TY - JOUR
T1 - How high-risk comorbidities co-occur in readmitted patients with hip fracture
T2 - Big data visual analytical approach
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
N1 - Funding Information:
The authors thank James Goodwin, Allan Brasier, and Gautam Vallabha for their support and feedback. Funding for SB was provided in part by the Clinical and Translational Science Award (UL1 TR001439) from the NCATS, National Institutes of Health, the PCORI (ME-1511-33194), and the UTMB Claude D Pepper Older Americans Independence Center funded by NIA, National Institutes of Health (P30 AG024832); funding for SV was provided in part by the NLM, National Institutes of Health (R01 LM012095); funding for KB was provided in part by the National Science Foundation (DMR-1507371 and IOS-1546858); funding for MR and YK was provided in part by the NIDA, National Institutes of Health (R01 DA039192); funding for KO was provided in part by the NIA, National Institutes of Health (K07 AG064031), and by the CLDR, National Institutes of Health (P2CHD065702). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, PCORI, or NSF.
Funding Information:
R (supported by the R Foundation for Statistical Computing) packages and functions used for the analyses. [DOCX File , 13 KB-Multimedia Appendix 4]
Publisher Copyright:
©Suresh K Bhavnani, Bryant Dang, Rebekah Penton, Shyam Visweswaran, Kevin E Bassler, Tianlong Chen, Mukaila Raji, Rohit Divekar, Raed Zuhour, Amol Karmarkar, Yong-Fang Kuo, Kenneth J Ottenbacher.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
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.
KW - Bipartite networks
KW - Precision medicine
KW - Unplanned hospital readmission
KW - Visual analytics
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U2 - 10.2196/13567
DO - 10.2196/13567
M3 - Article
AN - SCOPUS:85097452529
VL - 8
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
SN - 2291-9694
IS - 10
M1 - e13567
ER -