Unassisted assessment of stroke severity using telemedicine

Philippe Paquier, Jacques De Keyser and Raf BrounsRobbert-Jan Van Hooff, Ann De Smedt, Sylvie De Raedt, Maarten Moens, Peter Mariën,

Unassisted Assessment of Stroke Severity Using Telemedicine

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Evidence is accumulating that advanced telemedicine technology for stroke (telestroke) is beneficial and cost

efficient in case direct access to stroke expertise is not avail-able.1–3 Research on prehospital telestroke systems is recom-mended by the American Heart Association because it may facilitate early stroke diagnosis, assessment of stroke severity, and selection of patients for specific stroke treatments.4,5 The experience with audio/video communication between ambu-lance-located telestroke systems and remote stroke experts, however, is limited to the TeleBAT project6,7 and a recent pilot study conducted in healthy actors.8

Because specialized care at the patient’s bedside is often unavailable in the prehospital setting, there is a need for quan-tification of stroke severity without assistance from a third party. Many stroke scales are available, but none satisfy this requirement. The National Institutes of Health Stroke Scale (NIHSS) is the current gold standard for bedside practice and for remote assessment by telemedicine,9–16 but several draw-backs, among which the rather long examination time (>9 minutes for remote evaluation),17,18 have inspired researchers

to modify the scale.19–22 Both the standard NIHSS and the modified NIHSS (mNIHSS) have been used successfully in interhospital telestroke consultations,10,11 but results in the pre-hospital setting are disappointing.8

The purpose of this study was to develop a simple, rapid, and reliable measure to evaluate stroke severity obtained in an unassisted way via a telestroke system (Unassisted TeleStroke Scale [UTSS]).

MethodsPatientsThis prospective monocentric study was approved by the Medical Ethics Committee of the Universitair Ziekenhuis Brussel. Patients were recruited over an 8-week period at the Stroke Unit and at the Emergency Department of the Universitair Ziekenhuis Brussel. Patients with unstable vital functions and those for whom telestroke examination would delay any diagnostic or therapeutic intervention were excluded. All patients or their proxy gave written informed con-sent before the evaluation. Patient characteristics of the study cor-pus of 45 subjects with suspicion of acute stroke are described in the online-only Data Supplement (Table I).

Original Contribution

Background and Purpose—Quantification of stroke severity through telemedicine consultation is challenging and relies on professional support at the patient’s bedside. We aimed to develop a novel scale for assessing stroke severity through telemedicine without assistance from a third party (Unassisted TeleStroke Scale [UTSS]).

Methods—Stroke severity was assessed in 45 patients with suspicion of acute stroke by bedside examination using the National Institutes of Health Stroke Scale (NIHSS) and by teleconsultation using the UTSS. Scale reliability was evaluated by intrarater and interrater variability, internal consistency, and rater agreement. Concurrent and predictive validity were tested by relating the UTSS with the NIHSS and long-term outcome (modified Rankin Scale and mortality at 6 months). Clinimetric analysis of the UTSS was obtained via the Rasch model.

Results—The mean examination time for the UTSS was 3.1 minutes (SD, 1.1) versus 8.5 minutes for the NIHSS (SD, 2.6; P<0.001). Both UTSS and NIHSS showed excellent intrarater variability (r=0.97 and 0.98; P<0.001) and interrater variability (r=0.96 and 0.98; P<0.001), as well as excellent internal consistency and rater agreement. The UTSS correlated strongly with the NIHSS and was identified as an independent predictor of stroke outcome in logistic regression analysis. Rasch analysis indicated that the UTSS represents a unidimensional scale of stroke severity.

Conclusions—The UTSS is a rapid, reliable, and valid tool for unassisted assessment of stroke severity through telemedicine. (Stroke. 2013;44:00-00.)

Key Words: acute ■ diagnostic methods ■ stroke ■ stroke management ■ telemedicine

Unassisted Assessment of Stroke Severity Using TelemedicineRobbert-Jan Van Hooff, MD; Ann De Smedt, MD; Sylvie De Raedt, MD; Maarten Moens, MD; Peter Mariën, PhD; Philippe Paquier, PhD; Jacques De Keyser, MD, PhD; Raf Brouns, MD, PhD

Received October 20, 2012; accepted December 11, 2012.From the Department of Neurology (R.-J.V.H., A.D.S., S.D.R., J.D.K., R.B.), and Department of Neurosurgery (M.M.), Universitair Ziekenhuis Brussel,

Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium; Department of Neurology and Memory Clinic, ZNA Middelheim Hospital, Antwerp, Belgium (P.M.); Department of Neurology and Neuropsychology, University Hospital Erasme, Université Libre de Bruxelles, Brussels, Belgium (P.P.); Department of Clinical and Experimental Neurolinguistics, Vrije Universiteit Brussel, Brussels, Belgium (P.M., P.P.); Unit of Translational Neurosciences, School of Medicine and Health Sciences, Universiteit Antwerpen, Antwerp, Belgium (P.P.); and Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (J.D.K.).

The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA. 111.680868/-/DC1.

Correspondence to Raf Brouns, MD, PhD, Department of Neurology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium. E-mail [email protected]

© 2013 American Heart Association, Inc.

Stroke is available at http://stroke.ahajournals.org DOI: 10.1161/STROKEAHA.111.680868

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2 Stroke May 2013

Scale DevelopmentWe constructed a preliminary scale, including 388 items from 20 exist-ing acute stroke impairment scales (the online-only Data Supplement Table II).12,19,21,23–37 The various items of these scales were submitted to 5 stroke neurologists (A.D.S., J.D.K., R.B., R.-J.V.H., and S.D.R.), 2 neurolinguistic experts (P.M. and P.P. native speakers of Dutch and French, respectively), and a neurosurgeon with expertise in critical care and neurovascular interventions (M.M.) for assessment of ap-propriateness and technical feasibility in the prehospital situation (for instance, patient fixated on a stretcher in a fast-moving ambulance). The remaining 16 items were rephrased to short instructions with a dichotomous response (Table 1).

InstrumentBecause the UTSS is mainly intended for prehospital use with data transmission through the 3G or the 4G network, we first identified the restrictions of the available bandwidth on the audio/video teleconfer-ence. Exhaustive testing in ambulances serving the area around the

Universitair Ziekenhuis Brussel showed that bidirectional live video streaming through a commercial 3G network can be obtained for the common intermediate format (320×240 pixels) at 6 frames per second. Simultaneously, a telestroke system for in-hospital use was developed. The key parts of this system involve real-time wireless bidirectional au-diovisual communication over a secured Internet connection and a pro-prietary software tool for standardized assessment of the UTSS in the language preferred by the patient (Dutch, French, or English; Figure 1).

ProcedureAll patients were lying on a stretcher or in a hospital bed as they were evaluated through telestroke consultation using the UTSS. Viewing size, compression factor, frame rate, and resolution of the audio/video communication were restricted to the common intermediate format (320×240 pixels) at 6 frames per second, mimicking our field experi-ence. Patient response was encouraged by repetitive stimulation and use of pantomime in patients with reduced consciousness or verbal comprehension problems. Items that, despite these measures, elicited

Table 1. The Unassisted TeleStroke Scale (UTSS)

Item Instruction Scale Definition

1. Consciousness “How are you?” 0 Verbal or motor response to speech

1 No verbal or motor response to speech

2. Orientation “Please tell me what month we are in” 0 Correct

1 Incorrect

3. Eye position at rest “Please look straight ahead” 0 Normal eye position

1 Conjugate deviation, divergent position or involuntary movement

4. Voluntary eye movement “Please look left and right without moving your head” 0 Normal

1 Limited horizontal range of one or both eyes

5. Head position at rest Observe the spontaneous head position 0 Midline position

1 Deviation to one side

6. Motor face “Please close your eyes tightly” Observe motor activity of the entire facial musculature

0 Normal, symmetrical facial movement

1 Asymmetrical or absent facial movement

7. Motor left arm “Please hold your left arm extended at 45° for 5 s” 0 Normal

1 Arm cannot be held at 45° for 5 s

8. Motor right arm “Please hold your right arm extended at 45° for 5 s” 0 Normal

1 Arm cannot be held at 45° for 5 s

9. Motor left hand “Please spread the fingers of your left hand as wide apart as you can” 0 Normal

1 The fingers cannot be spread widely

10. Motor right hand “Please spread the fingers of your right hand as wide apart as you can” 0 Normal

1 The fingers cannot be spread widely

11. Motor left foot “Please pull the toes of your left foot toward you” 0 Normal dorsiflexion

1 Incomplete or absent dorsiflexion

12. Motor right foot “Please pull the toes of your right foot toward you” 0 Normal dorsiflexion

1 Incomplete or absent dorsiflexion

13. Naming “How do you call a horse with black and white stripes?” 0 Correct

1 Incorrect

14. Repetition “Please repeat after me. He forgot to store his bike indoors” 0 Correct repetition

1 Incorrect repetition

15. Articulation Assess the clarity of articulation throughout the entire examination 0 Normal articulation

1 Dysarthria

16. Spatial attention and left/right orientation

Assess spatial attention and left/right orientation throughout the entire examination

0 Normal

1 Hemi-inattention or left/right confusion

Assessment of the 16 items is facilitated and standardized by a software tool, presenting the instructions to the rater in the language preferred by the patient (Dutch, French, or English). The text between quotation marks should be read literally to the patient.



Van Hooff et al Unassisted TeleStroke Scale 3

an inaccurate response were scored as abnormal by convention. Before or after the teleconsultation, a standardized bedside assessment of the NIHSS was obtained. Both assessments were conducted consecutive-ly, with a maximal time interval of 10 minutes. Two raters (rater 1 and 2; R.B. and R.-J.V.H.) were randomized to perform either bedside NIHSS or real-time telestroke UTSS. All examination times were re-corded. Two other raters (rater 3 and 4; A.D.S. and S.D.R.) were ran-domized to assess the NIHSS and the UTSS on video registrations of rater 1 and 2 for evaluation of interrater variability. The same video re-cordings were used for reassessment by rater 1 and 2 with a delay of 6 months for assessment of intrarater variability. All raters were NIHSS-certified and blinded for the results of other raters. The mNIHSS was derived from the NIHSS, as previously described.19 Patient satisfaction with the teleconsultation was evaluated by means of a questionnaire (the online-only Data Supplement Table III).

Scale ReliabilityIntrarater and interrater variability of the UTSS, NIHSS, and mNI-HSS were tested by Pearson correlations, and the internal consisten-cy of the each scale was assessed by Cronbach α.38 The agreement among raters was summarized using the intraclass correlation coef-ficient and κ statistics. Weighted κ statistics were used for scale items that contain >2 possible responses.38 Statistical computations were performed with the SPSS software package version 17.0 (SPSS Inc, Chicago, IL) and SAS version 9 (SAS Institute Inc, Cary, NC).

Scale ValidityConcurrent validity of the UTSS was tested by Pearson correla-tion, with the NIHSS and the mNIHSS and with linear regression analysis.38 To assess predictive validity, the association between the 3 stroke scales at baseline and the modified Rankin Scale (mRS) at 3 and at 6 months was evaluated with Pearson correlations. The relationship with all-cause mortality at 3 and 6 months was evalu-ated using the Student t test.38 Univariate testing was performed to identify associations between possible confounding factors (age, sex, language preference [Dutch or French]), stroke subtype (transient ischemic attack, ischemic stroke, intracerebral hemorrhage, or stroke mimic), stroke lateralization (left, right, or bilateral), Oxfordshire Community Stroke Project (OCSP) classification, Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, cerebrovascular risk factors (arterial hypertension, diabetes mellitus, atrial fibrilla-tion, coronary artery disease, heart failure, smoking, previous stroke,

dyslipidemia), and the outcome parameters (mRS score and mortal-ity at 3 and 6 months). The relationship between the stroke scales at baseline and long-term outcome was then assessed by logistic re-gression analysis. A backward stepwise method was performed with entry and removal criteria of 0.05 and 0.10, respectively, including all variables with P values <0.05 in univariate analysis.

The Rasch ModelThe Rasch model estimates the item difficulty and the patient diffi-culty level on a common linear scale from the responses given to each item within a probabilistic framework.39 Fit statistics were used to ex-amine unidimensionality of the UTSS. The infit mean square (MNSQ) is sensitive to unexpected behavior, affecting responses to items near the person’s proficiency measure; the outfit MNSQ is sensitive to un-expected behavior by persons on items far from the person’s profi-ciency level.40 MNSQ can be transformed to a t statistic, termed the standardized Z value (ZSTD). Items with both infit and outfit ZSTD beyond ±2 were considered poor fitting.39 The WINSTEPS software (version 3.74.0) was used to perform the Rasch analysis.

ResultsGeneral ResultsForty-five of 69 patients admitted to the Stroke Unit during the study period were included. Twenty-four patients could not participate in the study because they presented when both raters were not simultaneously available. Demographics and stroke characteristics of these patients were not different from the study population. The mean examination time for the UTSS was 3.1 minutes (SD, 1.1 minute), which was sig-nificantly less than the duration to assess the NIHSS (8.5 minutes; SD, 2.6 minutes; P<0.001) and the mNIHSS (6.9 minutes; SD, 2.2 minutes; P<0.001). The mean total scores (SD) on the UTSS, the NIHSS, and the mNIHSS were 3.0 (4.6), 6.5 (9.3), and 4.7 (7.4). The median total scores (range) were 1 (0–15), 3 (0–38), and 2 (0–29), respectively. Patient satisfaction with the teleconsultation for assessment of the UTSS was high (the online-only Data Supplement Table III).

Figure 1. Screen print of the telestroke system showing the software tool for assessment of the Unassisted TeleStroke Scale (UTSS). The audio/video communication is integrated in the software. The view shows testing for UTSS item 8 “Motor right hand.”



4 Stroke May 2013

Scale ReliabilityThe results of intrarater and interrater variability, internal consistency, and agreement among raters for the total scores of the UTSS, the NIHSS, and the mNIHSS were excellent (Table 2). Calculation of κ statistics for the individual items of the UTSS showed excellent interobserver agreement (κ statistic, 0.75–1.0) for 12 items (75%) and moderate agreement (κ statistic, 0.4–0.75) for 2 items (13%). The items “Eye position” and “Head position” had poor κ statistics (0.37 and 0.31; P=0.012 and 0.004, respectively). Nine items of the NIHSS (60%) displayed excellent interobserver agreement, and 4 items (27%) had moderate κ statistics. The items “Facial palsy” and “Limb ataxia” yielded poor agreement (κ statistics, 0.33 and 0.32; P=0.010 for both).

Scale ValidityThe UTSS score correlated strongly with the NIHSS and the mNIHSS score (r=0.91; P<0.001 for both). Based on linear regression analysis, the NIHSS score can be predicted as fol-lows: 1.04+1.83×UTSS score (P<0.001). The mNIHSS score can be calculated using the formula: 0.34+1.46×UTSS score (P<0.001).

