A practical stroke severity scale predicts hospital outcomes

A Practical Stroke Severity Scale Predicts Hospital Outcomes Patrick S. Reynolds, MD,* Cheryl T. Crenshaw, MSN,* David S. Lefkowitz, MD,* Brent J. Shelton, PhD,† John S. Preisser, PhD,‡ and Charles H. Tegeler, MD*

Goal: To develop a practical severity scale (Wake Forest Stroke Severity Scale [WFSSS]) to predict acute hospital outcomes and resource use after acute ischemic stroke based on the admission neurologic exam. Background: A useful scheme enabling physicians and other health care providers to stratify stroke severity on admission to predict acute hospital outcomes and improve efficiency of inpatient care has not been described. Methods: The study subjects consisted of 271 consecutive acute stroke patients admitted to the neurology department from July 1995 to June 1996 who were prospectively examined and whose stroke severity was classified on the basis of admission neurologic exam (level of consciousness, strength, dysphasia, neglect, and gait) as mild, moderate, or severe, based on the WFSSS. National Institutes of Health stroke scale (NIHSS) was performed early in admission (70% within 24 hours). Discharge disposition (home, inpatient rehabilitation [rehab], skilled nursing facility [SNF], or death); length of stay (LOS); and hospital charges were associated with initial stroke severity ratings using chisquare and Kruskal-Wallis tests. Results: Fifty-percent (136) of strokes were classified as mild, 22% (60) as moderate, and 28% (75) as severe. Initial severity ratings were significantly related to discharge disposition, LOS, and hospital charges (all P values ⬍.001). Conclusions: A practical clinical severity scale (WFSSS) for acute ischemic stroke patients based on admission neurologic examination predicts hospital disposition, LOS, and hospital charges, and may allow more accurate severity-adjusted comparisons among institutions. Key Words: Acute ischemic stroke—Severity scale—Outcomes. Copyright © 2001 by National Stroke Association

Background Specialized stroke care units have been shown to improve mortality rates and both short- and long-term outcomes of stroke patients.1-8 Stroke units also increase

From the *Department of Neurology, Wake Forest University School of Medicine, Winston–Salem, NC; the †Department of Biostatistics, School of Public Health, University of Alabama—Birmingham, Birmingham, AL; and the ‡Department of Biostatistics, School of Public Health, University of North Carolina—Chapel Hill, Chapel Hill, NC. Received May 21, 2001; accepted July 19, 2001. Address reprint requests to Patrick S. Reynolds, MD, Assistant Professor of Neurology, Department of Neurology, Wake Forest University School of Medicine, Medical Center Blvd, Winston–Salem, NC 27157. Copyright © 2001 by National Stroke Association 1052-3057/01/1005-0006$35.00/0 doi:10.1053/jscd.2001.29824

efficiency of care by decreasing length of stay (LOS) and hospital charges/costs.9-13 As the health care system moves forward into the arena of managed care, the ability to predict resource use, improve efficiency of care, and track outcomes becomes more important. Various models to predict long-term outcome for acute ischemic stroke have been described.14-32 Several of these are complex, multivariate models factoring in age, comorbidities, and some components of the neurologic exam (usually level of consciousness and degree of motor weakness) to derive a total score, which correlates with long-term functional outcome.14-18 These complex models can be inconvenient and difficult to implement. Barer and Mitchell, Gladman, Harwood, and Barer, and Taub et al. have all analyzed multivariate models to predict long-term outcome after stroke and found them cumbersome to use and less useful than originally conceived.21,22,28 They concluded that these multivariate schemes offer little

Journal of Stroke and Cerebrovascular Diseases, Vol. 10, No. 5 (September-October), 2001: pp 231-235

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Table 1. Stroke severity classification Routine neurologic exam

NIHSS correlates

Mild stroke Minimal to mild weakness (4⫹ or better Medical Research Council Scale) No language deficit or mild dysphasia not interfering with communication needs Alert No evidence of inattention/neglect or isolated extinction to 1 modality Moderate stroke Considerable weakness but still able to have some antigravity strength (⬎3⫹) Considerable dysphasia that impairs communication ability Lethargic Considerable inattention or extinction to more than 1 sensory modality Unsafe gait (high fall risk, more than just unsteadiness) Severe stroke Severe weakness: at best, antigravity only (3⫹ or less) Global or near-global aphasia Obtunded or comatose Severe hemi-inattention or anosognosia

