ABSTRACT
Background. In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the NIH RECOVER Initiative to develop a machine learning (ML) pipeline to identify patients with a high probability of having post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID. However, the increased home testing, missing documentation, and reinfections that characterize the latter years of the pandemic necessitate reengineering our original model to account for these changes in the COVID-19 research landscape. Methods. Our updated XGBoost model gathers data for each patient in overlapping 100-day periods that progress through time, and issues a probability of Long COVID for each 100-day period. If a patient has known acute COVID-19 during any 100-day window (including reinfections), we censor the data from 7 days prior to the diagnosis/positive test date through 28 days after. These fixed time windows replace the prior model's reliance on a documented COVID-19 index date to anchor its data collection, and are able to account for reinfections. Results. The updated model achieves an area under the receiver operating characteristic curve of 0.90. Precision and recall can be adjusted according to a given use case, depending on whether greater sensitivity or specificity is warranted. Discussion. By eschewing the COVID-19 index date as an anchor point for analysis, we are now able to assess the probability of Long COVID among patients who may have tested at home, or with suspected (but untested) cases of COVID-19, or multiple SARS-CoV-2 reinfections. We view this exercise as a model for maintaining and updating any ML pipeline used for clinical research and operations.
Subject(s)
COVID-19ABSTRACT
Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to predict patient/cluster assignment over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.
Subject(s)
COVID-19 , Acute DiseaseABSTRACT
It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first 7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements.
Subject(s)
COVID-19ABSTRACT
Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.
Subject(s)
COVID-19ABSTRACT
BackgroundMore 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 POINTSO_ST_ABSQuestionC_ST_ABSWhat 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.
Subject(s)
Dementia , Substance-Related Disorders , Pulmonary Disease, Chronic Obstructive , Depressive Disorder , Psychoses, Substance-Induced , Obesity , COVID-19 , CardiomyopathiesABSTRACT
Naming a newly discovered disease is always challenging; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux. The deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Our results include a characterization of common diagnostics, treatment-oriented procedures, and medications associated with U09.9-coded patients, which give us insight into current practice patterns around Long COVID. We also established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into three major categories: cardiopulmonary, neurological, and metabolic. We aim to apply the patterns gleaned from this analysis to flag probable Long COVID cases occurring prior to the existence of U09.9, thus establishing a mechanism to ensure patients with earlier cases of Long-COVID are no less ascertainable for current and future research and treatment opportunities.
Subject(s)
COVID-19ABSTRACT
We previously found that type 2 immunity promotes COVID-19 pathogenesis in a mouse model. To test relevance to human disease we used electronic health record databases and determined that patients on dupilumab (anti-IL-4R monoclonal antibody that blocks IL-13 and IL-4 signaling) at the time of COVID-19 infection had lower mortality.