C-GRApH: A Validated Scoring System for Early Stratification of Neurologic Outcome After Out-of-Hospital Cardiac Arrest Treated With Targeted Temperature Management

The early change in pH values after out-of-hospital cardiac arrest is not associated with neurological outcome at hospital discharge

Background

The association between pH values and outcome for patients after out-of-hospital cardiac arrest (OHCA) was not fully elucidated; besides, the relationship of change in pH values and neurological outcome was unknown. The aim was to explore the association of pH values as well as change in pH values and neurological outcome for OHCA cardiac patients.

Methods

The adult patients with non-traumatic out-of-hospital cardiac arrest, shock-refractory ventricular fibrillation or pulseless ventricular tachycardia, and at least two arterial blood gases analysis recorded after admission were included. The change in pH values is calculated as the difference between the second and first pH value, and divided by time interval got the rate of change in pH values. The primary outcome was modified Rankin Score (mRS), dichotomized to good (mRS 0-3) and poor (mRS 4-6) outcomes at hospital discharge. The independent relationship of the first pH value, second pH value, and changes in pH values with neurological outcome was investigated with multivariable logistic regression models, respectively.

Results

A total of 1388 adult patients were included for analysis, of which 514 (37%) had good neurological outcome. The median first pH value and second pH value after admission were 7.21 (interquartile range [IQR] 7.09-7.29) and 7.28 (IQR 7.20-7.36), respectively. The median absolute, relative change, and rate of changes in pH values were 0.08 (IQR 0.01-0.16), 1.10% (IQR 0.11-2.22%), and 0.02 (IQR 0-0.06) per hour, respectively. After adjusting for confounders, the higher first pH value (odds ratio [OR] 3.81, confidence interval [CI] 1.60-9.24, P = 0.003) and higher second pH value (OR 9.54, CI 3.45-26.87, P < 0.001) after admission were associated with good neurological outcome, respectively. The absolute (OR 1.58, CI 0.58-4.30, P = 0.368) and relative (OR 1.03, CI 0.96-1.11, P = 0.399) change as well as the rate of change (OR 0.98, CI 0.33-2.71, P = 974) in pH values were not associated with neurological outcome.

Conclusions

For OHCA patients, abnormality in pH values was very common, with a more acidic pH value indicating poor neurological outcome. However, the change in pH values was not associated with outcomes.

Complications in patients with cardiogenic shock on veno-arterial extracorporeal membrane oxygenation therapy: distribution and relevance. Results from an international, multicentre cohort study

AIMS

Veno-arterial extracorporeal membrane oxygenation therapy (VA-ECMO) restores circulation and tissue oxygenation in cardiogenic shock (CS) patients, but can also lead to complications. This study aimed to quantify VA-ECMO complications and analyse their association with overall survival as well as favourable neurological outcome (cerebral performance categories 1 + 2).

METHODS AND RESULTS

All-comer patients with CS treated with VA-ECMO were retrospectively enrolled from 16 centres in four countries (2005-2019). Neurological, bleeding, and ischaemic adverse events (AEs) were considered. From these, typical VA-ECMO complications were identified and analysed separately as device-related complications. n = 501. Overall, 118 were women (24%), median age was 56.0 years, median lactate was 8.1 mmol/L. Acute myocardial infarction caused CS in 289 patients (58%). Thirty-days mortality was 40% (198/501 patients). At least one device-related complication occurred in 252/486 (52%) patients, neurological AEs in 108/469 (23%), bleeding in 192/480 (40%), ischaemic AEs in 123/478 (26%). The 22% of patients with the most AEs accounted for 50% of all AEs. All types of AEs were associated with a worse prognosis. Aside from neurological ones, all AEs and device-related complications were more likely to occur in women; although prediction of AEs outside of neurological AEs was generally poor.

CONCLUSION

Therapy and device-related complications occur in half of all patients treated with VA-ECMO and are associated with a worse prognosis. They accumulate in some patients, especially in women. Aside from neurological events, identification of patients at risk is difficult, highlighting the need to establish additional quantitative markers of complication risk to guide VA-ECMO treatment in CS.

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.

Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors

As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.

A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest

Background

Out-of-hospital cardiac arrest (OHCA) represents a major burden for society and health care, with an average incidence in adults of 67 to 170 cases per 100,000 person-years in Europe and in-hospital survival rates of less than 10%. Patients and practitioners would benefit from a prognostication tool for long-term good neurological outcomes.

Objective

We aim to develop a machine learning (ML) pipeline on a local database to classify patients according to their neurological outcomes and identify prognostic features.

Methods

We collected clinical and biological data consecutively from 595 patients who presented OHCA and were routed to a single regional cardiac arrest centre in the south of France. We applied recursive feature elimination and ML analyses to identify the main features associated with a good neurological outcome, defined as a Cerebral Performance Category score less than or equal to 2 at six months post-OHCA.

Results

We identified 12 variables 24 h after admission, capable of predicting a six-month good neurological outcome. The best model (extreme gradient boosting) achieved an AUC of 0.96 and an accuracy of 0.92 in the test cohort.

