TY  - JOUR
AU  - Bhavnani, Suresh K
AU  - Dang, Bryant
AU  - Penton, Rebekah
AU  - Visweswaran, Shyam
AU  - Bassler, Kevin E
AU  - Chen, Tianlong
AU  - Raji, Mukaila
AU  - Divekar, Rohit
AU  - Zuhour, Raed
AU  - Karmarkar, Amol
AU  - Kuo, Yong-Fang
AU  - Ottenbacher, Kenneth J
PY  - 2020
DA  - 2020/10/26
TI  - How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach
JO  - JMIR Med Inform
SP  - e13567
VL  - 8
IS  - 10
KW  - unplanned hospital readmission
KW  - visual analytics
KW  - bipartite networks
KW  - precision medicine
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2020/10/e13567
UR  - https://doi.org/10.2196/13567
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33103657
DO  - 10.2196/13567
ID  - info:doi/10.2196/13567
ER  -