient population. Accumulating evidence has shown an increased risk of thrombotic complications in patients with coronavirus disease 2019 (COVID-19)2 whose prevalence of thromboembolism is up to 20–25%3 compared with a lifetime risk of 8% in the general population4. Indeed, the figure may be an underestimate taking into account the postmortem finding that over 50% of COVID-19 patients may have undetected thromboembolism before demise5. A previous study reported an increased risk of thromboembolic complications in patients with severe COVID-19 infection, particularly those admitted to the intensive care unit (ICU)2. In concert with this finding, patients with severe COVID-19 could experience a 3.76-fold elevation in risk for thromboembolism compared to that in those with a non-severe disease according to a previous meta-analysis3. The risk of thrombosis was also considered high in COVID-19 patients with obesity and acute respiratory distress syndrome as well as those undergoing extracorporeal membrane oxygenation (ECMO) and those with hypercoagulability  and/or marked inflammatory syndrome6.Venous thromboembolism represents the third most common vascular disease after acute myocardial infarction and stroke8. Pulmonary embolism (PE) and deep vein thrombosis (DVT) are the two COVID-19-related thromboembolic complications with the prevalence of the former being two-fold higher than that of the latter.3 A small autopsy series identified PE as the cause of death in up to one-third of patients with COVID-19 5.Prognostically, thromboembolism is believed to contribute to mortality and morbidity in patients infected with COVID-1910 that is also observed in other septic situations, COVID-19 is characterized by another micro-thrombotic pattern prevailing in the lungs resulting from a massive coagulation activation accompanied by intense inflammatory and immune reactions12. The latter, which is termed "immuno-thrombosis"12, could cause widespread occlusive thrombotic micro-angiopathy and destruction of alveoli11. Pathologically, entering of SARS-CoV-2 into the airway epithelial cells triggers a cascade of inflammatory and immune reactions, including alveolar infiltrations of macrophages, monocytes, and T cells as well as the generation of chemokines and cytokines including TNF-α, IL-1β, IL-6, and IL-8, leading to an elevated fibrin degradation and an increase in D-dimer levels12. A previous clinical investigation has shown a persistent elevation in D-dimer levels (> 500 ng/mL) in patients with COVID-19 up to four months after their convalescence despite the normalization of other coagulation and inflammation markers13, highlighting the immunological nature of COVID-19-related hypercoagulation.Regarding the mechanism underlying the hypercoagulable state in patients with COVID-19, previous studies have identified two distinct phenotypes of thrombotic manifestations. In addition to thromboembosis14, has become a standard treatment protocol for patients with confirmed diagnosis of COVID-196.Accordingly, anticoagulant prophylaxis against thrombosis, which commonly involves the use of unfractionated heparin or low molecular weight heparin16 at least partly attributable to heparin resistance14. Because of the limitations in previous clinical trials on thromboprophylactic strategies, a collaborative effort has been proposed to conduct pooled analyses and expedite the implementation of effective interventions15. Indeed, the risk factors for PE, the impact of PE on mortality, and the effectiveness of anticoagulant prophylaxis against PE in patients with COVID-19 infection remain pressing issues that have not been systematically addressed. Therefore, through analysing available data from eligible trials, the present systematic review and meta-analysis aimed at investigating the association of PE with mortality, identifying the risk factors for PE as well as assessing the therapeutic benefit of anticoagulant prophylaxis in patients infected with COVID-19.Nevertheless, despite standard anticoagulant thromboprophylaxis, the incidence of COVID-19-related thrombosis remains highWe registered the protocol of the current study with PROSPERO (CRD42020213355).17. We searched the databases of Embase, PubMed, Cochrane controlled trials register, and Web of Science to obtain a list of all published eligible trials using the keywords "thromboembolism", "clot", "deep vein thrombosis (DVT)", "venous thromboembolism", "pulmonary embolism", "thrombosis", "venous thrombosis", "severe acute respiratory syndrome", "coronavirus 2", "coronavirus", "corona virus", "covid-19", "nCoV", "2019nCoV" or "Wuhan virus" from inception to October 10, 2020. References from relevant studies were searched to find additional articles. No publication date or language restriction was applied.We conducted the present meta-analysis according to Preferred Reporting Items Systematic Reviews and Meta-Analysis (PRISMA) guidelines18 were used to assess the degree of agreement between the two reviewers.Two reviewers independently examined the titles and abstracts of the articles to identify potentially eligible studies. The inclusion criteria for eligibility of trials for the current study included studies which compared the patient characteristics, laboratory profiles, and outcomes in COVID-19 patients with or without the occurrence of PE. The exclusion criteria were (1) studies that focused on patients with pregnancy, pediatric population, patients receiving extracorporeal membrane oxygenation (ECMO) as well as those with immune diseases ; (2) those whose information regarding outcomes  was unavailable; (3) postmortem studies; (4) case series; and (5) those with mixed outcomes from patients with DVT and PE without distinct information from patients with PE. Two authors independently investigated the selected studies for the final analysis. In the situation of disagreements, a third author was involved until a consensus was reached. Kappa statistics for interrater agreement evaluation Two authors were responsible for extracting relevant data from each selected trial and entering them into predefined databases. Divergences were resolved through discussion. The corresponding authors of the included studies that did not provide data on primary or secondary outcomes were contacted for further information. The data extracted from each trial were as follows: year of publication, author, study setting , sample size, patient characteristics , body mass index (BMI), the use of anticoagulant prophylaxis, laboratory profiles , and outcomes .The primary endpoint was the risk of mortality, while the secondary outcomes were potential risk factors for PE and changes in laboratory profiles with or without the occurrence of PE during the study period. If the same laboratory parameter was available at different time points, only the maximum value was selected for analysis.19, which scores each study based on three domains, namely, study group selection, group comparability, and outcome of interest ascertainment for cohort studies. A maximum of four, three, and two stars were assigned to the Selection, Comparability, and Outcome domains, respectively. The higher the number of stars, the better the quality of the study 19. For the second item of the Outcome domain, we awarded one star to a study if the patients were discharged from hospital or succumbed to the disease. For non-hospitalised patients or lack of relevant information  for inpatients, no star was assigned with the assumption of inadequate follow-up. One star was given to the last item of the Outcome domain for (1) studies with a follow-up rate ≥ 80%, or (2) those with a follow-up rate < 80% but including a description of lack of significant difference in demographic characteristics between the follow-up and lost-to-follow-up groups. Studies with less than six stars were considered to be of low-quality.Two authors assessed the risks of bias of the included non-randomised studies with the Newcastle-Ottawa Scale (NOS) for quality of cohort studies2 statistics was adopted to assess the heterogeneity, which was categorised as low (0–50%); moderate (51–75%), and high (76–100%). Sensitivity analyses were performed to evaluate the potential influence of a single trial on the overall results by removing the studies from the meta-analysis one at a time. In addition, to refine the quality of the present meta-analysis, we re-assessed the overall results after removing the low-quality studies. Funnel plots were used for investigating the potentials of reporting and publication bias when a particular outcome was reported in 10 or more studies. Statistical significance was set at 0.05 for all analyses. Cochrane Review Manager  and MetaXL (version 5.1) was used for data synthesis. Statistical Product and Service Solutions  was used for the calculation of kappa coefficient.For dichotomous outcomes, a random effects model was used to calculate the odd ratios (ORs) with 95% confidence intervals (CIs). The Mantel–Haenszel (MH) method was used to pool dichotomous data and to compute pooled ORs with 95% CIs. For continuous outcome, the selected effect size was expressed as mean difference (MD). The I34 . The study populations included hospitalised adult patients  (number of studies = 11)34, both inpatients and outpatients (number of study = 1)28, non-hospitalised patients  (number of study = 1)27, and only ICU patients32 (number of study = 3). Computed tomography is the gold standard for the diagnosis of PE35. Although most studies described the indications for computed tomography pulmonary angiography (CTPA)34    of the comparative cohort studies demonstrated an overall low risk of bias. The number of stars awarded to each of the included studies ranged from four to eight with a median NOS score of 8. Details on the numbers of stars assigned to the domains of Selection, Comparability, and Outcome for all the included studies are shown in Table 33. Pooled analysis showed a comparable risk of mortality between PE and non-PE groups   were available for mortality analysis31. A forest plot demonstrated a higher risk of mechanical ventilation in the PE group compared with that in the non-PE group   were eligible for the analysis of the risk of mechanical ventilation34. Forest plot analysis revealed a higher risk of ICU admission in the PE group compared to that in the non-PE group   were available for assessing the impact of PE on the risk of ICU admission34 and age 34 analyses, respectively. Funnel plot exhibited apparent symmetry    were eligible for analysis on the association between BMI and risk of PE   34, and fibrinogen 33analyses, respectively. The D-dimer      when the study by Leonard-Lorant et al.29  was omitted.Ten studies involving a total of 2404 patients with COVID-19  were eligible for the analysis of the impact of anticoagulant prophylaxis on the risk of PE4%) Fig. D. Howeve4, our results showed no significant correlations between PE and such comorbidities when compared with