Risk prediction models for out-of-hospital cardiac arrest outcomes in England

Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: 2024 Update of the Utstein Out-of-Hospital Cardiac Arrest Registry Template

The Utstein Out-of-Hospital Cardiac Arrest Resuscitation Registry Template, introduced in 1991 and updated in 2004 and 2015, standardizes data collection to enable research, evaluation, and comparisons of systems of care. The impetus for the current update stemmed from significant advances in the field and insights from registry development and regional comparisons. This 2024 update involved representatives of the International Liaison Committee on Resuscitation and used a modified Delphi process. Every 2015 Utstein data element was reviewed for relevance, priority (core or supplemental), and improvement. New variables were proposed and refined. All changes were voted on for inclusion. The 2015 domains-system, dispatch, patient, process, and outcomes-were retained. Further clarity is provided for the definitions of out-of-hospital cardiac arrest attended resuscitation and attempted resuscitation. Changes reflect advancements in dispatch, early response systems, and resuscitation care, as well as the importance of prehospital outcomes. Time intervals such as emergency medical service response time now emphasize precise reporting of the times used. New flowcharts aid the reporting of system effectiveness for patients with an attempted resuscitation and system efficacy for the Utstein comparator group. Recognizing the varying capacities of emergency systems globally, the writing group provided a minimal dataset for settings with developing emergency medical systems. Supplementary variables are considered useful for research purposes. These revisions aim to elevate data collection and reporting transparency by registries and researchers and to advance international comparisons and collaborations. The overarching objective remains the improvement of outcomes for patients with out-of-hospital cardiac arrest.

Cardiac arrest and cardiopulmonary resuscitation outcome reports: 2024 update of the Utstein Out-of-Hospital Cardiac Arrest Registry template

The Utstein Out-of-Hospital Cardiac Arrest Resuscitation Registry Template, introduced in 1991 and updated in 2004 and 2015, standardizes data collection to enable research, evaluation, and comparisons of systems of care. The impetus for the current update stemmed from significant advances in the field and insights from registry development and regional comparisons. This 2024 update involved representatives of the International Liaison Committee on Resuscitation and used a modified Delphi process. Every 2015 Utstein data element was reviewed for relevance, priority (core or supplemental), and improvement. New variables were proposed and refined. All changes were voted on for inclusion. The 2015 domains-system, dispatch, patient, process, and outcomes-were retained. Further clarity is provided for the definitions of out-of-hospital cardiac arrest attended resuscitation and attempted resuscitation. Changes reflect advancements in dispatch, early response systems, and resuscitation care, as well as the importance of prehospital outcomes. Time intervals such as emergency medical service response time now emphasize precise reporting of the times used. New flowcharts aid the reporting of system effectiveness for patients with an attempted resuscitation and system efficacy for the Utstein comparator group. Recognizing the varying capacities of emergency systems globally, the writing group provided a minimal dataset for settings with developing emergency medical systems. Supplementary variables are considered useful for research purposes. These revisions aim to elevate data collection and reporting transparency by registries and researchers and to advance international comparisons and collaborations. The overarching objective remains the improvement of outcomes for patients with out-of-hospital cardiac arrest.

Association between out-of-hospital cardiac arrest quality indicator and prehospital management and clinical outcomes for major trauma

INTRODUCTION

It is unclear whether emergency medical service (EMS) agencies with good out-of-hospital cardiac arrest (OHCA) quality indicators also perform well in treating other emergency conditions. We aimed to evaluate the association of an EMS agency's non-traumatic OHCA quality indicators with prehospital management processes and clinical outcomes of major trauma.

METHODS

This retrospective cross-sectional study analyzed data from registers of nationwide, population-based OHCA (adult EMS-treated non-traumatic OHCA patients from 2017 to 2018) and major trauma (adult, EMS-treated, and injury severity score ≥16 trauma patients in 2018) in South Korea. We developed a prehospital ROSC prediction model to categorize EMS agencies into quartiles (Q1-Q4) based on the observed-to-expected (O/E) ROSC ratio for each EMS agency. We evaluated the national EMS protocol compliance of on-scene management according to O/E ROSC ratio quartile. The association between O/E ROSC ratio quartiles and trauma-related early mortality was determined in a multi-level logistic regression model by adjusted odds ratios (OR) and 95 % confidence intervals (95 % CI).

