JMIR Publications

ABSTRACT

Background:

When elderly patients with hip-fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their one-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to the HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of HFx patients.

Objective:

Our objectives in this study was to use a combination of supervised and unsupervised visual analytical methods to (1) get 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 dataset consisting of 16,886 patients (8443 readmitted HFx patients, 8443 matched controls), and a replication dataset consisting of 16,222 patients (8111 readmitted HFx patients, 8111 matched controls) from the 2010 and 2009 Medicare database respectively. The analyses consisted of: (1) supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, (2) unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted HFx patients, and (3) integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders infer the processes that precipitate readmission in patient subgroups, and to propose targeted interventions.

Results:

The analyses helped to identify (1) eleven comorbidity combinations that conferred significantly higher risk (P = .00–.01, after FDR correction) for 30-day readmission, (2) seven biclusters of patients and comorbidities with significant bicluster modularity (P < .001, Medicare = 0.444, Random-Mean = 0.379) indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter and intra-cluster 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 comorbidity risk, comorbidity co-occurrence, and patient subgroups was useful for enabling a team of clinical and methodological stakeholders to infer the processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after hip fracture. These results have direct implications for (1) the management of comorbidities targeted to high-risk subgroups of patients with the goal of preemptively reducing their risk of readmission, and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.