those in patients without PE. On the other hand, our results demonstrated that PE was significantly linked to the male gender as well as increased risks of mechanical ventilation and ICU admission. Besides, the current study demonstrated a positive correlation of PE with circulating D-dimer and CRP concentrations, which was consistent with the finding of previous studies37. Although some findings of the present meta-analysis were consistent with those previously reported, some of our results raised clinical issues that need to be addressed.The present study represented the first meta-analysis investigating the mortality risk associated with pulmonary embolism (PE), the risk factors for PE, and the effectiveness of anticoagulant prophylaxis against PE in patients with COVID-19. The study addressed several important clinical issues. Contrary to previous findings of positive associations of PE with mortality, age, BMI, and other systemic diseases3. The present meta-analysis further demonstrated that PE is a significant risk factor for mechanical ventilation and ICU admission for COVID-19 patients. Our findings were consistent with those of a previous multicenter cohort study that showed significantly higher rates of ICU transfer and mechanical ventilation in the PE group26. On the other hand, the lack of a significant association between the development of PE and mortality in the current study was contradictory to that in the non-COVID setting in which about 20% of patients with PE would die before diagnosis or shortly thereafter, especially for those with hemodynamic instability38. Albeit seemingly paradoxical, our finding may be attributed to the observation that despite the development of multiple thrombi in both medium- and small-sized blood vessels39 as well as fibrinous microthrombi in the arterioles of the lungs and other organs40, they may not be the direct cause of death40; an autopsy series on 21 COVID-19 patients attributed the primary cause of death to respiratory failure from exudative diffuse alveolar damage as well as massive capillary congestion with or without superimposed bronchopneumonia, while PE was noted in only four of the patients 41. Besides, the wide adoption of anticoagulation prophylaxis against COVID-19-associated hypercoagulability in the included trials (11 out of 16) of the current study may have decreased the frequency of recurrent PE, which has been reported to be a major contributor to PE-associated mortality in the non-COVID-19 setting38. In addition, proactive survey programs for patients with COVID-196 may limit the progression of PE and also the subsequent mortality.Previous review studies have shown a positive association between the severity of COVID-19 and the risk of thromboembolism26, the published review studies did not reveal this finding3. Consistently, a large-scale study on patients without COVID-19 did not show an increased incidence of PE in males38. The disproportionately higher risk for males may be attributable to preexisting cardiovascular disease as well as COVID-19-associated cardiovascular injury42. On the other hand, although age is a known risk factor for PE in non-COVID patients35, our result showed no significant association between age and PE in those with COVID-19 on sensitivity analysis that demonstrated notable impacts from individual studies. Similarly, other systemic comorbidities known to be associated with PE in the non-COVID setting, including pulmonary diseases, obesity, heart failure, and cancer4 were also non-significant risk factors for PE.Another interesting finding of the present study was the identification of the male gender as a significant risk factor for PE in COVID-19 patients. Although a previous multi-center cohort study has identified the male gender as a risk factor for PE in patients with COVID-19p = 0.03, I2 = 75%) after omitting the study by Leonard-Lorant et al29. in which prophylactic anticoagulants were only given to COVID-19 patients highly suspected of having PE  instead of being a routine strategy. After confirming the diagnosis of PE through computed tomographic angiography, that study showed that 78% of patients in the PE group and 23% in the non-PE group received anticoagulant prophylaxis. Conceivably, that study may underestimate the benefit of anticoagulant prophylaxis as a routine treatment as reflected by the relatively low proportion of patients undergoing prophylaxis in the PE group, in which close to one-fourth (22%) did not receive prophylaxis because of the highly selective nature of the program. Consistently, a previous meta-analysis has demonstrated a positive association between a high rate of pharmacologic thromboprophylaxis (defined as ≥ 60%) and a lower incidence of thromboembolism3.Despite the lack of overall therapeutic benefit of anticoagulant prophylaxis against PE in the present meta-analysis, sensitivity analysis revealed a significant reduction in the risk of PE  , reflects an ongoing activation of the hemostatic system and serves as an indicator of thrombosis9 and an elevated CRP concentration37 as risk factors for PE in COVID-19 patients. Because CRP is more a marker of bacterial infections than that of viral diseases45, an elevated circulating CRP level may suggest secondary bacterial infections. Indeed, superimposing bacterial infection is known to contribute to mortalities and morbidities among patients with COVID-1946; a previous study reported an incidence of secondary bacterial infection in up to 41% of COVID-19 patients in critical condition37. Besides, a recent meta-analysis has identified procalcitonin, CRP, D-dimer, and lactate dehydogense as predictors of the severity of COVID-19 infections47. Consistently, our study demonstrated an elevated CRP level in COVID-19 patients with PE compared to that in those without. In terms of treatment, previous meta-analytical studies have shown a positive impact of immunological treatment by demonstrating the effectiveness of tocilizumab, a humanized monoclonal antibody clinically used as an immunosuppressive agent targeting the interleukin-6 receptor, for reducing the expressions of biomarkers48 and mortality rate49 in patients with COVID-19, underscoring the therapeutic potential of combining anti-cytokine and anticoagulant in patients with moderate to severe COVID-19 infections.Focusing on CRP, a marker of systemic inflammation, the current study highlighted its positive association with PE. Our result was consistent with that of previous studies that identified vascular inflammationOur finding of significant positive associations of PE with the risks of mechanical ventilation and ICU admission but not mortality in COVID-19 patients may suggest an increased awareness among clinicians regarding the probability of PE to expedite the implementation of preemptive measures in this patient population. In addition, the lack of significant correlation between PE and systemic comorbidities previously reported to be related to PE  may highlight the need for suspecting PE in patients with COVID-19 even in the absence of conventional risk factors.50, our results demonstrated only a DVT prevalence of 11% in COVID-19 patients with PE. Therefore, instead of being dislodgement from a venous thrombus, our finding may implicate a different mechanism underlying clot formation in the pulmonary vasculature of patients with COVID-19 infection. The autopsy findings of diffuse alveolar damage with fibrinous microthrombi in the edematous and congested alveolar capillaries as well as evidence of damage to the airway surface epithelium10 appear to support this hypothesis.Moreover, although DVT is the major cause of PE in the non-COVID-19 setting36, our results suggested that circulating D-dimer concentration could serve as a diagnostic clue for PE but not necessarily a prognostic indicator. Similarly, the lack of significant relationship between fibrinogen concentration and PE in COVID-19 patients in the current study may not support its use as a diagnostic tool for PE in this patient population. Furthermore, although the finding of an association between circulating CRP level and PE in the present study may suggest superimposing bacterial infection, evidence from autopsy of COVID-19 patients implicated the role of platelet activation rather than infectious pathogens in thrombosis formation39. The therapeutic potential of anti-platelet agents for prophylaxis against PE among COVID-19 patients remains to be elucidated.Contrary to previous findings that supported the use of D-dimer level as a prognostic indicator for COVID-19 patients38, our results support a beneficial role of prophylaxis in patients with COVID-19.One of the strengths of the present meta-analysis was our investigation into the risk factors and mortality as well as the risks of mechanical ventilation and ICU admission associated with PE in patients with COVID-19 instead of merely studying the incidence of PE and DVT. In addition to identifying unreported risk factors for PE in COVID-19 patients after analyzing the available clinical evidence, we found the lack of significant correlations between PE and a number of comorbidities previously proposed to be linked to PE development . Furthermore, although a previous large-scale multicenter study on non-COVID-19 patients suggested a failure of anticoagulant prophylaxis against PE as one-third of patients were under prophylaxis at the time of PE occurrence51, no information was available for evaluating a possible ethnical association with PE and its associated mortality in the current study. Third, the relatively short follow-up period in the majority of included studies  may bias the outcomes. Fourth, because the optimal doses of anticoagulant for prophylaxis against PE remain unclear2, there were discrepancies in anticoagulant dosages in the included studies. Nevertheless, we demonstrated effectiveness of anticoagulant prophylaxis after exclusion of one study29. Fifth, although the presence of right ventricular hypokinesis has been reported to double the risk of mortality within three months of the PE diagnosis38, only a limited number of studies included in the present meta-analysis (7 out of 16) provided the information  as well as the severity of their diseases may impact the study outcomes.Nevertheless, the study had its limitations that need to be taken into consideration for accurate interpretation of its findings. First, for the purpose of the current study, only the highest value of the parameters was selected for analysis during a time course. The possibility that the data were acquired after confirmation of the diagnosis of PE by computed tomography pulmonary angiography (CTPA) may contribute to the wide range of variation in certain parameters . Besides, the difference in indications for CTPA  may also affect the data for analysis. Second, despite the known ethnical impact on clinical outcomes among COVID-19 patientsThrough systematically reviewing the eligible clinical trials, the present meta-analysis demonstrated significant associations of pulmonary embolism with the male gender, risks of mechanical ventilation and ICU admission as well as elevated circulating concentrations of D-dimer and CRP in COVID-19 patients despite the lack of correlation between pulmonary embolism and mortality. After omitting one trial with strict patient selection criteria for anticoagulant prophylaxis, our results showed significant therapeutic benefit of anticoagulant prophylaxis against pulmonary embolism in those with COVID-19 infection. Because of limited data from the included observational studies, further large-scale clinical trials are warranted to support our findings.Supplementary Tables.


Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely “Long COVID”, but also “COVID-19 syndrome (PACS)” or, “post-acute sequelae of SARS-CoV-2 infection (PASC)”. In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat. Individual symptoms and disease severity vary widely among patients, with some patients developing mild or even asymptomatic infections, while others experience acute respiratory distress syndrome (ARDS), sepsis, and other life-threatening conditions.8SARS-CoV-2 emerged in late 2019 as the third human coronavirus identified in the 21st century. As of early 2021, new impacts of the virus are still being identified. The virus initially targets epithelial cells, endothelial cells, alveolar macrophages (via ACE2 proteins and the TMPRSS2 protease) causing symptoms attributable to the lungs, digestive tract, kidneys, heart, brain, and other organs.14 but anecdotal reports are available describing patients with ongoing symptoms as long as a year post-infection.16 Symptoms experienced after the acute illness represent a significant challenge for patients, physicians, and society as a whole. The causes, patient profile, and even symptom patterns associated with Long COVID remain difficult to isolate, and the natural history of this condition remains uncharacterized.As more information about patient recovery has been collected, and as pathophysiologic mechanisms are revealed, a wide range of outcomes following acute COVID-19 have emerged. Some patients experience residual symptoms and others develop new symptoms long after the initial infection. These symptoms can present across a wide range of organ systems and tissues. Given the timeline of SARS-CoV-2’s emergence, studies to date have tracked patients’ clinical course up to six months post-infection,17 Giardia lamblia, Coxiella burnetii, Borrelia burgdorferi (Lyme disease) and Ross River virus,18 are also associated with an increased risk for post-infectious sequelae. These sequelae include symptoms such as disabling fatigue, musculoskeletal pain, neurocognitive difficulties, and mood disturbance.19 Chronic fatigue syndrome (CFS) is frequently preceded by a viral infection.20 However, although these sequelae are well documented, they are still not well understood, and the molecular mechanisms underlying these post-acute presentations have yet to be elucidated.The fact that some COVID-19 patients experience symptoms following recovery from acute infection is not unexpected. Other infectious diseases, including Epstein-Barr Virus,21 following recovery from the acute infection.23 For SARS, follow-ups have been conducted up to 15 years post-infection. In addition to fatigue, studies reported effects on lung health and capacity,27 psychological health,21 bone health,27 and lipid metabolism,28 with the latter two attributed to treatments involving large doses of steroids.28 Most of the improvements among SARS patients occurred within the first one to two years following infection,30 although some patients continued to experience decreased quality of life for more than a decade following the acute illness.28 Though follow-up studies in MERS patients are more sparse, effects on pulmonary function were observed at one year post-infection, with patients who experienced more severe cases at greater risk for long-term effects.31Post-infectious sequelae have also been documented following infection by other coronaviruses. A subset of patients with severe acute respiratory syndrome (SARS), caused by the coronavirus SARS-CoV, and Middle-Eastern Respiratory Syndrome (MERS), caused by the coronavirus MERS-CoV, were observed to experience persistent or new-onset symptoms, including fatigue,32 After 30 days, loss of smell, loss of taste, memory loss, chest pain, and muscle weakness were the symptoms enriched in patients who were positive for SARS-CoV-2 at the time of their acute illness. The association between these symptoms and COVID-19 diagnosis fluctuated slightly at 60 and 90 days, with muscle weakness no longer associated at 60 days, difficulty concentrating emerging at 60 days, and confusion and bone or joint pain emerging at 90 days. Many of the symptoms most strongly associated with Long COVID are therefore distinct from those observed in post-acute SARS or MERS, and therefore may be challenging to identify based on research on other post-infectious sequelae.While post-acute sequelae are not an unexpected outcome of SARS-CoV-2 infection, the number of people affected and range of symptoms associated with Long COVID is unprecedented. The multisystem nature of Long COVID compared to previously studied post-acute sequelae of human coronaviruses has raised questions about how to most effectively identify indicators of Long COVID. An analysis of 32 symptoms in patients with and without SARS-CoV-2 infection identified several symptoms that were enriched in patients with COVID-19 in comparison to other illnesses of comparable severity.33 evaluated all research on Long COVID released prior to January 1, 2021, that included at least 100 patients; based on the 15 studies that met the inclusion criteria, the authors identified 55 symptoms of Long COVID. None of the most common symptoms were assessed by all 15 studies. They reported that the five most common symptoms evaluated in the literature were fatigue, headache, attention disorder, hair loss, and dyspnea. They also reported the frequency at which clinical measurements such as chest X-ray and biomarkers such as C-reactive protein and D-dimer were evaluated. The authors concluded that the symptoms of Long COVID are extremely heterogeneous, and that the assessment of these symptoms varies widely among studies.Furthermore, regardless of whether they are unique to Long COVID, symptoms frequently reported by Long COVID patients are not assessed consistently across studies. A systematic review available as a preprint35 Observing residual or new symptoms months after experiencing COVID-19, patients have established online communities to provide support and identify similarities in their experiences.36 Some Long COVID patients who are also researchers have led efforts to systematically categorize the range of experiences associated with Long COVID. An extensive patient-led survey (Patient-Led Research Collaborative) performed deep longitudinal characterization of the Long COVID symptoms and trajectories in suspected and confirmed COVID-19 patients who reported illness lasting more than 28 days.13 Evaluating data from 3,762 respondents to 257 survey questions, this analysis documented 205 phenotypic features associated with Long COVID. The symptoms most frequently reported after 6 months were fatigue, post-exertional malaise, and cognitive dysfunction. Patients who reported symptoms lasting for longer than six months following acute infection experienced an average of 14 symptoms in month 7, and 86% of patients experienced relapses during the period assessed, with exercise, physical or mental activity, and stress reported as common triggers. The diversity of the symptoms reported by Long COVID patients underscores the urgent need to understand the natural history of COVID-19 following the initial infection in order to manage medical care of affected individuals.However, Long COVID’s emergence has followed a different trajectory than that of most medical syndromes. Rather than building from a clinically determined framework of the illness, to date, much of the growing awareness of Long COVID and its symptoms has been driven by patient-led efforts.33 utilized a survey-based approach, meaning that they were able to analyze only symptoms identified a priori as concerns. These studies also varied in whether they included formerly hospitalized patients exclusively or a mixture of patients with mild, moderate, and severe cases of acute COVID-19. Long COVID can occur following either severe or relatively mild acute illness,37 and it has been suggested that the severity of acute illness affects the clinical course of Long COVID,32 as it does in SARS and MERS.38 Additionally, patients who are treated in the intensive care unit (ICU) would be assumed to be particularly likely to experience ongoing health challenges due to the well-documented occurrence of post-intensive care syndrome (PICS).39 Several different frameworks have been proposed to describe Long COVID cases, without any clear criteria emerging about how to define the condition or how to stratify patients. This ambiguity presents a concern as more and more data is collected: as of the end of 2020, at least 239 papers and preprints about the post-acute effects of COVID-19 had been released, and approximately 20 additional papers become available each month.40 These papers do not conform to a single definition of Long COVID and do not evaluate consistent symptoms or markers of the disorder (or constituent disorders). In addition, the differences between the common symptoms as identified in a systematic review of the literature33 compared to the patient-led assessment13 indicates that current research on Long COVID may fail to address the full diversity of and even the most significant symptoms identified by patients with lived experience of Long COVID. Additionally, because proactive self-report is an important component of the patient-led research collaborative, symptoms and experiences of persons with low access to and uptake of technology may be under-represented in Long COVID studies thus far.Given Long COVID’s very recent emergence, no standard framework has yet been established for identifying and assessing associated symptoms or other clinical indicators. Most of the studies analyzed in the systematic reviewcovid.cd2h.org/enclave) to provide guidance towards defining and identifying symptoms and patient variables that must be considered while designing and developing studies of Long COVID. Given that Long COVID is poised to produce an additional public health crisis on top of the COVID-19 pandemic,36 rapid harmonization of existing data and the integration of this information into new efforts to characterize Long COVID will be the critical next steps in responding to this looming threat.In order to develop clinical management strategies to prevent or mitigate Long COVID, it will be