利用机器学习实现癌症患者的靶向提前护理规划：一项质量改进研究

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study

JOURNAL OF PAIN AND SYMPTOM MANAGEMENT

影响因子:3.5

分区:医学2区 / 临床神经病学2区卫生保健与服务2区医学：内科2区

发表日期:2024 Dec

作者: Mihir N Patel, Alexandria Mara, Yvonne Acker, Jamie Gollon, Noppon Setji, Jonathan Walter, Steven Wolf, S Yousuf Zafar, Suresh Balu, Michael Gao, Mark Sendak, David Casarett, Thomas W LeBlanc, Jessica Ma

DOI: 10.1016/j.jpainsymman.2024.08.036

摘要

预后预测的挑战导致癌症患者临终关怀（EOL）提前规划的延误。旨在评估一种基于死亡率预测的机器学习算法干预对ACP文档化和临终关怀的影响。我们在杜克大学医院实施了经过验证的固体恶性肿瘤患者的死亡风险预测模型。临床医师在识别出高风险患者时会收到电子邮件通知。我们比较了干预前后ACP文档化和临终关怀的结果，排除首次24小时内入ICU的患者。采用卡方检验/费舍尔精确检验和Wilcoxon秩和检验进行比较，按医师专长进行Cochran-Mantel-Haenszel检验。前后两个队列分别为88和77名患者。大多数患者为白人、非西班牙裔/拉丁裔且已婚。干预前，住院期间的ACP会话仅占2.3%，干预后升至80.5%（P<0.001）；当通知的主治医生为缓和医疗专科医师（4.1% vs. 84.6%）或肿瘤科医师（0% vs. 76.3%）时，差异更为显著（P<0.001）。两组在住院时间、安宁疗护转诊、代码状态变更、ICU入院及住院时间、30天再入院率、30天急诊访问以及住院和30天死亡率方面无显著差异。通过机器学习识别高风险癌症患者大幅增加了ACP会话的文档记录，但未影响临终关怀的具体措施。该干预显示出改变临床医师行为的潜力，相关模型在临床实践中的进一步整合正在进行中。由Elsevier公司发布。

Abstract

Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involved chi-square/Fisher's exact tests and Wilcoxon rank sum tests; comparisons stratified by physician specialty employ Cochran-Mantel-Haenszel tests.Preintervention and postintervention cohorts comprised 88 and 77 patients, respectively. Most were White, non-Hispanic/Latino, and married. ACP conversations were documented for 2.3% of hospitalizations preintervention vs. 80.5% postintervention (P<0.001), and if the attending physician notified was a palliative care specialist (4.1% vs. 84.6%) or oncologist (0% vs. 76.3%) (P<0.001). There were no differences between groups in length of stay (LOS), hospice referral, code status change, ICU admissions or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths.Identifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.Published by Elsevier Inc.