Med Decis Making - Application of an artificial neural network to predict postinduction hypotension during general anesthesia.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground periop hypotens associ advers outcom patient undergo surgeri computerbas model integr relat factor predict risk hypotens help clinic anesthesia purpos studi develop artifici neural network ann model identifi patient high risk postinduct hypotens general anesthesiamethod anesthesia record march novemb review record analyz eleven patientrel surgic anesthet variabl use develop ann logist regress lr model qualiti model evalu extern valid data set three clinician ask make predict valid data set casebycas basisresult ann model accuraci sensit specif accuraci lr model sensit specif area receiv oper characterist curv ann lr model clinician lowest predict accuraci sensit compar ann lr modelsconclus ann model develop studi good discrimin calibr provid decis support clinician increas vigil patient high risk postinduct hypotens general anesthesia

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