Conclusion

We demonstrated that it is possible to build accurate, locally optimised prediction and prognostication scores using datasets of limited size and breadth. We proposed and shared a generic machine-learning pipeline which allows external teams to replicate the approach locally.

Treatment of Refractory Cardiac Arrest by Controlled Reperfusion of the Whole Body: A Multicenter, Prospective Observational Study

Background: Survival following cardiac arrest (CA) remains poor after conventional cardiopulmonary resuscitation (CCPR) (6-26%), and the outcomes after extracorporeal cardiopulmonary resuscitation (ECPR) are often inconsistent. Poor survival is a consequence of CA, low-flow states during CCPR, multi-organ injury, insufficient monitoring, and delayed treatment of the causative condition. We developed a new strategy to address these issues. Methods: This all-comers, multicenter, prospective observational study (69 patients with in- and out-of-hospital CA (IHCA and OHCA) after prolonged refractory CCPR) focused on extracorporeal cardiopulmonary support, comprehensive monitoring, multi-organ repair, and the potential for out-of-hospital cannulation and treatment. Result: The overall survival rate at hospital discharge was 42.0%, and a favorable neurological outcome (CPC 1+2) at 90 days was achieved for 79.3% of survivors (CPC 1+2 survival 33%). IHCA survival was very favorable (51.7%), as was CPC 1+2 survival at 90 days (41%). Survival of OHCA patients was 35% and CPC 1+2 survival at 90 days was 28%. The subgroup of OHCA patients with pre-hospital cannulation showed a superior survival rate of 57.1%. Conclusions: This new strategy focusing on repairing damage to multiple organs appears to improve outcomes after CA, and these findings should provide a sound basis for further research in this area.

External validation of GO-FAR 2 calculator for outcomes after in-hospital cardiac arrest with comparison to GO-FAR and trial of expanded applications

Aim

Externally validate the GO-FAR 2 tool for predicting survival with good neurologic function after in-hospital cardiac arrest with comparison to the original GO-FAR tool. Additionally, we collected qualitative descriptors and performed exploratory analyses with various levels of neurologic function and discharge destination.

Methods

Retrospective chart review of all patients who underwent in-hospital resuscitation after cardiac arrest during the calendar years 2016-2019 in our institution (n = 397). GO-FAR and GO-FAR 2 scores were calculated based on information available in the medical record at the time of hospital admission. Cerebral performance category (CPC) scores at the time of admission and discharge were assessed by chart review.

Results

The GO-FAR 2 score accurately predicted outcomes in our study population with a c-statistic of 0.625. The original GO-FAR score also had accurate calibration with a stronger c-statistic of 0.726. The GO-FAR score had decreased predictive value for lesser levels of neurologic function (c-statistic 0.56 for alive at discharge) and discharge destination (0.69). Descriptors of functional status by CPC score were collected.

Conclusion

Our findings support the validity of the GO-FAR and GO-FAR 2 tools as published, but the c-statistics suggest modest predictive discrimination. We include functional descriptors of CPC outcomes to aid clinicians in using these tools. We propose that information about expected outcomes could be valuable in shared decision-making conversations.

Predicting Neurologically Intact Survival for Advanced Age Adults After Successful Resuscitation of Out-of-Hospital Cardiac Arrest

We sought to predict survival to hospital discharge with favorable neurologic outcome for advanced age adults (≥65 years) after successful resuscitation of non-traumatic out-of-hospital cardiac arrest (OHCA). A retrospective observational cohort analysis was performed using the national Cardiac Arrest Registry to Enhance Survival database from January 1, 2013 to December 31, 2021. All nontraumatic OHCA occurring in advanced age adults who survived to hospital admission were included. The primary outcome was survival with favorable neurologic outcome defined as a cerebral performance category score of 1 or 2 at hospital discharge. Multivariable logistic regression including patient variables (age category, gender, co-morbidities) and OHCA characteristics (location, rhythm category, witnessed status, and who initiated cardiopulmonary resuscitation) were used to predict hospital outcome. 83,574 patients met study inclusion criteria with 19,298 (23.1%) surviving with favorable neurologic outcome. The median age was 75 years (interquartile range 69 to 82 years), 58.9% were male, and a majority of events occurred at home (67.3%). Age was found to have a linear, negative association with outcome. Survival with cerebral performance category 1 or 2 ranged from 28.8% in those between the age of 65 to 69 years (n = 23,161) and 13.7% for those age >90 years (n = 4,666). The regression model produced outcome probabilities ranging from 2.6% to 80.8% with a cross-validated AUROC of 0.742 (95% confidence interval 0.738 to 0.746) and a Brier score of 0.151. In conclusion, a simple model with basic patient and OHCA characteristics can predict hospital outcomes in advanced age adults with good discrimination and calibration.

Clinical Grading Scales and Neuroprognostication in Acute Brain Injury

Prediction of neurological clinical outcome after acute brain injury is critical because it helps guide discussions with patients and families and informs treatment plans and allocation of resources. Numerous clinical grading scales have been published that aim to support prognostication after acute brain injury. However, the development and validation of clinical scales lack a standardized approach. This in turn makes it difficult for clinicians to rely on prognostic grading scales and to integrate them into clinical practice. In this review, we discuss quality measures of score development and validation and summarize available scales to prognosticate outcomes after acute brain injury. These include scales developed for patients with coma, cardiac arrest, ischemic stroke, nontraumatic intracerebral hemorrhage, subarachnoid hemorrhage, and traumatic brain injury; for each scale, we discuss available validation studies.

