Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study

Objective: Sudden Death (SD) is a high-mortality emergency event that typically occurs within one hour of symptom onset. Accurate risk prediction is essential for optimizing post-resuscitation care. This study aims to enhance the survival rate of patients experiencing sudden death by developing and validating a risk prediction model for in-hospital mortality following successful resuscitation. Method: This study is a retrospective analysis of data that were collected prospectively from a standardized clinical database. All data were recorded at the time of patient admission using a predefined protocol to ensure consistency and accuracy. We retrospectively analyzed the data collected from 295 patients who experienced sudden death and achieved successful resuscitation at Wuhan Central Hospital from January 2017 to June 2024. The patients were assigned to groups using a randomization process into training and validation sets using k-fold cross-validation and further categorized within these sets based on in-hospital mortality as the outcome. A prediction model was constructed, and its efficacy was validated using logistic regression analysis, which was visualized with nomograms. Results: The results of this regression analysis of the training set demonstrated the actual length of hospital stay, in-hospital norepinephrine dosage, post-resuscitation respiratory rate, and sinus rhythm after resuscitation as independent influencing factors (p < 0.05), which formed the basis of the prediction model. The analysis of the training set exhibited high discriminative ability, with an area under the ROC curve (AUC) of 0.860, which exceeds the commonly accepted threshold for good classification performance, and the calibration, applicability, and reasonableness were all favorable. When the model was applied to the validation set, the AUC was 0.758, and the discrimination, calibration, applicability, and reasonableness of the validation set were also satisfactory. Conclusions: the main conclusion is that a risk prediction model for in-hospital mortality following resuscitation from sudden death was successfully developed and internally validated, offering a significant advancement in clinical decision-making support.