prognostic information beyond what is available from the simple, baseline clinical variables of the stroke patients. Other authors have concluded that the most important prognostic indicator after acute stroke is the severity of the baseline neurologic deficits.19,24,26 Numeric rating scales such as the National Institutes of Health stroke scale (NIHSS), Canadian Neurological Scale (CNS), and the Scandinavian Stroke Scale (SSS) have been devised to quantify the neurologic examination after stroke and are routinely used in clinical trials.20,33,34,35 Severity of deficits as quantified by the NIHSS and the CNS have been shown to predict long-term functional outcome after stroke.29,32 The need for accurate, early assessments in acute stroke patients to help guide care and predict outcomes is manifestly important but also extremely difficult because of the marked heterogeneity of deficits in stroke patients. In addition to the many well-established scales currently available, Lyden and Lau have identified a need for a global rating scale for stroke severity.36 In this study, we sought to design a practical method to stratify stroke patients by a global measure of the severity of their neurologic deficits (mild, moderate, and severe), which would provide an accurate guide to length of stay (LOS), use of hospital resources, and ultimate acute hospital disposition, therefore improving efficiency of care.

Design/Methods From July 1995 through June 1996, 271 consecutive ischemic stroke patients admitted to the inpatient Neurology Service of the Wake Forest University Baptist Medical Center were the subjects of a prospective evaluation and data collection. Basic demographic data including age, gender, race, and primary insurance coverage

0 0 0 0

or 1 (items 5 and 6) or 1 (item 9) (item 1a) or 1 (item 11)

1 or 2 (items 5 and 6) 2 (item 9) 1 or 2 (item 1a) 2 (item 11) N/A 3 3 2 2

or 4 (items 5 and 6) (item 9) or 3 (item 1a) (item 11)

were obtained. Stroke severity was classified as mild, moderate, or severe, based on key components of the neurologic examination: level of consciousness, motor strength, dysphasia, neglect, and gait. Patients were classified based on the most severe category in any 1 rating domain (Table 1). This scale was designated the Wake Forest Stroke Severity Scale (WFSSS.) These ratings were established both by the admitting resident physician and 1 of 2 stroke neurologists who helped to formulate the rating scale. The NIHSS was also performed by 1 of the 2 stroke neurologists early in the patient’s admission (70% within the first 24 hours of admission) to provide another numerical rating of the severity of the patient’s deficit. Interrater reliability of the global stroke rating scale was evaluated by comparing the stroke neurologist’s scaling of the patients’ examination to the resident physician’s rating. Acute hospital discharge disposition (to home, inpatient rehabilitation [rehab], skilled nursing facility [SNF], or death) was related to demographic factors, stroke location, and the initial stroke severity ratings. LOS and acute stay hospital charges were also correlated with the demographic factors and the initial severity ratings. Statistical analysis was performed using chisquare and Kruskal-Wallis tests.

Results Table 2 shows the basic characteristics of the study population and the aggregate outcome measures for the entire study group. The average age of the patients was 65 years, and one third were African Americans. The third-party payer mix included 65% Medicare, 12% Medicaid, 20% private insurance or HMO, and 3% personal pay. Stroke localization was based on clinical examination and computed tomography (CT)/magnetic reso-

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Table 2. Demographics and aggregate outcome data Characteristics

Statistics

Number of patients Age (mean, range) Sex (% male/% female) Race (% white/% African American) Length of hospital stay in days (mean [range], median) Hospital charges (mean, median) Acute hospital disposition (all patients, N ⫽ 271)

271 65 (17-98) 53/47 68/32 9 (1-57), 7 $13,170, $8,100 To home: 68% To rehab: 11% To SNF: 14% Death: 7% Mild: 50% (NIHSS range, 0-10) Moderate: 22% (NIHSS range, 4-14) Severe: 28% (NIHSS range, 6-42)

