Unplanned hospitalization among advanced prostate cancer patients by diabetes status—a population-based study
Menée à partir de données des registres américains des cancers et de la base Medicare portant sur 12 240 patients atteints d'un cancer de la prostate de stade avancé, cette étude analyse le risque d'hospitalisation non programmée en fonction de la présence d'un diabète et d'une utilisation d'inhibiteurs des récepteurs aux androgènes
Background: Older adults with advanced prostate cancer (PCa) and type 2 diabetes mellitus (T2DM) are under-represented in trials of androgen receptor pathway inhibitors (ARPIs). This study examined changes in unplanned hospitalization rates in patients receiving ARPIs by T2DM status and assessed if unplanned hospitalization varies according to ARPI.
Methods: This population-based study of PCa patients over age 66 utilized SEER-Medicare data. Pre-post ARPI initiation changes and ARPI differences in unplanned hospitalization rates were estimated by adjusted incidence rate ratios (aIRR) with considerations for interactions between period, ARPI, and T2DM status. Linear contrasts were used to estimate and test conditional aIRRs. Tests were two-sided and p < .05 was considered statistically significant.
Results: The study included 12,240 patients: 3,160 with T2DM (25.8%), 7,191 (58.8%) received AAP and 5,049 (41.2%) ENZA. Unplanned hospitalization rates increased after ARPI initiation by 65% among patients with T2DM complications (aIRR 1.65; 95% CI 1.37, 1.98) and 109% in non-diabetics (aIRR 2.09; 95% CI 1.94, 2.26). Among patients with T2DM without complications, the increase in unplanned hospitalization rates depended on the ARPI initiated: 103% after AAP (aIRR 2.03; 95% CI 1.70, 2.43) and 47% after ENZA (aIRR 1.47; 95% CI 1.21, 1.80), a 38% greater increase in unplanned hospitalization rates after AAP than ENZA (ratio of aIRRAAP/aIRRENZA 1.38; 95% CI 1.06, 1.80).
Conclusions: All patients had higher unplanned hospitalization rates after ARPI. Our findings highlight the importance of using real-world data to better understand the interplay between pre-existing health conditions and treatment outcomes, a critical step toward precision medicine.
JNCI Cancer Spectrum , résumé, 2025