Usefulness of the NULL-PLEASE Score to Predict Survival in Out-of-Hospital Cardiac Arrest

Validation of the NULL-EASE Score for Predicting Survival in a Multiethnic Asian Cohort of Out-of-Hospital Cardiac Arrest

BACKGROUND

NULL-PLEASE is a simple and accurate clinical scoring system developed in a Western cohort of patients with out-of-hospital cardiac arrest (OHCA). The need for blood test results limits its use in early stages of care. We adapted and validated the NULL-EASE score (without laboratory tests) in an independent, multiethnic Asian cohort of patients with out-of-hospital cardiac arrest.

METHODS AND RESULTS

Using the Singapore OHCA registry, we included consecutive adult patients with out-of-hospital cardiac arrest who survived to hospital admission between April 2010 to December 2020. In-hospital mortality was the primary outcome. Logistic regression analyses were performed with STATA MP v18. Of 3274 patients (median age 64, interquartile range 54-75; 67.9% male) included in the study, 2476 (75.6%) had in-hospital mortality. NULL-EASE score was significantly lower in survivors compared with nonsurvivors (median [inter quartile range] 3 [1-4] versus 6 [4-7]; P<0.001) and strongly predictive of mortality (area under receiver operating characteristic, 0.81 [95% CI, 0.79-0.83]). Patients with a score of ≥3 had higher odds of mortality (adjusted odds ratio, 8.11 [95% CI, 6.57-10.00]) when compared with those with lower scores, after adjusting for sex, residential arrest, diabetes, respiratory disease, and stroke. A cutoff value of ≥3 predicted mortality with 92.2% sensitivity, 84.1% positive predictive value, 46.1% specificity, and 65.5% negative predictive value. NULL-EASE score performed better in younger compared with older patients (area under receiver operating characteristic, 0.82 versus 0.77, P=0.008).

CONCLUSIONS

The NULL-EASE score has good discriminative performance (sensitivity and accuracy) in our multiethnic Asian cohort, but the cutoff of ≥3 falls short of the desired level of specificity for therapeutic decision-making.

Simultaneous prognostic score validation in patients with out-of-hospital cardiac arrest by a post-hoc analysis based on national multicenter registry

Using a nationwide multicenter prospective registry in Japan's data, we calculated prognostic and predictive scores, including the Out-of-Hospital Cardiac Arrest (OHCA); Cardiac Arrest Hospital Prognosis (CAHP); Nonshockable rhythm, Unwitnessed arrest, Long no-flow or Long low-flow period, blood PH < 7.2, Lactate > 7.0 mmol/L, End-stage chronic kidney disease on dialysis, Age ≥ 85 years, Still resuscitation, and Extracardiac cause (NULL-PLEASE); revised post-Cardiac Arrest Syndrome for Therapeutic hypothermia (rCAST); and MIRACLE2 scores, for adult patients with cardiac arrest. The MIRACLE2 score was validated with the modified MIRACLE2 score, which excludes information of pupillary reflexes. Each score was calculated only for the cases with no missing data for the variables used. These scores' accuracies were compared using neurological outcomes 30 days after out-of-hospital cardiac arrest (OOHCA). Patients with a cerebral performance category scale of 1 or 2 were designated as having favorable neurological outcomes. Each score's discrimination ability was evaluated by the receiver operating characteristic curve's area under the curve (AUC). To assess in detail in areas of high specificity and high sensitivity, which are areas of interest to clinicians, partial AUCs were also used. The analysis included 11,924 hospitalized adult patients. The AUCs of the OHCA, MIRACLE2, CAHP, rCAST, and NULL-PLEASE scores for favorable neurological outcomes were 0.713, 0.727, 0.785, 0.761, and 0.831, respectively. The CAHP and NULL-PLEASE scores were significantly more accurate than the rest. Accuracies did not differ significantly between the CAHP and NULL-PLEASE scores. The NULL-PLEASE score was significantly better at discriminating favorable neurological prognoses at 30 days in patients with OOHCA compared to other scoring systems.

Clinical Factors Associated With Mode of Death Following Cardiac Arrest

BACKGROUND

Death after resuscitation from cardiac arrest is common. Although associated factors have been identified, knowledge about their relationship with specific modes of death is limited.

