ArticleWorld journal of cardiology2023
Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest.
Article in World journal of cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- External validation of the Prehospital Return of Spontaneous Circulation (P-ROSC) score for predicting prehospital return of spontaneous circulation among patients with out-of-hospital cardiac arrest.Resuscitation plus · 2026Article
- Influencing factors and predictive indicators of return of spontaneous circulation in in-hospital cardiac arrest.Frontiers in cardiovascular medicine · 2025Article
- Thoughts on recent articles on cardiopulmonary resuscitation.World journal of cardiology · 2024Article
- Comparison of blended e-learning and face-to-face-only education for resuscitation training in German schools - A cluster randomized-controlled prospective study.Resuscitation plus · 2024Article
- Correction: Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest.World journal of cardiology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOut-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide.
aimTo explore factors influencing prehospital return of spontaneous circulation (P-ROSC) in patients with OHCA and develop a nomogram prediction model.
methodsClinical data of patients with OHCA in Shenzhen, China, from January 2012 to December 2019 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were applied to select the optimal factors predicting P-ROSC in patients with OHCA. A nomogram prediction model was established based on these influencing factors. Discrimination and calibration were assessed using receiver operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was used to evaluate the model's clinical utility.
resultsAmong the included 2685 patients with OHCA, the P-ROSC incidence was 5.8%. LASSO and multivariate logistic regression analyses showed that age, bystander cardiopulmonary resuscitation (CPR), initial rhythm, CPR duration, ventilation mode, and pathogenesis were independent factors influencing P-ROSC in these patients. The area under the ROC was 0.963. The calibration plot demonstrated that the predicted P-ROSC model was concordant with the actual P-ROSC. The good clinical usability of the prediction model was confirmed using DCA.
conclusionThe nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.