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Background and aim <p>Out-of-hospital cardiac arrest (OHCA) is a major global health burden, with survival rates remaining critically low despite advancements in emergency care. Early identification of patients likely to achieve return of spontaneous circulation (ROSC) is vital for optimizing resuscitation strategies and improving clinical outcomes. This study aimed to develop and validate a multivariable predictive model for ROSC in patients with out-of-hospital cardiac arrest (OHCA) who received resuscitation in the emergency department, using readily available clinical variables.</p> Methods <p>We retrospectively analyzed clinical records of 902 OHCA patients who received resuscitative care in the emergency department of Hefei Second People’s Hospital between January 2021 and June 2024. Key variables including age, sex, number of defibrillations, total epinephrine dosage, dopamine (inotrope) administration, and CPR duration were extracted. Multivariable logistic regression was used to identify independent predictors of ROSC. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis (DCA).</p> Results <p>Among the 890 included patients, ROSC was achieved in 20.3%. Independent predictors of ROSC included number of defibrillations (OR: 1.40, 95% CI: 1.08–1.81), total epinephrine dose (OR: 0.59, 95% CI: 0.54–0.66), dopamine dose (OR: 1.01, 95% CI: 1.01–1.02), and CPR duration ≥ 30&#xa0;min (OR: 0.18, 95% CI: 0.12–0.29). The model demonstrated good discriminative ability with an AUC of 0.833. Calibration and DCA supported the model’s clinical utility.</p> Conclusion <p>We developed a robust and interpretable predictive model for ROSC using real-world data from OHCA patients. While not intended for real-time clinical decision-making, the model can support retrospective evaluation and risk stratification, offering insights to improve resuscitation strategies and future research.</p> Clinical trial registration <p>Not applicable.</p>

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Predicting return of spontaneous circulation in out-of-hospital cardiac arrest resuscitated in the emergency department: a single center retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Shuang Zuo,
  • Guofeng Wei,
  • Haishan Li

摘要

Background and aim

Out-of-hospital cardiac arrest (OHCA) is a major global health burden, with survival rates remaining critically low despite advancements in emergency care. Early identification of patients likely to achieve return of spontaneous circulation (ROSC) is vital for optimizing resuscitation strategies and improving clinical outcomes. This study aimed to develop and validate a multivariable predictive model for ROSC in patients with out-of-hospital cardiac arrest (OHCA) who received resuscitation in the emergency department, using readily available clinical variables.

Methods

We retrospectively analyzed clinical records of 902 OHCA patients who received resuscitative care in the emergency department of Hefei Second People’s Hospital between January 2021 and June 2024. Key variables including age, sex, number of defibrillations, total epinephrine dosage, dopamine (inotrope) administration, and CPR duration were extracted. Multivariable logistic regression was used to identify independent predictors of ROSC. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis (DCA).

Results

Among the 890 included patients, ROSC was achieved in 20.3%. Independent predictors of ROSC included number of defibrillations (OR: 1.40, 95% CI: 1.08–1.81), total epinephrine dose (OR: 0.59, 95% CI: 0.54–0.66), dopamine dose (OR: 1.01, 95% CI: 1.01–1.02), and CPR duration ≥ 30 min (OR: 0.18, 95% CI: 0.12–0.29). The model demonstrated good discriminative ability with an AUC of 0.833. Calibration and DCA supported the model’s clinical utility.

Conclusion

We developed a robust and interpretable predictive model for ROSC using real-world data from OHCA patients. While not intended for real-time clinical decision-making, the model can support retrospective evaluation and risk stratification, offering insights to improve resuscitation strategies and future research.

Clinical trial registration

Not applicable.