essential for studies to use a unified definition of Long COVID and its subforms so that data from different studies can be integrated to provide the foundation for robust statistical inferences about risk factors for the development of Long COVID, as well as the natural history and response to treatments. Additionally, it is essential that survey-based research efforts to investigate Long COVID operate from a framework that addresses the symptoms most common among and most debilitating to Long COVID patients. A rigorous framework for evaluating Long COVID will also help to elucidate the organ systems involved in the disease and its sub-forms; such a framework could help to distinguish for example pulmonary versus cardiovascular syndromes and whether these are interrelated.. In this analysis, we present methodologies, findings, and perspectives related to the extraction of data from the literature, from an extensive patient survey, and from the NCATS N3C Data Enclave , we included searches of specialized databases  and relied on expert recommended key articles, with snowball techniques to find similar studies. Both published articles and preprints were included for abstraction. The questions we explored in the review were: for observational studies of Long COVID, how are studies characterizing Long COVID, and what outcomes are reported and/or associated with this syndrome? In addition we explored whether any COVID-specific measures or tests have been developed or validated, whether any patient subgroups or medical specialties report unique signs or symptoms, and what patient-reported and patient-centered outcomes were reported . While sThe newly emergent nature of Long COVID and lack of definition complicate traditional search methods. Each study abstracted was analyzed to identify the relationship of participant recruitment in the study to formal definitions of Long COVID that have been proposed. This analysis required evaluating how the duration of long-term symptoms was defined relative to the acute illness and whether patients were selected or stratified based on variables related to clinical course. Due to the proposed definitions at the time of analysis, the variables considered were: definition of onset of disease course , time elapsed since onset (as defined in each manuscript), patient-reported symptoms or clinical measures assessed, and tests or measurements reported or developed.41 Although no firm criteria have been established to define the post-acute period or sub-categories within Long COVID, several sets of guidelines have been proposed for the classification of COVID-19-related disease phenotypes, and these criteria were compared to the definitions used in the literature. For example, a recently proposed public health framework classifies SARS-CoV-2-related disease into three categories.42 The first is acute COVID-19, or the disease most commonly associated with acute SARS-CoV-2 infection. The second category includes Multisystem Inflammatory Syndrome in Children (MIS-C) and in adults (MIS-A), a less common presentation of SARS-CoV-2 infection characterized by hyperinflammation that can appear 4–6 weeks after viral infection.43 The third category describes late sequelae.42 In terms of defining study cohorts, adherence with this definition would therefore require a clinical diagnosis, rather than a SARS-CoV-2 test alone, in order to distinguish MIS-C/A and COVID-19.Long COVID can be broadly defined as delayed recovery from an episode of COVID-19 and is characterized by lasting effects of the infection, e.g., persistence of symptoms or onset of new chronic diseases, for far longer than would be expected.44 Similarly, PACS has been defined operationally as extending beyond three weeks from the onset of first symptoms, and the term chronic COVID-19 has been proposed to refer to PACS cases where symptoms extend beyond 12 weeks;37 these PACS definitions are consistent with the virological data available thus far.45Other frameworks break down the “late sequela” category into subtypes depending on either timing or disease natural history. For example, the United Kingdom’s National Institute for Health and Care Excellence’s guideline on long COVID provides two definitions of postacute COVID-19: (1) ongoing symptomatic COVID-19 for people who still have symptoms between 4 and 12 weeks after the start of acute symptoms; and (2) post-COVID-19 syndrome for people who still have symptoms for more than 12 weeks after the start of acute symptoms.45 Some authors go further and subdivide Long COVID into three groups:patients who have experienced severe COVID with ARDS and experience long-term respiratory symptoms dominated by breathlessness;individuals with milder initial disease who were not necessarily hospitalized during the acute infection but but who present with a multisystem disease with cardiac, respiratory, or neurological manifestations of end-organ damage; and46people who have persistent fatigue and other symptoms but with no evidence of organ damage.However, other criteria recommend defining the post-acute period as starting once a patient is discharged from inpatient acute care for those hospitalized longer than three weeks.47) and created a table row for each symptom in each publication. We then used a Python script to extract symptoms;. symptoms remained exactly as described in the manuscripts except to adjust for capitalization, punctuation, plurals , spelling in British versus American English , and to standardize labels assigned to specific measures. The identifiers for specific assessments used were as follows: FLU-PRO for InFLUenza Patient-Reported Outcome,48 EQ-5D-5L for the 5-level EQ-5D,49 EQ VAS for the EQ visual analogue scale,49 and mMRC Dyspnea Scale Scores for Modified Medical Research Council Dyspnea Scale Scores.50 We tabulated the relationships between publications and the symptoms they reported; we then manually mapped symptoms to one or more body systems and visualized the result using a Sankey diagram  provides a standardized vocabulary of over 15,000 terms to describe phenotypic abnormalities observed in human disease.covid.cd2h.org/enclave) with the intention of identifying unique healthcare utilization patterns among COVID-positive patients that may differentiate them as Long COVID patients. To achieve this, we looked for patterns found only in COVID-positive patients compared to COVID-negative controls. Some patients were expected to be COVID-positive but non-Long COVID, so this analysis was expected to distinguish at least three categories: COVID-positive and Long COVID, COVID-positive and non-Long COVID, and COVID-negative.We performed analysis of electronic health record (EHR) data in the N3C Secure Data Enclave . We define COVID-negative as any non-deceased patient in the N3C enclave with at least one negative PCR, antibody, or antigen test for COVID who is not also in the positive group . We then further narrow the set of patients whose data is used for analysis in the following ways:We require all patients to have at least one year of history with their contributing health care system.For COVID-positive patients, we require that at least 90 days have passed since their COVID index date (minimum date of diagnosis or positive test).We define COVID-positive as any non-deceased patient in the N3C enclave with an ICD-10-CM diagnosis code for COVID (U07.1) or a positive PCR, antibody, or antigen test for COVID (Applying these restrictions resulted in a case (positive) cohort of 314,237 patients, and a control (negative) cohort of 1,917,935 patients.53 to perform nearest-neighbor propensity matching on the positive and negative patients, at a ratio of 2:1 (control:case). The following factors were used in matching: age, sex, race, site (exact match required), and comorbid conditions . A patient was defined as having a comorbid condition if they had two or more ICD-10-CM codes equating to that condition in their EHR data. Two sites  were removed from the matching process due to a significant amount of missing data required for matching. Additionally, 2,311 cases were dropped because they were not able to be matched with two controls at their same site. This resulted in a final case set of 297,704 patients, and a final control set of 595,408 patients. The case set was further split into two groups: cases who were hospitalized for COVID  and patients not hospitalized for COVID .We then employed the R package MatchItWe opted to model COVID-related healthcare utilization patterns among the cases and controls by counting occurrences of COVID- and Long COVID-related diagnoses See “,” below After representing the data as a matrix of pre- and post-diagnosis conditions, we applied nonnegative matrix factorization in order to extract conserved co-occurring sets of diagnoses that best represent the cohort. The result of this step is a data-driven representation of which sets of diagnoses occur together. We then compared the change in frequency of these diagnoses before and after COVID to identify potential signatures of Long COVID.54 OMOP2OBO is an algorithmic framework designed to generate clinically meaningful mappings between Open Biomedical Ontologies (OBO) and standard clinical terminologies in the OMOP common data model. Using version 1.0.0 of the mappings, each of the HPO concepts were processed and all reasonable matches returned. HPO concepts unable to be mapped using OMOP2OBO were manually mapped using Version 1.12.0.6.210309.1608 of the Athena - OHDSI Vocabulary Repository,55 which at the time of mapping was populated with OMOP Vocabulary version: v5.0 26-FEB-21. All manual mappings were discussed with one or more professional ontologist and/or clinical phenotyping experts. Upon ingestion into the N3C Enclave, HPO concept sets were extended to include all descendant concepts for each included OMOP concept identifier. Each of the completed concept sets received an additional round of review by clinical domain experts within the Enclave prior to use in classifying the cohorts as described above.Concept sets were obtained by mapping a subset of the manually curated HPO concepts to OMOP concept identifiers within the Conditions domain . These m56 or “discharged COVID-19 patients”57) or clinical course . A number of studies did refer to their participant groups using terms like “Long COVID”,68 “post-acute COVID-19”,70 “post-COVID syndrome”,71 or “post-acute COVID-19 syndrome”,72 but these terms were not standardized among studies. A few studies60 acknowledged the proposed distinction at 12 weeks post-infection between post-acute COVID-19 and chronic COVID-19,37 but otherwise the definitions used typically did not refer to any proposed operationalizations of Long COVID. Therefore, while operational definitions of the constituent components of Long COVID have been proposed,44 reviewing the Long COVID literature revealed that they are rarely used when describing cases or identifying study cohorts.The analysis of 