%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 8
%N 10
%P e13567
%T How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach
%A Bhavnani,Suresh K
%A Dang,Bryant
%A Penton,Rebekah
%A Visweswaran,Shyam
%A Bassler,Kevin E
%A Chen,Tianlong
%A Raji,Mukaila
%A Divekar,Rohit
%A Zuhour,Raed
%A Karmarkar,Amol
%A Kuo,Yong-Fang
%A Ottenbacher,Kenneth J
%+ Preventive Medicine and Population Health, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555-0129, United States, 1 409 772 1928, subhavna@utmb.edu
%K unplanned hospital readmission
%K visual analytics
%K bipartite networks
%K precision medicine
%D 2020
%7 26.10.2020
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. Results: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. Conclusions: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups. 
%M 33103657
%R 10.2196/13567
%U https://medinform.jmir.org/2020/10/e13567
%U https://doi.org/10.2196/13567
%U http://www.ncbi.nlm.nih.gov/pubmed/33103657