PDF(Pubmed)
Abstract：
OBJECTIVE: While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient\'s mortality risk.
METHODS: This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.
RESULTS: Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model\'s estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.
CONCLUSIONS: The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.
METHODS: This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.
RESULTS: Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model\'s estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.
CONCLUSIONS: The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.
摘要：
目标：虽然临床实践指南建议肿瘤学家讨论晚期癌症患者的治疗目标，据估计，住院的高危癌症患者中，只有不到20%的人与医疗服务提供者进行了临终讨论。虽然人们一直有兴趣开发死亡率预测模型来引发这样的讨论，很少有研究将这些模型与临床判断进行比较，以确定患者的死亡风险。
方法：本研究是对2022年2月7日至6月7日在纪念斯隆·凯特琳癌症中心的1,069例实体瘤内科肿瘤科住院患者（n=911例患者）的前瞻性分析。电子调查被送到医院，高级实践提供商，和医学肿瘤学家入院后的第一个下午，他们被要求估计患者在45天内死亡的可能性。将提供者对死亡率的估计与使用监督机器学习方法开发的预测模型进行了比较，并合并了常规实验室，人口统计学,生物识别，和录取数据。接收器工作特性曲线下面积(AUC),在临床医生估计值和模型预测值之间比较校准曲线和决策曲线.
结果：入院后45天内，911例患者中有229例（25%）死亡。该模型的性能优于临床医生的估计(AUC0.834vs.0.753，p<0.0001）。将临床医生的预测与模型的估计值相结合，进一步将AUC增加到0.853（p<0.0001）。临床医生高估了风险，而模型却经过了很好的校准。该模型证明了在广泛的阈值概率上的净收益。
结论：入院时的住院患者预后模型是协助临床提供者评估死亡风险的有力工具，最近已在我们机构的电子病历中实施，以改善住院癌症患者的临终护理计划。
方法：本研究是对2022年2月7日至6月7日在纪念斯隆·凯特琳癌症中心的1,069例实体瘤内科肿瘤科住院患者（n=911例患者）的前瞻性分析。电子调查被送到医院，高级实践提供商，和医学肿瘤学家入院后的第一个下午，他们被要求估计患者在45天内死亡的可能性。将提供者对死亡率的估计与使用监督机器学习方法开发的预测模型进行了比较，并合并了常规实验室，人口统计学,生物识别，和录取数据。接收器工作特性曲线下面积(AUC),在临床医生估计值和模型预测值之间比较校准曲线和决策曲线.
结果：入院后45天内，911例患者中有229例（25%）死亡。该模型的性能优于临床医生的估计(AUC0.834vs.0.753，p<0.0001）。将临床医生的预测与模型的估计值相结合，进一步将AUC增加到0.853（p<0.0001）。临床医生高估了风险，而模型却经过了很好的校准。该模型证明了在广泛的阈值概率上的净收益。
结论：入院时的住院患者预后模型是协助临床提供者评估死亡风险的有力工具，最近已在我们机构的电子病历中实施，以改善住院癌症患者的临终护理计划。