OBJECTIVE

To identify clinical factors associated with specific modes of death following cardiac arrest.

METHODS

This study involved a retrospective medical record review of patients admitted to a single health care center from January 2015 to March 2020 after resuscitation from cardiac arrest who died during their index hospitalization. Mode of death was categorized as either brain death, withdrawal of life-sustaining therapies due to neurologic causes, death due to medical causes, or withdrawal of life-sustaining therapies due to patient preference. Clinical characteristics across modes of death were compared.

RESULTS

The analysis included 731 patients. Death due to medical causes was the most common mode of death. Compared with the other groups of patients, those with brain death were younger, had fewer comorbidities, were more likely to have experienced unwitnessed and longer cardiac arrest, and had more severe acidosis and hyperglycemia on presentation. Patients who died owing to medical causes or withdrawal of life-sustaining therapies due to patient preference were older and had more comorbidities, fewer unfavorable cardiac arrest characteristics, and fewer days between cardiac arrest and death.

CONCLUSIONS

Significant associations were found between several clinical characteristics and specific mode of death following cardiac arrest. Decision-making regarding withdrawal of care after resuscitation from cardiac arrest should be based on a multimodal approach that takes account of a variety of personal and clinical factors.

A novel score to predict in-hospital mortality for patients with acute coronary syndrome and out-of-hospital cardiac arrest: the FACTOR study

INTRODUCTION

Acute coronary syndromes (ACS) represent a substantial global healthcare challenge. In its most severe form, it can lead to out-of-hospital cardiac arrest (OHCA). Despite medical advancements, survival rates in OHCA patients remain low. Further, the prediction of outcomes in these patients poses a challenge to all health care providers involved. This study aims at developing a score with variables available on admission to assess in-hospital mortality of patients with OHCA undergoing coronary angiography.

METHOD

All patients with OHCA due to ACS admitted to a tertiary care center were included. A multivariate logistic regression analysis was conducted to explore the association between clinical variables and in-hospital all-cause mortality. A scoring system incorporating variables available upon admission to assess individual patients' risk of in-hospital mortality was developed (FACTOR score). The score was then validated.

RESULTS

A total of 291 patients were included in the study, with a median age of 65 [56-73] years, including 47 women (16.2%). The in-hospital mortality rate was 41.2%. A prognostic model was developed in the derivation cohort (n = 138) and included the following variables: age, downtime, first detected rhythm, and administration of epinephrine. The area under the curve for the FACTOR score was 0.823 (95% CI 0.737-0.894) in the derivation cohort and 0.828 (0.760-0.891) in the validation cohort (n = 153).

CONCLUSION

The FACTOR score demonstrated a reliable prognostic tool for health care providers in assessing in-hospital mortality of OHCA patients. Early acknowledgement of a poor prognosis may help in patient management and allocation of resources.

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.

Cardiac Catheterization Laboratory Management of the Comatose Adult Patient With an Out-of-Hospital Cardiac Arrest: A Scientific Statement From the American Heart Association

Out-of-hospital cardiac arrest is a leading cause of death, accounting for ≈50% of all cardiovascular deaths. The prognosis of such individuals is poor, with <10% surviving to hospital discharge. Survival with a favorable neurologic outcome is highest among individuals who present with a witnessed shockable rhythm, received bystander cardiopulmonary resuscitation, achieve return of spontaneous circulation within 15 minutes of arrest, and have evidence of ST-segment elevation on initial ECG after return of spontaneous circulation. The cardiac catheterization laboratory plays an important role in the coordinated Chain of Survival for patients with out-of-hospital cardiac arrest. The catheterization laboratory can be used to provide diagnostic, therapeutic, and resuscitative support after sudden cardiac arrest from many different cardiac causes, but it has a unique importance in the treatment of cardiac arrest resulting from underlying coronary artery disease. Over the past few years, numerous trials have clarified the role of the cardiac catheterization laboratory in the management of resuscitated patients or those with ongoing cardiac arrest. This scientific statement provides an update on the contemporary approach to managing resuscitated patients or those with ongoing cardiac arrest.

