Predicting survival in out-of-hospital cardiac arrest patients undergoing targeted temperature management: The Polish Hypothermia Registry Risk Score

Prediction performance of scoring systems after out-of-hospital cardiac arrest: A systematic review and meta-analysis

INTRODUCTION

Ongoing changes in post resuscitation medicine and society create a range of ethical challenges for clinicians. Withdrawal of life-sustaining treatment is a very sensitive, complex decision to be made by the treatment team and the relatives together. According to the guidelines, prognostication after cardiopulmonary resuscitation should be based on a combination of clinical examination, biomarkers, imaging, and electrophysiological testing. Several prognostic scores exist to predict neurological and mortality outcome in post-cardiac arrest patients. We aimed to perform a meta-analysis and systematic review of current scoring systems used after out-of-hospital cardiac arrest (OHCA).

MATERIALS AND METHODS

Our systematic search was conducted in four databases: Medline, Embase, Central and Scopus on 24th April 2023. The patient population consisted of successfully resuscitated adult patients after OHCA. We included all prognostic scoring systems in our analysis suitable to estimate neurologic function as the primary outcome and mortality as the secondary outcome. For each score and outcome, we collected the AUC (area under curve) values and their CIs (confidence iterval) and performed a random-effects meta-analysis to obtain pooled AUC estimates with 95% CI. To visualize the trade-off between sensitivity and specificity achieved using different thresholds, we created the Summary Receiver Operating Characteristic (SROC) curves.

RESULTS

24,479 records were identified, 51 of which met the selection criteria and were included in the qualitative analysis. Of these, 24 studies were included in the quantitative synthesis. The performance of CAHP (Cardiac Arrest Hospital Prognosis) (0.876 [0.853-0.898]) and OHCA (0.840 [0.824-0.856]) was good to predict neurological outcome at hospital discharge, and TTM (Targeted Temperature Management) (0.880 [0.844-0.916]), CAHP (0.843 [0.771-0.915]) and OHCA (0.811 [0.759-0.863]) scores predicted good the 6-month neurological outcome. We were able to confirm the superiority of the CAHP score especially in the high specificity range based on our sensitivity and specificity analysis.

CONCLUSION

Based on our results CAHP is the most accurate scoring system for predicting the neurological outcome at hospital discharge and is a bit less accurate than TTM score for the 6-month outcome. We recommend the use of the CAHP scoring system in everyday clinical practice not only because of its accuracy and the best performance concerning specificity but also because of the rapid and easy availability of the necessary clinical data for the calculation.

Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management

Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.

External validation of cardiac arrest-specific prognostication scores developed for early prognosis estimation after out-of-hospital cardiac arrest in a Korean multicenter cohort

We evaluated the performance of cardiac arrest-specific prognostication scores developed for outcome prediction in the early hours after out-of-hospital cardiac arrest (OHCA) in predicting long-term outcomes using independent data. The following scores were calculated for 1,163 OHCA patients who were treated with targeted temperature management (TTM) at 21 hospitals in South Korea: OHCA, cardiac arrest hospital prognosis (CAHP), C-GRApH (named on the basis of its variables), TTM risk, 5-R, NULL-PLEASE (named on the basis of its variables), Serbian quality of life long-term (SR-QOLl), cardiac arrest survival, revised post-cardiac arrest syndrome for therapeutic hypothermia (rCAST), Polish hypothermia registry (PHR) risk, and PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages (PROLOGUE) scores and prediction score by Aschauer et al. Their accuracies in predicting poor outcome at 6 months after OHCA were determined using the area under the receiver operating characteristic curve (AUC) and calibration belt. In the complete-case analyses, the PROLOGUE score showed the highest AUC (0.923; 95% confidence interval [CI], 0.904–0.941), whereas the SR-QOLl score had the lowest AUC (0.749; 95% CI, 0.711–0.786). The discrimination performances were similar in the analyses after multiple imputation. The PROLOGUE, TTM risk, CAHP, NULL-PLEASE, 5-R, and cardiac arrest survival scores were well calibrated. The rCAST and PHR risk scores showed acceptable overall calibration, although they showed miscalibration under the 80% CI level at extreme prediction values. The OHCA score, C-GRApH score, prediction score by Aschauer et al., and SR-QOLl score showed significant miscalibration in both complete-case (P = 0.026, 0.013, 0.005, and < 0.001, respectively) and multiple-imputation analyses (P = 0.007, 0.018, < 0.001, and < 0.001, respectively). In conclusion, the discrimination performances of the prognostication scores were all acceptable, but some showed significant miscalibration.

Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework

BACKGROUND

Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.

METHODS

We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.

RESULTS

5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort.

CONCLUSION

We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.

Glasgow Coma Scale score of more than four on admission predicts in-hospital survival in patients after out-of-hospital cardiac arrest

AIM

The aim of the study was to assess the usefulness of the Glasgow Coma Scale (GCS) score assessed by EMS team in predicting survival to hospital discharge in patients after out-of-hospital cardiac arrest (OHCA).

METHODS

Silesian Registry of OHCA (SIL-OHCA) is a prospective, population-based regional registry of OHCAs. All cases of OHCAs between the 1st of January 2018 and the 31st of December 2018 were included. Data were collected by EMS using a paper-based, Utstein-style form. OHCA patients aged ≥18 years, with CPR attempted or continued by EMS, who survived to hospital admission, were included in the current analysis. Patients who did not achieve return of spontaneous circulation (ROSC) in the field, with missing data on GCS after ROSC or survival status at discharge were excluded from the study.

RESULTS

Two hundred eighteen patients with OHCA, who achieved ROSC, were included in the present analysis. ROC analysis revealed GCS = 4 as a cut-off value in predicting survival to discharge (AUC 0.735; 95%CI 0.655-0.816; p < 0.001). Variables significantly associated with in-hospital survival were young age, short response time, witnessed event, previous myocardial infarction, chest pain before OHCA, initial shockable rhythm, coronary angiography, and GCS > 4. On the other hand, epinephrine administration, intubation, the need for dispatching two ambulances, and/or a physician-staffed ambulance were associated with a worse prognosis. Multivariable logistic regression analysis revealed GCS > 4 as an independent predictor of in-hospital survival after OHCA (OR of 6.4; 95% CI 2.0-20.3; p < 0.0001). Other independent predictors of survival were the lack of epinephrine administration, previous myocardial infarction, coronary angiography, and the patient's age.

CONCLUSION

The survival to hospital discharge after OHCA could be predicted by the GCS score on hospital admission.