Neuron-specific enolase (NSE) improves clinical risk scores for prediction of neurological outcome and death in cardiac arrest patients: Results from a prospective trial.

AIM: Neuron-specific enolase (NSE) increases in response to brain injury and is recommended for outcome prediction in cardiac arrest patients. Our aim was to investigate whether NSE measured at different days after a cardiac arrest and its kinetics would improve the prognostic ability of two cardiac arrest specific risk scores. METHODS: Within this prospective observational study, we included consecutive adult patients after cardiac arrest. We calculated the Out-of-hospital cardiac arrest (OHCA) score and the Cardiac Arrest Hospital Prognosis (CAHP) score upon ICU admission and measured serum NSE upon admission and days 1, 2, 3, 5 and 7. We calculated logistic regression models to study associations of scores and NSE levels with neurological outcome defined by Cerebral Performance Category (CPC) scale and in-hospital death. RESULTS: From 336 included patients, 180 (54%) survived until hospital discharge, of which 150 (45%) had a good neurological outcome. NSE at day 3 showed the highest prognostic accuracy (discrimination) for neurological outcome (area under the curve (AUC) 0.89) and in-hospital mortality (AUC 0.88). These results were robust in reclassification statistics and across different subgroups. NSE kinetics with admission levels serving as a baseline did not further improve prognostication. NSE on day 3 significantly improved discrimination of both clinical risk scores (CAHP from AUC 0.81 to 0.91; OHCA from AUC 0.79 to 0.89). CONCLUSION: NSE measured at day 3 significantly improves clinical risk scores for outcome prediction in cardiac arrest patients and may thus add to clinical decision making about escalation or withdrawal of therapy in this vulnerable patient population.

Performance of clinical risk scores to predict mortality and neurological outcome in cardiac arrest patients.

AIM: Several scores are available to predict mortality and neurological outcome in cardiac arrest patients admitted to the intensive care unit (ICU). The aim of the study was to externally validate the prognostic value of four previously published risk scores. METHODS: For this observational, single-center study, we prospectively included 349 consecutive adult cardiac arrest patients upon ICU admission. We calculated two cardiac arrest specific risk scores (OHCA and CAHP) and two general severity of illness scores (APACHE II and SAPS II). The primary endpoint was in-hospital mortality. Secondary endpoints were neurological outcome at hospital discharge and 30-day mortality. RESULTS: 170 patients (49%) died until hospital discharge. All scores were independently associated with outcomes in logistic regression analysis and showed acceptable discrimination for in-hospital mortality with highest AUCs of the cardiac arrest specific risk scores (OHCA: 0.80 (95%CI 0.75-0.85) and CAHP: 0.84 (95%CI 0.79-0.88) compared to the severity of illness scores (APACHE II: 0.78 (95%CI 0.73-0.83) and SAPS II: 0.77 (95%CI 0.72-0.82). Results were robust in subgroup analysis except for worse performance in elderly patients (>75 years) and patients with respiratory cause of cardiac arrest. Results were similar for 30-days mortality and slightly higher for neurological outcome. CONCLUSIONS: This study confirms the good prognostic performance of cardiac arrest specific scores to predict mortality and neurological outcomes in cardiac arrest patients. Routine use of OHCA or CAHP score helps to objectively risk stratify these vulnerable patients and thereby may improve therapeutic decisions.

Routine blood markers from different biological pathways improve early risk stratification in cardiac arrest patients: Results from the prospective, observational COMMUNICATE study.

INTRODUCTION: Prognostication of cardiac arrest patients admitted to the intensive care unit (ICU) may influence treatment decision, but remains challenging. We evaluated the incremental usefulness of routine blood markers from different biological pathways for predicting fatal outcome and neurological deficits in cardiac arrest patients. METHODS: We prospectively included consecutive, adult cardiac arrest patients upon ICU admission. We recorded initial clinical parameters and measured blood markers of cardiac injury/stress (troponin, BNP, CK), inflammation/infection (WBC, CRP, procalcitonin) and shock (lactate, creatinine, urea). The primary and secondary endpoints were all-cause in-hospital mortality and bad neurological outcome defined by the Cerebral Performance Category (CPC) score. RESULTS: Mortality in the 321 included patients was 49% (n = 156). Procalcitonin (adjusted odds ratio 1.84, 95%CI 1.34 to 2.53, p < 0.001; AUC 0.73) and lactate (adjusted odds ratio 7.29, 95%CI 3.05 to 17.42, p < 0.001; AUC 0.70) were identified as independent prognostic factors for mortality and significantly improved discrimination of a parsimonious clinical model including resuscitation measures (no-flow time, shockable rhythm) and initial vital signs (Glasgow coma scale, respiratory rate) from an AUC of 0.79 to 0.84 (p < 0.001). Cardiac markers did not further improve the model. Results for neurological outcome were similar with model improvements by procalcitonin and lactate from AUC 0.83 to 0.87 (p = 0.004). CONCLUSION: Assessment of routine markers of inflammation/infection and shock provide significant improvements for prognostication of cardiac arrest patients, while cardiac markers did not further improve statistical models. Combination of blood markers and clinical parameters may help to improve initial management decisions in this vulnerable patient population.

Early rule-out and rule-in of myocardial infarction using sensitive cardiac Troponin I.

BACKGROUND: It is currently unknown, whether and to what extent sensitive cardiac troponin (s-cTn) allows shortening of the time required for safe rule-out and rule-in of acute myocardial infarction (AMI). METHODS: We aimed to develop and validate early rule-out and rule-in algorithms for AMI using a thoroughly-examined and commonly used s-cTnI assay in a prospective multicenter study including 2173 patients presenting to the emergency department with suspected AMI. S-cTnI was measured in a blinded fashion at 0 h, 1 h, and 2 h. The final diagnosis was centrally adjudicated by two independent cardiologists. In the derivation cohort (n = 1496), we developed 1h- and 2h-algorithms assigning patients to "rule-out", "rule-in", or "observe". The algorithms were then prospectively validated in the validation cohort (n = 677). RESULTS: AMI was the adjudicated diagnosis in 17% of patients. After applying the s-cTnI 1h-algorithm developed in the derivation cohort to the validation cohort, 65% of patients were classified as "rule-out", 12% as "rule-in", and 23% to "observe". The negative predictive value for AMI in the "rule-out" group was 98.6% (95% CI, 96.9-99.5), the positive predictive value for AMI in the "rule-in" group 76.3% (95% CI, 65.4-85.1). Overall, 30-day mortality was 0.2% in the "rule-out" group, 1.0% in the "observe" group, and 3.0% in the "rule-in" group. Similar results were obtained for the 2h-algorithm. CONCLUSION: When used in conjunction with other clinical information including the ECG, a simple algorithm incorporating s-cTnI values at presentation and after 1h (or 2h) will allow safe rule-out and accurate rule-in of AMI in the majority of patients.