The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest

Prognostic effects of cardiopulmonary resuscitation (CPR) start time and the interval between CPR to extracorporeal cardiopulmonary resuscitation (ECPR) on patient outcomes under extracorporeal membrane oxygenation (ECMO): a single-center, retrospective observational study

BACKGROUND

The impact of the chronological sequence of events, including cardiac arrest (CA), initial cardiopulmonary resuscitation (CPR), return of spontaneous circulation (ROSC), and extracorporeal cardiopulmonary resuscitation (ECPR) implementation, on clinical outcomes in patients with both out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA), is still not clear. The aim of this study was to investigate the prognostic effects of the time interval from collapse to start of CPR (no-flow time, NFT) and the time interval from start of CPR to implementation of ECPR (low-flow time, LFT) on patient outcomes under Extracorporeal Membrane Oxygenation (ECMO).

METHODS

This single-center, retrospective observational study was conducted on 48 patients with OHCA or IHCA who underwent ECMO at Hamad General Hospital (HGH), the tertiary governmental hospital of Qatar, between February 2016 and March 2020. We investigated the impact of prognostic factors such as NFT and LFT on various clinical outcomes following cardiac arrest, including 24-hour survival, 28-day survival, CPR duration, ECMO length of stay (LOS), ICU LOS, hospital LOS, disability (assessed using the modified Rankin Scale, mRS), and neurological status (evaluated based on the Cerebral Performance Category, CPC) at 28 days after the CA.

RESULTS

The results of the adjusted logistic regression analysis showed that a longer NFT was associated with unfavorable clinical outcomes. These outcomes included longer CPR duration (OR: 1.779, 95%CI: 1.218-2.605, P = 0.034) and decreased survival rates for ECMO at 24 h (OR: 0.561, 95%CI: 0.183-0.903, P = 0.009) and 28 days (OR: 0.498, 95%CI: 0.106-0.802, P = 0.011). Additionally, a longer LFT was found to be associated only with a higher probability of prolonged CPR (OR: 1.818, 95%CI: 1.332-3.312, P = 0.006). However, there was no statistically significant connection between either the NFT or the LFT and the improvement of disability or neurologically favorable survival after 28 days of cardiac arrest.

CONCLUSIONS

Based on our findings, it has been determined that the NFT is a more effective predictor than the LFT in assessing clinical outcomes for patients with OHCA or IHCA who underwent ECMO. This understanding of their distinct predictive abilities enables medical professionals to identify high-risk patients more accurately and customize their interventions accordingly.

Cardiac power output is associated with cardiovascular related mortality in the ICU in post-cardiac arrest patients

AIM

Although brain injury is the main determinant of poor outcome following cardiac arrest (CA), cardiovascular failure is the leading cause of death within the first days after CA. However, it remains unclear which hemodynamic parameter is most suitable for its early recognition. We investigated the association of cardiac power output (CPO) with early mortality in intensive care unit (ICU) after CA and with mortality related to post-CA cardiovascular failure.

METHODS

Retrospective analysis of adult comatose survivors of CA admitted to the ICU of a University Hospital. Exclusion criteria were treatment with extracorporeal cardiopulmonary resuscitation, ECMO or intra-aortic balloon pump. We retrieved CA characteristics; we recorded mean arterial pressure, cardiac output, CPO (as derived parameter) and the vasoactive-inotropic score for the first 72 hours after ROSC, at intervals of 8 hours. ICU death was defined as related to post-CA cardiovascular failure when death occurred as a direct consequence of shock, secondary CA or fatal arrhythmia, or related to neurological injury if this led to withdrawal of life-sustaining therapy or brain death.

RESULTS

Among the 217 patients (median age 66 years, 65% male, 61.8% out-of-hospital CA), 142 (65.4%) died in ICU: 99 (69.7%) patients died from neurological injury and 43 (30.3%) from cardiovascular-related causes. Comparing the evolution over time of CPO between survivors and non-survivors, a statistically significant difference was found only at +8 hours after CA (p = 0.0042). In multivariable analysis, CPO at 8-hour was significantly associated with cardiovascular-related mortality (p = 0.007).

CONCLUSIONS

In post-CA patients, the 8-hour CPO is an independent factor associated with ICU cardiovascular-related mortality.

