Abstract
Background and Purpose
A significant proportion of strokes occur while patients are hospitalized for other reasons. Numerous stroke scales have been developed and validated for use in pre-hospital and emergency department settings, and there is growing interest to adapt these scales for use in the inpatient setting. We aimed to validate existing stroke scales for inpatient stroke codes.
Methods
We retrospectively reviewed charts from inpatient stroke code activations at an urban academic medical center from January 2016 through December 2018. Receiver operating characteristic analysis was performed for each specified stroke scale including NIHSS, FAST, BE-FAST, 2CAN, FABS, TeleStroke Mimic, and LAMS. We also used logistic regression to identify independent predictors of stroke and to derive a novel scale.
Results
Of the 958 stroke code activations reviewed, 151 (15.8%) had a final diagnosis of ischemic or hemorrhagic stroke. The area under the curve (AUC) of existing scales varied from .465 (FABS score) to .563 (2CAN score). Four risk factors independently predicted stroke: (1) recent cardiovascular procedure, (2) platelet count less than 50 × 109 per liter, (3) gaze deviation, and (4) presence of unilateral leg weakness. Combining these 4 factors into a new score yielded an AUC of .653 (95% confidence interval [CI] .604-.702).
Conclusion
This study suggests that currently available stroke scales may not be sufficient to differentiate strokes from mimics in the inpatient setting. Our data suggest that novel approaches may be required to help with diagnosis in this unique population.
Keywords: inpatient strokes, acute cerebrovascular events, stroke scales, mimics, hospitalized patients
Introduction
The incidence of stroke in admitted patients hospitalized for a non-neurological reason is as high as 17%. 1 Stroke codes are rapid response systems meant to quickly identify patients with sudden neurological changes that are believed to be secondary to a recent cerebrovascular event. This enables providers to rapidly assess eligibility for acute interventions such as mechanical thrombectomy or tissue plasminogen activator.2,3 However, it has been shown that a large proportion of stroke codes are non-cerebrovascular events, known as mimics. 4 The etiology of stroke mimics ranges from encephalopathy secondary to a metabolic derangement and seizure to medical emergencies including sepsis and heart failure. 1 Rapid assessment and treatment are essential to improve short- and long-term outcomes for inpatient strokes and mimics alike.
Multiple scores have been developed and tested for non-neurologist medical providers as well as first responders to identify strokes and activate appropriate rapid response quickly and accurately in prehospital, emergency department (ED), and inpatient settings.5-8 Though some scores were created primarily for prehospital and ED application,7,9 there is increasing clinical interest to adapt scores for use in the inpatient setting. However, patients who experience stroke while admitted in the hospital are a unique population given their underlying comorbidities, active medical treatments, proximity to medical care, established baseline neurological examination determined by the primary team daily, and increased risk of complications due to recent procedures as well as medical therapeutic interventions such as anticoagulation.10,11 Thus, efforts should be made to ensure that existing scales for the detection of stroke are appropriately tailored to the needs of this particular population.
In this study, we aimed to compare the performance of existing scores for the detection of stroke applied to admitted patients evaluated through an in-hospital stroke code system. We secondarily aimed to develop a novel score to predict stroke in the inpatient setting.
Methods
Study Design
A retrospective review was performed on all inpatient stroke code activations at the University of Chicago Medical Center (UCMC) from January 2016 through December 2018. UCMC is an academic, urban medical center on the South Side of Chicago and an accredited comprehensive stroke center via the Joint Commission since 2013. Institutional Review Board of University of Chicago approved the protocol (IRB-16-1593). Patient consent was waived.
