JMIR Med Inform. 2022 May 2. doi: 10.2196/37239. Online ahead of print.
ABSTRACT
BACKGROUND: A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes, with the aim of designing targeted interventions. However, while several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups, and their modeling and interpretation for clinical applications.
OBJECTIVE: To develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a three-step modeling approach. (1) Visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities, and determine their statistical significance and clinical interpretability. (2) Classification modeling to classify patients into subgroups and measure its accuracy. (3) Prediction modeling to predict a patient’s risk for an adverse outcome, and compare its accuracy with and without patient subgroup information.
METHODS: The MIPS framework was developed using (1) bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities; (2) multinomial logistic regression to classify patients into subgroups; and (3) hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership, compared to standard logistic regression without subgroup membership. The MIPS framework was evaluated on three hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip/knee arthroplasty (THA/TKA). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge, and controls defined as patients not readmitted within 90 days of discharge, matched by age, gender, race, and Medicaid eligibility (n[COPD]=29,016, n[CHF]=51,550, n[THA/TKA]=16,498).
RESULTS: In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), were significantly replicated (RI=0.92, 0.94, 0.89; P<.001, <.001, <.01), and were clinically meaningful to clinicians. (2) In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.60%, 99.34%, 99.86%). (3) In two conditions (COPD, THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between the readmitted and not readmitted patients as measured by net reclassification improvement (NRI=.059, .11), but not as measured by the C-statistic or integrated discrimination improvement (IDI).
CONCLUSIONS: While the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and inter-cluster associations. The high accuracy of the classification models reflects the strong separation of the patient subgroups despite the size and density of the datasets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors for hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission and beyond.
PMID:35537203 | DOI:10.2196/37239