A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns

Haupert, Spencer R.; Shi, Xu; Chen, Chen; Fritsche, Lars G.; Mukherjee, Bhramar

doi:10.1016/j.jbi.2022.104237

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…Additionally, the intensity of the pandemic and misinformation might introduce confirmatory bias between healthcare providers and patients. Some articles [33,43] acknowledged the need for replication studies in other cohorts. Several articles excluded hospitalized COVID-19 patients, which might not reflect the complete spectrum of long COVID.…”

Section: Discussionmentioning

confidence: 99%

Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records

Luke,

Shaw,

Saarunya

et al. 2024

Informatics

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This scoping review explores the potential of electronic health records (EHR)-based studies to characterize long COVID. We screened all peer-reviewed publications in the English language from PubMed/MEDLINE, Scopus, and Web of Science databases until 14 September 2023, to identify the studies that defined or characterized long COVID based on data sources that utilized EHR in the United States, regardless of study design. We identified only 17 articles meeting the inclusion criteria. Respiratory conditions were consistently significant in all studies, followed by poor well-being features (n = 14, 82%) and cardiovascular conditions (n = 12, 71%). Some articles (n = 7, 41%) used a long COVID-specific marker to define the study population, relying mainly on ICD-10 codes and clinical visits for post-COVID-19 conditions. Among studies exploring plausible long COVID (n = 10, 59%), the most common methods were RT-PCR and antigen tests. The time delay for EHR data extraction post-test varied, ranging from four weeks to more than three months; however, most studies considering plausible long COVID used a waiting period of 28 to 31 days. Our findings suggest a limited utilization of EHR-derived data sources in defining long COVID, with only 59% of these studies incorporating a validation step.

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Section: Discussionmentioning

confidence: 99%

Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records

Luke,

Shaw,

Saarunya

et al. 2024

Informatics

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show abstract

“…It is crucial to acknowledge certain limitations inherent in utilizing raw EHR data [ 9 , 10 ]. The data, being an indirect reflection of the true patient state due to the recording process, introduces variability stemming from the dynamic nature of the patient's health status [ 11 ]. Patient states can significantly influence the value, existence, type, and timing of recorded measurements, thereby impacting the precision of analyses.…”

Section: Discussionmentioning

confidence: 99%

Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer

Huang,

Johnathan,

Shah

et al. 2023

Cureus

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This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association's ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.

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“…Covariates. Similar to our previous COVID-19 studies [13,34,[56][57][58], we considered the following key covariates to control for potential confounding factors. Age at the time of recruitment (UKB) or last observed visit (All of Us, MGI), whichever came last, was considered a continuous variable for each participant.…”

Section: Covid-19 Severity Polygenic Risk Score (Prs) Constructionmentioning

confidence: 99%

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

Fritsche,

Nam,

et al. 2023

PLoS Genet

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Objective To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity. Methods Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis. Results The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection. Conclusion By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

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A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns

Cited by 5 publications

References 52 publications

Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records

Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records

Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

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