Development and Validation of a Nomogram Prediction Model for In-hospital Mortality in Patients with Cardiac Arrest:...

Cardiac arrest (CA) is associated with high incidence and mortality rates. Hence, assessing the prognosis of CA patients is crucial for optimizing clinical treatment. This study aimed to develop and validate a clinically applicable nomogram for predicting the risk of in-hospital mortality in CA patients.

We retrospectively collected the clinical data of CA patients admitted to two hospitals in Zhejiang Province between January 2018 and June 2024. These patients were randomly assigned to the training set (70%) and the internal validation set (30%). Variables of interest included demographics, comorbidities, CA-related characteristics, vital signs, and laboratory results, and the outcome was defined as in-hospital death. Variables were selected using least absolute shrinkage and selection operator (LASSO) regression, recursive feature elimination (RFE), and eXtremely Gradient Boosting (XGBoost). Meanwhile, multivariate regression analysis was used to identify independent risk factors. Subsequently, prediction models were developed in the training set and validated in the internal validation set. Receiver operating characteristic (ROC) curves were plotted and the area under these curves (AUC) was calculated to compare the discriminative ability of the models. The model with the highest performance was further validated in an independent external cohort and was subsequently represented as a nomogram for predicting the risk of in-hospital mortality in CA patients.

This study included 996 CA patients, with an in-hospital mortality rate of 49.9% (497/996). The LASSO regression model significantly outperformed the RFE and XGBoost models in predicting in-hospital mortality, with an AUC value of 0.81 (0.78, 0.84) in the training set and 0.85 (0.80, 0.89) in the internal validation set. The AUC values for these sets in the RFE model were 0.74 (0.70, 0.78) and 0.77 (0.72, 0.83), respectively, and those for the XGBoost model were 0.75 (0.71, 0.79) and 0.77 (0.72, 0.83), respectively. For the optimal prediction model, the AUC value of the LASSO regression model in the external validation set was 0.84 (0.78, 0.90). The LASSO regression model was represented as a nomogram incorporating several independent risk factors, namely age, hypertension, cause of arrest, initial heart rhythm, vasoactive drugs, continuous renal replacement therapy (CRRT), temperature, blood urea-nitrogen (BUN), lactate, and Sequential Organ Failure Assessment (SOFA) scores. Calibration and decision curves confirmed the predictive accuracy and clinical utility of the model.

We developed a nomogram to predict the risk of in-hospital mortality in CA patients, using variables selected via LASSO regression. This nomogram demonstrated strong discriminative ability and clinical practicality.