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 survival post-cardiac arrest: An observational cohort study

Introduction

Over 400,000 out-of-hospital cardiac arrest (OHCA) occur each year in Canada and the United States with less than 10% survival to hospital discharge. Cardiac arrest is a heterogenous condition and patient outcomes are impacted by a multitude of factors. Prognostication is recommended at 72 hours after return of spontaneous circulation (ROSC), however there may be other factors that could predict patient outcome earlier in the post-arrest period. The objective of our study was to develop and internally validate a novel clinical prediction rule to risk stratify patients early in the post-cardiac arrest period.

Methods

We performed a retrospective cohort study of adult (≥18 years) post-cardiac arrest patients between 2010 and 2015 from the Epistry Cardiac Arrest database in Toronto. Our primary analysis used ordinal logistic regression to examine neurologic outcome at discharge using the modified Rankin Scale (mRS). Our secondary analysis used logistic regression for neurologic outcome and survival to hospital discharge. Models were internally validated using bootstrap validation.

Results

A total of 3432 patients met our inclusion criteria. Our clinical prediction model was able to predict neurologic outcome on an ordinal scale using our predefined variables with an AUC of 0.89 after internal validation. The predictive performance was maintained when examining neurologic outcome as a binary variable and survival to hospital discharge.

Conclusion

We were able to develop a model to accurately risk stratify adult cardiac arrest patients early in the post-cardiac arrest period. Future steps are needed to externally validate this model in other healthcare settings.

Survival by time-to-administration of amiodarone, lidocaine, or placebo in shock-refractory out-of-hospital cardiac arrest

BACKGROUND

Amiodarone and lidocaine have not been shown to have a clear survival benefit compared to placebo for out-of-hospital cardiac arrest (OHCA). However, randomized trials may have been impacted by delayed administration of the study drugs. We sought to evaluate how timing from emergency medical services (EMS) arrival on scene to drug administration affects the efficacy of amiodarone and lidocaine compared to placebo.

METHOD

This is a secondary analysis of the 10-site, 55-EMS-agency double-blind randomized controlled amiodarone, lidocaine, or placebo in OHCA study. We included patients with initial shockable rhythms who received the study drugs of amiodarone, lidocaine, or placebo before achieving return of spontaneous circulation. We performed logistic regression analyses evaluating survival to hospital discharge and secondary outcomes of survival to admission and functional survival (modified Rankin scale score ≤ 3). We evaluated the samples stratified by early (<8 min) and late administration groups (≥8 min). We compared outcomes for amiodarone and lidocaine compared to placebo and adjust for potential confounders.

RESULTS

There were 2802 patients meeting inclusion criteria, with 879 (31.4%) in the early (<8 min) and 1923 (68.6%) in the late (≥8 min) groups. In the early group, patients receiving amiodarone, compared to placebo, had significantly higher survival to admission (62.0% vs. 48.5%, p = 0.001; adjusted OR [95% CI] 1.76 [1.24-2.50]), survival to discharge (37.1% vs. 28.0%, p = 0.021; 1.56 [1.07-2.29]), and functional survival (31.6% vs. 23.3%, p = 0.029; 1.55 [1.04-2.32]). There were no significant differences with early lidocaine compared to early placebo (p > 0.05). Patients in the late group who received amiodarone or lidocaine had no significant differences in outcomes at discharge compared to placebo (p > 0.05).

CONCLUSIONS

The early administration of amiodarone, particularly within 8 min, is associated with greater survival to admission, survival to discharge, and functional survival compared to placebo in patients with an initial shockable rhythm.

A novel cardiac arrest severity score for the early prediction of hypoxic-ischemic brain injury and in-hospital death

INTRODUCTION

Out-of-hospital cardiac arrest (OHCA) outcomes are unsatisfactory despite postcardiac arrest care. Early prediction of prognoses might help stratify patients and provide tailored therapy. In this study, we derived and validated a novel scoring system to predict hypoxic-ischemic brain injury (HIBI) and in-hospital death (IHD).

METHODS

We retrospectively analyzed Korean Hypothermia Network prospective registry data collected from in Korea between 2015 and 2018. Patients without neuroprognostication data were excluded, and the remaining patients were randomly divided into derivation and validation cohorts. HIBI was defined when at least one prognostication predicted a poor outcome. IHD meant all deaths regardless of cause. In the derivation cohort, stepwise multivariate logistic regression was conducted for the HIBI and IHD scores, and model performance was assessed. We then classified the patients into four categories and analyzed the associations between the categories and cerebral performance categories (CPCs) at hospital discharge. Finally, we validated our models in an internal validation cohort.