RESULTS

Among 30,034 severe trauma patients, 4,836 were analyzed. Patients in Q4 showed the lowest early mortality rate (5.6 %, 5.5 %, 4.8 %, and 3.4 % in Q1, Q2, Q3, and Q4, respectively). In groups Q1 to Q4, increasing compliance with the national EMS on-scene management protocol (trauma center transport, basic airway management for patients with altered mentality, spinal motion restriction for patients with spinal injury, and intravenous access for patients with hypotension) was observed (p for trend <0.05). Multivariable multi-level logistic regression analysis showed significantly lower early mortality in Q4 than in Q1 (adjusted OR [95 % CI] 0.56 [0.35-0.91]).

CONCLUSION

Major trauma patients managed by EMS agencies with high success rates in achieving prehospital ROSC in non-traumatic OHCA were more likely to receive protocol-based care and exhibited lower early mortality.

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

Health inequalities in cardiopulmonary resuscitation and use of automated electrical defibrillators in out-of-hospital cardiac arrest

Out of hospital cardiac arrest (OHCA) outcomes can be improved by strengthening the chain of survival, namely prompt cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED). However, provision of bystander CPR and AED use remains low due to individual patient factors ranging from lack of education to socioeconomic barriers and due to lack of resources such as limited availability of AEDs in the community. Although the impact of health inequalities on survival from OHCA is documented, it is imperative that we identify and implement strategies to improve public health and outcomes from OHCA overall but with a simultaneous emphasis on making care more equitable. Disparities in CPR delivery and AED use in OHCA exist based on factors including sex, education level, socioeconomic status, race and ethnicity, all of which we discuss in this review. Most importantly, we discuss the barriers to AED use, and strategies on how these may be overcome.

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.

Prediction Models for Return of Spontaneous Circulation in Patients with Cardiac Arrest: A Systematic Review and Critical Appraisal

Objectives

Prediction models for the return of spontaneous circulation (ROSC) in patients with cardiac arrest play an important role in helping physicians evaluate the survival probability and providing medical decision-making reference. Although relevant models have been developed, their methodological rigor and model applicability are still unclear. Therefore, this study aims to summarize the evidence for ROSC prediction models and provide a reference for the development, validation, and application of ROSC prediction models.

Methods

PubMed, Cochrane Library, Embase, Elsevier, Web of Science, SpringerLink, Ovid, CNKI, Wanfang, and SinoMed were systematically searched for studies on ROSC prediction models. The search time limit was from the establishment of the database to August 30, 2022. Two reviewers independently screened the literature and extracted the data. The PROBAST was used to evaluate the quality of the included literature.

Results

A total of 8 relevant prediction models were included, and 6 models reported the AUC of 0.662-0.830 in the modeling population, which showed good overall applicability but high risk of bias. The main reasons were improper handling of missing values and variable screening, lack of external validation of the model, and insufficient information of overfitting. Age, gender, etiology, initial heart rhythm, EMS arrival time/BLS intervention time, location, bystander CPR, witnessed during sudden arrest, and ACLS duration/compression duration were the most commonly included predictors. Obvious chest injury, body temperature below 33°C, and possible etiologies were predictive factors for ROSC failure in patients with TOHCA. Age, gender, initial heart rhythm, reason for the hospital visit, length of hospital stay, and the location of occurrence in hospital were the predictors of ROSC in IHCA patients.

Conclusion

The performance of current ROSC prediction models varies greatly and has a high risk of bias, which should be selected with caution. Future studies can further optimize and externally validate the existing models.

Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest

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 characteristic (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.

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.

The development of a risk-adjustment strategy to benchmark emergency medical service (EMS) performance in relation to out-of-hospital cardiac arrest in Australia and New Zealand

INTRODUCTION

The aim of this study was to develop a risk adjustment strategy, including effect modifiers, for benchmarking emergency medical service (EMS) performance for out-of-hospital cardiac arrest (OHCA) in Australia and New Zealand.

METHOD

Using 2017-2019 data from the Australasian Resuscitation Outcomes Consortium (Aus-ROC) OHCA Epistry, we included adults who received an EMS attempted resuscitation for a presumed medical OHCA. Logistic regression was applied to develop risk adjustment models for event survival (return of spontaneous circulation at hospital handover) and survival to hospital discharge/30 days. We examined potential effect modifiers, and assessed model discrimination and validity.