Stroke code activations refer to protocols for the initiation of rapid diagnosis and treatment in cases of potential stroke. When stroke is suspected at our institution, health care personnel (physicians and nurses) quickly page appropriate responders including neurology residents and attendings, nurses, and computed tomography (CT) technicians, in order to devote hospital resources to the diagnosis and treatment of acute stroke. Activations are triggered when signs or symptoms suspicious of stroke are detected within 24 hours of onset. Criteria to activate the protocol have been previously described in detail 12 and include: (1) focal weakness or numbness of face or extremity, (2) difficulty producing or understanding speech, (3) change in vision, (4) acute alteration of consciousness, (5) acute onset of severe headache, and (6) acute onset of vertigo or gait disturbance. Once stroke codes have been activated, neurology residents are typically the first to respond and perform an urgent neurological examination at patient’s bedside, order CT imaging, and consult with the attending vascular neurologist to implement appropriate next steps of management.
Study Participants
Patients who had stroke codes activated during the study period were included. Patients were excluded if the initial neurology note was incomplete (missing data points needed to apply tested scales) or absent (usually if a stroke code was activated and subsequently cancelled). Patients were also excluded if stroke was diagnosed not as part of a stroke code activation, as can occur if strokes are incidentally found radiographically.
Data Collection
Two medical students (AS and NM) were trained to review patient charts, which were then re-examined and verified by UCMC neurology residents (FSV and ZB). The following variables were collected for each stroke code activation: baseline patient demographics, vascular risk factors, seizure history, baseline modified Rankin scale (mRS) score, initial National Institutes of Health Stroke Scale (NIHSS) score, laboratory values (white blood cell and platelet counts), presence of cardiac procedure during the current admission, and final neurologic diagnosis. The final determination of stroke vs stroke mimic was made by the attending vascular neurologist on call. A final diagnosis of stroke required imaging (CT or MRI) confirmation of acute ischemic or hemorrhagic infarct as determined by final neuroradiologist report. Non-vascular events were considered to be stroke mimics. Given the diagnostic uncertainty associated with imaging-negative transient ischemic attack (TIA), we combined patients with TIA with stroke mimics.
Primary Predictors
Based upon collected variables and final diagnosis of stroke vs stroke mimic, we sought to validate the following existing stroke scales: NIHSS; 13 Clinical deficit, Cardiac procedure during hospitalization, Atrial fibrillation history, and New admission within 24 hours (2CAN) 5 ; TeleStroke Mimic Score (TM-Score); 6 Face Arm Speech Time (FAST); 14 Balance, Eyes, Face, Arm, Speech, Time (BE-FAST); 15 absence of Facial droop, negative history of Atrial fibrillation, Age <50 years, systolic Blood pressure <150 mm Hg at presentation, history of Seizures, and isolated Sensory symptoms without weakness at presentation (FABS); 7 and the Los Angeles Motor Scale (LAMS). 16
Statistical Analysis
For the primary analysis, we compared c-statistics using receiver operating characteristic (ROC) curves. Secondarily, we sought to identify individual factors associated with strokes in order to create a novel scale. The analysis was made by an independent clinician blinded to the data collection. Data was analyzed using SPSS v.25 (IBM, Armonk, NY) as follows: continuous variables were reported as a median with an interquartile range, while categorical variables were reported as proportions. A Mann-Whitney U test was performed to analyze the differences in continuous variables, while χ2 test or Fisher’s exact test (for low sample sizes, defined as n < 5) were used to analyze the differences between proportions. We performed logistic regression to identify independent predictors of stroke. Beta coefficients were used to create a weighted score. Receiver operating characteristic analysis was performed for each prespecified stroke scale and the new score. Differences were considered to be significant at P < .05.
Results
From January 2016 through December 2018, there were 1110 inpatient stroke code activations. Of these, 152 were excluded from the analysis due to cancelled activation (n = 49) or incomplete medical records (n = 103). The remaining 958 stroke code activations were reviewed. Of the 958 stroke code activations reviewed, 15.8% (n = 151) had a final diagnosis of stroke. Among strokes, 79% were ischemic and 21% were hemorrhagic. Among mimics, multifactorial encephalopathy (27.6%) and seizure (11.4%) were the most common etiologies.