RESULTS

Among 1373 patients, 240 were excluded, and 1133 were randomized into the derivation (n = 754) and validation cohorts (n = 379). In the derivation cohort, 7 and 8 predictors were selected for HIBI (0-8) and IHD scores (0-11), respectively, and the area under the curves (AUC) were 0.85 (95% CI 0.82-0.87) and 0.80 (95% CI 0.77-0.82), respectively. Applying optimum cutoff values of ≥6 points for HIBI and ≥7 points for IHD, the patients were classified as follows: HIBI (-)/IHD (-), Category 1 (n = 424); HIBI (-)/IHD (+), Category 2 (n = 100); HIBI (+)/IHD (-), Category 3 (n = 21); and HIBI (+)/IHD (+), Category 4 (n = 209). The CPCs at discharge were significantly different in each category (p < 0.001). In the validation cohort, the model showed moderate discrimination (AUC 0.83, 95% CI 0.79-0.87 for HIBI and AUC 0.77, 95% CI 0.72-0.81 for IHD) with good calibration. Each category of the validation cohort showed a significant difference in discharge outcomes (p < 0.001) and a similar trend to the derivation cohort.

CONCLUSIONS

We presented a novel approach for assessing illness severity after OHCA. Although external prospective studies are warranted, risk stratification for HIBI and IHD could help provide OHCA patients with appropriate treatment.

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.

Tree-Based Algorithms and Association Rule Mining for Predicting Patients’ Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic: Application of Data Mining

Objective

The authors performed several tree-based algorithms and an association rules mining as data mining tools to find useful determinants for neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients as well as to assess the effect of the first-aid and basic characteristics in the EMS system.

Patients and Methods

This was a retrospective cohort study. The outcome was Cerebral Performance Categories grading on OHCA patients at hospital discharge. Decision tree-based models inclusive of C4.5 algorithm, classification and regression tree and random forest were built to determine an OHCA patient’s prognosis. Association rules mining was another data mining method which we used to find the combination of prognostic factors linked to the outcome.

Results

The total of 3520 patients were included in the final analysis. The mean age was 67.53 (±18.4) year-old and 63.4% were men. To overcome the imbalance outcome issue in machine learning, the random forest has a better predictive ability for OHCA patients in overall accuracy (91.19%), weighted precision (88.76%), weighted recall (91.20%) and F1 score (0.9) by oversampling adjustment. Under association rules mining, patients who had any witness on the spot when encountering OHCA or who had ever ROSC during first-aid would be highly correlated with good CPC prognosis.

Conclusion

The random forest has a better predictive ability for OHCA patients. This paper provides a role model applying several machine learning algorithms to the first-aid clinical assessment that will be promising combining with Artificial Intelligence for applying to emergency medical services.

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.

Targeted Temperature Management: A Program Evaluation

In the United States, more than 350 000 cardiac arrests occur annually. The survival rate after an out-of-hospital cardiac arrest remains low. The majority of patients who have return of spontaneous circulation will die of complications of hypoxic-ischemic brain injury. Targeted temperature management is the only recommended neuroprotective measure for those who do not regain consciousness after return of spontaneous circulation. Despite current practices, a review of the literature revealed that evidence on the ideal time to achieve target temperature after return of spontaneous circulation remains equivocal. A program evaluation of a targeted temperature management program at an academic center was performed; the focus was on timing components of targeted temperature management. The program evaluation revealed that nurse-driven, evidence-based protocols can lead to optimal patient outcomes in this low-frequency, high-impact therapy.

Kynurenine Pathway Metabolites as Potential Clinical Biomarkers in Coronary Artery Disease

Coronary artery disease (CAD) is one of the leading cause of mortality worldwide. Several risk factors including unhealthy lifestyle, genetic background, obesity, diabetes, hypercholesterolemia, hypertension, smoking, age, etc. contribute to the development of coronary atherosclerosis and subsequent coronary artery disease. Inflammation plays an important role in coronary artery disease development and progression. Pro-inflammatory signals promote the degradation of tryptophan via the kynurenine pathway resulting in the formation of several immunomodulatory metabolites. An unbalanced kynurenic pathway has been implicated in the pathomechanisms of various diseases including CAD. Significant improvements in detection methods in the last decades may allow simultaneous measurement of multiple metabolites of the kynurenine pathway and such a thorough analysis of the kynurenine pathway may be a valuable tool for risk stratification and determination of CAD prognosis. Nevertheless, imbalance in the activities of different branches of the kynurenine pathway may require careful interpretation. In this review, we aim to summarize clinical evidence supporting a possible use of kynurenine pathway metabolites as clinical biomarkers in various manifestations of CAD.

The ED-PLANN Score: A Simple Risk Stratification Tool for Out-of-Hospital Cardiac Arrests Derived from Emergency Departments in Korea

Early risk stratification of out-of-hospital cardiac arrest (OHCA) patients with insufficient information in emergency departments (ED) is difficult but critical in improving intensive care resource allocation. This study aimed to develop a simple risk stratification score using initial information in the ED. Adult patients who had OHCA with medical etiology from 2016 to 2020 were enrolled from the Korean Cardiac Arrest Research Consortium (KoCARC) database. To develop a scoring system, a backward logistic regression analysis was conducted. The developed scoring system was validated in both external dataset and internal bootstrap resampling. A total of 8240 patients were analyzed, including 4712 in the development cohort and 3528 in the external validation cohort. An ED-PLANN score (range 0–5) was developed incorporating 1 point for each: P for serum pH ≤ 7.1, L for serum lactate ≥ 10 mmol/L, A for age ≥ 70 years old, N for non-shockable rhythm, and N for no-prehospital return of spontaneous circulation. The area under the receiver operating characteristics curve (AUROC) for favorable neurological outcome was 0.93 (95% CI, 0.92–0.94) in the development cohort, 0.94 (95% CI, 0.92–0.95) in the validation cohort. Hosmer–Lemeshow goodness-of-fit tests also indicated good agreement. The ED-PLANN score is a practical and easily applicable clinical scoring system for predicting favorable neurological outcomes of OHCA patients.

