Modeling Serum Biomarkers S100B and NSE as Predictors of Outcomes Following Out-of-Hospital Cardiac Arrest
Editor's Note: Based on Einav S, Kaufman N, Algur N, Kark JD. Modeling serum biomarkers S100 beta and neuron-specific enolase as predictors of outcome after out-of-hospital cardiac arrest: an aid to clinical decision making. J Am Coll Cardiol 2012;60:304-11.
Hypoxic brain injury remains the leading cause of mortality and severe neurological impairment after cardiopulmonary resuscitation (CPR) and return of spontaneous circulation (ROSC),1,2 yet accurate prediction of these outcomes remains elusive. Serum levels of protein S100B and Neuron Specific Enolase (NSE) rise in clinical situations reflecting the three classical models of brain injury; hypoxia,3 trauma4-6 and ischemia.7-11 Commercial kits for simple measurement of blood levels of both biomarkers have become available. Nevertheless, neither biomarker is being used in clinical practice because of concerns regarding their discriminant ability.
This study assessed the added value of biomarkers to outcome prediction through joint modeling with clinical data. The hypothesis was that clinical and laboratory characteristics, the latter determined at clinically convenient times in a non-selective population, can be used to develop models enabling clinicians to select the likelihood of misdiagnosis acceptable in their clinical settings for classifying a potential survivor as a death.
A prospective 3-year study of victims of out-of-hospital cardiac arrest (OHCA) within the Jerusalem district, where CPR is performed in accordance with American Heart Association guidelines. Following approval by the local Institutional Review Board, data were recorded on patients aged ≥18 years with ROSC at a single hospital. Blood was sampled for biomarker levels at hospital arrival and on the mornings of days 1 and 3. Treating staff and investigators were blinded to biomarker levels that were determined. Patients were followed to either hospital discharge or death, whichever occurred first. Cerebral Performance Category (CPC) was determined by death or within 24 hours before discharge. The primary outcome measure was poor versus good patient outcome at discharge (CPC 3-5 versus CPC 1-2).
Of the 250 patients were screened, 55 were excluded due to their agonal state, 195 were eligible for study inclusion and 184 contributed blood samples. Participants were mostly males (66%) with an average age of 73±16 (range 19-111) years. Their presenting rhythm was most often asystole (62%), VT/VF (24%) or PEA (12%). There were 43 survivors to hospital discharge (17%).
Biomarker levels: Median S100B concentrations were consistently higher in patients with a poor outcome (7.7, 1.8 and 1.4 mcg/L at admission, day 1 and day 3, respectively) compared to patients with a good outcome (2.3, 0.3 and 0.2 mcg/L, respectively (p<0.0083 for each between group comparison, Bonferroni correction). Median NSE levels did not differ significantly between patients with poor and good outcomes at arrival, but differences emerged subsequently (35 vs. 22 mcg/L for day 1, and 61 vs. 16 mcg/L for day 3)(p<0.0083 for days 1 and 3).
Model for predicting a poor outcome upon arrival (n=158): Univariate regression modeling yielded 4 significant variables: age, presenting rhythm and the two biomarker levels. Multivariable modeling with age (youngest tertile or not) and a presenting rhythm of VT/VF (yes or no) as explanatory variables resulted in an area under the curve (AUC) of 0.880 (95% CI 0.806, 0.954). Adding the biomarker levels to the model led to retention of only three significant variables: age, VT/VF and the level of S100 at admission (AUC 0.932 (95% CI 0.887-0.976)). Comparison of the two ROC curves yielded a p-value of 0.027.
Model for predicting a poor outcome on day 3 after admission (n=74): The only variables contributing independently to the day-3 model were a presenting rhythm of VT/VF and the day 3 S100B concentration (AUC of 0.931 (95% CI 0.873-0.989)). Using the same method as above, it was determined that if the presenting rhythm was not VT/VF, S100B > 0.195 mcg/L predicts a poor outcome and if the presenting rhythm was VT/VF, serum S100B > 0.566 mcg/L predicts a poor outcome (sensitivity 86%, specificity 92%).
Joint modeling of clinical and biomarker data improves outcome prediction after out-of-hospital cardiac arrest. Although biomarker data independently contributed an ostensibly modest 5.2% to the arrival model AUC, they substantially reduced the probability of misclassifying a patient as non-survivor compared with using solely on clinical criteria. When a presenting rhythm of VT/VF and age were added as examples of key clinical covariates, cutoff values for S100 could be tabulated with their specificity/sensitivity characteristics and predictive values.
Most studies of biomarkers in post-ROSC patients seek simple cutoff points, disregarding patient demographic and clinical characteristics yet few clinicians would agree to cease ongoing resuscitation efforts based on the results of blood tests alone. Risk-stratification based upon combined clinical-laboratory data should thus be the preferred clinical approach for interpretation of biomarkers of brain damage. The model presented sought to identify those patients with a poor outcome, potentially enabling improved use of costly resources.
While researchers continue to seek the infallible brain biomarker, policy makers should determine the level of misclassification acceptable to their society within the clinical setting of CPR.
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Keywords: Biomarkers, Diagnostic Errors
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