The nomogram prediction model based on age, systolic blood pressure, Killip class, and coronary calcification predicted in-hospital cardiac arrest with a C-index of 0.828 in STEMI patients after successful PPCI.
Observational (n=5,121)
No
A newly developed nomogram incorporating age, systolic blood pressure, Killip class, and coronary calcification provides accurate, individualized risk prediction for in-hospital cardiac arrest in STEMI patients after successful primary PCI.
Effect estimate: C-index 0.823 in training set, 0.828 in validation set (95% CI 95% CI 0.764-0.881 (training); 95% CI 0.751-0.905 (validation))
In-hospital cardiac arrest (IHCA) is an infrequent but lethal complication in patients with ST-elevation myocardial infarction (STEMI). Data on the characteristics and predictors of IHCA in STEMI patients after successful primary percutaneous coronary intervention (PPCI) are limited. This study aimed to identify the risk factors of IHCA and construct a predictive nomogram. A total of 5121 STEMI patients treated with successful PPCI from January 2018 to July 2023 at Beijing Anzhen Hospital were retrospectively enrolled in our study. Subjects were randomly divided into a development group and a validation group in a 7:3 ratio. Optimal predictive variables were selected using the least absolute shrinkage and selection operator (Lasso) and logistic regression models. A nomogram based on these predictors was then created to estimate IHCA probability. Among them, 94 patients (1.8% 95% CI: 1.4%-2.1%) experienced IHCA after PPCI, and the in-hospital mortality rate was 35.1% in these patients. Lasso regression was implemented to identify predictors of IHCA, including age, Killip III-IV, systolic blood pressure, and moderate to severe calcification, with non-zero coefficients. A nomogram model for predicting the risk of IHCA after PPCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.823 (95%CI 0.764–0.881). The model’s calibration curve closely aligned with the ideal reference, indicating strong agreement between predicted and actual outcomes, and the internal validation showed a C-index of 0.828 (95%CI 0.751–0.905). Clinical decision analysis further confirmed that the nomogram model demonstrated good clinical effectiveness. Our study identified several independent risk factors for IHCA in STEMI patients after successful PPCI based on a Lasso-logistic regression model, offering individualized and reliable cardiac arrest risk predictions during hospitalization. Not applicable.
Luo et al. (Wed,) conducted a observational in Patients with ST-elevation myocardial infarction (STEMI) treated with successful primary percutaneous coronary intervention (PPCI) within 24 hours from onset (n=5,121). Nomogram prediction model based on age, systolic blood pressure, Killip classification III-IV, and moderate to severe coronary calcification vs. No nomogram prediction or standard clinical risk assessment such as GRACE-based model was evaluated on In-hospital cardiac arrest (IHCA) during hospitalization after successful PPCI (C-index 0.823 in training set, 0.828 in validation set, 95% CI 95% CI 0.764-0.881 (training); 95% CI 0.751-0.905 (validation)). The nomogram prediction model based on age, systolic blood pressure, Killip class, and coronary calcification predicted in-hospital cardiac arrest with a C-index of 0.828 in STEMI patients after successful PPCI.