39 studies revealed a variety of criteria were used to identify and evaluate patients with post-acute COVID-19 sequelae. With nearly as many definitions as studies, it is clear that there is no agreement on the definition of Long COVID . Studies46 subdivides Long COVID patients into three groups based on whether their long-term symptoms are primarily respiratory in nature following severe COVID-19 with ARDS, whether they present with a multisystem disease with cardiac, respiratory, and/or neurological manifestations of end-organ damage, or whether their primary symptoms are persistent fatigue and other symptoms that do not necessarily indicate organ damage.89 In the literature analyzed, this definition was never used to define cohorts. Many studies included patients with acute infections that varied in their severity, including both inpatient and outpatient convalescents . Additionally, of the studies available thus far, data directly assessing organ damage is rarely collected, and the concept of organ damage itself has not been operationalized in this context.Moreover, the existing operational definitions of Long COVID differ in important ways, many of which are not differentiated by existing studies. For example, one framework45 In the literature surveilled, most studies recruited and assayed patients based on time elapsed from a COVID-19-related milestone, but what the milestone was varied widely. Some studies use the date of diagnosis or positive test, others the onset of symptoms, others hospital discharge, and others by even broader criteria . Many studies used a relatively precise window for patient assessment , while others included participants at various distances from acute SARS-CoV-2 infection under the umbrella term of Long COVID.46 In the latter case, these patients could fall under either the Long COVID (PACS) or chronic COVID-19 definitions if using a 12-week cutoff.44 Because the relationship between infection, symptoms, and viral clearance occupies a wide distribution,91 this heterogeneity among and sometimes within studies could introduce significant variability in disease course within and among patient cohorts.Other efforts to define Long COVID identify the severity of the acute phase as an important consideration in determining the onset of the post-acute phase. Specifically, for individuals hospitalized for more than three weeks following symptom onset, some definitions identify the post-acute period as starting once the patient is discharged from inpatient acute care.79 anosmia,82 anosmia/ageusia,76 loss of smell,68 loss of taste,59 loss of smell and taste,66 loss of smell or taste,65 and loss of smell and/or taste.77 While in many cases there are parallels among studies , the lack of a strict definition prevents straight-forward symptom matching across analyses. Further, there seemed to be limited surveying of neurological and systemic symptoms in some cases, hence the absence of common symptoms like cognitive dysfunction or “brain fog”, sensorimotor symptoms, and post-exertional malaise. This is where standard use of a full terminology such as HPO would be useful to create expressive and consistent meaning across studies.Finally, studies varied wide in the terminology used to describe patient-reported symptoms. Comparing symptoms described across the literature reviewed revealed 142 unique terms related to symptoms, including scales used to assess symptom profiles  or other dimensions of recovery  . The mos37 definitions of PACS and chronic COVID. Instead, studies typically define a period of time to investigate symptoms agnostic of how this factors into the broader conversation on the disease. The exception is studies that state they are investigating Long COVID, which use a wide variety of definitions. The same is true for post-acute COVID-19 or post-COVID syndrome, which are typically not explicitly tied to working definitions or explicit disease phenotypes. Among studies, patient inclusion criteria can be based on any number of relevant milestones from the acute phase, and only a subset of studies separate patients based on the severity of disease they experienced in the acute phase. Finally, no standardized terminology is used for patient-reported symptoms, and studies often report symptoms using similar but non-identical terminology. Thus, the literature analysis suggests significant heterogeneity among studies with respect to how they define cohorts of interest and analyze the experiences of patients experiencing Long COVID.Therefore, the literature indicates that at present there is little consistency among studies in definitions of Long COVID, including the symptoms analyzed. Few studies use terminology with a proposed, narrow-scope definition such as Greenhalgh and colleaguesFrom the results of the above exploratory review, candidates were selected to comprise a cohort of studies for further abstraction and analysis. Because of the poor reporting by and heterogeneity within our initial set of literature, the numbers of studies in this cohort is much smaller than the set of studies summarized above. This highlights the need for improved quality and reporting of even small cohorts. Details such as the specific definition used to identify patients with COVID-19 , hospitalization status (outpatient versus inpatient versus ICU), the severity of illness represented among patients in the cohort, and the number of patients presenting with each symptom or other clinical measure are important to efforts to compare results across studies.97 and one preprint,13 were chosen for the in-depth phenotypic analysis using HPO. A total of 154 different phenotypic abnormalities could be encoded using HPO terms. Here, 21 studies, including 20 published studiesFor the EHR analysis, we focused on 77 HPO annotations commonly used in the literature. Of these, 76 were successfully mapped to at least 1 OMOP concept identifier within the Condition domain . The unmapped HPO concept, increased circulating brain natriuretic peptide concentration (HP:0033534), could not be reasonably aligned to an OMOP concept identifier within the Condition domain. When expanding each OMOP concept identifier to include its descendant concepts, the total number of OMOP concepts used was 7,542  and the median number of OMOP concept identifiers mapped to each HPO codeset was 16. The largest HPO codeset sets were paresthesia , pain , skin rash , and anxiety , which was not unexpected given the variability in the clinical presentation  of the conditions associated with these concepts.The 297,404 patients in the final case group represent the pool of patients from which we have the potential to detect Long COVID . 85,912 The analyses described above demonstrate the heterogeneity both in symptoms associated with Long COVID and in assessments and definitions used to study Long COVID present in the literature as well as an EHR-based approach for identifying natural language data associated with potential Long COVID patients available in N3C.37, which is consistent with the virological data available thus far.45 There are a number of dimensions in which the existing literature varies in efforts to operationalize definitions of Long COVID. These differences are expected to vary in their effects. An important goal in the next phase of Long COVID research needs to be identifying the most critical considerations in defining patient cohorts.The literature review revealed a wide variety of terms used in describing patient cohorts used for studies of symptoms occurring after the acute phase of COVID-19. Most studies do not seek to assign their patients to a particular diagnosis or operational definition, although several referred to the definition from Greenhalgh et al. (2020)98 One early study examining viral load in hospitalized patients reported that viral shedding continued for at least 28 days following symptom onset in some patients.99 Another study reported that the median period between a patient’s first positive PCR test and cessation of viral shedding was 17 days and that up to 70% of patients were still symptomatic when their viral shedding ceased.90 However, viral shedding  does not necessarily indicate the presence of replication-competent viral particles. Viable viral particles have been detected from 6 days prior to up to 9 days after symptom onset.102 Patients have also been observed to test positive by PCR following a negative test,105 but the virus could not be cultured. Both asymptomatic and symptomatic patients with retest-positive COVID-19 have been identified.103 Even in individuals whose nasopharyngeal swabs produce negative PCR results, some test positive for SARS-CoV-2 in the intestine.106 These results therefore suggest that after the initial infection, patients shed non-infectious, degraded viral particles.104Long COVID is typically defined based on an elapsed acute infectious period, but at present, the relationship between the timing of COVID-19 symptoms relative to SARS-CoV-2 infection is not well understood.107 In current studies, there is significant variability in the inclusion/exclusion criteria used for patient recruitment in terms of COVID-19 test status. While some studies require a positive test, others recruit patients with either a confirmed or suspected diagnosis  and when they were admitted to the hospital for MIS-C and that they were hospitalized for a median of 6 days.114 MIS-A has been reported only very rarely, with only 30 known cases as of October 2020.115 The importance of distinguishing the natural history of MIS-C/A from that of COVID-19 has been highlighted in some efforts to operationalize definitions of Long COVID,42 but at present, MIS-C/A is not widely discussed in the Long COVID literature, even though it too manifests in the post-acute phase of infection.While COVID-19 is a complex and heterogeneous multisystem illness, patients infected with SARS-CoV-2 can also develop distinct illnesses. A multisystem inflammatory illness has been observed in children and in some adults following acute infection with SARS-CoV-2. This syndrome, called multisystem inflammatory syndrome in children (MIS-C) and in adults (MIS-A),20 The broad relationship between these known sequelae of viral infections and the specific pathogenesis of SARS-CoV-2 remains to be identified, although some mechanisms have been proposed116. In terms of characterizing the long-term sequelae of SARS-CoV-2 infection, they may introduce additional ambiguity regarding the specific outcomes associated with this particular virus compared to viral infections more broadly.Similarly, preliminary findings suggest that patients with SARS-CoV-2 infection are at risk for chronic illnesses associated with post-viral sequelae. One example is that some presentations of Long COVID bear a resemblance to CFS, another chronic condition that is often triggered by a viral infection.46 specifically highlights the potential importance of distinguishing long-term symptoms arising