Performance of a Statistical Model to Predict Stroke Outcome in the Context of a Large, Simple, Randomized, Controlled Trial of Feeding

Counsel, Chief; Dennis, MS; Lewis, Steff; Warlow, Charles

doi:10.1161/01.str.0000044165.41303.50

Cited by 35 publications

(7 citation statements)

References 16 publications

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“…In the Oxfordshire Community Stroke Project [22, 25], which is the only externally validated predictive model, pre-stroke independence, and normal Glasgow Coma Scale verbal score, arm power, and ability to walk were significant predictors of survival free of dependency at 6 months, whereas increasing age and living alone were associated with a poorer outcome for this parameter. In the Perth Community Stroke Study, the major predictor of new disability at 5 years was recurrent stroke (odds ratio = 12.4, 95% CI 3.3–63); other independent predictors were increasing age and stroke severity [26].…”

Section: Predictive Factors For Stroke Prognosismentioning

confidence: 99%

Long-Term Outcome after Ischaemic Stroke/Transient Ischaemic Attack

2003

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During the first 30 days after a stroke, the case fatality is about 25% and the major cause of death is the index stroke and its sequelae. The most consistent predictor of 30-day mortality after stroke is stroke severity. Other predictors include increasing age, a history of previous stroke, cardiac failure, and a high blood glucose concentration and white blood cell count. Other less common, but important, causes of early mortality are recurrent ischaemic stroke and a coronary event. The risk of a recurrent cerebrovascular event is highest in the first month (4%) and year (12%) after a stroke and transient ischaemic attack (TIA), probably reflecting the presence of active, unstable atherosclerotic plaque. Thereafter, the risk of a recurrent cerebrovascular event falls to about 5% per year, similar to the risk of a coronary event. During years 1–5 after a TIA and ischaemic stroke, cardiovascular disease increasingly becomes the major cause of death, reflecting the generalized nature of atherothrombosis, the most common cause of the index stroke. The most robust predictor of death within 1–5 years after stroke is increasing age, closely followed by cardiac failure. Additional baseline predictors of longer-term mortality include a history of previous symptomatic atherothrombosis (TIA, ischaemic stroke, peripheral arterial disease, and early-onset ischaemic heart disease), risk factors for atherothrombosis (smoking), other heart diseases (cardiac failure, atrial fibrillation) and increasing stroke severity. Lacunar syndromes can be predictive of relative longevity. At 5 years after stroke, survival is about 40%, and about half of survivors are disabled and dependent. The most robust predictors of disability at 5 years after stroke are increasing age, stroke severity, and recurrent stroke. The most powerful predictor of early recurrent stroke (within 30 days after stroke) is an atherosclerotic ischaemic stroke caused by large-artery atherosclerosis with >50% stenosis, whereas the strongest predictor of stroke recurrence over 5 years is diabetes. Other predictors of recurrent stroke include increasing age, previous TIA, atrial fibrillation, high alcohol consumption, haemorrhagic index stroke, and hypertension at discharge. The clinical implication of these findings is that strategies for optimizing long-term outcome after TIA and stroke should be directed toward reducing the high risk of recurrent stroke and coronary events by removing/recanalizing the symptomatic atherosclerotic plaque, controlling the underlying causal vascular risk factors, and administering long-term, effective antiplatelet therapy.

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Section: Predictive Factors For Stroke Prognosismentioning

confidence: 99%

Long-Term Outcome after Ischaemic Stroke/Transient Ischaemic Attack

2003

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“…Both these models have been individually validated in several studies [ 11 – 16 ]; see Table 2 . However, their relative performance has received little attention.…”