RESULTS

Both OHCA survival outcome models included EMS agency and the Utstein variables (age, sex, location of arrest, witnessed arrest, initial rhythm, bystander cardiopulmonary resuscitation, defibrillation prior to EMS arrival, and EMS response time). The model for event survival had good discrimination according to the concordance statistic (0.77) and explained 28% of the variation in survival. The corresponding figures for survival to hospital discharge/30 days were 0.87 and 49%. The addition of effect modifiers did little to improve the performance of either model.

CONCLUSION

The development of risk adjustment models with good discrimination is an important step in benchmarking EMS performance for OHCA. The Utstein variables are important in risk-adjustment, but only explain a small proportion of the variation in survival. Further research is required to understand what factors contribute to the variation in survival between EMS.

Prediction of return of spontaneous circulation in out-of-hospital cardiac arrest with non-shockable initial rhythm using point-of-care testing: a retrospective observational study

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is a public health concern, and many studies have been conducted on return of spontaneous circulation (ROSC) and its prognostic factors. Rotational thromboelastometry (ROTEM^®), a point-of-care testing (POCT) method, has been useful for predicting ROSC in patients with OHCA, but very few studies have focused on patients with non-shockable rhythm. We examined whether the parameters of POCT could predict ROSC in patients with OHCA and accompanying non-shockable rhythm.

METHODS

This is a single-center, retrospective observational study. Complete blood count, blood gas, and ROTEM POCT measurements were used. This study included patients with non-traumatic OHCA aged 18 years or older who were transported to the emergency department and evaluated using POCT between January 2013 and December 2021. The patients were divided into the ROSC and non-ROSC groups. Prehospital information and POCT parameters were compared using receiver operating characteristic (ROC) curve analysis, and further logistic regression analysis was performed.

RESULTS

Sixty-seven and 135 patients were in the ROSC and non-ROSC groups, respectively. The ROC curves showed a high area under the curve (AUC) for K⁺ of 0.77 (95% confidence interval [CI]: 0.71-0.83) and EXTEM amplitude 5 min after clotting time (A5) of 0.70 (95%CI: 0.62-0.77). The odds ratios for ROSC were as follows: female sex 3.67 (95%CI: 1.67-8.04); K⁺ 0.64 (95%CI: 0.48-0.84); and EXTEM A5 1.03 (95%CI: 1.01-1.06).

CONCLUSION

In OHCA patients with non-shockable rhythm, K⁺ level and the ROTEM parameter EXTEM A5 may be useful in predicting ROSC.

End-tidal carbon dioxide (ETCO2) at intubation and its increase after 10 minutes resuscitation predicts survival with good neurological outcome in out-of-hospital cardiac arrest patients

AIM

To evaluate whether end-tidal carbon dioxide (ETCO2) value at intubation and its early increase (10 min) after intubation predict both the survival to hospital admission and the survival at hospital discharge, including good neurological outcome (CPC 1-2), in patients with out-of-hospital cardiac arrest (OHCA).

METHODS

All consecutive OHCA patients of any etiology between 2015 and 2018 in Pavia Province (Italy) and Ticino Region (Switzerland) were considered. Patients died before ambulance arrival, with a "do-not-resuscitate" order, without ETCO2 value or with incomplete data were excluded.

RESULTS

The study population consisted of 668 patients. An ETCO2 value at intubation > 20 mmHg and its increase 10 min after intubation were independent predictors (after correction for known predictors of OHCA outcome) of survival to hospital admission and survival at hospital discharge. Relative to hospital discharge with good neurological outcome, ETCO2 at intubation and its 10-min change were confirmed predictors both individually and in a bivariable analysis (OR 1.83, 95 %CI 1.02-3.3; p = 0.04 and OR 3.9, 95 %CI 1.97-7.74; p < 0.001, respectively). This was confirmed also when accounting for gender, age, etiology and location. After further adjustment for bystander and CPR status, presenting rhythm and EMS arrival time, the ETCO2 change remained an independent predictor.

CONCLUSIONS

ETCO2 value > 20 mmHg at intubation and its increase during resuscitation improve the prediction of survival at hospital discharge with good neurological outcome of OHCA patients. ETCO2 increase during resuscitation is a more powerful predictor than ETCO2 at intubation. A larger prospective study to confirm this finding appears warranted.

Resuscitation Using ECPR During In-Hospital Cardiac Arrest (RESCUE-IHCA) Mortality Prediction Score and External Validation

OBJECTIVES

The aim of this study was to develop and validate a score to accurately predict the probability of death for adult extracorporeal cardiopulmonary resuscitation (ECPR).