A retrospective analysis of fibrinolytic and adjunctive antithrombotic treatment during cardiopulmonary resuscitation

Synergistic effects of fibrinolytic and additional antithrombotic treatment during cardiopulmonary resuscitation in out-of-hospital cardiac arrest of assumed cardiac origin were evaluated retrospectively. Data were drawn from electronic files of the physician-staffed Emergency Medical Services Tyrol. During a 22-month observation period 53 adult patients were treated with tenecteplase (mean 7641 IU), 19 (32.1%) of whom received additional antithrombotic treatment with heparin (4000-5000 IU) and acetylsalicylic acid (250-500 mg). Lasting return of spontaneous circulation occurred in four of 34 patients who received fibrinolytic treatment only and in seven of 19 patients with additional antithrombotic treatment (p = 0.037). Four of five patients who were discharged from hospital had received additional antithrombotic treatment during CPR and were in appropriate neurological status (CPC 1). Considering the small sample size in this retrospective study, the argument may be still be made that fibrinolytic and adjunctive antithrombotic treatment during cardiopulmonary resuscitation in out-of-hospital cardiac arrest of assumed cardiac origin may increase the chances for survival.

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.

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.

Simultaneous external validation of various cardiac arrest prognostic scores: a single-center retrospective study

Background

This study aimed to compare and validate the out-of-hospital cardiac arrest (OHCA); cardiac arrest hospital prognosis (CAHP); non-shockable 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, age ≥ 85 years, still resuscitation, and extracardiac cause (NULL-PLEASE) clinical; post-cardiac arrest syndrome for therapeutic hypothermia (CAST); and revised CAST (rCAST) scores in OHCA patients treated with recent cardiopulmonary resuscitation strategies.

Methods

We retrospectively collected data on adult OHCA patients admitted to our emergency department between February 2015 and July 2018. OHCA, CAHP, NULL-PLEASE clinical, CAST, and rCAST scores were calculated based on the data collected. The predictive abilities of each score were tested using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Results

We identified 236 OHCA patients from computer-based medical records and analyzed 189 without missing data. In OHCA patients without bystander witnesses, CAHP and OHCA scores were not calculated. Although the predictive abilities of the scores were not significantly different, the NULL-PLEASE score had a large AUC of ROC curve in various OHCA patients. Furthermore, in patients with bystander-witnessed OHCA, the NULL-PLEASE score had large partial AUCs of ROC from sensitivity 0.8–1.0 and specificity 0.8–1.0.

Conclusions

The NULL-PLEASE score had a high, comprehensive predictive ability in various OHCA patients. Furthermore, the NULL-PLEASE score had a high predictive ability for good and poor neurological outcomes in patients with bystander-witnessed OHCA.

Predicting in-hospital mortality after an in-hospital cardiac arrest: A multivariate analysis

Aim of the study

Most survivors of an in-hospital cardiac arrest do not leave the hospital alive, and there is a need for a more patient-centered, holistic approach to the assessment of prognosis after an arrest. We sought to identify pre-, peri-, and post-arrest variables associated with in-hospital mortality amongst survivors of an in-hospital cardiac arrest.

Methods

This was a retrospective cohort study of patients ≥18 years of age who were resuscitated from an in-hospital arrest at our University Medical Center from January 1, 2013 to September 31, 2016. In-hospital mortality was chosen as a primary outcome and unfavorable discharge disposition (discharge disposition other than home or skilled nursing facility) as a secondary outcome.

Results

925 patients comprised the in-hospital arrest cohort with 305 patients failing to survive the arrest and a further 349 patients surviving the initial arrest but dying prior to hospital discharge, resulting in an overall survival of 29%. 620 patients with a ROSC of greater than 20 min following the in-hospital arrest were included in the final analysis. In a stepwise multivariable regression analysis, recurrent cardiac arrest, increasing age, time to ROSC, higher serum creatinine levels, and a history of cancer were predictors of in-hospital mortality. A history of hypertension was found to exert a protective effect on outcomes. In the regression model including serum lactate, increasing lactate levels were associated with lower odds of survival.

Conclusion

Amongst survivors of in-hospital cardiac arrest, recurrent cardiac arrest was the strongest predictor of poor outcomes with age, time to ROSC, pre-existing malignancy, and serum creatinine levels linked with increased odds of in-hospital mortality.