Primary Analysis
Receiver operating characteristic curves calculated for the 7 tested strokes scales are shown in Figure 1. The area under the curve (AUC) of existing scales varied from .465 (95% confidence interval [CI] .415-.515) for the FABS score to .563 (CI .511-.616) for the 2CAN score, with results for all the other tested scales falling between these 2 values (Table 1).
Figure 1.
Receiver operating curves for the initial NIHSS, 2CAN, TM-Score, FAST, FABS, BE-FAST, LAMS.
Table 1.
Area Under the Curve Calculations for Initial NIHSS, 2CAN, TM-Score, FAST, FABS, BE-FAST, and LAMS Scores.
| Stroke scale | Area under the curve | 95% confidence interval |
|---|---|---|
| Initial NIHSS | .540 | .488-.591 |
| 2CAN | .563 | .511-.616 |
| TeleStroke mimic | .527 | .477-.577 |
| FAST | .533 | .481-.584 |
| FABS | .465 | .415-.515 |
| BE-FAST | .558 | .507-.609 |
| LAMS | .510 | .459-.560 |
Secondary Analysis
Compared to patients without a stroke, those diagnosed with a stroke were more frequently postoperative after a cardiovascular procedure, more commonly had left ventricular assist device (LVAD), had higher proportions of unilateral leg motor weakness, had higher proportions of gaze deviation, and had lower median platelet counts (Table 2). There were no significant differences between the stroke and mimic groups in any of the other measured baseline variables.
Table 2.
Baseline Characteristics Comparing Strokes to Stroke Mimics.
| Clinical variable | Stroke mimic n = 807 | Confirmed stroke n = 151 | P value |
|---|---|---|---|
| Age, median (IQR) | 66.0 (56.0-76.0) | 65.5 (55.5-75.5) | .74 |
| Women, n (%) | 451 (55.9) | 79 (52.3) | .42 |
| Initial NIHSS, a median (IQR) | 8.0 (0-16) | 10.5 (1-20) | .12 |
| Hypertension, n (%) | 614 (76.1) | 120 (79.5) | .40 |
| Hyperlipidemia, n (%) | 217 (26.9) | 51 (33.8) | .09 |
| Diabetes mellitus, n (%) | 208 (25.8) | 35 (23.2) | .54 |
| Tobacco use, b n (%) | 442 (54.8) | 84 (55.6) | .86 |
| Atrial fibrillation, n (%) | 167 (20.7) | 30 (19.9) | .91 |
| HFrEF, c n (%) | 217 (26.9) | 43 (28.5) | .69 |
| Active cancer, n (%) | 165 (20.4) | 31 (20.5) | 1.00 |
| History of seizures, n (%) | 97 (12.0) | 14 (9.3) | .41 |
| Cardiovascular procedure, n (%) | 160 (19.8) | 58 (38.4) | <.001 |
| Presence of LVAD, n (%) | 43 (5.3) | 19 (12.6) | .002 |
| Time from admission to code ≥24 hrs, n (%) | 621 (77.0) | 115 (76.2) | .83 |
| Unilateral arm motor weakness, n (%) | 217 (26.9) | 50 (33.1) | .14 |
| Unilateral leg motor weakness, n (%) | 167 (20.7) | 46 (30.5) | .01 |
| Facial weakness, n (%) | 170 (21.1) | 38 (25.2) | .28 |
| Gaze deviation, n (%) | 107 (13.3) | 38 (25.2) | <.001 |
| Hemiparesis, n (%) | 114 (14.1) | 29 (19.2) | .13 |
| Aphasia, n (%) | 371 (46.0) | 78 (51.7) | .21 |
| Dysarthria, n (%) | 283 (35.1) | 49 (32.5) | .58 |
| Platelets, d median (IQR) | 203 (132-274) | 179 (106.35-251.65) | .04 |
| Platelets <50, d n (%) | 55 (6.8) | 18 (11.9) | .045 |
| White blood cells, median (IQR) | 8.7 (5.6-11.8) | 8.85 (6.1-11.6) | .76 |
| Systolic blood pressure, median (IQR) | 125 (105.5-144.5) | 126 (105-147) | .91 |
Bolded variables indicate significant differences at P < .05.
aNIHSS = National Institutes of Health Stroke Scale. Minimum score of 0 and maximum score of 42, with higher scores indicating greater impairment.
bCurrent or former.
cHeart failure with reduced ejection fraction, as determined by a documented diagnosis of heart failure with reduced ejection fraction and/or an echocardiogram report with documented left ventricular ejection fraction below 40%.
dPlatelets represented as n, whereas true value is n × 109/L.
