Impact of the Dedicated Neurointensivists on the Outcome in Patients with Ischemic Stroke Based on the Linked Big Data for Stroke in Korea

Clinical and paraclinical predictors of early neurological deterioration and poor outcome in spontaneous intracerebral hemorrhage

Background

Spontaneous intracerebral hemorrhage (sICH) is the second most common form of stroke. It is a major cause of morbidity and mortality. Several clinical and radiological parameters are related to its poor outcome. The aim of this study is to elucidate the clinical, laboratory, and radiological factors associated with early neurological deterioration and poor outcome in patients with ICH.

Results

seventy patients diagnosed with sICH were evaluated within the first 72 h from the onset of symptoms by Clinical, radiological, and laboratory parameters. Patients were assessed for early neurological deterioration (END) during the hospital stay (up to 7 days from admission) using Glasgow coma scale (GSC), and the National Institutes of Health Stroke Scale (NIHSS), and within 3 months from stroke onset using modified Rankin scale (mRS). ICH score and Functional Outcome in Patients with Primary Intracerebral Hemorrhage (FUNC) Score were calculated for prognostication. 27.1% and 71.42% of patients had END and showed unfavorable outcome, respectively. Clinical indices, as NIHSS > 7 on admission and age > 51 years, radiological characteristics, as large hematoma size, leukoaraiosis, and mass effect detected on CT scan, as well as serum biomarkers; serum urea level > 50 mg/dL, high neutrophil:lymphocyte ratio on admission, high ALT and AST, as well as low total, LDL, and HDL cholesterol levels, all were significantly associated with poor outcome in the patients. Stepwise multivariate logistic regression analysis found the presence of aspiration to be an independent predictor of END, and the scores of NIHSS > 7 on admission, age > 51 years, and urea level > 50 mg/dL were independent predictors of poor outcome.

Conclusions

There are several predictors for END as well as poor outcome in ICH. Some are clinical, others are radiological and laboratory. Aspiration was an independent predictor of END during hospital stay (3-7 days) in patients with ICH, while older age, high NIHSS and urea level on admission were independent predictors of poor outcome.

Association of Neurocritical Care Services With Mortality and Functional Outcomes for Adults With Brain Injury: A Systematic Review and Meta-analysis

Importance

Neurocritical care (NCC) aims to improve the outcomes of critically ill patients with brain injury, although the benefits of such subspecialized care are yet to be determined.

Objective

To evaluate the association of NCC with patient-centered outcomes in adults with acute brain injury who were admitted to intensive care units (ICUs). The protocol was preregistered on PROSPERO (CRD42020177190).

Data Sources

Three electronic databases were searched (Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials) from inception through December 15, 2021, and by citation chaining.

Study Selection

Studies were included for interventions of neurocritical care units (NCCUs), neurointensivists, or NCC consulting services compared with general care in populations of neurologically ill adults or adults with acute brain injury in ICUs.

Data Extraction and Synthesis

Data extraction was performed in keeping with PRISMA guidelines and risk of bias assessed through the ROBINS-I Cochrane tool by 2 independent reviewers. Data were pooled using a random-effects model.

Main Outcomes and Measures

The primary outcome was all-cause mortality at longest follow-up until 6 months. Secondary outcomes were ICU length of stay (LOS), hospital LOS, and functional outcomes. Data were measured as risk ratio (RR) if dichotomous or standardized mean difference if continuous. Subgroup analyses were performed for disease and models of NCC delivery.

Results

After 5659 nonduplicated published records were screened, 26 nonrandomized observational studies fulfilled eligibility criteria. A meta-analysis of mortality outcomes for 55 792 patients demonstrated a 17% relative risk reduction (RR, 0.83; 95% CI, 0.75-0.92; P = .001) in those receiving subspecialized care (n = 27 061) compared with general care (n = 27 694). Subgroup analyses did not identify subgroup differences. Eight studies including 4667 patients demonstrated a 17% relative risk reduction (RR, 0.83; 95% CI, 0.70-0.97; P = .03) for an unfavorable functional outcome with subspecialized care compared with general care. There were no differences in LOS outcomes. Heterogeneity was substantial in all analyses.

Conclusions and Relevance

Subspecialized NCC is associated with improved survival and functional outcomes for critically ill adults with brain injury. However, confidence in the evidence is limited by substantial heterogeneity. Further investigations are necessary to determine the specific aspects of NCC that contribute to these improved outcomes and its cost-effectiveness.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

PURPOSE OF REVIEW

To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors.

RECENT FINDINGS

Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.