Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

BMC Medical Research Methodology

Research

Harrison T. Reeder¹  Tingyi Cao²  Andrea S. Foulkes³ 

[1] Biostatistics, Massachusetts General Hospital, Boston, MA, USA;Department of Medicine, Harvard Medical School, Boston, MA, USA;Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA;Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA;Biostatistics, Massachusetts General Hospital, Boston, MA, USA;Department of Medicine, Harvard Medical School, Boston, MA, USA;

关键词: SARS-CoV-2;    Biomarkers;    Sparse group LASSO;    Functional data analysis;    Functional principal component analysis;   

DOI  :  10.1186/s12874-023-02076-3

 received in 2023-05-22, accepted in 2023-10-18,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundA substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including intubation and death. However, most existing studies considered each of multiple biomarkers independently and focused analysis on baseline or peak values.MethodsWe propose a two-stage analytic strategy combining functional principal component analysis (FPCA) and sparse-group LASSO (SGL) to characterize associations between biomarkers and 30-day mortality rates. Unlike prior reports, our proposed approach leverages: 1) time-varying biomarker trajectories, 2) multiple biomarkers simultaneously, and 3) the pathophysiological grouping of these biomarkers. We apply this method to a retrospective cohort of 12, 941 patients hospitalized at Massachusetts General Hospital or Brigham and Women’s Hospital and conduct simulation studies to assess performance.ResultsRenal, inflammatory, and cardio-thrombotic biomarkers were associated with 30-day mortality rates among hospitalized SARS-CoV-2 patients. Sex-stratified analysis revealed that hematogolical biomarkers were associated with higher mortality in men while this association was not identified in women. In simulation studies, our proposed method maintained high true positive rates and outperformed alternative approaches using baseline or peak values only with respect to false positive rates.ConclusionsThe proposed two-stage approach is a robust strategy for identifying biomarkers that associate with disease severity among SARS-CoV-2-infected individuals. By leveraging information on multiple, grouped biomarkers’ longitudinal trajectories, our method offers an important first step in unraveling disease etiology and defining meaningful risk strata.

【 授权许可】

CC BY   
© The Author(s) 2023

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