Dataset Information

The impact of post-stroke complications on in-hospital mortality depends on stroke severity.

ABSTRACT:

Introduction

Controversies remain on whether post-stroke complications represent an independent predictor of poor outcome or just a reflection of stroke severity. We aimed to identify which post-stroke complications have the highest impact on in-hospital mortality by using machine learning techniques. Secondary aim was identification of patient's subgroups in which complications have the highest impact.

Patients and methods

Registro Nacional de Ictus de la Sociedad Española de Neurología is a stroke registry from 42 centers from the Spanish Neurological Society. Data from ischemic stroke patients were used to build a random forest by combining 500 classification and regression trees, to weight up the impact of baseline characteristics and post-stroke complications on in-hospital mortality. With the selected variables, a logistic regression analysis was performed to test for interactions.

Results

12,227 ischemic stroke patients were included. In-hospital mortality was 5.9% and median hospital stay was 7(4-10) days. Stroke severity [National Institutes of Health Stroke Scale?>?10, OR?=?5.54(4.55-6.99)], brain edema [OR?=?18.93(14.65-24.46)], respiratory infections [OR?=?3.67(3.02-4.45)] and age [OR?=?2.50(2.07-3.03) for >77 years] had the highest impact on in-hospital mortality in random forest, being independently associated with in-hospital mortality. Complications have higher odds ratios in patients with baseline National Institutes of Health Stroke Scale <10.

Discussion

Our study identified brain edema and respiratory infections as independent predictors of in-hospital mortality, rather than just markers of more severe strokes. Moreover, its impact was higher in less severe strokes, despite lower frequency.

Conclusion

Brain edema and respiratory infections were the complications with a greater impact on in-hospital mortality, with the highest impact in patients with mild strokes. Further efforts on the prediction of these complications could improve stroke outcome.

SUBMITTER: Bustamante A

PROVIDER: S-EPMC6453178 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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The impact of post-stroke complications on in-hospital mortality depends on stroke severity.

Bustamante Alejandro A Giralt Dolors D García-Berrocoso Teresa T Rubiera Marta M Álvarez-Sabín José J Molina Carlos C Serena Joaquín J Montaner Joan J

European stroke journal 20161128 1

<h4>Introduction</h4>Controversies remain on whether post-stroke complications represent an independent predictor of poor outcome or just a reflection of stroke severity. We aimed to identify which post-stroke complications have the highest impact on in-hospital mortality by using machine learning techniques. Secondary aim was identification of patient's subgroups in which complications have the highest impact.<h4>Patients and methods</h4>Registro Nacional de Ictus de la Sociedad Española de Neu ...[more]

PMID: 31008302

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Project description:Background and Purpose: Clinical outcome and mortality after endovascular thrombectomy (EVT) in patients with ischemic stroke are commonly assessed after 3 months. In patients with acute kidney injury (AKI), unfavorable results for 3-month mortality have been reported. However, data on the in-hospital mortality after EVT in this population are sparse. In the present study, we assessed whether AKI impacts in-hospital and 3-month mortality in patients undergoing EVT. Materials and Methods: From a prospectively recruiting database, consecutive acute ischemic stroke patients receiving EVT between 2010 and 2018 due to acute large vessel occlusion were included. Post-contrast AKI (PC-AKI) was defined as an increase of baseline creatinine of ≥0.5 mg/dL or >25% within 48 h after the first measurement at admission. Adjusting for potential confounders, associations between PC-AKI and mortality after stroke were tested in univariate and multivariate logistic regression models. Results: One thousand one hundred sixty-nine patients were included; 166 of them (14.2%) died during the acute hospital stay. Criteria for PC-AKI were met by 29 patients (2.5%). Presence of PC-AKI was associated with a significantly higher risk of in-hospital mortality in multivariate analysis [odds ratio (OR) = 2.87, 95% confidence interval (CI) = 1.16-7.13, p = 0.023]. Furthermore, factors associated with in-hospital mortality encompassed higher age (OR = 1.03, 95% CI = 1.01-1.04, p = 0.002), stroke severity (OR = 1.05, 95% CI = 1.03-1.08, p < 0.001), symptomatic intracerebral hemorrhage (OR = 3.20, 95% CI = 1.69-6.04, p < 0.001), posterior circulation stroke (OR = 2.85, 95% CI = 1.72-4.71, p < 0.001), and failed recanalization (OR = 2.00, 95% CI = 1.35-3.00, p = 0.001). Conclusion: PC-AKI is rare after EVT but represents an important risk factor for in-hospital mortality and for mortality within 3 months after hospital discharge. Preventing PC-AKI after EVT may represent an important and potentially lifesaving effort in future daily clinical practice.

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Project description:Post-stroke mortality is higher among residents of disadvantaged neighborhoods, but it is not known whether neighborhood inequalities are specific to stroke survival or similar to mortality patterns in the general population. We hypothesized that neighborhood disadvantage would predict higher poststroke mortality, and neighborhood effects would be relatively larger for stroke patients than for individuals with no history of stroke. Health and Retirement Study participants aged ≥50 years without stroke at baseline (n=15 560) were followed ≤12 years for incident stroke (1715 events over 159 286 person-years) and mortality (5325 deaths). Baseline neighborhood characteristics included objective measures based on census tracts (family income, poverty, deprivation, residential stability, and percent white, black, or foreign-born) and self-reported neighborhood social ties. Using Cox proportional hazard models, we compared neighborhood mortality effects for people with versus people without a history of stroke. Most neighborhood variables predicted mortality for both stroke patients and the general population in demographic-adjusted models. Neighborhood percent white predicted lower mortality for stroke survivors (hazard ratio, 0.75 for neighborhoods in highest 25th percentile versus below, 95% confidence interval, 0.62-0.91) more strongly than for stroke-free adults (hazard ratio, 0.92; 95% confidence interval, 0.83-1.02; P=0.04 for stroke-by-neighborhood interaction). No other neighborhood characteristic had different effects for people with versus without stroke. Neighborhood-mortality associations emerged within 3 months after stroke, when associations were often stronger than among stroke-free individuals. Neighborhood characteristics predict mortality, but most effects are similar for individuals without stroke. Eliminating disparities in stroke survival may require addressing pathways that are not specific to traditional poststroke care.

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Dataset Information

The impact of post-stroke complications on in-hospital mortality depends on stroke severity.

Introduction

Patients and methods

Results

Discussion

Conclusion

Publications

The impact of post-stroke complications on in-hospital mortality depends on stroke severity.

Similar Datasets

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