The median mRS score 6 months after stroke was 2 (interquartile range, 0–3), and 14 patients (31.1%) had a score of >2. Six months after stroke, 5 patients (11.1%) were deceased. There was a moderate correlation between the 3 stroke scales at baseline and the mRS score 6 months after stroke (UTSS, r=0.59; NIHSS, r=0.64; and mNIHSS, r=0.64; P<0.001 for all). The mean UTSS score was signifi-cantly higher at baseline in patients with mRS score >2 (6 vs 2; P=0.002), and in patients who died within 6 months after stroke (9 vs 2; P<0.001). Univariate analysis for pos-sible confounding factors only identified a significant asso-ciation between the mRS score and mortality 6 months after stroke and the patient’s age at stroke onset (P=0.019 and P=0.010). Logistic regression analysis including the UTSS score and the patient’s age as covariates retained the UTSS score at baseline as an independent predictor of functional stroke outcome (mRS score at 6 months, dichotomized at 2) and all-cause mortality 6 months after stroke. Similar results were obtained for the mRS score and all-cause mortality at 3 months after stroke.

Rasch Analysis of the UTSSTable 3 summarizes the results of the Rasch model. The empirical explained variance (87.0%) was very similar to the modeled explained variance (86.2%), indicating that the UTSS represents a unidimensional scale of stroke severity. Stroke severity is expressed in logits, a linear unit defined as the natural logarithm of the odds of successful achievement by a patient for any item. This unit is constant along the measure scale, and the 0 of the scale is set by convention at the average difficulty of the whole item set. The items are classed in decreasing difficulty order (range, 3.93 to –2.31 logits), with higher logit values representing more difficult items. The item “Eye position” was identified as a misfit item, based on the infit ZSTD of 2.9 (MNSQ=2.45) and the outfit ZSTD of 2.4 (MNSQ=7.12). The variations of the infit ZSTD (SD=1.1) and the outfit ZSTD (SD=0.9) were similar and well below 2 SD. Both item and person reliability were good (reliability coefficient=0.71 and 0.82, respectively), indicating that the order of item hierarchy would be replicated with sufficient degree of probability if the UTSS were presented to other comparable cohorts, and that the UTSS can differentiate the patient ability according to stroke severity. The item separation index was 2.14, with a good separation reliability at 0.82, suggesting that 3 distinct strata for item difficulty (high, average, and low) can be distinguished. Figure 2 shows the map of persons and items along the continuum, illustrating that the items were spread out evenly except for a gap between the 2 most difficult items (“Eye position” and “Head position”) and “Consciousness.”

DiscussionAcute stroke is a time-critical medical emergency requiring specialized treatment (“time is brain” and “competence is brain”).41 Stroke care can be optimized by efficient telemedi-cine applications, bringing expertise to underserved geograph-ical areas and to the prehospital setting.5 Quantification of stroke severity is key for decision making with regard to indi-vidualized stroke treatment strategies and is required for clini-cal trials assessing neuroprotective interventions.42 However, remote assessment is challenging because current stroke scales require trained professional support at the patient’s bedside.

We developed the UTSS, which showed to be an easy to administer, simple, quantitative measure for stroke severity

Table 2. Reliability of the UTSS, the NIHSS, and the mNIHSS

Scale Feature Statistical Test

Stroke Severity Scales

UTSS NIHSS mNIHSS

Intrarater variability Pearson r between real-time evaluation and reassessment of videoregistration after 6 months

0.97 (P<0.001) 0.98 (P<0.001) 0.98 (P<0.001)

Interrater variability Pearson r between Team 1 and Team 2 0.96 (P<0.001) 0.98 (P<0.001) 0.99 (P<0.001)

Internal consistency Cronbach α 0.95 (95% CI, 0.92–0.97) 0.93 (95% CI, 0.89–0.96) 0.91 (95% CI, 0.87–0.95)

Agreement among raters

Intraclass correlation coefficient for single measures

0.96 (95% CI, 0.93–0.98; P<0.001)

0.98 (95% CI, 0.97–0.99; P<0.001)

0.99 (95% CI, 0.98–0.99; P<0.001)

Weighted κ statistics 0.81 (95% CI, 0.72–0.90; P<0.001)

0.84 (95% CI, 0.77–0.91; P<0.001)

0.88 (95% CI, 0.82–0.92; P<0.001)

CI indicates confidence interval; mNIHSS, modified National Institutes of Health Stroke Scale; NIHSS, National Institutes of Health Stroke Scale; and UTSS, Unassisted TeleStroke Scale.




that can be conducted with high patient satisfaction and without assistance of a third party. Compared with the current gold standard, the UTSS was associated with a time gain of 5 minutes and a good reliability. Concurrent and predictive validity were confirmed by strong correlations between the UTSS score and stroke severity (NIHSS score) and long-term outcome (mRS score and mortality 6 months after stroke). Similar to the development of the NIHSS, the items included in the UTSS were selected on the basis of multidisciplinary expert opinion and extensive literature review, thus satisfying the requirements for content validity.38 Clinimetric analysis of the UTSS using the Rasch model was also satisfactory. Only the items “Eye position” and “Head position” had poor interobserver agreement, which may be linked to the restrictive settings for audio/video communication (ie, limited resolution and low frame rate) aiming to mimic our field experience in

the first and misinterpretation of voluntary head turning as spontaneous deviation in the latter. It is arguably an option to delete these items from the UTSS. However, this would result in loss of clinically pertinent information, especially in case of severe hemispheric stroke or brain stem stroke. Moreover, deletion of the 2 items did not relevantly alter the reliability or validity of the scale (data not shown).