A simple scoring rule to predict survival to discharge after out of hospital cardiac arrest at the time of ED arrival

BACKGROUND

It is important to be able to predict the chance of survival to hospital discharge upon ED arrival in order to determine whether to continue or terminate resuscitation efforts after out of hospital cardiac arrest. This study was conducted to develop and validate a simple scoring rule that could predict survival to hospital discharge at the time of ED arrival.

METHODS

This was a multicenter retrospective cohort study based on a nationwide registry (Korean Cardiac Arrest Research Consortium) of out of hospital cardiac arrest (OHCA). The study included adult OHCA patients older than 18 years old, who visited one of 33 tertiary hospitals in South Korea from September 1st, 2015 to June 30th, 2020. Among 12,321 screened, 5471 patients were deemed suitable for analysis after exclusion. Pre-hospital ROSC, pre-hospital witness, shockable rhythm, initial pH, and age were selected as the independent variables. The dependent variable was set to be the survival to hospital discharge. Multivariable logistic regression (LR) was performed, and the beta-coefficients were rounded to the nearest integer to formulate the scoring rule. Several machine learning algorithms including the random forest classifier (RF), support vector machine (SVM), and K-nearest neighbor classifier (K-NN) were also trained via 5-fold cross-validation over a pre-specified grid, and validated on the test data. The prediction performances and the calibration curves of each model were obtained. Pre-processing of the registry was done using R, model training & optimization using Python.

RESULTS

A total of 5471 patients were included in the analysis. The AUROC of the scoring rule over the test data was 0.7620 (0.7311-0.7929). The AUROCs of the machine learning classifiers (LR, SVM, k-NN, RF) were 0.8126 (0.7748-0.8505), 0.7920 (0.7512-0.8329), 0.6783 (0.6236-0.7329), and 0.7879 (0.7465-0.8294), respectively.

CONCLUSION

A simple scoring rule consisting of five, binary variables could aid in the prediction of the survival to hospital discharge at the time of ED arrival, showing comparable results to conventional machine learning classifiers.

Bayesian network predicted variables for good neurological outcomes in patients with out-of-hospital cardiac arrest

Out-of-hospital cardiac arrest (OHCA) is linked to a poor prognosis and remains a public health concern. Several studies have predicted good neurological outcomes of OHCA. In this study, we used the Bayesian network to identify variables closely associated with good neurological survival outcomes in patients with OHCA. This was a retrospective observational study using the Japan Association for Acute Medicine OHCA registry. Fifteen explanatory variables were used, and the outcome was one-month survival with Glasgow-Pittsburgh cerebral performance category (CPC) 1-2. The 2014-2018 dataset was used as training data. The variables selected were identified and a sensitivity analysis was performed. The 2019 dataset was used for the validation analysis. Four variables were identified, including the motor response component of the Glasgow Coma Scale (GCS M), initial rhythm, age, and absence of epinephrine. Estimated probabilities were increased in the following order: GCS M score: 2-6; epinephrine: non-administered; initial rhythm: spontaneous rhythm and shockable; and age: <58 and 59-70 years. The validation showed a sensitivity of 75.4% and a specificity of 95.4%. We identified GCS M score of 2-6, initial rhythm (spontaneous rhythm and shockable), younger age, and absence of epinephrine as variables associated with one-month survival with CPC 1-2. These variables may help clinicians in the decision-making process while treating patients with OHCA.

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions.

Scoring Systems to Predict Survival or Neurological Recovery after Out-of-hospital Cardiac Arrest

Numerous prediction scores have been developed to better inform clinical decision-making following out-of-hospital cardiac arrest (OHCA), however, there is no consensus among clinicians over which score to use. The aim of this review was to identify and compare scoring systems to predict survival and neurological recovery in patients with OHCA. A structured literature search of the MEDLINE database was carried out from inception to December 2021. Studies developing or validating scoring systems to predict outcome following OHCA were selected. Relevant data were extracted and synthesised for narrative review. In total, 16 scoring systems were identified: one predicting the probability of return of spontaneous circulation, six predicting survival to hospital discharge and nine predicting neurological outcome. NULL-PLEASE and CAST are recommended as the best scores to predict mortality and neurological outcome, respectively, due to the extent of external validation, ease of use and high predictive value of the variables. Whether use of these scores can lead to more cost-effective service delivery remains unclear.

