Chao-Shun Lin1,2, Chuen-Chau Chang2, Jainn-Shiun Chiu3, Yuan-Wen Lee2,4,5, Jui-An Lin2, Martin S Mok2, Hung-Wen Chiu4,6, Yu-Chuan Li4,5. 1. Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei City, Taiwan (CSL) 2. Department of Anesthesiology, Taipei Medical University Hospital, Taipei City, Taiwan (CSL, CCC, YWL, JAL, MSM) 3. Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan (JSC) 4. Graduate Institute of Medical Informatics, Taipei Medical University, Taipei City, Taiwan (HWC, YCL) 5. Department of Dermatology, Taipei Medical University—Wan Fang Hospital, Taipei City, Taiwan (YCL) 6. Taipei Medical University-Cancer Excellency of Clinical Research, Taipei City, Taiwan (HWC)
Abstract
BACKGROUND: Perioperative hypotension is associated with adverse outcomes in patients undergoing surgery. A computer-based model that integrates related factors and predicts the risk of hypotension would be helpful in clinical anesthesia. The purpose of this study was to develop artificial neural network (ANN) models to identify patients at high risk for postinduction hypotension during general anesthesia. METHODS: Anesthesia records for March through November 2007 were reviewed, and 1017 records were analyzed. Eleven patient-related, 2 surgical, and 5 anesthetic variables were used to develop the ANN and logistic regression (LR) models. The quality of the models was evaluated by an external validation data set. Three clinicians were asked to make predictions of the same validation data set on a case-by-case basis. RESULTS: The ANN model had an accuracy of 82.3%, sensitivity of 76.4%, and specificity of 85.6%. The accuracy of the LR model was 76.5%, the sensitivity was 74.5%, and specificity was 77.7%. The area under the receiver operating characteristic curve for the ANN and LR models was 0.893 and 0.840. The clinicians had the lowest predictive accuracy and sensitivity compared with the ANN and LR models. CONCLUSIONS: The ANN model developed in this study had good discrimination and calibration and would provide decision support to clinicians and increase vigilance for patients at high risk of postinduction hypotension during general anesthesia.
BACKGROUND: Perioperative hypotension is associated with adverse outcomes in patients undergoing surgery. A computer-based model that integrates related factors and predicts the risk of hypotension would be helpful in clinical anesthesia. The purpose of this study was to develop artificial neural network (ANN) models to identify patients at high risk for postinduction hypotension during general anesthesia. METHODS: Anesthesia records for March through November 2007 were reviewed, and 1017 records were analyzed. Eleven patient-related, 2 surgical, and 5 anesthetic variables were used to develop the ANN and logistic regression (LR) models. The quality of the models was evaluated by an external validation data set. Three clinicians were asked to make predictions of the same validation data set on a case-by-case basis. RESULTS: The ANN model had an accuracy of 82.3%, sensitivity of 76.4%, and specificity of 85.6%. The accuracy of the LR model was 76.5%, the sensitivity was 74.5%, and specificity was 77.7%. The area under the receiver operating characteristic curve for the ANN and LR models was 0.893 and 0.840. The clinicians had the lowest predictive accuracy and sensitivity compared with the ANN and LR models. CONCLUSIONS: The ANN model developed in this study had good discrimination and calibration and would provide decision support to clinicians and increase vigilance for patients at high risk of postinduction hypotension during general anesthesia.
Authors: Michael R Pinsky; Maurizio Cecconi; Michelle S Chew; Daniel De Backer; Ivor Douglas; Mark Edwards; Olfa Hamzaoui; Glenn Hernandez; Greg Martin; Xavier Monnet; Bernd Saugel; Thomas W L Scheeren; Jean-Louis Teboul; Jean-Louis Vincent Journal: Crit Care Date: 2022-09-28 Impact factor: 19.334