Several stroke scales, including the Orgogozo Scale,33 are easier to administer than the NIHSS, but until now, only the NIHSS and some of its variants have been studied for telestroke assessment in the in-hospital setting10,11,17,18,43 or through ambulance-located telemedicine systems.6–8 In total, data on 112 patients with suspicion of acute stroke are avail-able (average sample size per study: 22 patients, range 6–41 patients).6,10,11,17,18 Good interrater agreement was reported for most items, with the exception of Consciousness,11 Gaze,8 Visual fields,6 Facial palsy,6,18 Motor legs,6 Sensory,6 and Ataxia.11 Simplified versions of the NIHSS with removal of the items Visual fields, Sensory and Extinction/neglect43 or Consciousness, Facial palsy, Ataxia, and Dysarthria10 also proved to be reliable, but are more timeconsuming and laborious than the UTSS. The high reliability and predic-tive value of the UTSS may partially be explained by the absence of less robust items, such as Visual fields, Motor legs, Sensory, and Ataxia, as is the case in several modifi-cations of the NIHSS.10,19,20,43 The poor reliability score for “Eye position” in the UTSS is also in line with literature findings.8

Our study population is the largest reported so far in vali-dating stroke severity assessment through telemedicine, and is representative for the general stroke population with regard to demographics, cerebrovascular risk factors, stroke severity, stroke subtypes, and stroke syndromes. The use of restrictive settings for the audio/video communication, based on our field experience with the present limitations of wireless connectiv-ity, is another strength of this study. Reduced video resolution and frame rate may come at the cost of lower diagnostic accu-racy. This, however, is not supported by data from TeleBAT, which qualified much lower image resolution as sufficient.6,7 Aiming for better image quality in the prehospital setting has shown to be disappointing, based on currently available telecommunication solutions.8 As a result of ongoing techno-logical developments, improved video resolution and higher

Table 3. Difficulty, SE, Infit, and Outfit Statistics for All 16 Items of the Unassisted TeleStroke Scale

Item* Difficulty Logit SE Logit Infit ZSTD Outfit ZSTD

Eye position 3.93 0.92 2.9 2.4

Head position 3.93 0.92 –1.5 –0.6

Consciousness 1.54 0.90 –1.9 –0.8

Motor right hand 0.81 0.81 –0.2 1.5

Motor face 0.24 0.71 0.0 1.0

Motor right hand 0.24 0.71 0.0 1.0

Eye movement –0.21 0.64 –0.9 –0.9

Spatial attention –0.21 0.64 –0.3 –0.6

Motor right foot –0.59 0.60 –0.6 –0.5

Orientation –0.93 0.57 –0.8 –0.5

Motor left hand –0.93 0.57 –0.1 –0.2

Motor left foot –1.24 0.54 –1.1 –0.3

Repetition –1.24 0.54 0.5 0.1

Articulation –1.24 0.54 1.5 0.7

Motor left arm –1.79 0.51 –0.4 0.1

Naming –2.31 0.50 1.0 0.4

Mean 0.00 0.67 –0.1 0.2

SD 1.76 0.14 1.1 0.9

ZSTD indicates standardized Z value.*The items are arranged in descending order of difficulty.

Figure 2. Item and person map from the Rasch model illustrating the distribution of patients (X) and all items of the Unassisted TeleStroke Scale (UTSS) along the continuum of logits representing stroke severity.



6 Stroke May 2013

frame rates are to be expected, which probably will further enhance diagnostic accuracy of telemedicine systems.

Some limitations of the study have to be taken into account. First, the study was performed in hemodynamically sta-ble patients admitted at the Stroke Unit or the Emergency Department. The results can therefore not yet be extrapolated to the prehospital setting. Prospective testing of the UTSS using an ambulance-located telestroke system is currently ongoing. Second, the items Visual fields, Motor leg, Sensory, and Ataxia from the NIHSS remain unaddressed in the UTSS. Although this may result in loss of information, it has been demonstrated in previous reports that these items have poor reliability,6,11 and that simplified versions of the NIHSS with-out these items proved to be reliable.10,19,20,43 Unassisted evalu-ation of these symptoms is even more troublesome in the prehospital situation, which mostly involves a patient fixated on a stretcher in a fast-moving ambulance. However, prospec-tive validation of the UTSS in patients presenting with iso-lated anterior cerebral artery stroke or posterior circulation symptoms is required. Finally, the rather small sample size may have negatively affected the statistical power. It should, however, be acknowledged that our results originate from the largest validation study on stroke severity through telemedi-cine reported so far, and that study populations > 30 patients are generally accepted for this purpose.40

Summingup, the UTSS is a patient-friendly and easy to administer tool for rapid and efficient assessment of stroke severity through telemedicine, without the need for assistance by a trained healthcare professional. This scale has the potential to greatly facilitate emergency telestroke consultation because it showed to be less timeconsuming, reliable, and valid com-pared with the current gold standard. Prospective validation in the in-hospital and prehospital settings is currently ongoing.

