Predicting severe diabetes complications using administrative claims data in Maryland.
1/5 보강
[OBJECTIVE] To describe operations and performance of a large-scale predictive model of severe type 2 diabetes complications (DC) for Medicare beneficiaries in Maryland.
APA
Goetschius L, Barefoot D, et al. (2026). Predicting severe diabetes complications using administrative claims data in Maryland.. The American journal of managed care, 32(3), e66-e70. https://doi.org/10.37765/ajmc.2026.89898
MLA
Goetschius L, et al.. "Predicting severe diabetes complications using administrative claims data in Maryland.." The American journal of managed care, vol. 32, no. 3, 2026, pp. e66-e70.
PMID
41886341
Abstract
[OBJECTIVE] To describe operations and performance of a large-scale predictive model of severe type 2 diabetes complications (DC) for Medicare beneficiaries in Maryland.
[STUDY DESIGN] Retrospective longitudinal multivariable regression.
[METHODS] Using Medicare fee-for-service (FFS) claims from March 2021 to July 2024, we created an analytic data set of 219 candidate risk factors spanning 12,611,063 person-months. Multivariable discrete-time survival modeling was used to assess the relation between risk factors and the risk of incurring a future hospitalization due to severe DC. Using stepwise variable selection to retain only statistically significant risk factors, DC risk scores were created by applying training coefficients to risk factors from the most recently available month of data. Risk scores for 346,614 individuals were released on June 7, 2024. Predictive performance of scores was assessed using actual events in the month following the release of the scores, compared with the performance of Hierarchical Condition Category (HCC) scores.
[RESULTS] As of April 2024, the model retained 95 significant risk factors. The mean risk score was 0.0124, and utilization- and condition-based risk factors primarily accounted for the top 15 risk factors. The June 2024 risk scores were strongly predictive of true events: Individuals with the top 10% of DC risk scores accounted for 56.9% of severe DC events in the following month. This significantly outperformed HCC scores, which accounted for 37.5% of true events in the same period.
[CONCLUSIONS] A risk prediction model based on administrative claims can predict severe DC events for the Medicare FFS population in Maryland.
[STUDY DESIGN] Retrospective longitudinal multivariable regression.
[METHODS] Using Medicare fee-for-service (FFS) claims from March 2021 to July 2024, we created an analytic data set of 219 candidate risk factors spanning 12,611,063 person-months. Multivariable discrete-time survival modeling was used to assess the relation between risk factors and the risk of incurring a future hospitalization due to severe DC. Using stepwise variable selection to retain only statistically significant risk factors, DC risk scores were created by applying training coefficients to risk factors from the most recently available month of data. Risk scores for 346,614 individuals were released on June 7, 2024. Predictive performance of scores was assessed using actual events in the month following the release of the scores, compared with the performance of Hierarchical Condition Category (HCC) scores.
[RESULTS] As of April 2024, the model retained 95 significant risk factors. The mean risk score was 0.0124, and utilization- and condition-based risk factors primarily accounted for the top 15 risk factors. The June 2024 risk scores were strongly predictive of true events: Individuals with the top 10% of DC risk scores accounted for 56.9% of severe DC events in the following month. This significantly outperformed HCC scores, which accounted for 37.5% of true events in the same period.
[CONCLUSIONS] A risk prediction model based on administrative claims can predict severe DC events for the Medicare FFS population in Maryland.