from organ damage from those arising from other etiologies. Given that a large number of Long COVID patients suffer from fatigue, which is associated with other post-viral syndromes but for which there are limited treatment options,20 identifying whether and when Long COVID patients have sustained long-term organ damage may provide additional options for treatment and understanding of the disease. However, few studies of Long COVID to date have conducted analyses elucidating the presence or extent of organ damage. Many assessments to collect evidence of long-term organ damage are intensive, meaning that their feasibility may vary with the strain on hospitals during the course of the COVID-19 pandemic. However, preliminary investigations of a number of organ systems have identified organ damage in Long COVID patients. These findings are also important because they highlight the possibility of asymptomatic Long COVID patients, who could sustain organ damage due to the SARS-CoV-2 virus that does not immediately present with symptoms. Therefore, an improved understanding of organ damage as an outcome of acute COVID-19 or as a long-term sequelae of the SARS-CoV-2 virus may present new options for patients experiencing persistent symptoms or elucidate new information about how the SARS-CoV-2 virus interacts with a range of organ systems.One definition of Long COVID119 AKI, especially moderate/severe AKI, is a risk factor for the development of chronic kidney disease (CKD).120 Apoptosis, maladaptive repair, and fibrosis have been postulated as mechanisms involved in the transition from AKI to CKD.121 The kidney is an organ of interest in Long COVID because acute SARS-CoV-2 infection is associated with kidney injury.123 SARS-CoV-2-associated microvascular injury may cause perfusion abnormalities within the pancreatic islets, skeletal muscle, heart and or brain. In the islet, for example, microcirculation is essential for both glucose sensing and insulin secretion; abnormal islet capillary architecture and fragmentation contributes to beta cell dysfunction in type 1 and type 2 diabetes.124 Diabetes is a known contributor to CKD. Both CKD and diabetes are major risk factors for cardiovascular disease (CVD)125 and long term disability, which may overlap with the complicated picture of PASC.126During acute SARS-CoV-2 infection, diffuse endothelial injury, leads to end organ perfusion abnormalities and microthrombi. This reduced perfusion contributes to acute kidney injury (AKI), and possibly to new-onset diabetes.De novo or progressive CKD was noted in 25.7% & 74.3% of cases based on estimated glomerular filtration rate (eGFR) + serum creatinine (SCr) and only SCr measures, respectively. A second study in a Swedish cohort127 similarly investigated kidney dysfunction following acute illness. In a group of 60 ICU patients admitted for COVID-19 infection, they found that inpatient AKI severity was associated with higher CKD stages at 3- to 6-month follow up.127 They found no differences between patients with CKD progression compared to those without progression in terms of demographics, comorbid conditions, or ICU admission characteristics.127 Similarly, in the unpublished study, neither inpatient AKI recovery nor a history of CKD prior to admission were associated with worsening renal function at follow up. Both of these analyses are limited due to a small sample size. Ongoing study at Stony Brook’s Post-AKI COVID clinic will include additional patients and longer follow-up and therefore should provide a more accurate estimate of CKD risk. It is not yet known whether inadequate renal repair after severe injury or persistence of SARS-Cov-2 in the kidney drives post-AKI CKD in COVID-19.An unpublished investigation and a complementary published analysis provide evidence highlighting the relevance of kidney damage to medium-to-long COVID-19 outcomes. A pilot investigation (unpublished) was conducted on a subgroup of 35 COVID-19 AKI survivors who were admitted at Stony Brook University Hospital, NY between March and June 2020 and subsequently followed in a “Post-AKI COVID clinic.” Patients were observed at a 6-month follow-up to have a high incidence of persistently reduced renal function after moderate/severe AKI in the setting of hospitalization with COVID-19. 128 this association has not yet been extensively explored in the setting of COVID-19, given that the virus has been circulating for just over a year at this time and studies so far have mostly reported the persistence of renal dysfunction (AKD) at time of hospital discharge.130 Persistent organ damage is now considered part of the Long COVID syndrome,131 and kidney disease should be considered part of this syndrome. While these two studies are among the first reporting this association, further multi-center studies with larger sample sizes and with pathology data are needed to further analyze the relationship between AKI and development/progression of CKD in COVID-19.While AKI is an established independent risk factor for CKD,135. There have been a few focused imaging studies that attempt to dissect neuroimaging correlates associated with specific symptoms; for instance, olfactory bulb abnormalities were characterized in an MR imaging study of COVID-19 anosmic patients.136 One study comparing 35 Long COVID patients to 44 controls found significant hypometabolism in the brain, including the olfactory gyrus, right temporal lobe , the bilateral pons/medulla brainstem, and the bilateral cerebellum; notably, the clusters of hypometabolism were correlated with patient symptoms, including hyposmia and anosmia, memory and cognitive impairment, pain, and insomnia.137 There have also been additional suggestions that brainstem dysfunction might be involved in a variety of COVID-19 clinical manifestations. For instance, Yong138 cites a number of autopsy studies to support this hypothesis.140A variety of neuroimaging findings have been reported in COVID-19 patient populations, and efforts to better understand pathophysiologic origins and neuroanatomical correlates are ongoing. A number of studies have been carried out to characterize COVID-19 neuroimaging findings and associated neuropsychiatric symptoms, e.g.143 Kidney autopsies or biopsies in patients with Covid-19 related AKI do not generally reveal suggestions of direct viral cytotoxic effects such as nuclear, cytoplasmic inclusions or with extensive tissue necrosis and inflammation.144 Autopsy and kidney biopsy tissue studies have indicated that acute tubular injury is the most common pathologic finding Covid patients with AKI or proteinuria.145 Collapsing glomerulopathy and thrombotic microangiopathy are also been associated with Covid 19 AKI in autopsy and biopsy studies.145Autopsy analysis is an important method to obtain insights into the pathology associated with COVID-19 and the presence of the SARS-CoV-2 virus in diseased tissues. Kidney tissue provides a good example of the importance of autopsy analysis. AKI is very common in patients hospitalized with COVID-19 and is a major risk factor for mortality.1 reported the presence of viral load and also viral RNA and proteins in the kidney using in situ hybridization and indirect immunofluorescence with confocal microscopy. In an autopsy study of 26 patients with COVID-19, Su et al.146 found clusters of coronavirus-like particles in the kidney tissue on electron microscopy and also detected positive immunostaining with SARS-CoV nucleoprotein antibody associated with injury patterns on light microscopy.146 Autopsy studies have also shown that SARS-CoV-2 infects and replicates inside pancreatic beta cells, reducing insulin-sensing functions of those cells.147 This direct infection of the pancreatic beta cells is likely a lead cause of metabolic dysfunction and glycemia after SARS-CoV-2 infection. The N3C database offers a unique opportunity to study glycemia before and after SARS-CoV-2 infection, as well as how new-onset diabetes may contribute to PASC effects on quality of life for both adults and children.Multi-organ and especially renal tropism of SARS-CoV-2 has been observed in autopsy studies on COVID-19 patients. Puelles et al148 Interestingly, in one recent study, megakaryocytes were found in cortical capillaries in 33% of brain autopsy cases examined.149 The authors thereof a) noted that this observation was consistent with other observers who have noted megakaryocytes151 and b) suggest that these large cells could cause ischemic alternation in a distinct pattern and might be associated with COVID-19 neurological impairment.In a review of brain autopsy studies, summarizing 24 studies with results from 149 individuals chronic inflammation or neural changes typically associated with viral infections were found to be largely absent.in situ hybridization of the kidney tissue.152 This study raised concerns about the method and timing of post-mortem tissue collection and processing, since a significant degree of autolysis was noted in the kidney tissue in this study.153 In a recent study of immediate (≤3 hours) post-mortem renal biopsies of 16 patients with COVID-19 and 5 control patients with sepsis, investigators reported that the presence nCoV2019 N-Protein was detected in proximal and distal renal tubules in 9 of 16 cases, out of which 6 of the 9 were confirmed by in situ hybridization. This finding supported the presence of SARS-CoV-2 in the kidney.154 However, SARS-CoV-2 E and N1/N2 genes were detected by RT-PCR of the kidney total RNA in only one case, and classical viral inclusions were not detected via electron microscopy. Therefore, while autopsy can serve as an important tool in looking for the presence of SARS-CoV-2 in tissues and the associated pathology, the methodology used for autopsy is critical to providing accurate insights into disease patterns.The timing of autopsy is likely to be important in efforts to detect whether SARS-CoV-2 remains in tissue. In another study examining 42 postmortem samples of patients who died with COVID-19, no presence of SARS-CoV-2 was noted in analysis with immunofluorescence, electron microscopy or 158 While unique psychological stressors are likely to affect patients who experience COVID-19 and especially those with more severe cases, the impact of a broader societal decline in psychological health (including addiction/substance abuse disorder) may be difficult to identify in studies that evaluate only COVID-19 patients with and without Long COVID. Similarly, viral infections can exacerbate pain and other chronic conditions,159 but these effects are not specific to SARS-CoV-2 even though they may appear that way depending on study design. Similarly, the conditions of the pandemic have reduced access to healthy food choices in some places161 and reduced opportunities for exercise.162 Social distancing also presents unique challenges to patients with substance use disorders; as loneliness and stress can make people more inclined to substance use.164 