The prognostic value of early lactate clearance for survival after out-of-hospital cardiac arrest

Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis

Background/Objectives: To systematically review the literature and to characterize the utility of lactate and pH for predicting survival and long-term neurological outcomes after out-of-hospital cardiac arrest (OHCA). Methods: PRISMA guidelines were followed. PubMed, Embase, Web of Science, Cochrane Central, and Academic Search Premier were searched for relevant studies. The population included adults with OHCA. Studies with majority in-hospital cardiac arrest (>50%) and studies predicting return of spontaneous circulation (ROSC) were excluded. Pairs of investigators reviewed the studies for relevance. Data were extracted and risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-analyses were performed to characterize the relationship between lactate and pH with survival and neurological outcomes. Results: We included 21,120 patients over 49 studies. Most studies (78%) included OHCA only. Mean lactate of 7.24 (95%CI:6.05-8.44) was associated with favorable survival (n = 9155; 21 studies), while mean lactate of 7.15 (95%CI:6.37-7.93) was associated with favorable neurological outcome (n = 7534; 21 studies). Mean pH of 7.22 (95%CI:7.10-7.33) was associated with favorable survival (n = 4077; 7 studies), while a mean pH of 7.22 (95%CI:7.17-7.27) was associated with favorable neurological outcome (n = 6701; 13 studies). Poor outcomes were associated with lower pH and higher lactate values. Risk of bias was generally low to medium, while heterogeneity was high. Conclusions: A direct correlation exists between pH with survival and neurological outcome; the likelihood of favorable outcomes increases as pH increases. Conversely, an inverse relationship exists between lactate with survival and neurological outcome; higher lactate is associated with poorer outcomes. For lactate, the threshold for survival was more lenient than for favorable neurological outcome.

Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis

BACKGROUND

Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have developed ML models for predicting the occurrence and prognosis of CA, with certain models appearing to outperform traditional scoring tools. However, these models still lack systematic evidence to substantiate their efficacy.

OBJECTIVE

This systematic review and meta-analysis was conducted to evaluate the prediction value of ML in CA for occurrence, good neurological prognosis, mortality, and the return of spontaneous circulation (ROSC), thereby providing evidence-based support for the development and refinement of applicable clinical tools.

METHODS

PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched from their establishment until May 17, 2024. The risk of bias in all prediction models was assessed using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

In total, 93 studies were selected, encompassing 5,729,721 in-hospital and out-of-hospital patients. The meta-analysis revealed that, for predicting CA, the pooled C-index, sensitivity, and specificity derived from the imbalanced validation dataset were 0.90 (95% CI 0.87-0.93), 0.83 (95% CI 0.79-0.87), and 0.93 (95% CI 0.88-0.96), respectively. On the basis of the balanced validation dataset, the pooled C-index, sensitivity, and specificity were 0.88 (95% CI 0.86-0.90), 0.72 (95% CI 0.49-0.95), and 0.79 (95% CI 0.68-0.91), respectively. For predicting the good cerebral performance category score 1 to 2, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.86 (95% CI 0.85-0.87), 0.72 (95% CI 0.61-0.81), and 0.79 (95% CI 0.66-0.88), respectively. For predicting CA mortality, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.85 (95% CI 0.82-0.87), 0.83 (95% CI 0.79-0.87), and 0.79 (95% CI 0.74-0.83), respectively. For predicting ROSC, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.77 (95% CI 0.74-0.80), 0.53 (95% CI 0.31-0.74), and 0.88 (95% CI 0.71-0.96), respectively. In predicting CA, the most significant modeling variables were respiratory rate, blood pressure, age, and temperature. In predicting a good cerebral performance category score 1 to 2, the most significant modeling variables in the in-hospital CA group were rhythm (shockable or nonshockable), age, medication use, and gender; the most significant modeling variables in the out-of-hospital CA group were age, rhythm (shockable or nonshockable), medication use, and ROSC.

