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2019 HSR&D/QUERI National Conference Abstract

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1073 — Hospitalization, Re-Hospitalization, Mortality, and Utilization Patterns Predicted by Novel High-Risk Patient Latent Subgroups and Complexity Scores

Lead/Presenter: Xinhua Zhao,  COIN - Pittsburgh/Philadelphia
All Authors: Zhao X (Center for Health Equity Research & Promotion, Pittsburgh, PA), Vijan S (Center for Clinical Management Research, Ann Arbor, MI), Maciejewski M (Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC) Zulman D (Center for Innovation to Implementation, Palo Alto, CA) Thorpe J (Center for Health Equity Research & Promotion, Pittsburgh, PA) Batten A (Clinical Systems Development & Evaluation, Veterans Health Administration, Seattle, WA) Zhang H (Center for Health Equity Research & Promotion, Pittsburgh, PA) Daniels K (Center for Health Equity Research & Promotion, Pittsburgh, PA) Rosland AM (Center for Health Equity Research & Promotion, Pittsburgh, PA)

The ability to define latent groupings among high-risk complex patients has been demonstrated, but these groups' relevance to tailored interventions depends on their ability to predict distinct health outcomes and utilization patterns over time.

We used Mixture Item Response Theory (MixIRT) to define 6 consistent clusters of chronic comorbidities and group-specific patient theta (‘complexity') scores among 934,787 high-risk VA primary care patients. We included 934,787 patients whose predicted probability of hospitalization over 12 months was > = 25% during any week in 2014. We used regression models to assess the associations between subgroup membership and patient theta scores with subsequent rates of all-cause mortality, all-cause acute hospitalizations, and 30-day re-hospitalization, and described VA utilization patterns by group.

81% of patients were matched with a group at a probability of > = 80%. High-risk patient subgroups were characterized by high levels of Substance Use Disorder (SUD, 11% of patients assigned), Cardiometabolic Conditions (CM, 21%), Mental Health Conditions (MH,14%), Pain and Arthritis (PA, 16%), Cancer (13%), and Chronic Liver Disease (12%). The overall rate of one-year mortality was 8% and VA hospitalization was 25%. 17% of hospitalizations resulted in 30-day readmission. Mortality varied significantly among subgroups (SUD reference; AOR (95%CI): Liver 3.2 (3.0, 3.3), Cancer 2.3 (2.2, 2.4), CM 1.6 (1.5, 1.7), PA 1.06 (1.02, 1.11), and MH 0.7 (0.66, 0.73)). One-year hospitalization rates ranged from 61 per 100 patients (Liver) to 31 per 100 patients (MH) (AIRR with SUD = reference: Liver 1.27, Cancer 0.95, CM 0.88, PA 0.75, MH 0.6, all p < 0.001). Among all hospitalizations (n = 253,933), 30-day re-admission rate ranged from Liver 24% to MH 15%; (AIRR Liver 1.1, SUD reference, Cancer 0.99, CM 0.92, PA 0.82, MH 0.73, all p < 0.001 except Cancer p = 0.69). MixIRT derived patient theta scores independently predicted mortality (AOR 1.16, CI 1.15, 1.17), any hospitalization (AOR 1.17, CI 1.16, 1.17), and ?one > = 30-day re-hospitalization (AOR 1.29, CI 1.28, 1.30). Variations in utilization among groups included primary care use (range 5.4 PCP visits/year for MH to 4.2 for SUD, specialty care use (range 13 visits/year for Cancer to 4.8 for SA), and ED visit rate (range 2.7 visits/year for SA to 1.9 for Cancer).

MixIRT models were able to assign high-risk patients to subgroups with distinct hospitalization, re-hospitalization, and utilization profiles, despite coming from a single group previously defined by a homogenously high risk score. High rates of re-hospitalization were seen among groups not typically targeted in re-admission prevention programs (Liver, Substance Use Disorder).

Bundled intervention content and intensity could be tailored to the unique characteristics and risk profiles of each high-risk patient group.