Using Out-of-Hospital Cardiac Arrest (OHCA) and Cardiac Arrest Hospital Prognosis (CAHP) Scores with Modified Objective Data to Improve Neurological Prognostic Performance for Out-of-Hospital Cardiac Arrest Survivors

This study aimed to determine whether accuracy and sensitivity concerning neurological prognostic performance increased for survivors of out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM), using OHCA and cardiac arrest hospital prognosis (CAHP) scores and modified objective variables. We retrospectively analyzed non-traumatic OHCA survivors treated with TTM. The primary outcome was poor neurological outcome at 3 months after return of spontaneous circulation (cerebral performance category, 3–5). We compared neurological prognostic performance using existing models after adding objective data obtained before TTM from computed tomography (CT), magnetic resonance imaging (MRI), and biomarkers to replace the no-flow time component of the OHCA and CAHP models. Among 106 patients, 61 (57.5%) had poor neurologic outcomes. The area under the receiver operating characteristic (AUROC) curve for the OHCA and CAHP models was 0.89 (95% confidence interval (CI) 0.81–0.94) and 0.90 (95% CI 0.82–0.95), respectively. The prediction of poor neurological outcome improved after replacing no-flow time with a grey/white matter ratio measured using CT, high-signal intensity (HSI) on diffusion-weighted MRI (DWI), percentage of voxel using apparent diffusion coefficient value, and serum neuron-specific enolase levels. When replaced with HSI on DWI, the AUROC and sensitivity of the OHCA and CAHP models were 0.96 and 74.5% and 0.97 and 83.8%, respectively (100% specificity). Prognoses concerning neurologic outcomes improved compared with existing OHCA and CAHP models by adding new objective variables to replace no-flow time. External validation is required to generalize these results in various contexts.

A systematic review and meta-analysis of the effect of routine early angiography in patients with return of spontaneous circulation after Out-of-Hospital Cardiac Arrest

BACKGROUND

Early coronary angiography (CAG) has been reported in individual studies and systematic reviews to significantly improve outcomes of patients with return of spontaneous circulation (ROSC) after cardiac arrest (CA).

METHODS

We undertook a systematic review and meta-analysis to evaluate the impact of early CAG on key clinical outcomes in comatose patients after ROSC following out-of-hospital CA of presumed cardiac origin. We searched the PubMED, EMBASE, CINAHL, ERIC and Cochrane Central Register of Controlled Trials databases from 1990 until April 2020. Eligible studies compared patients undergoing early CAG to patients with late or no CAG. When randomized controlled trials (RCTs) existed for a specific outcome, we used their results to estimate the effect of the intervention. In the absence of randomized data, we used observational data. We excluded studies at high risk of bias according to the Robins-I tool from the meta-analysis. The GRADE system was used to assess certainty of evidence at an outcome level.

RESULTS

Of 3738 citations screened, 3 randomized trials and 41 observational studies were eligible for inclusion. Evidence certainty across all outcomes for the RCTs was assessed as low. Randomized data showed no benefit from early as opposed to late CAG across all critical outcomes of survival and survival with favourable neurologic outcome for undifferentiated patients and for patient subgroups without ST-segment-elevation on post ROSC ECG and shockable initial rhythm.

CONCLUSION

These results do not support routine early CAG in undifferentiated comatose patients and patients without STE on post ROSC ECG after OHCA.

REVIEW REGISTRATION

PROSPERO - CRD42020160152.

PROLOGUE (PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages): Development and validation of a scoring system for early prognostication in unselected adult cardiac arrest patients

BACKGROUND

Early prognostication after cardiac arrest would be useful. We aimed to develop a scoring model for early prognostication in unselected adult cardiac arrest patients.

METHODS

We retrospectively analysed data of adult non-traumatic cardiac arrest patients treated at a tertiary hospital between 2014 and 2018. The primary outcome was poor outcome at hospital discharge (cerebral performance category, 3-5). Using multivariable logistic regression analysis, independent predictors were identified among known outcome predictors, that were available at intensive care unit admission, in patients admitted in the first 3 years (derivation set, N = 671), and a scoring system was developed with the variables that were retained in the final model. The scoring model was validated in patients admitted in the last 2 years (validation set, N = 311).

RESULTS

The poor outcome rates at hospital discharge were similar between the derivation (66.0%) and validation sets (64.3%). Age <59 years, witnessed collapse, shockable rhythm, adrenaline dose <2 mg, low-flow duration <18 min, reactive pupillary light reflex, Glasgow Coma Scale motor score ≥2, and levels of creatinine <1.21 mg dl^-1, potassium <4.4 mEq l^-1, phosphate <5.8 mg dl^-1, haemoglobin ≥13.2 g dl^-1, and lactate <8 mmol l^-1 were retained in the final multivariable model and used to develop the scoring system. Our model demonstrated excellent discrimination in the validation set (area under the curve of 0.942, 95% confidence interval 0.917-0.968).

CONCLUSIONS

We developed a scoring model for early prognostication in unselected adult cardiac arrest patients. Further validations in various cohorts are needed.

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

PURPOSE

Out-of-hospital cardiac arrest (OHCA) carries a very high mortality rate even after successful cardiopulmonary resuscitation. Currently, information given to relatives about prognosis following resuscitation is often emotive and subjective, and varies with clinician experience. We aimed to validate the NULL-PLEASE score to predict survival following OHCA.

