APACHE II Score Immediately after Cardiac Arrest as a Predictor of Good Neurological Outcome in Out-of-Hospital Cardiac Arrest Patients Receiving Targeted Temperature Management

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.

Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks

BACKGROUND

To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN).

METHODS

The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively.

CONCLUSIONS

The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.