In a multivariable analysis, 4 independent risk factors for stroke were identified: (1) recently postoperative from a cardiovascular procedure during the same admission, (2) platelet count less than 50 × 109/L, (3) gaze deviation, and (4) presence of unilateral leg weakness (Table 3). A new score was developed based on these independently significant factors (Table 4) and achieved an AUC of .653 (95% CI .604-.702).
Table 3.
Multivariable Analysis of Predictors of Inpatient Stroke.
| Clinical variable | Beta coefficient | Adjusted odds ratio | P value | 95% CI for OR | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Cardiovascular procedure | .967 | 2.630 | <.001 | 1.799 | 3.845 |
| Platelets less than 50 a | .655 | 1.926 | .026 | 1.080 | 3.436 |
| Gaze deviation | .651 | 1.917 | .004 | 1.233 | 2.979 |
| Unilateral leg weakness | .463 | 1.589 | .025 | 1.061 | 2.378 |
aPlatelets represented as n, whereas true value is n × 109/L.
Table 4.
New Score Elements and Point System.
| Variables | Points |
|---|---|
| Recent cardiovascular procedure | 2 |
| Platelets less than 50 × 109/L | 1.5 |
| Gaze preference | 1.5 |
| Unilateral leg weakness | 1 |
| Total | 6 |
Discussion
In this single-center validation study, we found that 7 previously published stroke scales performed poorly for prediction of inpatient stroke. A novel combination of independent risk factors could be used to develop a new stroke scale which performed better than existing scales. Independent predictors included recent cardiovascular procedure, thrombocytopenia with less than 50 × 109 platelets per liter, gaze deviation, and unilateral leg weakness. However, all scores, including the new one, performed modestly at best, suggesting that their utility may be limited in the inpatient population.
Recognized stroke scales including the TM-Score, FABS, LAMS, FAST, and BE-FAST, which were designed for use in ED and prehospital settings, were unable to be validated in our inpatient sample. Hospitalized patients may have medical comorbidities which can limit both the application and validity of existing scales developed for use in other settings. For example, existing medical conditions may make it challenging for providers to distinguish focal neurologic deficits in the inpatient setting. 17 Symptoms may be misinterpreted as manifestations of the original illness or side effects from medications used during hospitalization. 10 Moreover, a majority of inpatient strokes do not occur on the neurology floor where expertise is greatest. 18 Interventions performed in the hospital setting (such as intubation or administration of sedative medications) may further limit the ability of non-neurology providers to do a full neurological examination and thus appropriately apply existing scores to patients suspected of having strokes.
Even stroke scales commonly used in hospitalized patients were unable to be validated in our study. For example, though the NIHSS is often used in hospitalized patients, it has been found to have low inter-rater reliability of certain items including loss of consciousness, and hospitalized patients are at high risk of having active comorbid conditions including unresponsiveness and encephalopathy. 19 Moreover, we were unable to validate other scales designed for inpatient settings at other institutions. For example, though the 2CAN study was conducted based on stroke codes at an urban academic medical center like our own, our 2 institutions may have different thresholds for activating these codes which ultimately affects the overall prevalence of stroke in our populations. Another recent study was unable to validate the 2CAN score in an inpatient sample with a low prevalence of stroke mimics. 20 This further suggests that variability of stroke code activation criteria may affect the prevalence of inpatient stroke and thus the generalizability of newly developed scales.