Delayed arrival of advanced life support adversely affects the neurological outcome in a multi-tier emergency response system

AIM

Prehospital management of out-of-hospital cardiac arrest (OHCA) is based on basic life support, with the addition of advanced life support (ALS) if possible. This study aimed to investigate the effect of delayed arrival of ALS on neurological outcomes of patients with OHCA at hospital discharge.

METHODS

This was a retrospective study of a registry of patients with OHCA. A multi-tier emergency response system was established in the study area. ALS was initiated when the second-arrival team arrived at the scene. A restricted cubic spline curve was used to investigate the relationship between the response time interval of the second-arrival team and neurological outcomes at hospital discharge. Multivariable logistic regression analysis was performed to assess the independent association between the response time interval of the second-arrival team and neurological outcomes of patients at hospital discharge.

RESULTS

A total of 3186 adult OHCA patients who received ALS at the scene were included in the final analysis. A restricted cubic spline curve showed that a long response time interval of the second-arrival team was correlated with a high likelihood of poor neurological outcomes. Meanwhile, multivariable logistic regression analysis showed that a long response time interval of the second-arrival team was independently associated with poor neurological outcomes (odds ratio, 1.10; 95% confidence interval, 1.03-1.17).

CONCLUSION

In a multi-tiered prehospital emergency response system, the delayed arrival of ALS was associated with poor neurological outcomes at hospital discharge.

A retrospective study of mortality for perioperative cardiac arrests toward a personalized treatment

Perioperative cardiac arrest (POCA) is associated with a high mortality rate. This work aimed to study its prognostic factors for risk mitigation by means of care management and planning. A database of 380,919 surgeries was reviewed, and 150 POCAs were curated. The main outcome was mortality prior to hospital discharge. Patient demographic, medical history, and clinical characteristics (anesthesia and surgery) were the main features. Six machine learning (ML) algorithms, including LR, SVC, RF, GBM, AdaBoost, and VotingClassifier, were explored. The last algorithm was an ensemble of the first five algorithms. k-fold cross-validation and bootstrapping minimized the prediction bias and variance, respectively. Explainers (SHAP and LIME) were used to interpret the predictions. The ensemble provided the most accurate and robust predictions (AUC = 0.90 [95% CI, 0.78–0.98]) across various age groups. The risk factors were identified by order of importance. Surprisingly, the comorbidity of hypertension was found to have a protective effect on survival, which was reported by a recent study for the first time to our knowledge. The validated ensemble classifier in aid of the explainers improved the predictive differentiation, thereby deepening our understanding of POCA prognostication. It offers a holistic model-based approach for personalized anesthesia and surgical treatment.

Association between sex and survival after non-traumatic out of hospital cardiac arrest: A systematic review and meta-analysis

BACKGROUND

Existing studies have shown conflicting results regarding the relationship of sex with survival after out of hospital cardiac arrest (OHCA). This systematic review evaluates the association of female sex with survival to discharge and survival to 30 days after non-traumatic OHCA.

METHODS

We searched Medline, Embase, CINAHL, Web of Science, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews from inception through June 2021 for studies evaluating female sex as a predictor of survival in adult patients with non-traumatic cardiac arrest. Random-effects inverse variance meta-analyses were performed to calculate pooled odds ratios (ORs) with 95% confidence intervals (CI). The GRADE approach was used to assess evidence quality.

RESULTS

Thirty studies including 1,068,788 patients had female proportion of 41%. There was no association for female sex with survival to discharge (OR 1.03, 95% CI 0.95-1.12; I²=89%). Subgroup analysis of low risk of bias studies demonstrated increased survival to discharge for female sex (OR 1.20, 95% CI 1.18-1.23; I²=0%) and with high certainty, the absolute increase in survival was 2.2% (95% CI 0.1%-3.6%). Female sex was not associated with survival to 30 days post-OHCA (OR 1.02, 95% CI 0.92-1.14; I²=79%).

CONCLUSIONS

In adult patients experiencing OHCA, with high certainty in the evidence from studies with low risk of bias, female sex had a small absolute difference for the outcome survival to discharge and no difference in survival at 30 days. Future models that aim to stratify risk of survival post-OHCA should focus on sex-specific factors as opposed to sex as an isolated prognostic factor.

