A Predictive Risk Model for Outcomes of Ischemic Stroke

Johnston, Karen C.; Connors, Alfred F.; Wagner, Douglas P.; Knaus, William A.; Wang, X.-Q.; Haley, E. Clarke

doi:10.1161/01.str.31.2.448

Cited by 174 publications

(171 citation statements)

References 49 publications

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“…45,69 Some studies used Ͼ1 ADL measurement in their prognostic investigations. 23,42,61 The Barthel Index (BI; Kϭ24) 23,24,26,29,31,32,34,37,41,42,45,46,51-53,58 -62,64 -66,68 was the most frequently used scale to assess ADL outcome, followed by the modified Rankin scale (Kϭ18), 21-23,25,30,35,37,38,42,44, …”

Section: Study Characteristicsmentioning

confidence: 99%

Early Prediction of Outcome of Activities of Daily Living After Stroke

Veerbeek

Kwakkel

Wegen

et al. 2011

Stroke

455

310

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Background and Purpose-Knowledge about robust and unbiased factors that predict outcome of activities of daily living (ADL) is paramount in stroke management. This review investigates the methodological quality of prognostic studies in the early poststroke phase for final ADL to identify variables that are predictive or not predictive for outcome of ADL after stroke. Methods-PubMed, Ebsco/Cinahl and Embase were systematically searched for prognostic studies in which stroke patients were included Յ2 weeks after onset and final outcome of ADL was determined Ն3 months poststroke. Risk of bias scores were used to distinguish high-and low-quality studies and a qualitative synthesis was performed. Results-Forty-eight of 8425 identified citations were included. The median risk of bias score was 17 out of 27 (range, 6 -22) points. Most studies failed to report medical treatment applied, management of missing data, rationale for candidate determinants and outcome cut-offs, results of univariable analysis, and validation and performance of the model, making the predictive value of most determinants indistinct. Six high-quality studies showed strong evidence for baseline neurological status, upper limb paresis, and age as predictors for outcome of ADL. Gender and risk factors such as atrial fibrillation were unrelated to this outcome. Conclusions-Because of insufficient methodological quality of most prognostic studies, the predictive value of many clinical determinants for outcome of ADL remains unclear. Future cohort studies should focus on early prediction using simple models with good clinical performance to enhance application in stroke management and research. (Stroke.

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Section: Study Characteristicsmentioning

confidence: 99%

Early Prediction of Outcome of Activities of Daily Living After Stroke

Veerbeek

Kwakkel

Wegen

et al. 2011

Stroke

455

310

View full text Add to dashboard Cite

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“…The sample size was large (larger than the sum total of all 17 previous studies identified in systematic review), 4 includes patients admitted with acute stroke to all hospitals in England and Wales, and reflects the outcomes of contemporary stroke care. Many of the previously developed models were based on data from single sites 22 or interventional cohorts, 6,23 or were derived from historic cohorts 24 of patients who had stroke at a time before the widespread use of interventions proven to improve outcomes after stroke, such as organized stroke unit care. 25 The validity of these models in predicting the outcomes of contemporary patients with stroke is therefore uncertain.…”

Section: Strokementioning

confidence: 99%

Derivation and External Validation of a Case Mix Model for the Standardized Reporting of 30-Day Stroke Mortality Rates

Bray

Campbell

Cloud

et al. 2014

Stroke

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Background and Purpose-Case mix adjustment is required to allow valid comparison of outcomes across care providers.However, there is a lack of externally validated models suitable for use in unselected stroke admissions. We therefore aimed to develop and externally validate prediction models to enable comparison of 30-day post-stroke mortality outcomes using routine clinical data. Methods-Models were derived (n=9000 patients) and internally validated (n=18 169 patients) using data from the Sentinel Stroke National Audit Program, the national register of acute stroke in England and Wales. External validation (n=1470 patients) was performed in the South London Stroke Register, a population-based longitudinal study. Models were fitted using general estimating equations. Discrimination and calibration were assessed using receiver operating characteristic curve analysis and correlation plots. Results-Two final models were derived. Model A included age (<60, 60-69, 70-79, 80-89, and ≥90 years), NationalInstitutes of Health Stroke Severity Score (NIHSS) on admission, presence of atrial fibrillation on admission, and stroke type (ischemic versus primary intracerebral hemorrhage). Model B was similar but included only the consciousness component of the NIHSS in place of the full NIHSS. Both models showed excellent discrimination and calibration in internal and external validation. The c-statistics in external validation were 0.87 (95% confidence interval, 0.84-0.89) and 0.86 (95% confidence interval, 0.83-0.89) for models A and B, respectively. Conclusions-We have derived and externally validated 2 models to predict mortality in unselected patients with acute stroke using commonly collected clinical variables. In settings where the ability to record the full NIHSS on admission is limited, the level of consciousness component of the NIHSS provides a good approximation of the full NIHSS for mortality prediction. (Stroke. 2014;45:3374-3380.)

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“…Various studies have concluded that it is predictive of stroke outcomes. [16][17][18] One study demonstrated that severity of stroke assessed by NIHSS at admission to be predictive of 3 month mortality and another study established it as an independent predictor of 30 days mortality.…”

Section: Discussionmentioning

confidence: 99%