Cardiovascular disease is the leading cause of mortality among breast cancer (BC) patients aged 50 and above. Machine Learning (ML) models are increasingly utilized as prediction tools, and recent evidence suggests that incorporating social determinants of health (SDOH) data can enhance its performance. This study included females ≥ 18 years diagnosed with BC at any stage. The outcomes were the diagnosis and time-to-event of major adverse cardiovascular events (MACEs) within two years following a cancer diagnosis. Covariates encompassed demographics, risk factors, individual and neighborhood-level SDOH, tumor characteristics, and BC treatment. Race-specific and race-agnostic Extreme Gradient Boosting ML models with and without SDOH data were developed and compared based on their C-index. Among 4309 patients, 11.4% experienced a 2-year MACE. The race-agnostic models exhibited a C-index of 0.78 (95% CI 0.76–0.79) and 0.81 (95% CI 0.80–0.82) without and with SDOH data, respectively. In non-Hispanic Black women (NHB;n= 765), models without and with SDOH data achieved a C-index of 0.74 (95% CI 0.72–0.76) and 0.75 (95% CI 0.73–0.78), respectively. Among non-Hispanic White women (n= 3321), models without and with SDOH data yielded a C-index of 0.79 (95% CI 0.77–0.80) and 0.79 (95% CI 0.77–0.80), respectively. In summary, including SDOH data improves the predictive performance of ML models in forecasting 2-year MACE among BC females, particularly within NHB.
心血管疾病是50岁及以上乳腺癌患者的主要死亡原因。机器学习模型日益被用作预测工具，近期证据表明纳入健康社会决定因素数据可提升其预测效能。本研究纳入年龄≥18岁、任何分期的女性乳腺癌患者。研究终点为癌症诊断后两年内主要不良心血管事件的诊断及发生时间。协变量涵盖人口统计学特征、风险因素、个体及社区层面的健康社会决定因素、肿瘤特征及乳腺癌治疗方案。研究构建了包含与不包含健康社会决定因素数据的种族特异性及种族无关极限梯度提升机器学习模型，并通过C指数进行比较。在4309例患者中，11.4%发生2年内主要不良心血管事件。种族无关模型在不包含与包含健康社会决定因素数据时的C指数分别为0.78（95% CI 0.76–0.79）和0.81（95% CI 0.80–0.82）。在非西班牙裔黑人女性中（n=765），不包含与包含健康社会决定因素数据的模型C指数分别为0.74（95% CI 0.72–0.76）和0.75（95% CI 0.73–0.78）。在非西班牙裔白人女性中（n=3321），不包含与包含健康社会决定因素数据的模型C指数均为0.79（95% CI 0.77–0.80）。综上所述，纳入健康社会决定因素数据可提升机器学习模型预测女性乳腺癌患者2年内主要不良心血管事件的效能，这一提升在非西班牙裔黑人群体中尤为显著。
肿瘤(癌症)患者之家