A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

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. Although 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: This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information.

Methods: The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility.

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), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement.

Conclusions: Although 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 intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of 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.

Keywords: Bipartite Network analysis; Medicare; hospital readmission; modeling; precision medicine; visual analytics.

Figure 1

Inputs and outputs for the 3-step modeling in MIPS consisting of the visual analytical model, classification model, and prediction model. MIPS: Modeling and Interpreting Patient Subgroups.

Figure 2

The chronic obstructive pulmonary disease (COPD) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently co-occurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk of readmission (shown in blue text). GI: Gastrointestinal disorders; HD: Heart disease; MV: History of mechanical ventilation.

Figure 3

The congestive heart failure (CHF) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently co-occurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk of readmission (shown in blue text). CABG: History of coronary artery bypass graft surgery; COPD: Chronic obstructive pulmonary disease; GI: Gastrointestinal disorders; HD: Heart disease.

Figure 4

The total hip arthroplasty/total knee arthroplasty (THA/TKA) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently co-occurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk for readmission (shown in blue text). CHF: Congestive heart failure; COPD: Chronic obstructive pulmonary disease; OB: Obesity.

Figure 5

Predictive accuracy of the Standard Model compared with the Hierarchical Model in chronic obstructive pulmonary disease (COPD), as measured by the C-statistic. The C-statistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup-1 has lower accuracy than Subgroup-3 and Subgroup-4. (C-statistics in A and B cannot be compared, as they are based on models from different populations).

Figure 6

(A) Predictive accuracy of the Standard Model compared with the Hierarchical Model in congestive heart failure (CHF) as measured by the C-statistic. The C-statistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup-1 has lower accuracy than Subgroup-3 and Subgroup-4. (C-statistics in A and B cannot be compared, as they are based on models from different populations).

Figure 7

(A) Predictive accuracy of the Standard Model compared with the Hierarchical Model in total hip arthroplasty/total knee arthroplasty (THA/TKA) as measured by the C-statistic. The C-statistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup-1 has lower accuracy than Subgroup-7. (C-statistics in A and B cannot be compared, as they are based on models developed from different populations).