Background: Time spent in the prehospital phase of acute stroke care is multifactorial and has an effect on the possibilities for acute treatment. Communication between paramedics and the in-hospital stroke team directly affects time to treatment. A mutual stroke scale such as the National Institutes of Health Stroke Scale (NIHSS) may improve communication quality. The Paramedic Norwegian Acute Stroke Prehospital Project (ParaNASPP) was a stepped-wedge, randomized trial of stroke screening using NIHSS in the ambulance where the intervention was training paramedics in stroke and the NIHSS, with the use of NIHSS made into a mobile app to guide the examination and facilitate communication with the in-hospital stroke team.
Objective: The aim of this study was to validate the digital training model from the ParaNASPP clinical trial.
Methods: In total, 24 paramedics were recruited from Oslo University Hospital in Norway to complete the ParaNASPP training model; 20 exclusive videos with predefined NIHSS scores were recorded; and 4 stroke physicians from Oslo University Hospital were included for reference. Bland-Altman plots with 95% limits of agreement (LoA) were calculated—first comparing paramedics and stroke physicians to the predefined scores and then with each other. The predefined LoA were set to 3 points. To align with clinical practice, NIHSS scores were also dichotomized into 2 categories: from 0-5 (minor stroke) or ≥6 (moderate and major stroke), and agreement was calculated using Cohen κ.
Results: The videos (n=20) had a median (range) NIHSS score of 7 (0-31). The paramedics’ scores were slightly higher than the predefined scores with a mean difference of –0.38 and the LoA ranging from –4.04 to 3.29. The paramedics scored higher than the stroke physicians with a mean difference of –0.39, with the LoA ranging from –4.58 to 3.80. When the NIHSS scores were dichotomized, Cohen κ was 0.89 between the predefined scores and paramedics, 0.92 between the predefined scores and stroke physicians, and 0.81 between the paramedics and stroke physicians, all indicating very good agreement.
Conclusions: The paramedics scored higher than both the predefined scores and stroke physicians’ scores, hence the predefined LoA were not met. However, the width of the LoA was smaller than seen when experienced neurologists are compared. When the NIHSS scores were dichotomized, the paramedics achieved very good agreement with both the predefined scores and stroke physicians’ scores. This study demonstrates the possibilities for the transfer of clinical competence in digital simulation training.
The correct and timely triage of patients with acute stroke to the right level of care is largely based on the prehospital assessment [, ]. Prehospital stroke symptom identification and the prenotification of in-hospital stroke teams are known to affect time to acute treatment [ , ]. Prenotification communication with the receiving facility is important as it prepares the stroke team on the patient’s condition and secures the efficient in-hospital reception of the patient [ , ]. The National Institutes of Health Stroke Scale (NIHSS; ) is the most frequently used stroke scale by stroke physicians and stroke nurses today [ ]. The NIHSS has been considered too complex and time-consuming and, therefore, less suited for prehospital use [ , ], and consequently, most prehospital scales are the modified and shortened versions of the NIHSS [ , ]. Fair agreement has been found when comparing the NIHSS scores achieved by neurologists and nonneurologists [ - ], but little is known on how the full-scale NIHSS when performed by paramedics compare to stroke physicians’ scores. Traditional simulation training is to a large degree based on physical attendance and, thus, is both time- and resource-consuming. Alternative solutions for training medical personnel, including video-based training, have been investigated [ , ] and proven to be reliable in NIHSS training and certification [ , ]. Video-based training supplemented with electronic learning (e-learning) has shown better performance in NIHSS scoring [ ]. For the Paramedic Norwegian Acute Stroke Prehospital Project (ParaNASPP)—a stepped-wedge, randomized trial of stroke screening using NIHSS in the ambulance—we developed a complete digital training model for paramedics [ ]. An e-learning program was combined with unique videos for scoring NIHSS in the (native) Norwegian language.
The aim of this study was to validate the training model in the ParaNASPP clinical trial.
In the ParaNASPP clinical trial , paramedics in Oslo, Norway, were trained in the full-scale NIHSS as the intervention. The participant enrollment period was from June 3, 2019, to July 1, 2021. The intervention included a structured learning program, a mobile app for NIHSS scoring, and the transfer of data from paramedics to the on-call stroke team physician. In October 2018, we tested the intervention for feasibility and identified the needed adjustments in the e-learning and simulation training before the start of the trial. To validate the training model, we decided to test the interrater agreement between paramedics and stroke physicians, and we planned for a pilot study. Due to the COVID-19 pandemic, a need for digital training emerged. For practical reasons, we decided to test the interrater agreement after digital simulation training.