External validation of the simple NULL-PLEASE clinical score in predicting outcomes of out-of-hospital cardiac arrest in the Danish population - A nationwide registry-based study

AIM

The NULL-PLEASE score (Nonshockable rhythm, Unwitnessed arrest, Long no-flow or Long low-flow period, blood pH < 7.2, Lactate > 7.0 mmol/L, End-stage renal disease on dialysis, Age ≥85 years, Still resuscitation, and Extracardiac cause) may identify patients with out-of-hospital cardiac arrest (OHCA) unlikely to survive. We aimed to validate the NULL-PLEASE score in a nationwide setting.

METHODS

We used Danish nationwide registry data from 2001 to 2019 and identified OHCA survivors with return of spontaneous circulation (ROSC) or ongoing cardiopulmonary resuscitation at hospital arrival. The primary outcome was 1-day mortality. Secondary outcomes were 30-day mortality and the combined outcome of 1-year mortality or anoxic brain damage. The risks of outcomes were estimated using logistic regression with a NULL-PLEASE score of 0 as reference (range 0-14). The predictive ability of the score was examined using the area under the receiver operating characteristics (AUCROC) curve.

RESULTS

A total of 3,881 patients were included in the analyses. One-day mortality was 35%, 30-day mortality was 61%, and 68% experienced the combined outcome. For a NULL-PLEASE score ≥9 (n = 244) the absolute risks were: 1-day mortality: 80.7% (95% confidence interval [CI]: 75.8-85.7%); 30-day mortality: 98.0% (95% CI: 96.2-99.7%); and the combined outcome: 98.4% (95% CI: 96.8-100.0%). Corresponding AUCROC values were 0.800 (95% CI: 0.786-0.814) for 1-day mortality, 0.827 (95% CI: 0.814-0.840) for 30-day mortality, and 0.828 (95% CI: 0.815-0.841) for the combined outcome.

CONCLUSIONS

In a nationwide OHCA-cohort, AUCROC values for the predictive ability of NULL-PLEASE were high for all outcomes. However, some survived even with high NULL-PLEASE scores.

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.

Differential Effectiveness of Hypothermic Targeted Temperature Management According to the Severity of Post-Cardiac Arrest Syndrome

International guidelines recommend targeted temperature management (TTM) to improve the neurological outcomes in adult patients with post-cardiac arrest syndrome (PCAS). However, it still remains unclear if the lower temperature setting (hypothermic TTM) or higher temperature setting (normothermic TTM) is superior for TTM. According to the most recent large randomized controlled trial (RCT), hypothermic TTM was not found to be associated with superior neurological outcomes than normothermic TTM in PCAS patients. Even though this represents high-quality evidence obtained from a well-designed large RCT, we believe that we still need to continue investigating the potential benefits of hypothermic TTM. In fact, several studies have indicated that the beneficial effect of hypothermic TTM differs according to the severity of PCAS, suggesting that there may be a subgroup of PCAS patients that is especially likely to benefit from hypothermic TTM. Herein, we summarize the results of major RCTs conducted to evaluate the beneficial effects of hypothermic TTM, review the recent literature suggesting the possibility that the therapeutic effect of hypothermic TTM differs according to the severity of PCAS, and discuss the potential of individualized TTM.

Predicting Survived Events in Nontraumatic Out-of-Hospital Cardiac Arrest: A Comparison Study on Machine Learning and Regression Models

BACKGROUND

Prediction of early outcomes of nontraumatic out-of-hospital cardiac arrest (OHCA) by emergency physicians is inaccurate.

OBJECTIVE

Our aim was to develop and validate practical machine learning (ML)-based models to predict early outcomes of nontraumatic OHCA for use in the emergency department (ED). We compared their discrimination and calibration performances with the traditional logistic regression (LR) approach.

