Bayesian networks to identify potential high-risk multimorbidity and intervention clusters in inpatients: an explorative data mining study.
Roth JA., Sakoparnig T., Gerber M., Hug BL.
AIMS OF THE STUDY: Based on large sets of routine hospital data from inpatient cases, we aimed to explore multimorbidity and intervention clusters showing high risks for in-hospital mortality and unplanned readmissions using data-driven analytical methods. METHODS: We performed an explorative, historical cohort study of consecutive inpatient cases at a tertiary care centre with an integrated platform for routine healthcare data in Switzerland. From January 2012 through to December 2017, all inpatients aged ≥18 years at hospital admission were eligible for study inclusion. We predefined all-cause in-hospital death and unplanned hospital readmission as co-primary outcomes. In a first step, we explored and visualised multimorbidity and intervention clusters using mutual information analysis. In a subsequent step, we trained multi-layer Bayesian networks to identify clusters associated with in-hospital death and/or unplanned hospital readmission. RESULTS: Among 190,837 inpatient cases, 7994 unique diagnoses and 6639 interventions were routinely recorded during the six-year study period. Based on the mutual information analysis, we identified 32 multimorbidity clusters and 24 intervention clusters – of which several were directly related to in-hospital mortality and/or unplanned readmission in the subsequent Bayesian network analysis. CONCLUSIONS: Bayesian network analysis may be used as a tool to mine large healthcare databases in order to explore intervention targets for quality improvement programmes. However, the resulting associations should be substantiated in consecutive investigations using specific causal models. (Trial registration no EKNZ 2016-02128.).