METHODS

A multicenter cohort study was conducted, with retrospective and prospective validation in consecutive unselected patients presenting with OHCA. The NULL-PLEASE score was calculated by attributing points to the following variables: Nonshockable initial rhythm, Unwitnessed arrest, Long low-flow period, Long no-flow period, pH <7.2, Lactate >7.0 mmol/L, End-stage renal failure, Age ≥85 years, Still resuscitation, and Extracardiac cause. The primary outcome was in-hospital death.

RESULTS

We assessed 700 patients admitted with OHCA, of whom 47% survived to discharge. In 300 patients we performed a retrospective validation, followed by prospective validation in 400 patients. The NULL-PLEASE score was lower in patients who survived compared with those who died (0 [interquartile range 0-1] vs 4 [interquartile range 2-4], P < .0005) and strongly predictive of in-hospital death (C-statistic 0.874; 95% confidence interval, 0.848-0.899). Patients with a score ≥3 had a 24-fold increased risk of death (odds ratio 23.6; 95% confidence interval, 14.840-37.5; P < .0005) compared with those with lower scores. A score ≥3 has a 91% positive predictive value for in-hospital death, while a score <3 predicts a 71% chance of survival.

CONCLUSION

The easy-to-use NULL-PLEASE score predicts in-hospital mortality with high specificity and can help clinicians explain the prognosis to relatives in an easy-to-understand, objective fashion, to realistically prepare them for the future.

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.

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.

Prognostic value of OHCA, C-GRApH and CAHP scores with initial neurologic examinations to predict neurologic outcomes in cardiac arrest patients treated with targeted temperature management

OBJECTIVE

The aim of this study in out-of-hospital cardiac arrest (OHCA) patients treated with targeted temperature management (TTM) was to evaluate the prognostic value of OHCA, C-GRApH, and CAHP scores with initial neurologic examinations for predicting neurologic outcomes.

METHODS

This retrospective study included OHCA patients treated with TTM from 2009 to 2017. We calculated three cardiac arrest (CA)-specific risk scores (OHCA, C-GRApH, and CAHP) at the time of admission. The initial neurologic examination included an evaluation of the Full Outline of UnResponsiveness brainstem reflexes (FOUR_B) and Glasgow Coma Scale motor (GCS_M) scores. The primary outcome was the neurologic outcome at hospital discharge.

RESULTS

Of 311 subjects, 99 (31.8%) had a good neurologic outcome at hospital discharge. The OHCA score had an area under the receiver operating characteristic curve (AUROC) of 0.844 (95% confidence interval (CI): 0.798-0.884), the C-GRApH score had an AUROC of 0.779 (95% CI: 0.728-0.824), and the CAHP score had an AUROC of 0.872 (95% CI: 0.830-0.907). The addition of the FOUR_B or GCS_M score to the OHCA score improved the prediction of poor neurologic outcome (with FOUR_B: AUROC = 0.899, p = 0.001; with GCS_M: AUROC = 0.880, p = 0.004). The results were similar with the C-GRApH and CAHP scores in predicting poor neurologic outcome.

CONCLUSIONS

This study confirms the good prognostic performance of CA-specific scores to predict neurologic outcomes in OHCA patients treated with TTM. By adding new variables associated with the initial neurologic examinations, the prognoses of neurologic outcomes improved compared to the existing scoring models.

Development and External Validation of an Early Prognostic Model for Survivors of Out-of-hospital Cardiac Arrest

INTRODUCTION AND OBJECTIVES

Despite therapeutic hypothermia, unconscious survivors of out-of-hospital cardiac arrest have a high risk of death or poor neurologic function. Our objective was to assess the usefulness of the variables obtained in the early moments after resuscitation in the prediction of 6-month prognosis.

METHODS

A multicenter study was performed in 3 intensive cardiac care units. The analysis was done in 153 consecutive survivors of out-of-hospital cardiac arrest who underwent targeted temperature management between January 2007 and July 2015. Significant neurological sequelae at 6 months were considered to be present in patients with Cerebral Performance Categories Scale > 2. An external validation was performed with data from 91 patients admitted to a third hospital in the same time interval.

RESULTS

Among the 244 analyzed patients (median age, 60 years; 77.1% male; 50.0% in the context of acute myocardial ischemia), 107 patients (43.8%) survived with good neurological status at 6 months. The prediction model included 5 variables (Shockable rhythm, Age, Lactate levels, Time Elapsed to return of spontaneous circulation, and Diabetes - SALTED) and provided an area under the curve of 0.90 (95%CI, 0.85-0.95). When external validation was performed, the predictive model showed a sensitivity of 73.5%, specificity of 78.6%, and area under the curve of 0.82 (95%CI, 0.73-0.91).

CONCLUSIONS

A predictive model that includes 5 clinical and easily accessible variables at admission can help to predict the probability of survival without major neurological damage following out-of-hospital cardiac arrest.

