Cos Heidy, Li Dingwen, Williams Gregory, Chininis Jeffrey, Dai Ruixuan, Zhang Jingwen, Srivastava Rohit, Raper Lacey, Sanford Dominic, Hawkins William, Lu Chenyang, Hammill Chet W
Washington University in St Louis, St Louis, MO, United States.
Barnes-Jewish Hospital and the Alvin J Siteman Cancer Center, St Louis, MO, United States.
J Med Internet Res. 2021 Mar 18;23(3):e23595. doi: 10.2196/23595.
Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity.
The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning.
In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves.
Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875.
Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics.
胰腺癌是癌症相关死亡的第三大主要原因，尽管胰腺切除术目前是唯一的治愈性治疗方法，但它会带来显著的发病率。
本研究的目的是评估可穿戴远程监测技术利用患者活动指标和机器学习预测治疗结果的效用。
在这项前瞻性、单中心、单队列研究中，计划进行胰腺切除术的患者在手术前配备了可穿戴远程监测设备。收集患者临床数据，并使用美国外科医师学会国家外科质量改进计划手术风险计算器（ACS-NSQIP SRC）对所有患者进行评估。开发机器学习模型以预测患者是否会有理想的治疗结果，并使用受试者操作特征曲线下面积（AUROC）与ACS-NSQIP SRC进行比较。
2019年2月至2020年2月期间，48名患者完成了研究。在手术前平均27.8天收集患者活动指标。患者每天平均走4162.1步（标准差4052.6），平均心率为每分钟75.6次（标准差14.8）。28名（58%）患者胰腺切除术后有理想的治疗结果。未达到理想治疗结果的20名（42%）患者包括14名有严重并发症的患者和11名需要再次入院的患者。ACS-NSQIP SRC预测未达到理想治疗结果的AUROC曲线为0.6333，而我们结合患者临床特征和患者活动数据的模型以0.7875的AUROC曲线取得了最高性能。
在预测胰腺切除术后的理想治疗结果方面，机器学习模型优于ACS-NSQIP SRC估计。当机器学习模型纳入患者临床特征和活动指标时，观察到最高性能。