Stroke scales tested in this paper yielded AUC values less than those reports in each scale’s respective original papers. This discrepancy is likely due to the variability in composition of tested populations, with the most notable difference being that our study looked specifically at hospitalized patients. This suggests that external validation of the scales is limited and may be affected by composition of the sample (hospitalized patients compared to those from the community) along with variations in prevalence of stroke in the cohorts studied.
Though hospitalized patients are at increased risk of stroke, our study shows that a large proportion of inpatient stroke code activations are ultimately mimics. Given that many inpatient stroke codes are called for mimics, and stroke scales tested in our study were unable to be validated in this population, a new approach that acknowledges this heterogeneity may be needed. Rather than activating stroke codes, hospitals might instead consider adopting a more general “acute neurologic emergency code” which readies resources for the diagnosis of strokes and mimics alike. For example, most strokes codes in our study were actually for mimics, with multifactorial encephalopathy and seizures being the most common etiologies. These mimics can be emergencies with time-sensitive interventions available in the inpatient setting, and pathways to approach these emergencies could provide more rapid diagnoses and treatment. In the case of an “acute neurologic emergency code,” trained responding providers could order and interpret imaging to help diagnose stroke, while simultaneously initiating a rapid medical workup including respiratory support, basic labs, and cardiac markers of injury including troponin and electrocardiogram (ECG). Quickly casting a wide diagnostic net accomplishes the goal of ruling out stroke while also detecting mimics, such as seizures or myocardial infarction, which can be life-threatening.
Hospitalized patients are unique given their comorbid conditions and active medical treatments, and providers should recognize the challenges this poses to stroke code teams and consider broadening their workups. Further development of effective means to identify acute stroke and rule out mimics is important to improving outcomes for hospitalized patients. Future studies in this area should examine the utility of existing scales in comparison to novel approaches including imaging and serologic biomarker algorithms. 21 For example, the utility of portable and bedside neuroimaging (CT Ref. [22] and MRI Ref. [23, 24]), electroencephalography, 25 transcranial Doppler, 26 and near-infrared spectroscopy 27 needs further study.
Limitations
There are several limitations to this study. It was conducted in a single urban academic medical center and thus may not be generalizable. Moreover, as a retrospective review, our results are dependent upon the accuracy of chart documentation and limited by exclusions due to incomplete records. Though we made an effort to examine previously published scales, we acknowledge the existence of several additional scales, such as Recognition of Stroke in the Emergency Room (ROSIER) 28 and Finnish Prehospital Stroke Scale (FPSS) 29 which could not be assessed due to lack of documentation of all the required score elements in our retrospective review or due to redundancy with included scales. Additionally, the prevalence of stroke within our sample of stroke code activations was somewhat lower (15%) than prior studies5,6 (35%-67%). This disparity in prevalence could negatively impact the performance of tested scales.
Further, this study was limited by exclusion of patients with TIA. Due to the diagnostic uncertainty of imaging-negative TIA, this group was excluded from our study but it may be beneficial for future prospective studies to examine and develop scales for detection of a combined cerebrovascular event inclusive of TIA. This could broaden the clinical relevance of scale application in the hospital setting. Moreover, though we developed a new score, it was not applied to separate derivation and validation cohorts. Future studies should consider validation of this score prior to its use.
Conclusion
In conclusion, this study suggests that presently available stroke scales may not be sufficient to differentiate strokes from mimics in the inpatient setting. Though we developed a new score for use in hospitalized patients, there is a need for further development of inpatient stroke scales. Use of novel diagnostic approaches and perhaps the creation and implementation of an “acute neurologic emergency code,” rather than a specific stroke code activation, may be required to quickly diagnose and treat both strokes and mimics in this unique population.
