Predicting Prognoses in Patients with Acute Stroke

MARK H. EBELL, MD, MS,
Athens, Georgia

American Family Physician. 2008;77(12):1719-1720.

Author disclosure: Nothing to disclose.

Clinical Question

What is the long-term prognosis for patients with acute stroke?

Evidence Summary

A number of clinical decision models and scoring systems have been developed and validated to assist physicians in assessing the prognosis of patients with acute stroke. This assessment can be helpful to patients, families, and physicians as they plan for long-term care and prepare advance directives. The National Institutes of Health Stroke Scale (NIHSS) predicts seven-day and three-month prognoses and is widely used in the research setting. The NIHSS is available at http://www.ninds.nih.gov/disorders/stroke/strokescales.htm.

In a prospective study of 1,281 patients with acute stroke, a good or excellent outcome at three months was observed in 95 percent of patients with an NIHSS score of 0 to 3 points, 87 percent with 4 to 6 points, 78 percent with 7 to 10 points, 56 percent with 11 to 15 points, 42 percent with 16 to 22 points, and only 18 percent with 23 or more points.1 However, the NIHSS has 13 items, is fairly complex, and requires training for accurate use and good reproducibility.

Other models are simpler and potentially more useful at the point of care. Guy's prognostic score (G-score), a simplification of the Guy's Hospital score, includes patient age and five clinical signs. It has been prospectively validated in several populations, but most of the validation studies are at least 10 years old.2 Because of changes in the care of patients with acute stroke, this article presents only models validated since 2000.

The six simple variable model has been prospectively validated in a study of dietary management in 2,955 patients with stroke at 112 hospitals in 16 countries.3 The mean age of patients was 73 years, and about one half were men; more than 90 percent of patients were independent in daily activities before the stroke. Separate six simple variable models were created to predict survival free of dependency at six months (Table 1), overall survival at six months, and probability of being alive and at home within six months.3 The models had good accuracy, with an area under the receiver operating characteristic curve of 0.79. The models tended to be somewhat pessimistic in patients with severe strokes and optimistic in patients with milder strokes. The models require calculations but can be put into a spreadsheet fairly easily.

A simpler model was developed in 223 patients with acute stroke in an Australian teaching hospital and was validated in 217 patients at the same hospital (Table 2).4 The mean age of patients in the validation group was 69 years, and 58 percent were men. Although easier to use at the point of care, the model has not been prospectively validated in other populations and should be used with caution.

Table 1 Six Simple Variable Model for Predicting the Probability of a Good Outcome Six Months After Acute Stroke

VariablePoints
Age (years)
50−2.55
55−2.81
60−3.06
65−3.32
70−3.57
75−3.82
80−4.08
85−4.34
Living alone
Yes/unknown+0.661
No+1.322
Independent before the stroke
Yes/unknown−2.744
No−5.488
Normal GCS verbal score
Yes−2.16
No/unknown−4.32
Able to lift arms
Yes−2.106
No/unknown−4.212
Able to walk
Yes−1.311
No/unknown−2.622
Constant+12.34
Total:_________

note: A good outcome is defined as survival free of dependence. Probability of independent survival = exp(total score)/(1+ exp[total score]).

GCS = Glasgow Coma Scale.

Example: A 60-year-old patient living alone was independent before a stroke. The patient has a normal GCS verbal score, but is unable to lift the arms or walk. The patient's total score is −1.797 (−3.06 + 0.661 − 2.744 − 2.16 − 4.212 − 2.622+ 12.34). Because exp(−1.797) = 0.1657, the probability of independent survival is 0.1657/(1 + 0.1657) = 0.14 or 14%. Note that you can find answers to exp(x) calculations by searching Google for exp(x), where x is the number (i.e., enter exp(−1.797) into the Google search engine).

Information from reference 3.

Table 2 Model for Predicting One-Year Mortality Risk In Patients with Acute Stroke

VariablePoints
Urinary incontinence9
Dysphagia (moderate or severe)7
Both sides of the brain affected4
Hyperthermia (body temperature > 99.5°F [37.5°C])4
History of ischemic heart disease3
History of peripheral vascular disease3
Unconscious on admission3
History of diabetes mellitus2
Total:_________

note: This model has been prospectively validated in the original study population, but not in other settings.

Interpretation: Patients with a score of 10 points or greater had a 60% one-year mortality (high risk); patients with a score of less than 10 points had an 11% one-year mortality; no patient with a score of 15 points or greater survived at one year.

Example: A patient with an acute stroke has hyperthermia and a history of diabetes, but no other risk factors. The patient has a score of 6 points (4 + 2) and is considered at low risk.

Information from reference 4.

Address correspondence to Mark Ebell, MD, MS, at ebell@uga.edu. Reprints are not available from the author.

Author disclosure: Nothing to disclose.

  1. 1.Adams HP, Davis PH, Leira EC, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology. 1999;53(1):126-131.
  2. 2.Gompertz P, Pound P, Ebrahim S. Predicting stroke outcome: Guy's prognostic score in practice. J Neurol Neurosurg Psychiatry. 1994;57(8):932-935.
  3. 3.Counsel C, Dennis MS, Lewis S, Warlow C for the FOOD Trial Collaboration; Feed Or Ordinary Diet. Performance of a statistical model to predict stroke outcome in the context of a large, simple, randomized controlled trial of feeding. Stroke. 2003;34(1):127-133.
  4. 4.Wang Y, Lim LL, Heller RF, Fisher J, Levi CR. A prediction model of 1-year mortality for acute ischemic stroke patients. Arch Phys Med Rehabil. 2003;84(7):1006-1011.

This guide is one in a series that offers evidence-based tools to assist family physicians in improving their decision-making at the point of care.

This series is coordinated by Mark H. Ebell, MD, MS, deputy editor for evidence-based medicine.

A collection of Point-of-Care Guides published in AFP is available at https://www.aafp.org/afp/poc.

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