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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Intensive Care Cardiovascular Medicine
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1510710

Development and Validation of a Prediction Model of Hospital Mortality for Patients with Cardiac Arrest Survived 24 hours after Cardiopulmonary Resuscitation

Provisionally accepted
Renwei Zhang Renwei Zhang 1*Zhenxing Liu Zhenxing Liu 2Yumin Liu Yumin Liu 1Li Peng Li Peng 1
  • 1 Zhongnan Hospital, Wuhan University, Wuhan, China
  • 2 Yiling People's Hospital, Yichang, Hebei Province, China

The final, formatted version of the article will be published soon.

    Objective: Research on predictive models for hospital mortality in patients who have survived 24 hours following cardiopulmonary resuscitation (CPR) is limited. We aim to explore the factors associated with hospital mortality in these patients and develop a predictive model to aid clinical decision-making and enhance the survival rates of patients post-resuscitation. Methods: We sourced the data from a retrospective study within the Dryad dataset, dividing patients who suffered cardiac arrest following CPR into a training set and a validation set at a 7:3 ratio. We identified variables linked to hospital mortality in the training set using Least Absolute Shrinkage and Selection Operator (LASSO) regression, as well as univariate and multivariate logistic analyses. Utilizing these variables, we developed a prognostic nomogram for predicting mortality post-CPR. Calibration curves, the area under receiver operating curves (ROC), decision curve analysis (DCA), and clinical impact curve were used to assess the discriminability, accuracy, and clinical utility of the nomogram. Results:. The study population comprised 374 patients, with 262 allocated to the training group and 112 to the validation group. Of these, 213 patients were dead in the hospital. Multivariate logistic analysis revealed age (OR 1.05, 95%CI 1.03-1.08), witnessed arrest (OR 0.28, 95%CI 0.11-0.73), time to return of spontaneous circulation (ROSC) (OR 1.05, 95%CI 1.02-1.08), non-shockable rhythm (OR 3.41,, alkaline phosphatase (OR 1.01, 95%CI 1-1.01), and sequential organ failure assessment (SOFA) (OR 1.27, 95%CI 1.15-1.4) were independent risk factors for hospital mortality for patients who survived 24 hours after CPR. ROC of the nomogram showed the AUC in the training and validation group was 0.827 and 0.817, respectively. Calibration curves, DCA, and clinical impact curve demonstrated the nomogram with good accuracy and clinical utility. Conclusion: Our prediction model had accurate predictive value for hospital mortality in patients who survived 24 hours after CPR, which will be beneficial for assisting in identifying high-risk patients and intervention. Further confirmation of the model's accuracy required external validation data.

    Keywords: Hospital Mortality, nomogram, Cardiac arrest, LASSO, Cardiopulmonary Resuscitation

    Received: 18 Oct 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Zhang, Liu, Liu and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Renwei Zhang, Zhongnan Hospital, Wuhan University, Wuhan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.