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.

Out-of-hospital cardiac arrest in the Algarve region of Portugal: a retrospective registry trial with outcome data

BACKGROUND AND IMPORTANCE

Out-of-hospital cardiac arrest is a leading cause of death in Europe. An understanding of region-specific factors is essential for informing strategies to improve survival.

DESIGN

This retrospective observational study included all out-of-hospital cardiac arrest patients attended by the Emergency Medical Service of the Algarve in 2019. Outcome data were derived from hospital records.

MAIN RESULTS

In 2019, there were 850 out-of-hospital cardiac arrests treated with cardiopulmonary resuscitation in the Algarve, representing a population incidence of 189/100 000. Return of spontaneous circulation occurred in 83 patients (9.8%), of whom 17 (2.0%) had survival to hospital discharge and 15 (1.8%) had survival with good neurologic outcome. Among patients in the Utstein comparator group, survival to hospital discharge was 21.4%. Predictors of return of spontaneous circulation were age, witnessed arrest, initial shockable rhythm, time of year, time to cardiopulmonary resuscitation, and time to advanced life support. Predictors of survival to hospital discharge were age, initial shockable rhythm, time to rhythm analysis, and time to advanced life support. Predictors of survival with good neurologic outcome were age, initial shockable rhythm, and time to return of spontaneous circulation.

CONCLUSIONS

The incidence of out-of-hospital cardiac arrest with cardiopulmonary resuscitation in the Algarve was higher than in other jurisdictions while return of spontaneous circulation, survival to hospital discharge, and survival with good neurologic outcome were comparatively low. An aging population, a geographically diverse region, and a low incidence of bystander cardiopulmonary resuscitation may have contributed to these outcomes. These results confirm the importance of early cardiopulmonary resuscitation, early rhythm assessment, and early advanced life support, all of which are potentially modifiable through public education, broadening of the defibrillator network and increased availability of advanced life support teams.

Computed-Tomography as First-line Diagnostic Procedure in Patients With Out-of-Hospital Cardiac Arrest

Background

Mortality after out-of-hospital cardiac arrest (OHCA) with return of spontaneous circulation (ROSC) remains high despite numerous efforts to improve outcome. For patients with suspected coronary cause of arrest, coronary angiography is crucial. However, there are other causes and potentially life-threatening injuries related to cardiopulmonary resuscitation (CPR), which can be detected by routine computed tomography (CT).

Materials and Methods

At Hannover Medical School, rapid coronary angiography and CT are performed in successfully resuscitated OHCA patients as a standard of care prior to admission to intensive care. We analyzed all patients who received CT following OHCA with ROSC over a three-year period.

Results

There were 225 consecutive patients with return of spontaneous circulation following out-of-hospital cardiac arrest. Mean age was 64 ± 13 years, 75% were male. Of them, 174 (77%) had witnessed arrest, 145 (64%) received bystander CPR, and 123 (55%) had a primary shockable rhythm. Mean time to ROSC was 24 ± 20 min. There were no significant differences in CT pathologies in patients with or without ST-segment elevations in the initial ECG. Critical CT findings qualifying as a potential cause for cardiac arrest were intracranial bleeding (N = 6), aortic dissection (N = 5), pulmonary embolism (N = 17), pericardial tamponade (N = 3), and tension pneumothorax (N = 11). Other pathologies were regarded as consequences of CPR and relevant for further treatment: aspiration (N = 62), rib fractures (N = 161), sternal fractures (N = 50), spinal fractures (N = 11), hepatic bleeding (N = 12), and intra-abdominal air (N = 3).

Conclusion

Early CT fallowing OHCA uncovers a high number of causes and consequences of OHCA and CPR. Those are relevant for post-arrest care and are frequently life-threatening, suggesting that CT can contribute to improving prognosis following OHCA.

Initial Blood pH, Lactate and Base Deficit Add No Value to Peri-Arrest Factors in Prognostication of Neurological Outcome After Out-of-Hospital Cardiac Arrest

Background: In cardiac arrest survivors, metabolic parameters [pH value, lactate concentration, and base deficit (BD)] are routinely added to peri-arrest factors (including age, sex, bystander cardiopulmonary resuscitation, shockable first rhythm, resuscitation duration, adrenaline dose) to enhance early outcome prediction. However, the additional value of this strategy remains unclear.

