Dataset Information

Routine angiography in survivors of out of hospital cardiac arrest with return of spontaneous circulation: a single site registry.

ABSTRACT:

Background

Coronary revascularization in resuscitated out of hospital cardiac arrest (OOHCA) patients has been associated with improved survival.

Methods

This was a retrospective review of patients with OOHCA between 01/07/2007 and 31/03/2009 surviving to hospital admission. Cardiac risk factors, demographics, treatment times, electrocardiogram (ECG), angiographic findings and in-hospital outcomes were recorded.

Results

Of the 78 patients, 63 underwent coronary angiography. Traditional cardiac risk factors were common in this group. Chest pain occurred in 33.3% pre-arrest, 59.0% were initially treated at a peripheral hospital, 83.3% had documented ventricular tachycardia or ventricular fibrillation, 55.1% had specific ECG changes, 65.4% had acute myocardial infarction (AMI) as the cause of OOHCA and the majority had multi-vessel disease. ST elevation strongly predicted AMI. The in-hospital survival was 67.9% with neurological deficit in 13.2% of survivors. The group of patients who had an angiogram were more likely to have AMI as a cause of cardiac arrest (71.4% vs 40.0%, p = 0.01) and more likely to have survived to discharge (74.6% vs 40.0%, p < 0.01). Poor outcome was associated with older age, cardiogenic shock, longer transfer times, diabetes, renal impairment and a long duration to return of spontaneous circulation.

Conclusions

Acute myocardial infarction was the commonest cause of OOHCA and a high rate of survival to discharge was seen with a strategy of routine angiography and revascularization.

SUBMITTER: Wijesekera VA

PROVIDER: S-EPMC3944915 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWe performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods.MethodsWe performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome.ResultsA total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets.ConclusionWe established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

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