Analysis of risk factors of respiratory and cardiac arrest after medullary infarction

Objective To identify the risk factors of respiratory and cardiac arrest after medullary infarction (MI), and to establish a Nomogram model of respiratory and cardiac arrest after MI. Methods Total of 3168 patients with MI hospitalized in Tianjin Huanhu Hospital from January 2016 to January 2023 were included, including 66 patients in the respiratory and cardiac arrest group, and 3102 patients in the non ⁃ respiratory and cardiac arrest group. Potential risk factors of respiratory and cardiac arrest were collected, and samples were resampled using random undersampling (RUS), random oversampling (ROS), and synthetic minority over⁃sampling technique (SMOTE). Split the raw data and resampled data into training and testing sets. For the training set, univariate and multivariate stepwise Logistic regression models were used to analyze the risk factors of respiratory and cardiac arrest after MI. Drawn receiver operating characteristic (ROC) curve using the training and testing sets, compared the area under the curve (AUC) of 4 Logistic regression models using Delong test, and established a Nomogram model. Results Use the testing sets to test the Logistic regression models built on the raw data and 3 resampling methods. The results showed that the AUC of SMOTE resampling was the highest after testing (SMOTE∶raw data Z = 3.254, P = 0.000; SMOTE∶RUS Z = 4.385, P = 0.000; SMOTE∶ROS Z = 2.701, P = 0.007). For SMOTE resampling data, age increase (OR = 1.045, 95%CI: 1.021-1.070; P = 0.000), smoking history (OR = 22.216, 95%CI: 10.426-49.920; P = 0.000), the smaller the number of cigarettes smoked (OR = 0.943, 95%CI: 0.915-0.971; P = 0.000), alcohol history (OR = 1.847, 95%CI: 1.068-3.207; P = 0.028), cerebrovascular history (OR = 3.104, 95%CI: 1.842-5.344; P = 0.000), the higher the high ⁃ density lipoprotein cholesterol (HDL ⁃ C; OR = 5.863, 95%CI: 2.063-16.725, P = 0.000), the higher fibrinogen (FIB; OR = 1.413, 95%CI: 1.381-1.702; P = 0.001), left lateral medullary infarction [LMI; no medial medullary infarction (MMI; OR = 0.173, 95%CI: 0.093-0.312, P = 0.000), no right LMI (OR = 0.337, 95%CI: 0.176-0.634; P = 0.001)], combined with extramedullary infarction (OR = 31.354, 95%CI: 17.496-59.163; P = 0.000), higher Wada Drinking Water Test score (OR = 3.723, 95%CI: 2.913-4.862; P = 0.000), and patients with stress ulcer (OR = 5.266, 95%CI: 2.902-9.813; P = 0.000) were more likely to experience respiratory and cardiac arrest after MI. The Nomogram model showed that the Wada Drinking Water Test score had the greatest predictive effect, while the predictive effect of drinking history was the smallest. Conclusions Increasing age, high HDL⁃C, high FIB, smoking history, the smaller the number of cigarettes smoked, alcohol history, cerebrovascular history, left LMI, combined with extramedullary infarction, high Wada Drinking Water Test score and combined with stress ulcer are risk factors for respiratory and cardiac arrest after MI. The Nomogram model can be used to intuitively predict the probability of occurrence of respiratory and cardiac arrest after MI.