This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the aff...This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.展开更多
BACKGROUND Out-of-hospital cardiac arrest(OHCA)is a leading cause of death worldwide.AIM To explore factors influencing prehospital return of spontaneous circulation(P-ROSC)in patients with OHCA and develop a nomogram...BACKGROUND Out-of-hospital cardiac arrest(OHCA)is a leading cause of death worldwide.AIM To explore factors influencing prehospital return of spontaneous circulation(P-ROSC)in patients with OHCA and develop a nomogram prediction model.METHODS Clinical 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 charac-teristic(ROC)and calibration curves.Decision curve analysis(DCA)was used to evaluate the model’s clinical utility.RESULTS Among 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.CONCLUSION The nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.展开更多
文摘This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.
文摘BACKGROUND Out-of-hospital cardiac arrest(OHCA)is a leading cause of death worldwide.AIM To explore factors influencing prehospital return of spontaneous circulation(P-ROSC)in patients with OHCA and develop a nomogram prediction model.METHODS Clinical 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 charac-teristic(ROC)and calibration curves.Decision curve analysis(DCA)was used to evaluate the model’s clinical utility.RESULTS Among 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.CONCLUSION The nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.
