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Identifying Bariatric Surgery Patients With the Most Favorable Cost Outcomes
Jie Gao1, Valerie A Smith1,2,3, Matthew L Maciejewski1,2,4,5
1Department of Population Health Sciences, Duke University, Durham, North Carolina, USA.
Objective:
To examine whether the economic benefits of bariatric surgery differ by patient subgroups, with the aim of identifying those that may yield the most favorable cost profile for improved return on investment in an exploratory analysis.
Study Setting And Design:
To identify patient subgroups via the "mTree" (matching + decision tree) method, we conducted analyses of total expenditures 3 years after surgery in a retrospective cohort of 16,538 bariatric patients and 16,538 matched non-surgical patients.
Data Sources And Analytic Sample:
This study used electronic health records from Kaiser Permanente, an integrated health system, from 1/1/2012 to 12/31/2019. The cohort was randomly divided into training and test samples, and differences in median total expenditures within each subgroup were then estimated in a held-out test sample. We adapted a novel causal machine learning method mTree, previously developed for randomized trial data, to characterize heterogeneous treatment effects of bariatric surgery on total healthcare expenditures in a non-randomized observational study. This approach combines pair-matching and conditional inference trees to identify subgroups of patients with differential treatment effects while maintaining within-subgroup balance on important confounders.
Principal Findings:
Significant heterogeneity in the effect of bariatric surgery on total expenditures was observed across the eight identified patient subgroups, which were masked by a null average treatment effect. Five of eight subgroups identified in the training sample were replicated in the test sample, and patients using insulin with a Gagne score ≤ 4 had some of the greatest post-surgical expenditure reductions (-$4311, 95% CI: [-$6154, -$2504]).
Conclusions:
Subgroup identification is critical for providing context to average treatment effects by identifying patients who may generate a more (or less) promising return on investment. Patients from subgroups with more favorable post-surgical cost profiles may inform prioritization for bariatric surgery if these subgroups are validated in independent cohorts.
