Dataset Information

Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke.

ABSTRACT:

Background

Long-term mortality prediction can guide feasible discharge care plans and coordinate appropriate rehabilitation services. We aimed to develop and validate a prediction model to identify patients at risk of mortality after acute ischemic stroke (AIS).

Methods

The primary outcome was all-cause mortality, and the secondary outcome was cardiovascular death. This study included 21,463 patients with AIS. Three risk prediction models were developed and evaluated: a penalized Cox model, a random survival forest model, and a DeepSurv model. A simplified risk scoring system, called the C-HAND (history of Cancer before admission, Heart rate, Age, eNIHSS, and Dyslipidemia) score, was created based on regression coefficients in the multivariate Cox model for both study outcomes.

Results

All experimental models achieved a concordance index of 0.8, with no significant difference in predicting poststroke long-term mortality. The C-HAND score exhibited reasonable discriminative ability for both study outcomes, with concordance indices of 0.775 and 0.798.

Conclusions

Reliable prediction models for long-term poststroke mortality were developed using information routinely available to clinicians during hospitalization.

SUBMITTER: Lin CH

PROVIDER: S-EPMC9961287 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke.

Lin Ching-Heng CH Kuo Ya-Wen YW Huang Yen-Chu YC Lee Meng M Huang Yi-Wei YW Kuo Chang-Fu CF Lee Jiann-Der JD

International journal of environmental research and public health 20230209 4

<h4>Background</h4>Long-term mortality prediction can guide feasible discharge care plans and coordinate appropriate rehabilitation services. We aimed to develop and validate a prediction model to identify patients at risk of mortality after acute ischemic stroke (AIS).<h4>Methods</h4>The primary outcome was all-cause mortality, and the secondary outcome was cardiovascular death. This study included 21,463 patients with AIS. Three risk prediction models were developed and evaluated: a penalized ...[more]

PMID: 36833741

Similar Datasets

Project description:Predicting long-term stroke mortality is a clinically important and unmet need. We aimed to develop and internally validate a 10-year ischaemic stroke mortality prediction score. In this UK cohort study, 10,366 patients with first-ever ischaemic stroke between January 2003 and December 2016 were followed up for a median (interquartile range) of 5.47 (2.96-9.15) years. A Cox proportional-hazards model was used to predict 10-year post-admission mortality. The predictors associated with 10-year mortality included age, sex, Oxfordshire Community Stroke Project classification, estimated glomerular filtration rate (eGFR), pre-stroke modified Rankin Score, admission haemoglobin, sodium, white blood cell count and comorbidities (atrial fibrillation, coronary heart disease, heart failure, cancer, hypertension, chronic obstructive pulmonary disease, liver disease and peripheral vascular disease). The model was internally validated using bootstrap resampling to assess optimism in discrimination and calibration. A nomogram was created to facilitate application of the score at the point of care. Mean age (SD) was 78.5 ± 10.9 years, 52% female. Most strokes were partial anterior circulation syndromes (38%). 10-year mortality predictors were: total anterior circulation stroke (hazard ratio, 95% confidence intervals) (2.87, 2.62-3.14), eGFR < 15 (1.97, 1.55-2.52), 1-year increment in age (1.04, 1.04-1.05), liver disease (1.50, 1.20-1.87), peripheral vascular disease (1.39, 1.23-1.57), cancers (1.37, 1.27-1.47), heart failure (1.24, 1.15-1.34), 1-point increment in pre-stroke mRS (1.20, 1.17-1.22), atrial fibrillation (1.17, 1.10-1.24), coronary heart disease (1.09, 1.02-1.16), chronic obstructive pulmonary disease (1.13, 1.03-1.25) and hypertension (0.77, 0.72-0.82). Upon internal validation, the optimism-adjusted c-statistic was 0.76 and calibration slope was 0.98. Our 10-year mortality model uses routinely collected point-of-care information. It is the first 10-year mortality score in stroke. While the model was internally validated, further external validation is also warranted.

Project description:IntroductionWe aimed to develop and validate a prognostic score for disability at discharge and functional outcome at three months in patients with acute ischemic stroke based on clinical information available on admission.Patients and methodsThe Dutch Stroke Score (DSS) was developed in 1227 patients with ischemic stroke included in the Paracetamol (Acetaminophen) In Stroke study. Predictors for Barthel Index (BI) at discharge ('DSS-discharge') and modified Rankin Scale (mRS) at three months ('DSS-3 months') were identified in multivariable ordinal regression. The models were internally validated with bootstrapping techniques. The DSS-3 months was externally validated in the PRomoting ACute Thrombolysis in Ischemic StrokE study (1589 patients) and the Preventive Antibiotics in Stroke Study (2107 patients). Model performance was assessed in terms of discrimination, expressed by the area under the receiver operating characteristic curve (AUC), and calibration.ResultsAt model development, the strongest predictors of Barthel Index at discharge were age per decade over 60 (odds ratio = 1.55, 95% confidence interval (CI) 1.41-1.68), National Institutes of Health Stroke Scale (odds ratio = 1.24 per point, 95% CI 1.22-1.26) and diabetes (odds ratio = 1.62, 95% CI 1.32-1.91). The internally validated AUC was 0.76 (95% CI 0.75-0.79). The DSS-3 months, additionally consisting of previous stroke and atrial fibrillation, performed similarly at internal (AUC 0.75, 95% CI 0.74-0.77) and external validation (AUC 0.74 in PRomoting ACute Thrombolysis in Ischemic StrokE (95% CI 0.72-0.76) and 0.69 in Preventive Antibiotics in Stroke Study (95% CI 0.69-0.72)). Observed outcome was slightly better than predicted.Discussion: The DSS had satisfactory performance in predicting BI at discharge and mRS at three months in ischemic stroke patients.ConclusionIf further validated, the DSS may contribute to efficient stroke unit discharge planning alongside patients' contextual factors and therapeutic needs.

Dataset Information

Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke.

Background

Methods

Results

Conclusions

Publications

Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets