Tianyi Sun, Allison B. McCoy, Alan B. Storrow, Dandan Liu
{"title":"Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach","authors":"Tianyi Sun, Allison B. McCoy, Alan B. Storrow, Dandan Liu","doi":"10.1002/sim.10184","DOIUrl":null,"url":null,"abstract":"Clinical prediction models have been widely acknowledged as informative tools providing evidence‐based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real‐time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed “preconditioning”) method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing “one‐step‐sweep” approach as well as the imputation approach. In general, the simulation results show the preconditioning‐based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation‐based approach, while the “one‐step‐sweep” approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real‐time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/sim.10184","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Clinical prediction models have been widely acknowledged as informative tools providing evidence‐based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real‐time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed “preconditioning”) method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing “one‐step‐sweep” approach as well as the imputation approach. In general, the simulation results show the preconditioning‐based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation‐based approach, while the “one‐step‐sweep” approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real‐time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.
期刊介绍:
The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.