CONCLUSIONS

ML represents a currently promising approach for predicting the occurrence and outcomes of CA. Therefore, in future research on CA, we may attempt to systematically update traditional scoring tools based on the superior performance of ML in specific outcomes, achieving artificial intelligence-driven enhancements.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42024518949; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=518949.

EPOS-OHCA: Early Predictors of Outcome and Survival after non-traumatic Out-of-Hospital Cardiac Arrest

Background

Post-cardiac arrest syndrome (PCAS) after out-of-hospital cardiac arrest (OHCA) poses significant challenges due to its complex pathomechanisms involving inflammation, ischemia, and reperfusion injury. The identification of early available prognostic indicators is essential for optimizing therapeutic decisions and improving patient outcomes.

Methods

In this retrospective single-center study, we analyzed real-world data from 463 OHCA patients with either prehospital or in-hospital return of spontaneous circulation (ROSC), treated at the Cardiac Arrest Center of the University Hospital of Marburg (MCAC) from January 2018 to December 2022. We evaluated demographic, prehospital, and clinical variables, including initial rhythms, resuscitation details, and early laboratory results. Statistical analyses included logistic regression to identify predictors of survival and neurological outcomes.

Results

Overall, 46.9% (n = 217) of patients survived to discharge, with 70.1% (n = 152) achieving favorable neurological status (CPC 1 or 2). Age, initial shockable rhythm, resuscitation time to return of spontaneous circulation (ROSC), and early laboratory parameters like lactate, C-reactive protein, and glomerular filtration rate were identified as independent and combined Early Predictors of Outcome and Survival (EPOS), with high significant predictive value for survival (AUC 0.86 [95% CI 0.82-0.89]) and favorable neurological outcome (AUC 0.84 [95% CI 0.80-0.88]).

Conclusion

Integration of EPOS into clinical procedures may significantly improve clinical decision making and thus patient prognosis in the early time-crucial period after OHCA. However, further validation in other patient cohorts is needed.

Is the lactate value predictive of the return of spontaneous circulation during CPR in nontraumatic OHCA?

AIM OF THE STUDY

Cardiac arrest is a major public health issue, in which emergency medical services (EMS) initiating or continuing resuscitation in about 50% to 60% of cases. The aim of this study was to determine whether blood lactate levels and their course during cardiopulmonary resuscitation are prognostic indicators of the return of spontaneous cardiac activity (ROSC) in non-traumatic out-of-hospital cardiac arrest (OHCA).

METHODS

This was a prospective, interventional, multi-center study between 2017 and 2020. Patients above the age of 18 years (>50 years for women) who had non-traumatic OHCA and did not achieve ROSC before the arrival of the EMS, and for whom the medical team decided to initiate or continue cardiopulmonary resuscitation have been included. The primary endpoint was the return of spontaneous cardiac activity during out-of-hospital cardiopulmonary resuscitation, and secondary endpoint was survival at day 28. The lactate was initially measured simultaneously on a venous and capillary sample and then in capillary samples throughout the CPR, using POC device.

RESULTS

A total 60 patients were included. Median age was 71 [IQR: 62-84] and 21.3% were female. Among them, 25% underwent ROSC in out-of-hospital setting, and 13,3% were alive at D-28. The median venous lactate value in all patients at T0 (time at which the EMS set up the peripheral venous line) was 6.2 mmol/L [IQR: 4.6-8.1], with no difference between patients with or without ROSC: 6.4 mmol/L [IQR:4.7-7.9] for patients with ROSC and 6.2 mmol/L [IQR: 4.7-8] for patients without ROSC (p = 0.87). The variables independently associated with ROSC were initial EtCo2 value (aOR = 1.12; 95% CI 1.01-1.25); the initial shockable rhythm (aOR = 10.2; 95% CI 1.18-88.2); and the pre-ROSC adrenaline dose (aOR = 0.54; 95% CI 0.35-0.82).