External validation of a risk classification at the emergency department of post-cardiac arrest syndrome patients undergoing targeted temperature management

INTRODUCTION

There are no established risk classification for post-cardiac arrest syndrome (PCAS) patients at the Emergency Department (ED) undergoing targeted temperature management (TTM). The aim of this study was to externally validate a simplified version of our prognostic score, the "post-Cardiac Arrest Syndrome for Therapeutic hypothermia score" (revised CAST [rCAST]) and estimate the predictive accuracy of the risk classification based on it.

METHODS

For the external validation, we used data from an out-of-hospital cardiac arrest (OHCA) registry of the Japanese Association for Acute Medicine (JAAM), which is a multicenter, prospective registry of OHCA patients across Japan. Eligible patients were PCAS patients treated with TTM at 33-36 °C between June 2014 and December 2015. We validated the accuracy of rCAST for predicting the neurological outcomes at 30 and 90 days.

RESULTS

Among the 12,024 OHCA patients, the data of 460 PCAS patients treated by TTM were eligible for the validation. The areas under the curve of rCAST for predicting the neurological outcomes at 30 and 90 days were 0.892 and 0.895, respectively. The estimated sensitivity and specificity of the risk categories for the outcomes were as follows: 0.95 (95% CI: 0.92-0.98) and 0.47 (0.40-0.55) for the low (rCAST: ≤5.5), 0.62 (0.56-0.68) and 0.48 (0.40-0.55) for the moderate (rCAST: 6.0-14.0), and 0.57 (0.51-0.63) and 0.95 (0.91-0.98) for the high severity category (rCAST: ≥14.5).

CONCLUSIONS

The rCAST was useful for predicting the neurological outcomes with high accuracy in PCAS patients, and the three grades was developed for a risk classification based on the rCAST.

Initial arterial pH as a predictor of neurologic outcome after out-of-hospital cardiac arrest: A propensity-adjusted analysis

BACKGROUND

Lower pH after out-of-hospital cardiac arrest (OHCA) has been associated with worsening neurologic outcome, with <7.2 identified as an "unfavorable resuscitation feature" in consensus treatment algorithms despite conflicting data. This study aimed to describe the relationship between decremental post-resuscitation pH and neurologic outcomes after OHCA.

METHODS

Consecutive OHCA patients treated with targeted temperature management (TTM) at multiple US centers from 2008 to 2017 were evaluated. Poor neurologic outcome at hospital discharge was defined as cerebral performance category ≥3. The exposure was initial arterial pH after return of spontaneous circulation (ROSC) analyzed in decremental 0.05 thresholds. Potential confounders (demographics, history, resuscitation characteristics, initial studies) were defined a priori and controlled for via ATT-weighting on the inverse propensity score plus direct adjustment for the linear propensity score.

RESULTS

Of 723 patients, 589 (80%) experienced poor neurologic outcome at hospital discharge. After propensity-adjustment with excellent covariate balance, the adjusted odds ratios for poor neurologic outcome by pH threshold were: ≤7.3: 2.0 (1.0-4.0); ≤7.25: 1.9 (1.2-3.1); ≤7.2: 2.1 (1.3-3.3); ≤7.15: 1.9 (1.2-3.1); ≤7.1: 2.4 (1.4-4.1); ≤7.05: 3.1 (1.5-6.3); ≤7.0: 4.5 (1.8-12).

CONCLUSIONS

No increased hazard of progressively poor neurologic outcomes was observed in resuscitated OHCA patients treated with TTM until the initial post-ROSC arterial pH was at least ≤7.1. This threshold is more acidic than in current guidelines, suggesting the possibility that post-arrest pH may be utilized presently as an inappropriately-pessimistic prognosticator.

Lactate, lactate clearance and outcome after cardiac arrest: A post-hoc analysis of the TTM-Trial

BACKGROUND

Admission lactate and lactate clearance are implemented for risk stratification in sepsis and trauma. In out-of-hospital cardiac arrest, results regarding outcome and lactate are conflicting.

METHODS

This is a post-hoc analysis of the Target Temperature Management trial in which 950 unconscious patents after out-of-hospital cardiac arrest were randomized to a temperature intervention of 33°C or 36°C. Serial lactate samples during the first 36 hours were collected. Admission lactate, 12-hour lactate, and the clearance of lactate within 12 hours after admission were analyzed and the association with 30-day mortality assessed.

RESULTS

Samples from 877 patients were analyzed. In univariate logistic regression analysis, the odds ratio for death by day 30 for each mmol/L was 1.12 (1.08-1.16) for admission lactate, P < .01, 1.21 (1.12-1.31) for 12-hour lactate, P < .01, and 1.003 (1.00-1.01) for each percentage point increase in 12-hour lactate clearance, P = .03. Only admission lactate and 12-hour lactate levels remained significant after adjusting for known predictors of outcome. The area under the receiver operating characteristic curve was 0.65 (0.61-0.69), P < .001, 0.61 (0.57-0.65), P < .001, and 0.53 (0.49-0.57), P = .15 for admission lactate, 12-hour lactate, and 12-hour lactate clearance, respectively.

CONCLUSIONS

Admission lactate and 12-hour lactate values were independently associated with 30-day mortality after out-of-hospital cardiac arrest while 12-hour lactate clearance was not. The clinical value of lactate as the sole predictor of outcome after out-of-hospital cardiac arrest is, however, limited.