Pituitary · 2026
Importance of clinical profiling to determine excess mortality in prolactinomas: insights from a large, registry-based cohort.
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Shwaish Tarik Ziyad Tarik, Gori Davide, Sartori Claudio, Guaraldi Federica
Аннотация
PURPOSE: Population-based studies overall report no excess mortality among patients with prolactinoma compared with the general population. However, since prolactinomas are clinically heterogeneous, mortality may not be uniform across data-driven patient profiles.
METHODS: We analyzed 3,378 adult prolactinoma cases (26,118 person-years) from the Surveillance, Epidemiology, and End Results database (2004-2022). Standardized mortality ratios (SMRs) were calculated using US life tables matched by age, sex, calendar year, race/ethnicity, and geography. Among 2,481 patients with available tumor size (18,097 person-years), unsupervised clustering (Fuzzy C-Means) based on age, tumor size, and sex defined patient profiles. Excess mortality ratios (EMRs) compared observed-to-expected mortality across profiles, adjusting for race, calendar year, surgery, and radiotherapy.
RESULTS: Prolactinoma was associated with significant excess all-cause mortality (SMR 1.21, 95% CI 1.03-1.41). Two profiles emerged: SAYF (Small and Young, Female-predominant; 63%) and LOOM (Large or Old, Male-predominant; 37%). Among patients with tumor size data, mortality was increased in LOOM (SMR 1.36, 95% CI 1.11-1.65) and reduced in SAYF (SMR 0.48, 95% CI 0.22-0.90), with significant between-profile heterogeneity (EMR 2.92, 95% CI 1.44-5.90; p = 0.003). Results were consistent in sensitivity analyses with imputed data, although the survival advantage in SAYF was attenuated and no longer statistically significant.
CONCLUSION: Prolactinoma captured in a population-based tumor registry was associated with excess all-cause mortality, with heterogeneity across patient profiles. An older, male-predominant profile with larger tumors showed excess mortality, whereas a younger, female-predominant profile with small tumors showed directionally lower mortality. These hypothesis-generating findings warrant further investigation into the underlying biological and contextual factors.
METHODS: We analyzed 3,378 adult prolactinoma cases (26,118 person-years) from the Surveillance, Epidemiology, and End Results database (2004-2022). Standardized mortality ratios (SMRs) were calculated using US life tables matched by age, sex, calendar year, race/ethnicity, and geography. Among 2,481 patients with available tumor size (18,097 person-years), unsupervised clustering (Fuzzy C-Means) based on age, tumor size, and sex defined patient profiles. Excess mortality ratios (EMRs) compared observed-to-expected mortality across profiles, adjusting for race, calendar year, surgery, and radiotherapy.
RESULTS: Prolactinoma was associated with significant excess all-cause mortality (SMR 1.21, 95% CI 1.03-1.41). Two profiles emerged: SAYF (Small and Young, Female-predominant; 63%) and LOOM (Large or Old, Male-predominant; 37%). Among patients with tumor size data, mortality was increased in LOOM (SMR 1.36, 95% CI 1.11-1.65) and reduced in SAYF (SMR 0.48, 95% CI 0.22-0.90), with significant between-profile heterogeneity (EMR 2.92, 95% CI 1.44-5.90; p = 0.003). Results were consistent in sensitivity analyses with imputed data, although the survival advantage in SAYF was attenuated and no longer statistically significant.
CONCLUSION: Prolactinoma captured in a population-based tumor registry was associated with excess all-cause mortality, with heterogeneity across patient profiles. An older, male-predominant profile with larger tumors showed excess mortality, whereas a younger, female-predominant profile with small tumors showed directionally lower mortality. These hypothesis-generating findings warrant further investigation into the underlying biological and contextual factors.