METHODS

Between October 1, 2017 and March 31, 2020, prehospital resuscitation was performed on 17,166 OHCA patients. There were 8157 patients 18 years or older with nontraumatic OHCA who received continued resuscitation in the ED included for analysis. Eleven demographic and resuscitation predictor variables were extracted to predict survived events, defined as any sustained return of spontaneous circulation until in-hospital transfer of care. Prediction models based on random forest (RF), multilayer perceptron (MLP), and LR were created with hyperparameter optimization. Model performances on internal and external validation were compared using discrimination and calibration statistics.

RESULTS

The three models showed similar discrimination performances with c-statistics values of 0.712 (95% confidence interval [CI] 0.711-0.713) for LR, 0.714 (95% CI 0.712-0.717) for RF, and 0.712 (95% CI 0.710-0.713) for MLP models on external validation. For calibration, MLP model had a better performance (slope of calibration regression line = 1.10, intercept = -0.09) than LR (slope = 1.17, intercept = -0.11) and RF (slope = 1.16, intercept= -0.10).

CONCLUSIONS

Two practical ML-based and one regression-based clinical prediction models of nontraumatic OHCA for survived events were developed and validated. The ML-based models did not outperform LR in discrimination, but the MLP model showed a better calibration performance.

Performance of OHCA, NULL-PLEASE and CAHP scores to predict survival in Out-of-Hospital Cardiac Arrest due to acute coronary syndrome

AIM

Out-of-hospital Cardiac Arrest (OHCA) carries a poor prognostic with high mortality rates and multiple scoring systems have been developed to assess its prognostic. This study sought to evaluate the performance of three prognostic scores to predict survival in OHCA patients due to acute coronary syndrome (ACS).

METHODS AND RESULTS

This is an observational, monocentric study including 386 consecutive patients treated for OHCA due to ACS, treated by percutaneous coronary intervention, between 2007 and 2019. The OHCA, NULL-PLEASE and CAHP scores were calculated respectively for 370 patients (95.9%), 371 patients (96.1%) and 350 patients (90.7%). A C-statistic analysis was performed to determine score performance. The areas under the curve for the OHCA, NULL-PLEASE and CAHP scores were 0.861 (95% CI, 0.823-0.898), 0.789 (95% CI, 0.744-0.834) and 0.830 (95% CI, 0.788-0.872) respectively demonstrating good performance. The OHCA score performed better than the NULL-PLEASE score (p = 0.001), and there was no difference between the CAHP and the NULL-PLEASE score (p = 0.062) nor between the OHCA and the CAHP score (p = 0.105).

CONCLUSION

The OHCA score, the NULL-PLEASE score and the CAHP score performed well in predicting in-hospital death in patients presenting OHCA secondary to ACS. The NULL-PLEASE score is the easiest to use but performed less accurately than the OHCA score.

Out-of-hospital cardiac arrest: A systematic review of current risk scores to predict survival

IMPORTANCE

The arrest and the post-arrest period are an incredibly emotionally traumatic time for family and friends of the affected individual. There is a need to assess prognosis early in the patient pathway to offer objective, realistic and non-emotive information to the next-of-kin regarding the likelihood of survival.

OBJECTIVE

To present a systematic review of the clinical risk scores available to assess patients on admission following out-of-hospital cardiac arrest (OHCA) which can predict in-hospital mortality.

EVIDENCE REVIEW

A systematic search of online databases Embase, MEDLINE and Cochrane Central Register of Controlled Trials was conducted up until 20th November 2020.

FINDINGS

Out of 1,817 initial articles, we identified a total of 28 scoring systems, with 11 of the scores predicting mortality following OHCA included in this review. The majority of the scores included arrest characteristics (initial rhythm and time to return of spontaneous circulation) as prognostic indicators. Out of these, the 3 most clinically-useful scores, namely those which are easy-to-use, comprise of commonly available parameters and measurements, and which have high predictive value are the OHCA, NULL-PLEASE, and rCAST scores, which appear to perform similarly. Of these, the NULL-PLEASE score is the easiest to calculate and has also been externally validated.

CONCLUSIONS

Clinicians should be aware of these risk scores, which can be used to provide objective, nonemotive and reproducible information to the next-of-kin on the likely prognosis following OHCA. However, in isolation, these scores should not form the basis for clinical decision-making.