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Development of a prediction model for hypotension after induction of anesthesia using machine learning.

ABSTRACT: Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients' demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient's lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.

SUBMITTER: Kang AR

PROVIDER: S-EPMC7162491 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of a prediction model for hypotension after induction of anesthesia using machine learning.

Kang Ah Reum AR Lee Jihyun J Jung Woohyun W Lee Misoon M Park Sun Young SY Woo Jiyoung J Kim Sang Hyun SH

PloS one 20200416 4

Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between trache ...[more]

PMID: 32298292

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Project description:IntroductionInduction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context.Material and methodsWe aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10-fold cross-validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia.ResultsOur sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783-0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565-0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery.ConclusionsThis prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling.

Project description:Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks' gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms.

Dataset Information

Development of a prediction model for hypotension after induction of anesthesia using machine learning.

Publications

Development of a prediction model for hypotension after induction of anesthesia using machine learning.

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