Machine Learning to Predict Mortality and Improve End-of-Life Outcomes among Minorities with Advanced Cancer

机器学习预测死亡率并改善少数晚期癌症患者的临终结局

• 批准号: 10521902
• 负责人: Cardinale B Smith
• 金额: $ 30万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10521902
• 关键词: Address; Advanced Malignant Neoplasm; Algorithms; Attitude; Belief; Cancer Patient; Caring; Cessation of life; Characteristics; Clinical Decision Support Systems; Data; Development; Discrimination; Disease; Documentation; Educational workshop; Electronic Health Record; Emergency department visit; Enrollment; Ethnic Origin; Feedback; Focus Groups; Goals; Health; Health system; Healthcare; Hospitalization; Incidence; Inferior; Intervention; Intervention Studies; Interview; Life; Life Expectancy; Link; Machine Learning; Malignant Neoplasms; Minority; Modality; Modeling; Multicenter Trials; Nurses; Oncologist; Oncology; Outcome; Patient-Focused Outcomes; Patients; Perception; Persons; Physicians; Predictive Value; Procedures; Process; Prognosis; Provider; Race; Randomized Controlled Trials; Recommendation; Reporting; Research; Resources; Safety; Social Workers; Solid; Solid Neoplasm; Strategic Planning; System; Time; Uncertainty; base; cancer care; clinical decision-making; clinical implementation; data registry; data tools; design; disparity reduction; end of life; experience; health care service utilization; high risk; hospice environment; improved; improved outcome; individual patient; machine learning algorithm; machine learning model; machine learning prediction; minority patient; mortality; mortality risk; neoplasm registry; predictive modeling; predictive tools; prognostic; prognostic model; prognostication; racial bias; standardized care; tool

PROJECT SUMMARY/ABSTRACT Multiple studies show that minority patients with advanced cancer have inadequate discussions about treatment, prognosis and goals of care which contributes to higher utilization of health care among minorities at the end-of-life. A primary contributor to the low rates of prognosis and goals of care discussions relates to oncologists inability to accurately predict mortality. Clinical decision support systems (CDSS) are designed to directly aid clinical decision making by utilizing individual patient characteristics to generate patient-specific assessments. Limited studies indicate CDSS can reduce disparities in process of care and care standardization. However, existing tools do not identify patients at highest risk of mortality, have not been linked to patient outcomes and have not been routinely evaluated in minority patients. Machine learning (ML) predictive models allow more accurate prognoses by modeling patient and disease- specific interactions and has the potential to obviate the racial bias that exists in the use of oncologist- specific prognostication. ML models utilizing electronic health record (EHR) data can accurately predict short-term mortality among oncology patients. However, little evidence exists that these models assist with clinical decision making or improve outcomes for minority patients with cancer. We will address systemic race/ethnicity-related barriers that contribute to disparities in end-of-life outcomes among minority cancer patients by: 1) Developing and validating a predictive model to identify patients with advanced solid cancers at high risk of death within 90-days; 2) Creating a CDSS system intervention that incorporates mortality predictive tool data to prompt goals of care conversations for solid cancer patients at high risk of mortality within 90 days; and 3) Conducting a stepped-wedge cluster randomized controlled trial to evaluate whether implementing a clinical decision support system for patients with advanced solid cancer at risk of death within 90 days increases goals of care discussions and decreases utilization of aggressive care at the end-of-life among minorities versus non-minorities. The predictive model will be created from cancer registry data linked to the EHR. We will then create a CDSS by conducting focus groups among an interdisciplinary team of oncology clinicians (physicians, advance practice providers, nurses and social workers). Additionally, we will conduct co-design workshops with the oncology clinicians to inform the implementation of the CDSS. Next, we will conduct a stepped-wedge cluster randomized controlled trial to evaluate whether utilization of the CDSS increases goals of care discussions, and decreases healthcare utilization at the end-of-life among minority versus non-minority patients with advanced solid cancer at risk of death within 90 days. Finally, we will perform exit interviews to refine the intervention and study procedures. Findings will inform a larger multi-center trial aimed at implementation of the CDSS among those predicted to have high mortality to improve the end-of-life outcomes of minority patients with cancer.

项目总结/摘要 多项研究表明，少数晚期癌症患者没有充分讨论 治疗、预后和护理目标，这有助于提高 少数民族在生命的尽头预后和护理目标低的主要原因 讨论涉及肿瘤学家不能准确预测死亡率。临床决策支持系统 （CDSS）旨在通过利用个体患者特征直接辅助临床决策， 生成患者特异性评估。有限的研究表明，CDSS可以减少过程中的差距， 护理标准化。然而，现有的工具不能识别死亡风险最高的患者， 与患者结局无关，也未在少数患者中进行常规评价。 机器学习（ML）预测模型通过对患者和疾病进行建模， 具体的相互作用，并有可能消除种族偏见，存在于使用肿瘤学家- 具体说明。利用电子健康记录（EHR）数据的ML模型可以准确地预测 肿瘤患者的短期死亡率。然而，几乎没有证据表明这些模型有助于 临床决策或改善少数癌症患者的预后。我们将系统地 导致少数癌症患者临终结局差异的种族/族裔相关障碍 1）开发和验证预测模型以识别患有晚期实体癌的患者 90天内死亡的高风险; 2）创建CDSS系统干预， 预测工具数据，以提示高死亡风险实体癌患者的护理对话目标 90天内;和3）进行阶梯楔形随机对照试验，以评估 是否实施临床决策支持系统的晚期实体癌患者的风险， 90天内死亡增加了护理讨论的目标，减少了对积极护理的利用， 少数群体与非少数群体之间的寿命结束。预测模型将根据癌症创建 登记数据链接到EHR。然后，我们将通过在一个 肿瘤学临床医生的跨学科团队（医生，高级实践提供者，护士和社会 工人）。此外，我们将与肿瘤临床医生共同举办设计研讨会， 实施CDSS。接下来，我们将进行一项阶梯楔形分组随机对照试验， 评估CDSS的使用是否增加了护理讨论的目标， 少数群体与非少数群体晚期实体癌高危患者临终时的利用率 90天内死亡。最后，我们将进行离职面谈，以完善干预和研究 程序.研究结果将为一项更大规模的多中心试验提供信息，该试验旨在实施CDSS， 那些预计死亡率高的人，以改善少数癌症患者的临终结局。