Stroke severity (all patients, N ⫽ 271) (Range of NIHSS scores among patients in each severity grouping)

nance imaging (MRI), which was available for all patients. Cortically based, anterior circulation events (middle or anterior cerebral artery territory) accounted for 45% of the strokes. Vertebrobasilar events accounted for 28% of the strokes, and the remaining 27% were anterior circulation lacunar infarcts. Left brain events accounted for 52% of the patients, right brain events occurred in 37%, and the remaining 11% had bilateral disease. Two thirds of patients (68%) were discharged to home, and 19 (7%) patients died. The mean hospital LOS was 9 days, with a mean hospital cost (charges to the patient) of $13,170. Based on the WFSSS, 50% of the patients were classified as having suffered mild strokes, 22% as moderate, and 28% as severe. There was no statistically significant association noted between age or sex and severity. Within each qualitative severity grouping on the WFSSS there was a range of corresponding NIHSS scores with considerable overlap of scores among the 3 groups. There was no statistically significant relationship noted in univariate models of age, race, gender, type of insurance, or anatomic stroke location and outcome measures. Both the NIHSS and the WFSSS predicted outcome measures, but the strongest predictor of outcome was the initial stroke severity based on the WFSSS. Table 3 relates the initial stroke severity to the outcome measures. The initial severity ratings were strongly related to final hospital discharge disposition,

LOS, and hospital charges (all P values ⬍.001). Ninety percent of patients with mild strokes were ultimately discharged to home, whereas only one third of patients suffering severe strokes improved enough to go home from the acute hospital setting. The interrater reliability of the global rating scale was tested on 134 consecutive stroke patients and found to be excellent, with a kappa statistic of 86.8% (CI 79.4%, 94.2%).

Discussion The marked variability of pathophysiologic mechanism, neurologic deficits, and medical comorbidities of inpatients with stroke creates difficulties when trying to stratify and predict outcomes based on initial, or even serial, examinations. The benefits of an accurate, easy-touse rating scale for outcome prediction are obvious, and such a scale would be important in improving efficiency of patient care, planning resource use, and tracking outcomes. Our study shows that a simple, practical severity scale based on key elements of the admission neurologic exam is highly accurate in predicting acute hospital outcomes and resource use in a population of stroke patients. We believe that the prospective nature in which the severity ratings were applied makes this scale a very powerful and accurate tool. To our knowledge, the WF-

Table 3. Stroke severity and outcomes WFSSS stroke severity (N)

Median NIHSS (range)

Median LOS (days)

Median charges

Home

Rehab

SNF

Death

Mild (136) Moderate (60) Severe (75)

3 (0-10) 8 (4-14) 17 (6-42)

5 7 11

$ 6,400 $ 7,800 $12,300

89% 62% 33%

7% 18% 15%

4% 16% 29%

1% 3% 23%

NOTE. All P values ⬍.001.

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SSS is the first scale designed specifically to predict acute hospital outcomes; however, this approach has been confirmed by 2 recent studies. The Copenhagen Stroke Study has shown that the SSS is predictive of acute hospital outcomes and related to LOS.13 These data are difficult to compare with the American health system, as the Copenhagen LOS included both acute care and rehabilitation as the same admission. The NIHSS was shown in the Trial of Org 10172 in Acute Stroke (TOAST) to predict both acute (7-day) and long-term (3-month) outcomes based on Barthel index scores and Glasgow Outcome scores.37 The Copenhagen study did not analyze acute hospital disposition. Our study confirms the predictive power of the NIHSS but also shows its limitations as a prognostic tool for acute outcome. The marked heterogeneity of stroke patients, as well as the tendency of the neurologic deficits of stroke patients to fluctuate, is manifest in the spectrum of outcomes noted within each severity level of the WFSSS. This is also reflected in the large range and overlap among the severity groups with the NIHSS scores for these patients. Although the NIHSS was broadly associated with outcomes in our study, as was seen in the TOAST study, we believe that the large overlap of NIHSS scores among the 3 severity groups limits its use in isolation as a prognostic indicator of acute stroke outcome. Its usefulness as an adjunct, or modifier, to the qualitative severity ratings is currently being investigated.

Conclusions The WFSSS is a simple, easy-to-use, practical clinical severity scale for acute ischemic stroke based on key features of the neurologic exam. It has high interrater reliability, and it accurately predicts acute hospital disposition, LOS, and hospital charges. Severity stratification of stroke patients on admission is a powerful tool with several important applications. First, such a stratification scheme is useful for designing and implementing severity-adjusted critical pathways for management of acute stroke patients to improve patient care and resource use and could be useful for outcome tracking and research. It is also useful for comparing resource use and outcomes among institutions, as the most important deficiency of such comparisons currently is the lack of any real severity adjustment based on patients’ clinical stroke deficits. Finally, the simplicity of the scale makes it easy to use by other members of the health care team, not just physicians, and may allow its use in retrospective data collections as well. Acknowledgments: The authors express their gratitude to Miss Anne Watterson and Ms. Vickie Brown for their editorial comments and manuscript preparation.

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