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Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia

Author

Listed:
  • Chao-Shun Lin
  • Chuen-Chau Chang
  • Jainn-Shiun Chiu
  • Yuan-Wen Lee
  • Jui-An Lin
  • Martin S. Mok
  • Hung-Wen Chiu
  • Yu-Chuan Li

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.

Suggested Citation

  • Chao-Shun Lin & Chuen-Chau Chang & Jainn-Shiun Chiu & Yuan-Wen Lee & Jui-An Lin & Martin S. Mok & Hung-Wen Chiu & Yu-Chuan Li, 2011. "Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia," Medical Decision Making, , vol. 31(2), pages 308-314, March.
    • Handle: RePEc:sae:medema:v:31:y:2011:i:2:p:308-314
    DOI: 10.1177/0272989X10379648
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    Cited by:

    1. Hsueh-Yi Lu & Chen-Yuan Huang & Chwen-Tzeng Su & Chen-Chiang Lin, 2014. "Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    2. Chong-Jian Wang & Yu-Qian Li & Ling Wang & Lin-Lin Li & Yi-Rui Guo & Ling-Yun Zhang & Mei-Xi Zhang & Rong-Hai Bie, 2012. "Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.

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