Duke University School of Medicine, Durham, North Carolina.
Atrium Health Levine Cancer Institute, Concord, North Carolina.
J Pain Symptom Manage. 2024 Dec;68(6):539-547.e3. doi: 10.1016/j.jpainsymman.2024.08.036. Epub 2024 Sep 3.
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.
预后预测挑战导致接近生命终末期（EOL）的癌症患者的提前护理计划（ACP）延迟。
检查质量改进死亡率预测算法干预对 ACP 文件记录和 EOL 护理的影响。
我们在杜克大学医院的专门实体恶性肿瘤病房中实施了一种针对从急诊室（ED）入院的实体恶性肿瘤患者的验证死亡率风险预测机器学习模型。当患者被确定为高风险时，临床医生会收到一封电子邮件。我们比较了通知干预前后的 ACP 文件记录和 EOL 护理结果。我们排除了在头 24 小时内入住重症监护病房（ICU）的患者。比较涉及卡方/Fisher 精确检验和 Wilcoxon 秩和检验；按医生专业分层的比较采用 Cochran-Mantel-Haenszel 检验。
干预前和干预后队列分别包括 88 例和 77 例患者。大多数是白人、非西班牙裔/拉丁裔和已婚。干预前住院期间记录的 ACP 对话占 2.3%，而干预后为 80.5%（P<0.001），如果主治医生是姑息治疗专家（4.1% vs. 84.6%）或肿瘤学家（0% vs. 76.3%）通知（P<0.001）。两组之间的住院时间（LOS）、临终关怀转介、代码状态变更、ICU 入院或 LOS、30 天再入院、30 天 ED 就诊、住院和 30 天死亡均无差异。
通过机器学习识别患有癌症和高死亡率风险的患者大大增加了记录的 ACP 对话，但对 EOL 护理没有影响。我们的干预措施在改变临床医生行为方面显示出了希望。该模型在临床实践中的进一步整合仍在进行中。
