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 3 publications

(2 citation statements)

References 52 publications

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“…Additionally, the intensity of the pandemic and misinformation might introduce con rmatory bias between healthcare providers and patients. Some articles [34,45] acknowledged the need for replication studies in other cohorts. Several articles excluded hospitalized COVID-19 patients, which might not re ect the complete spectrum of long COVID.…”

Section: Discusssionmentioning

confidence: 99%

Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review

Luke,

Shaw,

Clarke

et al. 2023

Preprint

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Objective Long COVID, or post-COVID condition, is characterized by a range of physical and psychological symptoms and complications that persist beyond the acute phase of the coronavirus disease of 2019 (COVID-19). However, this condition still lacks a clear definition. This scoping review explores the potential of electronic health records (EHR)-based studies to characterize long COVID. Methods We screened all peer-reviewed publications in the English language from PubMed/MEDLINE, Scopus, and Web of Science databases until September 14, 2023. We identified studies that defined or characterized long COVID based on EHR data, regardless of geography or study design. We synthesized these articles based on their definitions, symptoms, and predictive factors or phenotypes to identify common features and analytical methods. Results We identified only 20 studies meeting the inclusion criteria, with a significant majority (n = 17, 85%) conducted in the United States. Respiratory conditions were significant in all studies, followed by poor well-being features (n = 17, 85%) and cardiovascular conditions (n = 14, 70%). Some articles (n = 8, 40%) used a long COVID-specific marker to define the study population, relying mainly on International Classification of Diseases, Tenth Revision (ICD-10) codes and clinical visits for post-COVID conditions. Among studies exploring plausible long COVID (n = 12, 60%), reverse transcription-polymerase chain reaction and antigen tests were the most common identification methods. 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. Conclusion Our findings suggest a limited global utilization of EHR-derived data in defining or characterizing long COVID, with 60% of these studies incorporating a validation step. Future meta-analyses are essential to assess the homogeneity of results across different studies.

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Section: Discusssionmentioning

confidence: 99%

Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review

Luke,

Shaw,

Clarke

et al. 2023

Preprint

View full text Add to dashboard Cite

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

Cited by 3 publications

References 52 publications

Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review

Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review

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

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

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