Supplemental Material
Supplemental Material for Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting by Adriana Sari, MD, Faddi G. Saleh Velez, MD, Nathan Muntz, BS, Zachary Bulwa, MD, and Shyam Prabhakaran, MD, MS in The Neurohospitalist
Appendix 1
Non-standard Abbreviations and Acronyms:
- 2CAN
Clinical deficit, Cardiac procedure during hospitalization, Atrial fibrillation history, and New admission within 24 hours
- AUC
area under the curve
- BE-FAST
Balance, Eyes, Face, Arm, Speech, Time
- CI
confidence interval
- CT
computed tomography
- ECG
electrocardiogram
- ED
emergency department
- FABS
absence of Facial droop, negative history of Atrial fibrillation, Age <50 years, systolic Blood pressure <150 mm Hg at presentation, history of Seizures, and isolated Sensory symptoms without weakness at presentation
- FAST
Face Arm Speech Time
- HFrEF
heart failure with reduced ejection fraction
- LAMS
Los Angeles Motor Scale
- LVAD
left ventricular assist device
- MRI
magnetic resonance imaging
- NIHSS
National Institutes of Health Stroke Scale
- ROC
receiver operating characteristic
- TIA
transient ischemic attack
- TM-Score
TeleStroke Mimic Score
- UCMC
University of Chicago Medical Center
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
ORCID iDs
Adriana Sari https://orcid.org/0000-0002-1546-4110
Faddi G. Saleh Velez https://orcid.org/0000-0002-2626-6259
References
- 1.Cumbler E, Simpson J. Code stroke: Multicenter experience with in-hospital stroke alerts. J Hosp Med. 2015;10:179-183. [DOI] [PubMed] [Google Scholar]
- 2.Nolan S, Naylor G, Burns M. Code gray—An organized approach to inpatient stroke. Crit Care Nurs Q. 2003;26:296-302. [DOI] [PubMed] [Google Scholar]
- 3.Gomez CR, Malkoff MD, Sauer CM, Tulyapronchote R, Burch CM, Banet GA. Code stroke. An attempt to shorten inhospital therapeutic delays. Stroke. 1994;25:1920-1923. [DOI] [PubMed] [Google Scholar]
- 4.Merino JG, Luby M, Benson RT, et al. Predictors of acute stroke mimics in 8187 patients referred to a stroke service. J Stroke Cerebrovasc Dis. 2013;22:e397-e403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chang P, Ruff I, Mendelson SJ, Caprio F, Bergman DL, Prabhakaran S. The 2CAN score. Stroke. 2018;49:2866-2871. [DOI] [PubMed] [Google Scholar]
- 6.Ali SF, Viswanathan A, Singhal AB, et al. The TeleStroke Mimic (TM)-score: A prediction rule for identifying stroke mimics evaluated in a telestroke network. J Am Heart Assoc. 2014;3:e000838. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Goyal N, Georgios T, Shailesh M, et al. Fabs: An intuitive tool for screening of stroke mimics in the emergency department. Stroke. 2016;47:2216-2220. [DOI] [PubMed] [Google Scholar]
- 8.El Ammar F, Ardelt A, Del Brutto VJ, et al. BE-FAST: A sensitive screening tool to identify in-hospital acute ischemic stroke. J Stroke Cerebrovasc Dis. 2020;29:104821. [DOI] [PubMed] [Google Scholar]
- 9.Joseph H, Omar H, Damian J, Davis J, Louw SJ, Ford GA. Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the face arm speech test. Stroke. 2003;34:71-76. [DOI] [PubMed] [Google Scholar]
- 10.Cumbler E. In-hospital ischemic stroke. Neurohospitalist. 2015;5:173-181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kassardjian CD, Willems JD, Skrabka K, et al. In-patient code stroke: A quality improvement strategy to overcome knowledge-to-action gaps in response time. Stroke. 2017;48:2176-2183. [DOI] [PubMed] [Google Scholar]
- 12.Del Brutto VJ, Ardelt A, Loggini A, et al. Clinical characteristics and emergent therapeutic interventions in patients evaluated through the in-hospital stroke alert protocol. J Stroke Cerebrovasc Dis. 2019;28:1362-1370. [DOI] [PubMed] [Google Scholar]
