Study participantsA total of 564 patients who met the study inclusion criteria were collected from the third-class first-class general hospital. Among them, 37 patients to transfer to another hospital, 23 patients discontinued treatment and left the hospital, and 17 patients experienced unplanned extubation during MV. Based on the offline outcome, these 487 patients were divided into two groups: 323 patients (66.32%) were simple weaning, and 164 patients (33.68%) faced difficult weaning. This study was approved by the hospital ethics committee (number: 2022-051).In this study, we conducted a retrospective cohort study to select patients who underwent invasive MV in the ICU of the third-class first-class general from June 2018 to December 2022. Inclusion criteria were as follows: (i) age ≥18 years old, (ii) patients with tracheal intubation and ventilator-assisted ventilation, (iii) the primary disease was under control and a SBT test was performed based on the doctor’s advice, with the plan to remove the tracheal intubation, (iv) complete preservation of case data. Exclusion criteria were as follows: (i) patients with pre-intubation airway dysfunction that could not be improved in a short time, such as airway compression due to tumor, congenital airway malformation, airway burn, or airway surgery, (ii) long-term MV exceeding 21 days, (iii) incomplete data.Data collectionThe study utilized the following information: (i) Demographic: age, sex, consciousness, cough reflex intensity15 (0 = no cough response, 1 = audible movement of air through the endotracheal tube but no audible cough, 2 = strong cough with phlegm under the end of endotracheal tube, 3 = strong cough with phlegm coming out of the end of endotracheal tube) ,body mass index (BMI), nutritional risk screening (NRS-2002), acute physiology and chronic health status score II (APAHE II), sequential organ failure score (SOFA), (ii) Questionnaire on related factors: duration of MV, vasoactive drugs on the day of weaning, last day liquid balance, temperature at SBT, heart rate(HR) at SBT, blood pressure at (BP)SBT, respiratory rate(RR) at SBT, (iii) Blood gas analysis parameters: PH value, PaO2, PaCO2, PaO2/FiO2, serum potassium, serum potassium, base excess (Be), LAC (lactic acid), (iv) ventilator parameters: tidal volume, PS, PEEP, (v) Laboratory inspection index: hemoglobin, hematocrit, platelet count, white blood cell count, high-sensitivity C-reactive protein, PCT procalcitonin, total protein, albumin and globulin. A total of 36 variables were included in the analysis.Machine learning modelsWith the advancement of computing power and the availability of high-frequency medical data, artificial intelligence (AI) methods based on machine learning (ML) have been widely adopted in medical decision-making. By analyzing large amounts of medical data, including clinical records, medical images, and genomics data, machine learning models can learn patient-specific patterns and accurately predict future events16,17. Currently, commonly used ML algorithms include logistic regression (LR), random forest (RF), support vector machines (SVM), light gradient boosting machine (Light GBM), and XG Boost. LR18,19,20,21 is a regression algorithm for solving classification problems and uses the sigmoid function to map linear regression model outputs to probabilities between 0 and 1. RF22 is an ensemble classification and regression algorithm that uses decision trees. SVM23 creates a binary classifier and is used in small sample data and binary classification. Light GBM24 and XG Boost25 both use gradient boosting with decision trees, but Light GBM features exclusive feature bundling and gradient-based one-sided sampling, while XG Boost uses regularization and column sampling to prevent overfitting and improve efficiency.Statistical analysisThe SPSS 26.0 software was used for statistical analysis in this study. Categorical variables were represented as number and percentage and were compared using the Chi-square test, Fisher’s exact test, and non-parametric Mann–Whitney U test. Continuous variables were compared using t-test and non-parametric Mann–Whitney U test.The predictive model was developed and validated using the Python 3.9 language platform. The dataset was randomly divided into a 7:3 ratio for training and verification sets, the model is trained on the training set, and the generalization ability of the model is evaluated on the test set. Five models, namely Logistic, SVM, Random Forest, XGBOOST, light GBM, are used to model and predict, and the hyperparameter of the model is selected by grid search and 50% cross-validation. Reach the confusion matrix models. According to the confusion matrix, the precision rate, recall rate, accuracy rate and F1 value of the model, respectively. Draw the ROC curves of the five models. Compare ROC curves of five models. Select the best model among the five models.