Sources of FundingThis research was supported by the Brussels Institute for Research and Innovation, the King Baudouin Foundation, and the Scientific Fund Willy Gepts. A.D.S. is a research assistant of the Fund for Scientific research Flanders (Fonds voor Wetenschappelijk Onderzoek Vlaanderen, FWO). Dr Moens is clinical investigator of The Research Foundation Flanders (FWO).

DisclosuresNone.

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7. LaMonte MP, Xiao Y, Hu PF, Gagliano DM, Bahouth MN, Gunawardane RD, et al. Shortening time to stroke treatment using ambulance telemedi-cine: TeleBAT. J Stroke Cerebrovasc Dis. 2004;13:148–154.

8. Liman TG, Winter B, Waldschmidt C, Zerbe N, Hufnagl P, Audebert HJ, et al. Telestroke ambulances in prehospital stroke management: concept and pilot feasibility study. Stroke. 2012;43:2086–2090.

9. Dewey HM, Donnan GA, Freeman EJ, Sharples CM, Macdonell RA, McNeil JJ, et al. Interrater reliability of the National Institutes of Health Stroke Scale: rating by neurologists and nurses in a community-based stroke incidence study. Cerebrovasc Dis. 1999;9:323–327.

10. Meyer BC, Lyden PD, Al-Khoury L, Cheng Y, Raman R, Fellman R, et al. Prospective reliability of the STRokE DOC wireless/site independent telemedicine system. Neurology. 2005;64:1058–1060.

11. Shafqat S, Kvedar JC, Guanci MM, Chang Y, Schwamm LH. Role for telemedicine in acute stroke. Feasibility and reliability of remote admin-istration of the NIH stroke scale. Stroke. 1999;30:2141–2145.

12. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20:864–870.

13. Adams HP Jr, Davis PH, Leira EC, Chang KC, Bendixen BH, Clarke WR, 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;53:126–131.

14. Lyden P, Brott T, Tilley B, Welch KM, Mascha EJ, Levine S, et al. Improved reliability of the NIH Stroke Scale using video training. NINDS TPA Stroke Study Group. Stroke. 1994;25:2220–2226.

15. Brott T, Marler JR, Olinger CP, Adams HP Jr, Tomsick T, Barsan WG, et al. Measurements of acute cerebral infarction: lesion size by computed tomography. Stroke. 1989;20:871–875.

16. Demaerschalk BM, Vegunta S, Vargas BB, Wu Q, Channer DD, Hentz JG. Reliability of real-time video smartphone for assessing National Institutes of Health Stroke Scale scores in acute stroke patients. Stroke. 2012;43:3271–3277.

17. Wang S, Lee SB, Pardue C, Ramsingh D, Waller J, Gross H, et al. Remote evaluation of acute ischemic stroke: reliability of National Institutes of Health Stroke Scale via telestroke. Stroke. 2003;34:e188–e191.

18. Handschu R, Littmann R, Reulbach U, Gaul C, Heckmann JG, Neundörfer B, et al. Telemedicine in emergency evaluation of acute stroke: interrater agreement in remote video examination with a novel multimedia system. Stroke. 2003;34:2842–2846.

19. Lyden PD, Lu M, Levine SR, Brott TG, Broderick J; NINDS rtPA Stroke Study Group. A modified National Institutes of Health Stroke Scale for use in stroke clinical trials: preliminary reliability and validity. Stroke. 2001;32:1310–1317.

20. Meyer BC, Hemmen TM, Jackson CM, Lyden PD. Modified National Institutes of Health Stroke Scale for use in stroke clinical trials: prospec-tive reliability and validity. Stroke. 2002;33:1261–1266.

21. Tirschwell DL, Longstreth WT Jr, Becker KJ, Gammans RE Sr, Sabounjian LA, Hamilton S, et al. Shortening the NIH Stroke scale for use in the prehospital setting. Stroke. 2002;33:2801–2806.

22. De Raedt S, Brouns R, De Smedt A, Aries MJ, Uyttenboogaart M, Luijckx GJ, et al. The sNIHSS-4 predicts outcome in right and left ante-rior circulation strokes. Clin Neurol Neurosurg. 2012

23. Tuthill JE, Pozen TJ, Kennedy FB. A neurologic grading system for acute strokes. Am Heart J. 1969;78:53–57.

24. Mathew NT, Rivera VM, Meyer JS, Charney JZ, Hartmann A. Double-blind evaluation of glycerol therapy in acute cerebral infarction. Lancet. 1972;2:1327–1329.

25. Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical perfor-mance. Scand J Rehabil Med. 1975;7:13–31.




26. Oxbury JM, Greenhall RC, Grainger KM. Predicting the outcome of stroke: acute stage after cerebral infarction. Br Med J. 1975;3:125–127.

