Al-Droubi Samer S, Jahangir Eiman, Kochendorfer Karl M, Krive Marianna, Laufer-Perl Michal, Gilon Dan, Okwuosa Tochukwu M, Gans Christopher P, Arnold Joshua H, Bhaskar Shakthi T, Yasin Hesham A, Krive Jacob
Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN 37232, USA.
Department of Health Informatics at Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, 3200 South University Drive, Fort Lauderdale, FL 33328-2018, USA.
Eur Heart J Digit Health. 2023 May 8;4(4):302-315. doi: 10.1093/ehjdh/ztad031. eCollection 2023 Aug.
There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.
De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals.
Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.
肿瘤学家目前没有使用全面的机器学习（ML）工具来协助进行风险识别以及转介至心脏肿瘤学领域。本研究应用ML算法来识别有心血管疾病风险的肿瘤患者，以便将其转介至心脏肿瘤学领域，并生成风险评分以支持医疗质量。
从范德比尔特大学医学中心获取了去识别化的患者数据。目标患者包括患有乳腺癌、肾癌和B细胞淋巴瘤的患者。此外，该研究还纳入了接受免疫治疗药物治疗黑色素瘤、肺癌或肾癌的患者。应用随机森林（RF）和人工神经网络（ANN）ML模型对每个队列进行分析：共分析了20023条记录（乳腺癌6299条；B细胞淋巴瘤9227条；肾癌2047条；以及三种涵盖癌症的免疫治疗患者2450条）。数据被随机分为训练集（80%）和测试集（20%）。随机森林和人工神经网络的准确率和曲线下面积（AUC）均超过90%。所有人工神经网络模型的表现均优于随机森林模型，并能做出准确的转介。
预测模型已准备好转化为肿瘤学实践，以识别和护理有心血管疾病风险的患者。这些模型正在与电子健康记录应用程序集成，作为应转介至心脏肿瘤学领域进行监测和/或定制治疗的患者报告。模型在操作上支持心脏肿瘤学实践。有限的验证发现，86%的淋巴瘤患者和58%的肾癌患者存在心脏毒性的重大风险，但未被转介至心脏肿瘤学领域。