As governmental and societal responses to SARS-CoV-2 evolve, it is possible that quality of life and psychosocial reports from Long COVID patients may shift along with those of the population more broadly.165The circumstances surrounding infection with SARS-CoV-2 and the pandemic itself are likely to have a significant impact on patients’ health. In particular, psychosocial health, nutrition, and physical fitness may all be impacted by the broader societal response to SARS-CoV-2. Early in the pandemic, the WHO released recommendations to support psychosocial health in light of the pandemic . Since then, many reports have indicated concern about a rise of psychosocial distress internationally.167 Thousands of clinical trials are being registered, initiated and, in many cases, completed on COVID-19 treatment and prevention in the USA and across the planet.168 While this response is impressive, there are risks to rapidly planning and performing expedited clinical trials.169 For example, recent reviews of registered protocols have revealed methodologic flaws and a wide array of outcomes measures, particularly patient-reported outcome measures (PROMs), being used,171 most of which have not been vetted for relevance to COVID-19 patients.172 Moreover, the lack of available terminological standards greatly impede the ability to compare studies.Because of the pandemic, there has been an incredible surge of research and a call for the surveillance of COVID-19 patients.173There are two obstacles to the design of clinical research in this area. First, there has not yet been any rigorous large-scale effort to characterize the constellation (incidence and breadth) of outcomes most important to Long COVID patients. Without this characterization it is not possible to design inclusion criteria for responsible clinical studies. A second and related obstacle is there have not yet been efforts to define the sets of core domains and outcomes for patients in future clinical studies. Heretofore the lack of uniformity in outcome measurement across clinical research creates multiple problems: it undermines the validity of this research, shows a lack of relevance to the patient perspective, and limits our ability to compare findings between studies or to pool data for meta-analyses.176 Core outcomes are instruments  that measure particular core domains , the latter of which are specific symptoms or broader symptom categories. A CDS is an agreed upon selection of symptoms or symptom domains (categories) that should be measured and reported in all clinical trials for a particular health condition. A COS is defined as an agreed minimum selection of outcomes that should be measured and reported in all clinical trials for a particular health condition.176 CDSs must be developed prior to the development of COSs of measurement instruments. Given that the scientific community has only recently started to examine Long COVID, a CDS is the first necessary step. A CDS would increase the reporting of patient important outcomes in Long COVID, reduce the risk of selective outcome reporting,177 and increase the feasibility of conducting meta-analyses on such topics in the future.177 In relation to value-based health care, core domain and outcome sets are key to performing research that inform quality indicators related directly to patient outcomes and are routinely being used by national health-care organizations in the USA and abroad184 and in particular can be used in the measurement of quality of care in the COVID era.185In an effort to reduce heterogeneity in outcomes measured across clinical trials, and to improve the clinical monitoring of patients, the development of core domain sets (CDS) and core outcome sets (COS) in specific health conditions has been routinely recommended.188 While this work is important for the acute period of COVID-19, these efforts do not focus on the long-term outcomes associated with Long COVID. To date no work has been done to explore what is important to patients with Long COVID. Without a CDS informed by a large sample of patients that had COVID-19, clinicians and clinical trialists will lack an essential assessment tool to adequately measure patient specific and patient important outcomes and changes across time. A CDS would provide a critical means of comparing results across trials, which is extremely difficult in the current conditions where many different PROMs are being used in many different samples of patients.Some work has been done to create various types of CDSs and COSs for clinical trials of acute COVID-19.These problems undermine the relevance and usefulness of this evidence for decision-making, and the research does not focus on what is most important to patients. Because evidence suggests long-term effects of COVID-19 on health-related quality of life, working to identify the domains and corresponding measures  that are most relevant to COVID-19 patients following the acute infection is urgently needed given the rapid expansion of clinical research in this group. The incidence of those with Long COVID will climb, and soon much clinical care and research will be directed at this group, as evidenced by the increase in research in the area.189 This concern is underscored by reports that Long COVID patients experience increased disability related to breathlessness and decreased quality of life.190 Understanding the needs of these patients will allow for the development of healthcare, rehabilitation, and other resources needed to support their recovery.192 However, identifying patient needs is contingent on developing a research infrastructure that accurately assesses the natural history of this illness.Available evidence suggests that Long COVID is a substantial public health problem with severe consequences for affected individuals and society at large. Patients commonly report being emotionally affected by health problems related to Long COVID. In the United States, patients have reported mild to severe financial impacts related to acute or chronic COVID-19,73 It should be noted that available studies have investigated COVID-19 patients who have come to medical attention, and much less data are available at the population level about the extent of late sequelae.42Given the heterogeneity of clinical presentations of individuals with prolonged clinical manifestations following acute COVID-19, it is likely that clinical management should be tailored to individuals. However, the clinical management of Long COVID remains challenging because there are no evidence-based guidelines. Existing studies do not always provide comprehensive information about the clinical course, and often present aggregated results for individuals with differing clinical courses, such as for instance severe COVID requiring admission to an ICU and moderate COVID requiring hospitalization but not care in the ICU. Existing literature is contradictory with respect to the natural history of Long COVID. For instance, one study found that persistent fatigue is independent of severity of initial infection, but another found that 10 of 16 individuals (63%) with severe acute COVID-19 but only 26/65 (40%) individuals with moderate COVID had persistent fatigue.Most studies to date use survey-based methods to ascertain patient-reported symptoms of Long COVID, although some studies are beginning to use imaging and other technologies to identify the physical signs of organ damage. Vital signs are a third category of indicators that are likely to prove valuable in efforts to investigate Long COVID. Vital signs have several attractive properties for the study of COVID-19. Data are often available from prior to the illness allowing for pre-post comparisons and are routinely collected in affected and unaffected individuals allowing for case-control comparisons. Moreover, analyses of discontinuities in a vital sign’s trajectory of time are possible. An ecosystem where associations between patient-reported symptoms, data available in EHR, and results of simple and/or complex clinical assessments with Long COVID have been evaluated and standardized will introduce a positive feedback cycle where clinicians are able to collect the data needed for the elucidation of Long COVID phenotype.While heterogeneity in the presentation of Long COVID has been identified, the specific variables influencing outcomes remain to be characterized. The number of syndromes within Long COVID and the extent to which symptom profiles, frequency of occurrence, and duration are unique to these groups remains to be explored. At present, however, data is not collected in a way to allow for these subtle differences to be parsed. In order to develop clinical management strategies to prevent or mitigate Long COVID, it will be essential for studies to use a unified definition of Long COVID and its subforms so that data from different studies can be integrated to provide the foundation for robust statistical inferences about risk factors for the development of Long COVID, as well as the natural history and response to treatments.Among the reasons for needing an unambiguous definition of Long COVID is the need to make clear contrasts and comparisons between affected and unaffected people. In addition, a clear definition is necessary to understand whether or to what extent defining phenotypic features of Long COVID were present prior to COVID-19 illness in patients affected by Long COVID or potentially serving as controls in studies. The identification of appropriate unaffected people and pre-illness time periods for comparisons is foundational to advancing the state of the art in Long COVID research. It is imperative that patient-reported symptoms be taken into account alongside deep clinical characterization and large scale observational data such as in the N3C. However, all three sources of data are subject to biases and all sources are needed to provide a more complete picture of Long COVID characterization for individuals and populations.The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH.Use of the N3C data for this study is authorized under the following IRB Protocol:ncats.nih.gov/n3c/resources.The N3C Data Enclave is approved under the authority of the NIH Institutional Review Board for Protocol 000082 associated with NIH iRIS reference number: 546652 entitled: “NCATS National COVID-19 Cohort Collaborative (N3C) Data Enclave Repository.” Further information can be found at Supplement 1Supplement 2Supplement 3