Methods: We used our resuscitation database to screen all patients ≥18 years who had suffered in- or out-of-hospital cardiac arrest (IHCA, OHCA) between January 1st, 2005 and May 1st, 2019. Patients with incomplete data, without return of spontaneous circulation or treatment with sodium bicarbonate were excluded. To analyse the added value of metabolic parameters to prognosticate neurological function, we built three models using logistic regression. These models included: (1) Peri-arrest factors only, (2) peri-arrest factors plus metabolic parameters and (3) metabolic parameters only. Receiver operating characteristics curves regarding 30-day good neurological function (Cerebral Performance Category 1-2) were analysed.

Results: A total of 2,317 patients (OHCA: n = 1842) were included. In patients with OHCA, model 1 and 2 had comparable predictive value. Model 3 was inferior compared to model 1. In IHCA patients, model 2 performed best, whereas both metabolic (model 3) and peri-arrest factors (model 1) demonstrated similar power. PH, lactate and BD had interchangeable areas under the curve in both IHCA and OHCA.

Conclusion: Although metabolic parameters may play a role in IHCA, no additional value in the prediction of good neurological outcome could be found in patients with OHCA. This highlights the importance of accurate anamnesis especially in patients with OHCA.

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.

Albumin Level at Admission to the Intensive Care Unit Is Associated With Prognosis in Cardiac Arrest Patients

Aim Cardiac arrest is a global health concern with consistently high mortality. It is also a common condition seen in the intensive care unit (ICU). We aimed to investigate the importance of albumin level on admission, which is a widely available and simple test, to predict in-hospital mortality in cardiac arrest patients. Methods The retrospective study collected data from the eICU Collaborative Research Database. It contains data from 171 hospitals, 276 ICU wards, and 4,529 patients who were treated for cardiac arrest from 2014 to 2015. We analyzed the patients' laboratory results and vital signs during the first 24 hours after admission to the ICU. The primary outcome was in-hospital mortality, and the secondary outcome was the length of ICU stay among survivors. Results In total, 2,414 patients were eligible. After adjusting for severity scores, including the Acute Physiology and Chronic Health Evaluation (APACHE) IV and Sequential Organ Failure Assessment (SOFA) scores, serum albumin was found to be a protective factor for survival (odds ratio of mortality: 0635, 95% confidence interval: 0.458-0.734, P<0.001). Among patients who survived until discharge, those with hypoalbuminemia had a long duration of stay in the ICU (P=0.005). Conclusion The higher albumin level at admission to the ICU was associated with lower mortality in patients with cardiac arrest.

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models

AIM

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm is useful in selecting the choice of resuscitative treatment by clinicians. This study aimed to develop and validate a machine learning-based outcome prediction model for out-of-hospital cardiac arrest with initial shockable rhythm, which can be used on patient's arrival at the hospital.

METHODS

Data were obtained from a nationwide out-of-hospital cardiac arrest registry in Japan. Of 43,350 out-of-hospital cardiac arrest patients with initial shockable rhythm registered between 2013 and 2017, patients aged <18 years and those with cardiac arrest caused by external factors were excluded. Subjects were classified into training (n = 23,668, 2013-2016 data) and test (n = 6381, data from 2017) sets for validation. Only 19 prehospital variables were used for the outcome prediction. The primary outcome was death at 1 month or survival with poor neurological function (cerebral performance category 3-5; "poor" outcome). Several machine learning models, including those based on logistic regression, support vector machine, random forest, and multilayer perceptron classifiers were compared.

RESULTS

In validation analyses, all machine learning models performed satisfactorily with area under the receiver operating characteristic curve values of 0.882 [95% confidence interval [CI]: 0.869-0.894] for logistic regression, 0.866 [95% CI: 0.853-0.879] for support vector machine, 0.877 [95% CI: 0.865-0.890] for random forest, and 0.888 [95% CI: 0.876-0.900] for multilayer perceptron classifiers.

CONCLUSIONS

A favourable machine learning-based prognostic model available to use on patient arrival at the hospital was developed for out-of-hospital cardiac arrest with initial shockable rhythm.

