心不全の分析：患者の結果に関する新しい視点

タイトル: A Multi-State Markov Model for the Longitudinal Analysis of Clinical Composite Outcomes in Heart Failure

概要: BackgroundThe statistical analysis of composite outcomes is challenging. The Clinical Outcomes, HEalthcare REsource utilizatioN, and relaTed costs (COHERENT) model was developed to describe and compare all components (incidence, timing and duration) of composite outcomes, but its statistical analysis remained unsolved. The aim of the study is to assess a multi-State Markov model as one statistical solution for the COHERENT model. MethodsA cohort of 3280 patients admitted to the emergency department or hospital for heart failure during year 2018 were followed during one year. The state of the patient was registered at the end of each day during 365 days as: home, emergency department (ED), hospital, re-hospital, re-ED, and death. Outcomes of patients with or without severe renal disease (sRD) were compared as an example. A Multi-State Markov model was developed to explain transitions to and from these states during follow-up. ResultsA Multi-State Markov model showed, adjusted for age and sex, a significantly lower likelihood of patients with sRD to return home regardless of the state in which they were (ED [->] HOME (HR, 0.72; 95%CI, 0.54-0.95), RE-ED [->] HOME (HR, 0.83; 95%CI, 0.75-0.93), HOSPITAL [->] HOME (HR, 0.77; 95%CI, 0.69-0.86), RE-HOSPITAL [->] HOME (HR, 0.82; 95%CI, 0.74-0.92) and a higher mortality risk, in particular at the hospital and at home (HOME [->] Death [HR, 1.54; 95%CI, 1.01-2.37] and HOSPITAL [->] Death [HR, 1.71; 95%CI, 1.30-2.24]. ConclusionMulti-state Markov models offer a statistical solution for the comprehensive analysis of composite outcomes assessed as transitions from different clinical states. Clinical PerspectiveO_LIWhat is new? O_LIAn integrated analysis of all components of composite endpoints including its incidence and duration is possible using the COHERENT model with analysis of transition risks. C_LIO_LIA statistical approach based on Markov chain models is a new potential statistical solution for the multivariate estimation of the risk of transitions in mutually exclusive composite endpoints. C_LI C_LIO_LIWhat are the clinical implications? O_LIThe use of the COHERENT model and Markov models is an opportunity to analyze composite endpoints and understand better the relationships between its components and, potentially, to improve the performance of statistical analysis in randomized controlled trials. C_LIO_LIThe utilization of the COHERENT model and Markov models in randomized controlled trials should be validated in future observational studies and in randomized controlled trials. C_LI C_LI