Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma

Abstract Background Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-thre...

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Autores principales:	Ji Soo Kim, Ami A. Shah, Laura K. Hummers, Scott L. Zeger
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Bayesian hierarchical models Longitudinal profiles Multivariate mixed models Sequentially-updated prediction Scleroderma Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/4e8342060869466793bee7e2250f66f5
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spelling	oai:doaj.org-article:4e8342060869466793bee7e2250f66f52021-11-14T12:39:40ZPredicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma10.1186/s12874-021-01439-y1471-2288https://doaj.org/article/4e8342060869466793bee7e2250f66f52021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01439-yhttps://doaj.org/toc/1471-2288Abstract Background Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients. Methods We use a Bayesian mixed model approach to simultaneously characterize each individual’s future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model. Results The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual’s risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient’s visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR). Conclusions This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.Ji Soo KimAmi A. ShahLaura K. HummersScott L. ZegerBMCarticleBayesian hierarchical modelsLongitudinal profilesMultivariate mixed modelsSequentially-updated predictionSclerodermaMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian hierarchical models Longitudinal profiles Multivariate mixed models Sequentially-updated prediction Scleroderma Medicine (General) R5-920
spellingShingle	Bayesian hierarchical models Longitudinal profiles Multivariate mixed models Sequentially-updated prediction Scleroderma Medicine (General) R5-920 Ji Soo Kim Ami A. Shah Laura K. Hummers Scott L. Zeger Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
description	Abstract Background Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients. Methods We use a Bayesian mixed model approach to simultaneously characterize each individual’s future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model. Results The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual’s risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient’s visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR). Conclusions This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.
format	article
author	Ji Soo Kim Ami A. Shah Laura K. Hummers Scott L. Zeger
author_facet	Ji Soo Kim Ami A. Shah Laura K. Hummers Scott L. Zeger
author_sort	Ji Soo Kim
title	Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
title_short	Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
title_full	Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
title_fullStr	Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
title_full_unstemmed	Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma
title_sort	predicting clinical events using bayesian multivariate linear mixed models with application to scleroderma
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4e8342060869466793bee7e2250f66f5
work_keys_str_mv	AT jisookim predictingclinicaleventsusingbayesianmultivariatelinearmixedmodelswithapplicationtoscleroderma AT amiashah predictingclinicaleventsusingbayesianmultivariatelinearmixedmodelswithapplicationtoscleroderma AT laurakhummers predictingclinicaleventsusingbayesianmultivariatelinearmixedmodelswithapplicationtoscleroderma AT scottlzeger predictingclinicaleventsusingbayesianmultivariatelinearmixedmodelswithapplicationtoscleroderma
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Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma

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