Cost-effectiveness of a novel smartphone application to mobilize first responders after witnessed OHCA in Belgium

Background

EVapp (Emergency Volunteer Application) is a Belgian smartphone application that mobilizes volunteers to perform cardiopulmonary resuscitation (CPR) and defibrillation with publicly available automatic external defibrillators (AED) after an emergency call for suspected out of hospital cardiac arrest (OHCA). The aim is to bridge the time before the arrival of the emergency services.

Methods

An accessible model was developed, using literature data, to simulate survival and cost-effectiveness of nation-wide EVapp implementation. Initial validation was performed using field data from a first pilot study of EVapp implementation in a city in Flanders, covering 2.5 years of implementation.

Results

Simulation of nation-wide EVapp implementation resulted in an additional yearly 910 QALY gained over the current baseline case scenario (worst case 632; best case 3204). The cost per QALY associated with EVapp implementation was comparable to the baseline scenario, i.e., 17 vs 18 k€ QALY⁻¹.

Conclusions

EVapp implementation was associated with a positive balance on amount of QALY gained and cost of QALY. This was a consequence of both the lower healthcare costs for patients with good neurological outcome and the more efficient use of yet available resources, which did not outweigh the costs of operation.

Evaluation of prognostic prediction models for out-of-hospital cardiac arrest

Introduction:

Accurate prognostic prediction of out-of-hospital cardiac arrest is challenging but important for the emergency team and patient’s family members. A number of prognostic prediction models specifically designed for out-of-hospital cardiac arrest are developed and validated worldwide.

Objective:

This narrative review provides an overview of the prognostic prediction models out-of-hospital cardiac arrest patients for use in the emergency department.

Discussion:

Out-of-hospital cardiac arrest prognostic prediction models are potentially useful in clinical, administrative and research settings. Development and validation of such models require prehospital and hospital predictor and outcome variables which are best in the standardised Utstein Style. Logistic regression analysis is traditionally employed for model development but machine learning is emerging as the new tool. Examples of such models available for use in the emergency department include ROSC After Cardiac Arrest, CaRdiac Arrest Survival Score, Utstein-Based Return of Spontaneous Circulation, Out-of-Hospital Cardiac Arrest, Cardiac Arrest Hospital Prognosis and Cardiac Arrest Survival Score. The usefulness of these models awaits future studies.

Neurologic prognostication after resuscitation from cardiac arrest

Out-of-hospital cardiac arrest remains a leading cause of mortality in the United States, and the majority of patients who die after achieving return of spontaneous circulation die from withdrawal of care due to a perceived poor neurologic prognosis. Unfortunately, withdrawal of care often occurs during the first day of admission and research suggests this early withdrawal of care may be premature and result in unnecessary deaths for patients who would have made a full neurologic recovery. In this review, we explore the evidence for neurologic prognostication in the emergency department for patients who achieve return of spontaneous circulation after an out-of-hospital cardiac arrest.

Early risk stratification after resuscitation from cardiac arrest

Emergency clinicians often resuscitate cardiac arrest patients, and after acute resuscitation, clinicians face multiple decisions regarding disposition. Recent evidence suggests that out-of-hospital cardiac arrest patients with return of spontaneous circulation have higher odds of survival to hospital discharge, long-term survival, and improved functional outcomes when treated at centers that can provide advanced multidisciplinary care. For community clinicians, a high volume cardiac arrest center may be hours away. While current guidelines recommend against neurological prognostication in the first hours or days after return of spontaneous circulation, there are early findings suggestive of irrecoverable brain injury in which the patient would receive no benefit from transfer. In this Concepts article, we describe a simplified approach to quickly evaluate neurological status in cardiac arrest patients and identify findings concerning for irrecoverable brain injury. Characteristics of the arrest and resuscitation, initial neurological assessment, and brain computed tomography together can identify patients with high likelihood of irrecoverable anoxic injury. Patients who may benefit from centers with access to continuous electroencephalography are discussed. This approach can be used to identify patients who may benefit from rapid transfer to cardiac arrest centers versus those who may benefit from care close to home. Risk stratification also can provide realistic expectations for recovery to families.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Background

More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known.

Methods and Results

We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c‐statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was −1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out‐of‐hospital cardiac arrest score (9 validations; median c‐statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c‐statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c‐statistic, 0.76).

Conclusions

Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

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.