- 13.Brott T, Adams HP, Olinger CP, et al. Measurements of acute cerebral infarction: A clinical examination scale. Stroke. 1989;20:864-870. [DOI] [PubMed] [Google Scholar]
- 14.Kothari R, Pancioli A, Liu T, Brott T, Broderick J. Cincinnati prehospital stroke scale: Reproducibility and validity. Ann Emerg Med. 1999;33:373-378. [DOI] [PubMed] [Google Scholar]
- 15.Aroor S, Singh R, Goldstein LB. BE-FAST (Balance, Eyes, Face, Arm, Speech, Time): Reducing the proportion of strokes missed using the FAST mnemonic. Stroke. 2017;48:479-481. [DOI] [PubMed] [Google Scholar]
- 16.Llanes JN, Kidwell CS, Starkman S, Leary MC, Eckstein M, Saver JL. The Los Angeles Motor Scale (LAMS): A new measure to characterize stroke severity in the field. Prehosp Emerg Care. 2004;8:46-50. [DOI] [PubMed] [Google Scholar]
- 17.El Husseini N, Goldstein L. “Code stroke”: Hospitalized versus emergency department patients. J Stroke Cerebrovasc Dis. 2013;22:345-348. [DOI] [PubMed] [Google Scholar]
- 18.Vera R, Lago A, Fuentes B, et al. In-hospital stroke: A multi-centre prospective registry. Eur. J. Neurol. 2011;18:170-176. [DOI] [PubMed] [Google Scholar]
- 19.Meyer BC, Lyden PD. The Modified National Institutes of Health Stroke Scale (mNIHSS): Its time has come. Int J Stroke Off J Int Stroke Soc. 2009;4:267-273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Parrino CR, Noles A, Lalla R, et al. Optimizing the recognition and treatment of in-hospital stroke: Evaluation of the 2CAN score. J Stroke Cerebrovasc Dis. 2021;30:106032. [DOI] [PubMed] [Google Scholar]
- 21.Cyrous A, O’Neal B, Freeman WD. New approaches to bedside monitoring in stroke. Expert Rev Neurother. 2012;12:915-928. [DOI] [PubMed] [Google Scholar]
- 22.John S, Stock S, Cerejo R, et al. Brain imaging using mobile CT: Current status and future prospects. J Neuroimaging. 2016;26:5-15. [DOI] [PubMed] [Google Scholar]
- 23.Yuen MM, Prabhat AM, Mazurek MH, et al. Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke. Sci Adv. 2022;8:eabm3952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sheth KN, Mazurek MH, Yuen MM, et al. Assessment of brain injury using portable, low-field magnetic resonance imaging at the bedside of critically ill patients. JAMA Neurol. 2021;78:41–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Shreve L, Kaur A, Vo C. et al. Electroencephalography measures are useful for identifying large acute ischemic stroke in the emergency department. J Stroke Cerebrovasc Dis 2019;28:2280–2286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sarkar S, Ghosh S, Ghosh SK, Collier A. Role of transcranial doppler ultrasonography in stroke. Postgrad Med J. 2007;83:683-689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Terborg C, Bramer S, Harscher S, Simon M, Witte OW. Bedside assessment of cerebral perfusion reductions in patients with acute ischaemic stroke by near-infrared spectroscopy and indocyanine green. J Neurol Neurosurg Psychiatry. 2004;75:38-42. [PMC free article] [PubMed] [Google Scholar]
- 28.Nor AM, Davis J, Sen B, et al. The Recognition of Stroke in the Emergency Room (ROSIER) scale: Development and validation of a stroke recognition instrument. Lancet Neurol. 2005;4:727–734. [DOI] [PubMed] [Google Scholar]
- 29.Ollikainen JP, Janhunen HV, Tynkkynen JA, et al. The finnish prehospital stroke scale detects thrombectomy and thrombolysis candidates—A propensity score-matched study. J Stroke Cerebrovasc Dis 2018;27:771–777. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Material for Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting by Adriana Sari, MD, Faddi G. Saleh Velez, MD, Nathan Muntz, BS, Zachary Bulwa, MD, and Shyam Prabhakaran, MD, MS in The Neurohospitalist