The present study aimed to identify the distinct classes of motivations to get vaccinated and to adopt preventive behaviors against the coronavirus disease 2019 (COVID-19) among people in Taiwan and to examine the roles of Protection Motivation Theory (PMT) cognitive constructs in these unique classes of motivations to get vaccinated and to adopt preventive behaviors. We recruited 1047 participants by using a Facebook advertisement. Participants’ motivations to get vaccinated and to adopt preventive behaviors against COVID-19, PMT constructs of threat appraisal (perceived severity and perceived vulnerability), and PMT constructs of coping appraisal  were determined. We analyzed participants’ motivations to get vaccinated and to adopt preventive behaviors against COVID-19 by using latent profile analysis. The present study identified three latent classes, including the participants with high motivation for vaccination and preventive behaviors (the class of Both High), those with low motivation for vaccination and preventive behaviors (the class of Both Low), and those with high motivation for vaccination but low motivation for preventive behaviors (the class of High Vaccination but Low Preventive Behaviors). Compared with the participants in the class of Both High, participants in the class of Both Low had lower levels of perceived vulnerability, perceived severity, self-efficacy to have vaccination, response efficacy of vaccination, knowledge about vaccination, and previous vaccination for seasonal influenza; participants in the class of High Vaccination but Low Preventive Behaviors had lower levels of perceived vulnerability and perceived severity but higher levels of response cost of vaccination. We concluded that varieties of motivations, threat, and coping appraisals should be considered in intervention programs aiming to increase motivation to adopt recommended protective behaviors against COVID-19. Coronavirus disease 2019 (COVID-19) has had a disastrous effect worldwide ,5,6,7,8.Although adopting preventive behaviors and getting vaccinated against COVID-19 are crucial during the COVID-19 pandemic, motivation to implement them is not universal. Many individuals have resisted adherence to social distancing , wearingResearch has demonstrated a significant association between the adoption of preventive behaviors against COVID-19 and motivation to receive a COVID-19 vaccination. However, adopting preventive behaviors did not guarantee a high motivation to receive a COVID-19 vaccination ,31. MostProtection Motivation Theory (PMT) is one of the theoretical frameworks that has been commonly applied to explain the motivation and behaviors regarding vaccination for respiratory infectious diseases ,35,36,37PMT has been applied to explain the motivation to adopt preventive behaviors ,44,45,46The present study aimed (1) to identify the distinct classes of motivations to get vaccinated and to adopt preventive behaviors against COVID-19 among people in Taiwan and (2) to examine the roles of PMT cognitive constructs in these unique classes of motivations to get vaccinated and to adopt preventive behaviors. We proposed two hypotheses. First, because adopting preventive behaviors did not guarantee high motivation to receive COVID-19 vaccination ,31, we hThe procedure of recruiting participants in this study is described elsewhere . In brieThe motivation to get vaccinated for COVID-19 was assessed using one item. The question and scoring are listed in Three items were adopted from the questionnaire developed by Liao et al.  to measup < 0.001) [We measured the constructs of threat appraisal (perceived severity of COVID-19 and perceived vulnerability to COVID-19) by the questionnaire transformed from the questionnaire for measuring risk perception of the H1N1 influenza . A previ< 0.001)  and supp< 0.001) . A previ< 0.001) . The Croprimary school or below; 1 = junior high school; 2 = senior high school; 3 = college or university; 4 = master’s degree; and 5 = doctorate) were collected. Participants were divided into three groups according to age .Sex , age, and education level (0 = tidyLPA [p < 0.05 indicates that the k class model is superior to the k − 1 class model (k represents the number of classes).We analyzed participants’ motivations to get vaccinated for COVID-19 and to adopt preventive behaviors against COVID-19 through Latent Profile Analysis (LPA) by using the R package tidyLPA  with thetidyLPA , and enttidyLPA . For BLRThe differences in sociodemographic characteristics between various latent classes of motivations to get vaccinated and to adopt preventive behaviors against COVID-19 were examined using multiple multinomial logistic regression with the latent class as the nominal outcome variable. The differences in the PMT constructs of perceived severity, perceived vulnerability, self-efficacy to vaccination, response efficacy and cost of vaccination, knowledge, and previous vaccination for seasonal influenza between the latent classes were examined using a multiple multinomial logistic regression with adjustment for sociodemographic characteristics.After adjustment for sociodemographic characteristics, compared with participants with high motivations for vaccination and preventive behaviors, participants with low motivation for vaccination and preventive behaviors had lower levels of perceived vulnerability, perceived severity, self-efficacy to have vaccination, response efficacy of vaccination, knowledge about the vaccination, and previous vaccination for seasonal influenza. Compared with participants with high motivations for vaccination and preventive behaviors, participants with high motivations for vaccination but low for preventive behaviors had lower levels of perceived vulnerability and perceived severity but a higher level of response cost of vaccination.The present study identified three latent classes according to the motivation to get vaccinated and to adopt preventive behaviors against COVID-19, namely high motivation for vaccination and preventive behaviors, low motivation for vaccination and preventive behaviors, and high motivation for vaccination but low for preventive behaviors. Several PMT cognitive constructs differed between these latent classes. Research has supported the prediction of motivation to get vaccinated for further actual behaviors of vaccination. For example, a one-year follow-up study confirmed that parents’ lower intention to have their children vaccinated significantly predicted the lower likelihood of influenza vaccination among children . A nine-The present study classified participants into three latent classes based on their motivations to get vaccinated and to adopt preventive behaviors against COVID-19. Participants with low motivation for getting vaccinated and adopting preventive behaviors accounted for 11.7% of the population; this group perceived lower vulnerability to COVID-19 and severity of COVID-19. Moreover, they reported lower self-efficacy to, response efficacy of, and knowledge regarding COVID-19 vaccination, and fewer previous experiences of vaccination for seasonal influenza compared with those with high motivations for getting vaccinated and adopting preventive behaviors. Lack of basic protection may put this group at the highest risk of contracting COVID-19. This group of the individuals should be the target of intervention programs for controlling the spread of COVID-19. Enhancing their threat appraisal toward COVID-19 and coping appraisal toward vaccination should be important components of intervention programs. However, despite media reports regarding the highly contagiousness and lethal nature of COVID-19 since February 2020, this group retained low motivation. Their cognitive processing model of risk perception warrants further study to provide evidence for developing effective intervention programs. Taiwan has succeeded in mass-vaccination programs against seasonal influenza  and hepaThe present study demonstrated that nearly half (48.4%) of the participants had high motivation to get vaccinated but low motivation to adopt preventive behaviors against COVID-19. They had lower levels of perceived vulnerability to and severity of COVID-19 but a higher level of response cost of vaccination compared with those with high motivations to get vaccinated and to adopt preventive behaviors. Low threat appraisal might partially account for their low motivation to adopt preventive behaviors against COVID-19. Moreover, low motivation to adopt preventive behaviors might result from repeated alerts regarding the COVID-19 pandemic but the low number of COVID-19 cases in Taiwan in the past 6 months. People might consider that getting vaccinated for COVID-19 is an alternative to adopting preventive behaviors in daily lives. Moreover, annual vaccination for seasonal influenza among people in Taiwan  may contThe high proportion of people with high motivation to get vaccinated but low motivation to adopt protective behaviors against COVID-19 is a warning regarding the poor prevention strategy in Taiwan. Although vaccination is the fundamental strategy to restrain the spread of COVID-19 ,11, no CThe present study found that, compared with participants with high motivations for vaccination and preventive behaviors, participants with high motivations for vaccination but low for preventive behaviors were more likely to belong to the age group of <35, whereas no difference in age distribution between the classes of both high and both low motivations for vaccination and preventive behaviors was found. Older age is identified as the major risk factor for COVID-19 . A studyThe present study is one of the first studies to classify individuals with various motivation levels to get vaccinated and to adopt protective behaviors against COVID-19 and to examine differences in PMT cognitive constructs across the individuals of various classes of motivation. The results can provide knowledge to develop intervention programs to subtly enhance motivation of people in various groups to adopt all types of protective behaviors. However, the present study had some limitations. First, this study used a Facebook advertisement to recruit participants. Although delivering study questionnaires through social media can provide a large number of responses in a short period , particuThe present study identified three latent classes of individuals with various motivations to get vaccinated and to adopt preventive behaviors against COVID-19. Furthermore, we determined the differences in threat and coping appraisals in terms of PMT between the latent classes. On the basis of the results, we suggest that designers of intervention programs aiming to increase motivation to adopt recommended protective behaviors against COVID-19 should consider the varieties of motivations, threat, and coping appraisals. Given that lack of vaccination and protective behaviors may put the people with low motivation for getting vaccinated and adopting preventive behaviors at the highest risk of contracting COVID-19, intervention programs should focus on enhancing their threat appraisal toward COVID-19 and coping appraisal toward vaccination. For people with high motivation to get vaccinated but low motivation to adopt preventive behaviors against COVID-19, enhancing their threat appraisal toward COVID-19 and discussing the advantages and disadvantages of vaccination against COVID-19 are necessary to ensure their vaccination against COVID-19.