Risk assessment and predictive modeling of suicide in multiple myeloma patients.

Abstract

Purpose: Despite advancements in treatment that have extended survival, multiple myeloma (MM) remains a distressing diagnosis with significant health impacts, including an elevated risk of suicide. This study aims to investigate suicide risk among MM patients and develop a predictive model to identify high-risk individuals.

Methods: We analyzed 83,333 MM cases from the latest Surveillance, Epidemiology, and End Results (SEER) database (2001-2020) to identify suicide risk predictors and develop prediction nomograms. The cohort was randomly allocated into training and validation groups. Validation included assessing the consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve.

Results: Among the cohort, 89 MM patients died by suicide, reflecting a significantly higher rate compared to the general US population (SMR = 2.186). Key risk factors included household income ≤ $50,000 (SMR = 3.82), male sex (SMR = 3.68), and age ≥ 80 years at diagnosis (SMR = 3.05). Additional predictors were unmarried status, Black race, and diagnosis post-2007. The nomogram incorporating these factors demonstrated strong predictive accuracy in both training and validation groups.

Conclusion: This study identified critical suicide risk factors in MM patients and developed a predictive nomogram that aids physicians in the early identification of at-risk individuals, facilitating more effective preventive measures.

Implications for cancer survivors: Utilizing the factors and predictive model for suicide risk among MM survivors allows for earlier identification and intervention, significantly enhancing their quality of life and psychological relief in the context of improved MM survival rates.