27. Norris JW. Steroid therapy in acute cerebral infarction. Arch Neurol. 1976;33:69–71.

28. Fawer R, Justafré JC, Berger JP, Schelling JL. Intravenous glycerol in cerebral infarction: a controlled 4-month trial. Stroke. 1978;9:484–486.

29. Multicenter trial of hemodilution in ischemic stroke--background and study protocol. Scandinavian stroke study group. Stroke. 1985;16:885–890

30. Côté R, Hachinski VC, Shurvell BL, Norris JW, Wolfson C. The Canadian Neurological Scale: a preliminary study in acute stroke. Stroke. 1986;17:731–737.

31. Adams RJ, Meador KJ, Sethi KD, Grotta JC, Thomson DS. Graded neu-rologic scale for use in acute hemispheric stroke treatment protocols. Stroke. 1987;18:665–669.

32. Gelmers HJ, Gorter K, de Weerdt CJ, Wiezer HJ. Assessment of interob-server variability in a Dutch multicenter study on acute ischemic stroke. Stroke. 1988;19:709–711.

33. Orgogozo JM, Asplund K, Boysen G. A unified form for neurologi-cal scoring of hemispheric stroke with motor impairment. Stroke. 1992;23:1678–1679.

34. Tsuji T, Liu M, Sonoda S, Domen K, Chino N. The stroke impairment assessment set: its internal consistency and predictive validity. Arch Phys Med Rehabil. 2000;81:863–868.

35. Hantson L, De Weerdt W, De Keyser J, Diener HC, Franke C, Palm R, et al. The European Stroke Scale. Stroke. 1994;25:2215–2219.

36. Gotoh F, Terayama Y, Amano T; Stroke Scale Committee of the Japan Stroke Society. Development of a novel, weighted, quantifiable stroke scale: Japan stroke scale. Stroke. 2001;32:1800–1807.

37. Flamand-Roze C, Falissard B, Roze E, Maintigneux L, Beziz J, Chacon A, et al. Validation of a new language screening tool for patients with acute stroke: the Language Screening Test (LAST). Stroke. 2011;42:1224–1229.

38. Lyden PD, Lau GT. A critical appraisal of stroke evaluation and rating scales. Stroke. 1991;22:1345–1352.

39. Bond TG, Fox CM. Applying the Rasch Model. 2nd ed. Mahwah, NJ: Lawrence Erlbaum; 2001.

40. Wright BD, Stone MH. Best Test Design. Chicago: MESA Press; 1979. 41. Kessler C, Khaw AV, Nabavi DG, Glahn J, Grond M, Busse O.

Standardized prehospital treatment of stroke. Dtsch Arztebl Int. 2011;108:585–591.

42. Neuroprotection as initial therapy in acute stroke. Third report of an ad hoc consensus group meeting. The european ad hoc consensus group. Cerebrovasc Dis. 1998;8:59–72

43. Gonzalez MA, Hanna N, Rodrigo ME, Satler LF, Waksman R. Reliability of prehospital real-time cellular video phone in assessing the simplified National Institutes Of Health Stroke Scale in patients with acute stroke: a novel telemedicine technology. Stroke. 2011;42:1522–1527.



SUPPLEMENTAL MATERIAL

Unassisted assessment of stroke severity using telemedicine

Robbert-Jan Van Hooff1 MD, Ann De Smedt1 MD, Sylvie De Raedt1 MD, Maarten Moens2

MD, Peter Mariën3,5 PhD, Philippe Paquier4,5,6 PhD, Jacques De Keyser1,7 MD PhD, Raf

Brouns1 MD PhD.

1. Department of Neurology, Universitair Ziekenhuis Brussel, Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussel, Belgium

2. Department of Neurosurgery, Universitair Ziekenhuis Brussel, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussel, Belgium

3. Department of Neurology and Memory Clinic, ZNA Middelheim Hospital, Antwerp, Belgium

4. Department of Neurology and Neuropsychology, University Hospital Erasme, Université Libre de Bruxelles, Brussels, Belgium

5. Department of Clinical and Experimental Neurolinguistics, Vrije Universiteit Brussel, Brussels, Belgium

6. Unit of Translational Neurosciences, School of Medicine and Health Sciences, Universiteit Antwerpen, Antwerp, Belgium

7. Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands



Supplemental material - Unassisted TeleStroke Scale

2

Supplemental Table S1: Baseline characteristics of the study corpus of 45 patients with suspicion of acute stroke

Characteristic Value*

Age (years) 74.5 ± 13.2

Male gender 22 (48.9%)

Language preference

Dutch

French

28 (62.2%)

17 (37.8%)

Stroke subtype

TIA

Ischemic stroke

Intracerebral hemorrhage

Stroke mimic

9 (20.0%)

28 (62.2%)

7 (15.6%)

1 (2.2%)

Stroke syndrome

Left hemisphere

Right hemisphere

Bihemispheric

Brainstem or cerebellum

20 (44.4%)

18 (40.0%)

1 (2.2%)

6 (13.3%)

OCSP classification

LACS

PACS

TACS

POCS

11 (24.4%)

23 (51.1%)

4 (8.9%)

7 (15.6%)

Risk factors

Arterial hypertension

Diabetes mellitus

Atrial fibrillation

Coronary artery disease

Heart failure

Smoking

Previous stroke

Dyslipidemia

28 (62.2%)

11 (24.4%)

10 (22.2%)

11 (24.4%)

2 (4.4%)

8 (17.8%)

10 (22.2%)

23 (51.1%)

* Data given as mean ± SD or as number (percentage)




3

LACS, lacunar syndrome; Oxfordshire Community Stroke Project; PACS, partial anterior circulation syndrome; POCS, posterior circulation syndrome; TACS, total anterior circulation syndrome; TIA, transient ischemic attack.