CONCLUSION

In this prospective multi-center study, there was no independent association between lactate values during cardiopulmonary resuscitation and ROSC in non-traumatic OHCA. However, the post-ROSC pre-hospital kinetics of lactate (i.e., during the first 30 min) seem to be associated with survival.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

BACKGROUND

Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear.

METHODS

The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram.

RESULTS

Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram.

CONCLUSIONS

The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Association Between Postresuscitation 12-Lead ECG Features and Early Mortality After Out-of-Hospital Cardiac Arrest: A Post Hoc Subanalysis of the PEACE Study

Background Once the return of spontaneous circulation after out-of-hospital cardiac arrest is achieved, a 12-lead ECG is strongly recommended to identify candidates for urgent coronary angiography. ECG has no apparent role in mortality risk stratification. We aimed to assess whether ECG features could be associated with 30-day survival in patients with out-of-hospital cardiac arrest. Methods and Results All the post-return of spontaneous circulation ECGs from January 2015 to December 2018 in 3 European centers (Pavia, Lugano, and Vienna) were collected. Prehospital data were collected according to the Utstein style. A total of 370 ECGs were collected: 287 men (77.6%) with a median age of 62 years (interquartile range, 53-70 years). After correction for the return of spontaneous circulation-to-ECG time, age >62 years (hazard ratio [HR], 1.78 [95% CI, 1.21-2.61]; P=0.003), female sex (HR, 1.5 [95% CI, 1.05-2.13]; P=0.025), QRS wider than 120 ms (HR, 1.64 [95% CI, 1.43-1.87]; P<0.001), the presence of a Brugada pattern (HR, 1.49 [95% CI, 1.39-1.59]; P<0.001), and the presence of ST-segment elevation in >1 segment (HR, 1.75 [95% CI, 1.59-1.93]; P<0.001) were independently associated with 30-day mortality. A score ranging from 0 to 26 was created, and by dividing the population into 3 tertiles, 3 classes of risk were found with significantly different survival rate at 30 days (score 0-4, 73%; score 5-7, 66%; score 8-26, 45%). Conclusions The post-return of spontaneous circulation ECG can identify patients who are at high risk of mortality after out-of-hospital cardiac arrest earlier than other forms of prognostication. This provides important risk stratification possibilities in postcardiac arrest care that could help to direct treatments and improve outcomes in patients with out-of-hospital cardiac arrest.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Impact of Lactate on 30-Day All-Cause Mortality in Patients with and without Out-of-Hospital Cardiac Arrest Due to Cardiogenic Shock

In patients with cardiogenic shock (CS) due to myocardial infarction, elevated lactate levels are known to be negative predictors. Studies regarding the prognostic impact in patients with CS complicated by out-of-hospital cardiac arrest (OHCA) are limited. Two hundred and sixty-three consecutive patients with CS were included. The prognostic value of lactate on days 1, 2, 3, 4 and 8 was tested stratified by OHCA and non-OHCA. Statistical analyses included the univariable t-test, Spearman's correlation, C-statistics, Kaplan-Meier analyses, as well as multivariable mixed analysis of variance (ANOVA) and Cox proportional regression analyses. The primary endpoint of all-cause mortality occurred in 49.4% of the non-OHCA group and in 63.4% of the OHCA group. Multivariable regression models showed an association of lactate values with 30-day all-cause mortality in the non-OHCA (p = 0.024) and OHCA groups (p = 0.001). In Kaplan-Meier analyses, patients with lactate levels ≥ 4 mmol/L (log-rank p = 0.001) showed the highest risk for 30-day all-cause mortality in the non-OHCA as well as in the OHCA group. However, in C-statistics lactate on days 1 and 8 had a better discrimination for 30-day all-cause mortality in the OHCA group compared to the non-OHCA group. In conclusion, patients presenting with CS lactate levels showed a good prognostic performance, with and without OHCA. Especially, lactate levels on days 1 and 8 were more accurate in the discrimination for all-cause mortality in CS-patients with OHCA.