Brain Natriuretic Peptide as a Marker of Adverse Neurological Outcomes Among Survivors of Cardiac Arrest

Abstract

Background Neurological prognosis after cardiac arrest remains ill-defined. Plasma brain natriuretic peptide (BNP) may relate to poor neurological prognosis in brain-injury patients, though it has not been well studied in survivors of cardiac arrest. Methods We performed a retrospective review and examined the association of BNP with mortality and neurological outcomes at discharge in a cohort of cardiac arrest survivors enrolled from January 2012 to December 2016 at the Wake Forest Baptist Hospital, in North Carolina. Cerebral performance category (CPC) and modified Rankin scales were calculated from the chart based on neurological evaluation performed at the time of discharge. The cohort was subdivided into quartiles based on their BNP levels after which multivariable adjusted logistic regression models were applied to assess for an association between BNP and poor neurological outcomes as defined by a CPC of 3 to 4 and a modified Rankin scale of 4 to 5. Results Of the 657 patients included in the study, 254 patients survived until discharge. Among these, poor neurological status was observed in 101 (39.8%) patients that had a CPC score of 3 to 4 and 97 patients (38.2%) that had a modified Rankin scale of 4 to 5. Mean BNP levels were higher in patients with poor neurological status compared to those with good neurological status at discharge ( P = .03 for CPC 3-4 and P = .02 for modified Rankin score 4-5). BNP levels however, did not vary significantly between patients that survived and those that expired ( P = .22). BNP did emerge as a significant discriminator between patients with severe neurological disability at discharge when compared to those without. The area under the curve for BNP predicting a modified Rankin score of 4 to 5 was 0.800 (95% confidence interval [CI] 0.756-0.844, P < .001) and for predicting CPC 3 to 4 was 0.797 (95% CI 0.756-0.838, P < .001). BNP was able to significantly improve the net reclassification index and integrated discriminatory increment ( P < .05). BNP was not associated with long-term all-cause mortality ( P > .05). Conclusions In survivors of either inpatient or out-of-hospital cardiac arrest, increased BNP levels measured at the time of arrest predicted severe neurological disability at discharge. We did not observe an independent association between BNP levels and long-term all-cause mortality. BNP may be a useful biomarker for predicting adverse neurological outcomes in survivors of cardiac arrest.

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

“…Comorbidities were identified using ICD-9/10 code. Based on previous research [ 8 , 9 , 13 , 14 ], clinical relevance, and general availability, the following data were extracted: demographic characteristics (age at the time of hospital admission, sex); vital signs (heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure(MBP), respiratory rate(RR), body temperature, saturation pulse oxygen (SPO 2 ); comorbidities (hypertension, congestive heart failure(CHF), myocardial infarction, diabetes mellitus(DM), and chronic obstructive pulmonary disease (COPD)) and laboratory variables (hematocrit(HCT), hemoglobin(HB), platelet count, white blood cells(WBC), prothrombin time (PT), international normalized ratio (INR), creatinine, blood urea nitrogen (BUN), glucose, potassium, sodium, calcium, chloride, the anion gap, bicarbonate, lactate, hydrogen ion concentration (pH); treatment information(ventilation, epinephrine, dopamine); marking system: sequential organ failure assessment(SOFA), simplified acute physiology score III (SAPS III), Glasgow coma scale (GCS). The primary outcome of the study was in-hospital mortality, defined as the vital status of the patient at discharge.…”

“…Given the high hospital mortality rate, identifying high-risk factors and accurately predicting prognosis in the early stages of hospitalization may have greater benefits for patients with cardiac arrest admitted to the ICU. Although there were several models for predicting mortality in hospitalized patients with CA available, the accuracy of these methods was not satisfactory (the sample size was less than 1000 or the C-statistic was not calculated), so they had not been widely used [8][9][10].…”