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6

Supplemental Table S3: Patient satisfaction with the teleconsultation as evaluated by a short questionnaire*

Question Response

Yes No

Were you able to clearly understand the physician? 37 (88%) 5 (12%)

Were you able to clearly see the physician? 37 (88%) 5 (12%)

Did you feel comfortable during the examination? 41 (98%) 1 (2%)

Did you miss the physical presence of the physician? 4 (9%) 38 (91%)

* 42 respondents.




7

References

1. Tuthill JE, Pozen TJ, Kennedy FB. A neurologic grading system for acute strokes. Am Heart J. 1969;78:53-57

2. Mathew NT, Rivera VM, Meyer JS, Charney JZ, Hartmann A. Double-blind evaluation of glycerol therapy in acute cerebral infarction. Lancet. 1972;2:1327-1329

3. Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:13-31

4. Oxbury JM, Greenhall RC, Grainger KM. Predicting the outcome of stroke: Acute stage after cerebral infarction. Br Med J. 1975;3:125-127

5. Norris JW. Steroid therapy in acute cerebral infarction. Arch Neurol. 1976;33:69-71

6. Fawer R, Justafre JC, Berger JP, Schelling JL. Intravenous glycerol in cerebral infarction: A controlled 4-month trial. Stroke. 1978;9:484-486

7. Multicenter trial of hemodilution in ischemic stroke--background and study protocol. Scandinavian stroke study group. Stroke. 1985;16:885-890

8. Cote R, Hachinski VC, Shurvell BL, Norris JW, Wolfson C. The canadian neurological scale: A preliminary study in acute stroke. Stroke. 1986;17:731-737

9. Adams RJ, Meador KJ, Sethi KD, Grotta JC, Thomson DS. Graded neurologic scale for use in acute hemispheric stroke treatment protocols. Stroke. 1987;18:665-669

10. Gelmers HJ, Gorter K, de Weerdt CJ, Wiezer HJ. Assessment of interobserver variability in a dutch multicenter study on acute ischemic stroke. Stroke. 1988;19:709-711

11. Brott T, Adams HP, Jr., Olinger CP, Marler JR, Barsan WG, Biller J, Spilker J, Holleran R, Eberle R, Hertzberg V, et al. Measurements of acute cerebral infarction: A clinical examination scale. Stroke. 1989;20:864-870

12. Orgogozo JM, Asplund K, Boysen G. A unified form for neurological scoring of hemispheric stroke with motor impairment. Stroke. 1992;23:1678-1679

13. Tsuji T, Liu M, Sonoda S, Domen K, Chino N. The stroke impairment assessment set: Its internal consistency and predictive validity. Arch Phys Med Rehabil. 2000;81:863-868

14. Hantson L, De Weerdt W, De Keyser J, Diener HC, Franke C, Palm R, Van Orshoven M, Schoonderwalt H, De Klippel N, Herroelen L, et al. The european stroke scale. Stroke. 1994;25:2215-2219

15. LaMonte MP, Cullen J, Gagliano DM, Gunawardane R, Hu P, Mackenzie C, Xiao Y. Telebat: Mobile telemedicine for the brain attack team. J Stroke Cerebrovasc Dis. 2000;9:128-135

16. LaMonte MP, Xiao Y, Hu PF, Gagliano DM, Bahouth MN, Gunawardane RD, MacKenzie CF, Gaasch WR, Cullen J. Shortening time to stroke treatment using ambulance telemedicine: Telebat. J Stroke Cerebrovasc Dis. 2004;13:148-154

17. Lyden PD, Lu M, Levine SR, Brott TG, Broderick J. A modified national institutes of health stroke scale for use in stroke clinical trials: Preliminary reliability and validity. Stroke. 2001;32:1310-1317




8

18. Gotoh F, Terayama Y, Amano T. Development of a novel, weighted, quantifiable stroke scale: Japan stroke scale. Stroke. 2001;32:1800-1807

19. Tirschwell DL, Longstreth WT, Jr., Becker KJ, Gammans RE, Sr., Sabounjian LA, Hamilton S, Morgenstern LB. Shortening the nih stroke scale for use in the prehospital setting. Stroke. 2002;33:2801-2806

20. Flamand-Roze C, Falissard B, Roze E, Maintigneux L, Beziz J, Chacon A, Join-Lambert C, Adams D, Denier C. Validation of a new language screening tool for patients with acute stroke: The language screening test (last). Stroke. 2011;42:1224-1229



Unassisted assessment of stroke severity using telemedicine

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