Development And Validation Of A Model To Predict Hypotension During Induction Of General Anaesthesia

Background: Post-induction hypotension（PIH）refers to arterial hypotension occurring within the first 20 minutes after anesthesia induction or from anesthesia induction to the beginning of surgery.PIH has a high incidence and is associated with poor prognosis of patients.Identifying high-risk patients with PIH is of great significance for medical staff to take corresponding preventive measures and formulating intervention plans.Therefore,This study aims to construct a PIH prediction model for patients undergoing general anesthesia and verify the performance of the model.Methods: This is a cross-sectional and observational study.The subjects included290 tumor patients who underwent elective surgery under general anesthesia in a tertiary hospital in southwest China from November 2020 to January 2021.The data came from medical records and anesthesia information collection system.Variables included patient age,gender,body mass index（BMI）,disease diagnosis,complications,drug use,Charlson comorbidity index（CCI）,American society of anesthesiologists physical status classification（ASA）,the last measured blood pressure in the ward,the blood pressure when entering the operating room,and the lowest blood pressure during anesthesia induction.PIH was defined as a decrease of mean arterial blood pressure（MAP）during induction of more than 30% compared to the MAP measured last time before induction.The sample data was divided into training set and validation set according to the ratio of 7:3.The least absolute shrinkage and selection operator（LASSO）binary logistic regression was used for feature selection and model training.The area under the receiver operating characteristic curve（AUROC）was used to check the discriminative power of the model.A calibration curve and the Hosmer-Lemeshow（H-L）chi-square test were used to evaluate the calibration degree of the model.Decision curve analysis（DCA）was used to evaluate the performance of the modeling in supporting clinical decision-making.The model was then visualized using a nomogram.Results: PIH was presented in 8% patients in the training set and 10% in the test set.The predictors of this model included BMI,changes in MAP,heart rate（HR）,and pre-operative use of angiotensin-converting enzyme inhibitors（ACEIs）/angiotensin receptor blockers（ARBs）.For the training and test sets,the AUROC curve using LASSO regression was 0.894（95% CI: 0.78-1.00）and 0.883（95% CI: 0.718-1.00）,with respective sensitivity（0.880 and 0.901）and specificity（0.875 and 0.889）.The H-L test of calibration curve was 3.42 and 11.265,with respective p value 0.905 and 0.187.The DCA demonstrated that using the model obtained higher net benefit（NB）than not using it.Conclusions: BMI,MAP change,HR,and ACEIs/ARBs were predictive of PIH by LASSO regression.This model composed of these four independent variables showed good discrimination,calibration,and clinical efficiency,which is helpful for medical staff to identify patients with high risk of PIH and formulate corresponding prevention and intervention strategies.