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Review 2: "Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as Part of the NIH RECOVER Program"

Reviewers find this study to be potentially informative to reliable and highlight the study’s limited generalizability and potential introduction of bias in the control selection process.

Published onOct 11, 2022
Review 2: "Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as Part of the NIH RECOVER Program"
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key-enterThis Pub is a Review of
Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program
Description

ABSTRACTBackgroundMore than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID).ObjectiveTo identify risk factors associated with PASC/long-COVID.DesignRetrospective case-control study.Setting31 health systems in the United States from the National COVID Cohort Collaborative (N3C).Patients8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system.MeasurementsRisk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC.ResultsAmong 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls.ConclusionsThis national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.KEY POINTSQuestionWhat risk factors are associated with post-acute sequelae of SARS-CoV-2 (PASC) in the National COVID Cohort Collaborative (N3C) EHR Cohort?FindingsThis national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, specific comorbidities, and the number of physicians per capita.MeaningClinicians can use these risk factors to identify patients at high risk for PASC while they are still in the acute phase of their infection and also to support targeted enrollment in clinical trials for preventing or treating PASC.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

In this matched case-control study using data from 31 US healthcare systems, the authors aimed to identify risk factors associated with the diagnosis of post-acute sequelae of SARS-CoV-2 (PASC). PASC was defined as a receipt of a long COVID diagnosis according to ICD code or a visit to a long COVID clinic. Three different control groups were defined: one unrestricted control group and two more restrictive control groups with the purpose to exclude individuals suffering from PASC but who do not have a corresponding diagnosis. Different analytical approaches were used, including logistic regression and machine learning methods. The study mainly confirms findings of previous work, showing that among others, middle-aged patients, females and those with certain co-morbidities and with higher severity of acute COVID-19 have a higher risk to develop PASC.

The study results are based on a very large and representative dataset. The different approaches for data analysis including traditional statistical analysis as well as machine learning techniques are important strengths of the study. Also, the results are very nicely presented and discussed.

An important limitation of the study, as acknowledged by the authors, is the complexity of selecting an adequate control group. As shown in a secondary analysis, higher physician density in the county of residence was associated with increased “risk” of PASC, reflecting differences in access to healthcare. Therefore, people with certain co-morbidities which require regular healthcare visits will be inherently more likely to receive a PASC diagnosis. To mitigate this detection bias, the authors used an algorithm to exclude individuals with a higher likelihood of having PASC based on co-variables. This of course biases the control group and pre-selects for certain populations. However, because results were not significantly different between the control groups, the authors argue that this circularity problem is not likely to substantially bias the results.

Another limitation of the study is the lack of a conceptual framework, which would a priori exclude certain variables of being entered into the model. For instance, the finding that tuberculosis is a risk factor for PASC is odd. This comorbidity, and potentially also others, share some of the symptomatology assigned to PASC and are very unlikely to be causally involved.

Finally, the study included only infections up to the end of 2021. This means that Omicron infections are - if at all - only minimally represented. New data show that the risk of PASC after Omicron is clearly decreased compared to previous variants. Also, the effect of SARS-CoV-2 vaccination is not factored in which has also been shown to potentially influence the risk of PASC. These limitations make it difficult to generalize and to use the proposed risk factor profile to predict PASC in individuals infected with the Omicron variant.


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