BACKGROUND

ECPR is being increasingly used to treat refractory in-hospital cardiac arrest (IHCA), but survival varies from 20% to 40%.

METHODS

Adult patients with extracorporeal membrane oxygenation for IHCA (ECPR) were identified from the American Heart Association GWTG-R (Get With the Guidelines-Resuscitation) registry. A multivariate survival prediction model and score were developed to predict hospital death. Findings were externally validated in a separate cohort of patients from the Extracorporeal Life Support Organization registry who underwent ECPR for IHCA.

RESULTS

A total of 1,075 patients treated with ECPR were included. Twenty-eight percent survived to discharge in both the derivation and validation cohorts. A total of 6 variables were associated with in-hospital death: age, time of day, initial rhythm, history of renal insufficiency, patient type (cardiac vs noncardiac and medical vs surgical), and duration of the cardiac arrest event, which were combined into the RESCUE-IHCA (Resuscitation Using ECPR During IHCA) score. The model had good discrimination (area under the curve: 0.719; 95% CI: 0.680-0.757) and acceptable calibration (Hosmer and Lemeshow goodness of fit P = 0.079). Discrimination was fair in the external validation cohort (area under the curve: 0.676; 95% CI: 0.606-0.746) with good calibration (P = 0.66), demonstrating the model's ability to predict in-hospital death across a wide range of probabilities.

CONCLUSIONS

The RESCUE-IHCA score can be used by clinicians in real time to predict in-hospital death among patients with IHCA who are treated with ECPR.

The epidemiology of out-of-hospital cardiac arrest in Australia and New Zealand: A binational report from the Australasian Resuscitation Outcomes Consortium (Aus-ROC)

INTRODUCTION

The Australasian Resuscitation Outcomes Consortium (Aus-ROC) out-of-hospital cardiac arrest (OHCA) Epistry (Epidemiological Registry) now covers 100% of Australia and New Zealand (NZ). This study reports and compares the Utstein demographics, arrest characteristics and outcomes of OHCA patients across our region.

METHODS

We included all OHCA cases throughout 2019 as submitted to the Epistry by the eight Australian and two NZ emergency medical services (EMS). We calculated crude and age-standardised incidence rates and performed a national and EMS regional comparison.

RESULTS

We obtained data for 31,778 OHCA cases for 2019: 26,637 in Australia and 5,141 in NZ. Crude incidence was 107.9 per 100,000 person-years in Australia and 103.2/100,000 in NZ. Overall, the majority of OHCAs occurred in adults (96%), males (66%), private residences (76%), were unwitnessed (63%), of presumed medical aetiology (83%), and had an initial monitored rhythm of asystole (64%). In non-EMS-witnessed cases, 38% received bystander CPR and 2% received public defibrillation. Wide variation was seen between EMS regions for all OHCA demographics, arrest characteristics and outcomes. In patients who received an EMS-attempted resuscitation (13,664/31,778): 28% (range across EMS=13.1% to 36.7%) had return of spontaneous circulation (ROSC) at hospital arrival and 13% (range across EMS=9.9% to 20.7%) survived to hospital discharge/30-days. Survival in the Utstein comparator group (bystander-witnessed in shockable rhythm) varied across the EMS regions between 27.4% to 42.0%.

CONCLUSION

OHCA across Australia and NZ has varied incidence, characteristics and survival. Understanding the variation in survival and modifiable predictors is key to informing strategies to improve outcomes.

Effects of Prehospital Factors on Survival of Out-Of-Hospital Cardiac Arrest Patients: Age-Dependent Patterns

Many prehospital factors that are known to influence survival rates after out-of-hospital cardiac arrest (OHCA) have been rarely studied as to how their influence varies depending on the age. In this study, we tried to find out what prehospital factors affect the survival rate after OHCA by age groups and how large the effect size of those factors is in each age group. We used the South Korean OHCA registry, which includes information on various prehospital factors relating OHCA and final survival status. The association between prehospital factors and survival was explored through logistic regression analyses for each age group. The effects of prehospital factors vary depending on the patient’s age. Being witnessed was relatively more influential in younger patients and the presence of first responders became more important as patients became older. While bystander cardiopulmonary resuscitation (CPR) did not appear to significantly affect survival in younger people, use of an automated external defibrillator (AED) showed the largest effect size on the survival in all age groups. Since the pathophysiology and etiologies of OHCA vary according to age, more detailed information on life support by age is needed for the development and application of more specialized protocols for each age.