Can a deep learning model based on intraoperative time-series monitoring data predict post-hysterectomy quality of recovery?

Perioperative Medicine

Junmei Xu¹  Lingzhong Meng²  Xu Zhao³  Wei Wang⁴  Ke Liao⁵ 

[1] Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China;Department of Anesthesiology, Yale University School of Medicine, 333 Cedar St, 06520, New Haven, CT, USA;Department of Anesthesiology, Yale University School of Medicine, 333 Cedar St, 06520, New Haven, CT, USA;Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China;Ricoh Software Research Center (Beijing) Co., Ltd., Beijing, China;Ricoh Software Research Center (Beijing) Co., Ltd., Beijing, China;School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan;

关键词: Deep learning;    Machine learning;    Time-series monitoring data;    Hysterectomy;    Quality of recovery;    Prediction;   

DOI  :  10.1186/s13741-021-00178-4

来源: Springer

【 摘 要 】

BackgroundIntraoperative physiological monitoring generates a large quantity of time-series data that might be associated with postoperative outcomes. Using a deep learning model based on intraoperative time-series monitoring data to predict postoperative quality of recovery has not been previously reported.MethodsPerioperative data from female patients having laparoscopic hysterectomy were prospectively collected. Deep learning, logistic regression, support vector machine, and random forest models were trained using different datasets and evaluated by 5-fold cross-validation. The quality of recovery on postoperative day 1 was assessed using the Quality of Recovery-15 scale. The quality of recovery was dichotomized into satisfactory if the score ≥122 and unsatisfactory if <122. Models’ discrimination was estimated using the area under the receiver operating characteristics curve (AUROC). Models’ calibration was visualized using the calibration plot and appraised by the Brier score. The SHapley Additive exPlanation (SHAP) approach was used to characterize different input features’ contributions.ResultsData from 699 patients were used for modeling. When using preoperative data only, all four models exhibited poor performance (AUROC ranging from 0.65 to 0.68). The inclusion of the intraoperative intervention and/or monitoring data improved the performance of the deep leaning, logistic regression, and random forest models but not the support vector machine model. The AUROC of the deep learning model based on the intraoperative monitoring data only was 0.77 (95% CI, 0.72–0.81), which was indistinct from that based on the intraoperative intervention data only (AUROC, 0.79; 95% CI, 0.75–0.82) and from that based on the preoperative, intraoperative intervention, and monitoring data combined (AUROC, 0.81; 95% CI, 0.78–0.83). In contrast, when using the intraoperative monitoring data only, the logistic regression model had an AUROC of 0.72 (95% CI, 0.68–0.77), and the random forest model had an AUROC of 0.74 (95% CI, 0.73–0.76). The Brier score of the deep learning model based on the intraoperative monitoring data was 0.177, which was lower than that of other models.ConclusionsDeep learning based on intraoperative time-series monitoring data can predict post-hysterectomy quality of recovery. The use of intraoperative monitoring data for outcome prediction warrants further investigation.Trial registrationThis trial (Identifier: NCT03641625) was registered at ClinicalTrials.gov by the principal investigator, Lingzhong Meng, on August 22, 2018.

【 授权许可】

CC BY