Section: Introductionmentioning

confidence: 99%

Validation and Recalibration of Two Multivariable Prognostic Models for Survival and Independence in Acute Stroke

et al. 2016

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IntroductionVarious prognostic models have been developed for acute stroke, including one based on age and five binary variables (‘six simple variables’ model; SSVMod) and one based on age plus scores on the National Institutes of Health Stroke Scale (NIHSSMod). The aims of this study were to externally validate and recalibrate these models, and to compare their predictive ability in relation to both survival and independence.MethodsData from a large clinical trial of oxygen therapy (n = 8003) were used to determine the discrimination and calibration of the models, using C-statistics, calibration plots, and Hosmer-Lemeshow statistics. Methods of recalibration in the large and logistic recalibration were used to update the models.ResultsFor discrimination, both models functioned better for survival (C-statistics between .802 and .837) than for independence (C-statistics between .725 and .735). Both models showed slight shortcomings with regard to calibration, over-predicting survival and under-predicting independence; the NIHSSMod performed slightly better than the SSVMod. For the most part, there were only minor differences between ischaemic and haemorrhagic strokes. Logistic recalibration successfully updated the models for a clinical trial population.ConclusionsBoth prognostic models performed well overall in a clinical trial population. The choice between them is probably better based on clinical and practical considerations than on statistical considerations.

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“…There is no cut-off to determine what constitutes an acceptable C statistic although, as the C statistic is identical to the area under a receiver operating curve for a dichotomous outcome, a value of 0.5 indicates the model is no better than chance [12]. …”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown C statistics for the prediction of 6-month post-stroke independent survival (OHS ≤2) with the SSV model of between 0.79 and 0.84 [6,12,13,14]. …”

Section: Discussionmentioning

confidence: 99%

Predicting Patient-Reported Stroke Outcomes: A Validation of the Six Simple Variable Prognostic Model

Teale

Young

Dennis

et al. 2013

Cerebrovasc Dis Extra

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Background: Case-mix represents the range of disease severity and baseline characteristics that may be the cause of variation in outcomes between individuals and populations. Adjustment for case-mix is therefore important to allow meaningful comparison of healthcare outcomes. The best available case-mix adjustment model for stroke (the Six Simple Variable [SSV] model) was developed to adjust the hard endpoints of independent survival, survival and alive and living at home. There is increasing interest in the measurement of patient-reported outcomes through self-completed questionnaires, though there are currently no robust adjustment models for any such outcome. We aimed to determine whether the SSV prognostic model derived to predict 6-month post-stroke independent survival has wider utility in case-mix adjustment of a patient-reported functional outcome measure, the Subjective Index of Physical and Social Outcome (SIPSO), collected by post 6 months after stroke onset. Methods: We examined data from 176 patients admitted following an acute stroke and recruited into a prospective cohort study in three participating acute hospitals in Yorkshire, UK. Patients in receipt of palliative care or with transient ischaemic attack were excluded. Using the beta coefficients from the published SSV model to predict independent survival, individual probabilities of ‘good' outcome as measured with the dichotomised SIPSO collected by post 6 months after stroke onset were calculated. The ability of the SSV case-mix adjustment model to discriminate patients with ‘good' over ‘poor' outcome was assessed through calculation of C statistics. Correct predictions were visualised with calibration plots. Results: The C statistics for the SSV model to predict the physical and social subscales of the SIPSO outcome measure were 0.73 (95% CI 0.65-0.79) and 0.66 (0.58-0.82), respectively. Inclusion of patients who died prior to follow-up and ascribing them a score of 0 improved the discrimination (0.76 [0.70-0.82] and 0.70 [0.64-0.76], respectively). Calibration plots demonstrated a tendency to over-optimistic predictions, although confidence limits were wide. Conclusions: The SSV model predicts adequately the physical component of the SIPSO patient-reported outcome measure and may be useful to adjust this outcome for case-mix following stroke in survivors to follow-up. This could be of benefit in observational studies, stratified randomisation for trials, and in comparison of between-institution clinical trials. Further exploration of the generalizability of the model to adjust other patient-reported stroke outcomes may be warranted.

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Performance of a Statistical Model to Predict Stroke Outcome in the Context of a Large, Simple, Randomized, Controlled Trial of Feeding

Cited by 35 publications

References 16 publications

Long-Term Outcome after Ischaemic Stroke/Transient Ischaemic Attack

Long-Term Outcome after Ischaemic Stroke/Transient Ischaemic Attack

Validation and Recalibration of Two Multivariable Prognostic Models for Survival and Independence in Acute Stroke

Predicting Patient-Reported Stroke Outcomes: A Validation of the Six Simple Variable Prognostic Model

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