Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients.

BACKGROUND AND OBJECTIVE: Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors. METHODS: We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals. RESULTS: A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60-0.71], EEG factors AUC = 0.82 [95% CI 0.77-0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80-0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76-0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001). CONCLUSIONS: Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.

Clinical outcomes in patients with generalized periodic discharges.

PURPOSE: Generalized periodic discharges (GPDs) are frequently identified in the EEGs of hospitalized patients but their prognostic significance remains unclear. We retrospectively reviewed clinical data in patients with GPDs to elucidate factors associated with in-hospital mortality. METHOD: We reviewed data from inpatients at three different hospitals affiliated with our institution in whom GPDs were reported on routine EEGs by fellowship-trained electroencephalographers during the years 2010-2012. Cox regression was used to determine statistical association between in-hospital death and demographics, medical comorbidities, neurological and neuroimaging abnormalities and antiepileptic drug use. RESULTS: We identified 113 patients with GPDs. The mean age was 70.4 years and 70 (61.9%) were women. There were 60 inpatient deaths (53.1%). The variables significantly associated with in-hospital mortality were dementia, poor mental status at the time of the EEG, chronic focal abnormalities on neuroimaging, cardiac arrest and chronic obstructive pulmonary disease (COPD). CONCLUSION: Dementia, poor mental status during EEG, chronic focal abnormalities on neuroimaging, cardiac arrest and COPD are independently associated with increased in-hospital mortality in patients with GPDs (P<0.05).

Thrombocytopenia is an independent predictor of mortality in pulmonary hypertension.

BACKGROUND: Established prognostic factors for pulmonary hypertension (PH) include brain natriuretic peptide, troponins and hemodynamic measures such as central venous pressure and cardiac output. The prognostic role of thrombocytopenia, however, has yet to be determined in patients with PH. The aim of this study was to evaluate effect of thrombocytopenia on mortality in patients with PH. METHODS: 521 patients with severe PH, defined by a pulmonary artery systolic pressure >60 mm Hg on transthoracic echocardiography and a platelet count measured within one month after diagnosis were enrolled from three hospitals of Montefiore Medical Center. The cohort was divided into two groups: mild thrombocytopenia to a normal platelet count (platelet count 100,000-450,000 per uL); and moderate to severe thrombocytopenia (platelet count <100,000 per uL). Inpatient and social security death records were used to determine 1-year all-cause mortality. RESULTS: Mean age was 70.3 ± 15.6 with 40% of patients being male. Overall mortality at 1 year was 30.7%, with increased mortality in PH patients with mild thrombocytopenia compared to those with moderate to severe thrombocytopenia (46.5% vs. 27.0%, p < 0.001). In multivariate analysis, moderate to severe thrombocytopenia remained an independent predictor of mortality (HR 1.798, 95% CI 1.240-2.607, p = 0.002). CONCLUSIONS: Moderate to severe thrombocytopenia is an independent predictor of higher mortality in patients with severe PH. These findings may support the use of thrombocytopenia as a useful prognostic indicator in patients with severe PH.