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.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

“…The remaining 5169 patients are all added to the validation cohort Sensitivity and specificity, discriminatory power and 95% confidence interval, number of tests avoided, negative predictive value, positive predictive value Not mentioned Isma'eel et al [ 42 ] The derivation cohort was randomly chosen 30 out of the 59 patients who tested positive were added randomly to the derivation cohort, and 30 out of the remaining 427 patients who tested negative were also added randomly to the derivation cohort. The remaining 426 patients (29 positive, 397 negative) were all added to the testing cohort; during the training phase, the 60 patients that are used for training were split 80% for pure training and 20% for validation Negative and positive predictive values, descriminatory power, percentage of avoided tests, sensitivity and specificity Not mentioned Jovanovic et al [ 43 ] The sample was randomly divided into 3 parts: training, testing, and validation sample Area under the receiver operating curve, sensitivity, specificity, and positive and negative predictive values Not mentioned Kang et al [ 27 ] Four-fold cross validation, 75/25 split (training/validation) Area under the receiver operating curve, accuracy, precision, recall Not mentioned Karhade et al [ 28 ] tenfold cross validation Discrimination (c-statistic or area under the receiver operating curve), calibration (calibration slope, calibration intercept), and overall performance (Brier score) Multiple imputation with the missForest methodology was undertaken for variables with less than 30% missing data Kebede et al [ 29 ] 10% cross validation, 90/10 split (training/testing) Area under the receiver operating curve; classification accuracy-true positive, false positive, precision, recall If information is incomplete, un-readable or their manual record is lost, patients were excluded from the study Khanji et al [ 47 ] Ten-fold cross validation Akaike Information Criterion, area under the receiver operating curve Excluded patients with missing data at the end of the study (± 6 months) Kim et al [ 30 ] …”

“…As noted above, one study using decision tree analysis used Quinlan’s C5.0 decision tree algorithm [ 15 ] while a second used an earlier version of this program (C4.5) [ 20 ]. Other decision tree analyses utilized various versions of R [ 18 , 19 , 22 , 24 , 27 , 47 ], International Business Machines (IBM) Statistical Package for the Social Sciences (SPSS) [ 16 , 17 , 33 , 47 ], the Azure Machine Learning Platform [ 30 ], or programmed the model using Python [ 23 , 25 , 46 ]. Artificial neural network analyses used Neural Designer [ 34 ] or Statistica V10 [ 35 ].…”

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension

“…In our future work, we will check the feasibility of the random forest model for large number of patients. We have previously described the development of a prediction model using an ANN [ 10 ]. Our previous model is comparable to Kendale et al [ 8 ], and achieves a better result in a similar condition.…”

“…In particular, data analyzed in our study came from mechanical ventilators and anesthetic workstation that have been overlooked in most literature in the past. In recent years, research on prediction models using machine learning has been steadily published in several clinical fields, such as arrhythmia, postoperative mortality, morbidity, and hypotension [ 10 , 21 , 31 , 32 , 33 , 34 ]. This trend clearly leads to many advantages, such as the development of the medical environment, improved patient safety, and improved prognosis.…”

“…If such a system that can predict the occurrence of hypotension at bedside is available, it may lead to reduced postoperative complications and mortality. Machine learning is currently applied to various clinical medical areas and several studies related to the prediction of hemodynamic changes using artificial neural networks have recently been published [ 8 , 9 , 10 ]. To develop this prediction model, it is essential to collect, store, and immediately analyze large data that arise from patient medical records and anesthesia.…”