The validation study took place in Oslo, Norway, in December 2020. Due to pragmatic and organizational reasons, we invited all (N=83) ambulance personnel employed at 3 geographically dispersed ambulance stations in the Prehospital Division of Oslo University Hospital to participate. To become an ambulance personnel in Norway, there is emergency medical technician training from upper secondary school. Paramedic training may be accomplished for emergency medical technicians and nurses with additional courses, and in recent years, a unique bachelor’s degree for paramedics has been developed as a higher education. To reflect the diversity in the ParaNASPP clinical trial study setting , we needed participants from this spectrum. For simplicity, we refer to the group as paramedics. Based on current protocol in the ambulance service, we expected the paramedics to have no or little formal competence or experience with the NIHSS. For comparison, selected stroke physicians that reflected the variations in the on-call team at the Stroke Unit of the Department of Neurology at Oslo University Hospital were also asked to participate.
Data that were collected from the participants included the number of years of experience in their respective services, level of education, and current status on the international certification in NIHSS . Written consent was obtained from all participants.
All enrolled paramedics completed a structured e-learning program in stroke assessment prior to a live, digital simulation training on the Teams chat-based collaboration platform (version 126.96.36.199; Microsoft). The digital simulation training lasted 4 hours. A stroke physician tutored the sessions, where the aim was to build an understanding of the assessment of neurological findings, the concept of the NIHSS, and the practical use of a mobile iOS app (the ParaNASPP app;). This is a specially developed app where each item from the NIHSS is displayed in pictograms, explanatory text is presented in a fixed sequence, and a total score is automatically calculated. A separate validation study of the ParaNASPP app has been published [ ]. All items in the NIHSS were demonstrated and simulated. Simulation cases in the live stream were unique and distinct from the forthcoming, predetermined cases to test the interrater agreement. The participants could ask questions, and they received immediate feedback and guidance from the instructors and stroke physician. Immediately after the live stream of the digital simulation training, the paramedics accessed the test material for the study.
In all, 20 exclusive videos (see an example in) with the role-playing of the neurological symptoms of a possible acute stroke were developed and used for testing interrater agreement. To achieve a trustworthy acting of neurological findings, a stroke physician performed as the patient in all videos, and a paramedic trained in the ParaNASPP model [ ] performed the NIHSS examination. The manuscripts for the videos were prepared in cooperation with stroke physicians who were not involved in this study. The video manuscripts represented the predefined NIHSS scores with a median (range) of 7 (0-31). The videos were intended to comprise the different items of the NIHSS to varying degrees; however, the cases of neurological findings not captured in the NIHSS were also acted out, such as dizziness and dysmetria. The distribution aimed to reflect a real population with stroke [ ] and was similar to comparable studies [ , ]. The videos had a mean (SD) duration of 2 minutes and 58 (23) seconds. The videos could be paused and rewound if warranted by the participants. When the paramedics scored the last NIHSS item in the app, a total score was transferred to the database, and this finalized the scoring opportunity for that video.
The paramedics’ NIHSS scores were compared to the predefined scores for each video. As this underlying predefinition is not available in clinical practice, the paramedics’ scores were also compared to the scores achieved by stroke physicians scoring the same videos.
All paramedics’ NIHSS scores were digitally entered in the ParaNASPP app. The time spent on NIHSS registration was recorded by start time (new registration) and end time (data submitted) and directly transferred to the database. The stroke physicians scored according to their daily practice on the original NIHSS paper form, independently from each other and the paramedics. The stroke physicians were responsible for documenting their own time stamps for each video. The time spent on scoring the NIHSS for each video was reported in whole minutes.
We presented continuous data as mean (SD) for symmetric data and median (range) for skewed data and data with outliers.
The NIHSS is a continuous scale, and Bland and Altman’s  approach for method comparison was applied to assess the interrater agreement. The limits of agreement (LoA) were estimated based on the observed differences between measurement methods, representing the actual variation in the data [ ]. These LoA were then compared to the acceptable variation, here set to 3 points on the NIHSS based on a clinical evaluation and the same a priori threshold in a comparable study [ ]. Bland and Altman’s [ ] original method was applied when comparing the NIHSS scores between the paramedics or stroke physicians and the predefined scores in the videos. When assessing the interrater agreement between the paramedics and stroke physicians, a mixed models version of method comparison was applied [ ], adjusting for the internal correlation structure in the data resulting from the 24 paramedics and 4 stroke physicians all evaluating the same 20 videos.
In clinical practice, a distinction in treatment regimens is often made for high versus low NIHSS scores [, ], and thus, in a secondary analysis, the interrater agreement for dichotomized NIHSS values were explored. The continuous NIHSS scores were dichotomized into a low-score category, from 0-5 (minor stroke), and a high-score category, ≥6 (moderate and major stroke). Cohen κ was used to calculate the agreement of the dichotomized data: first, between the paramedics or stroke physicians and the predefined scores and second, between the paramedics and stroke physicians. Note that currently, no version of the mixed models of Cohen κ exists, and the traditional Cohen κ used will likely underestimate the uncertainty in the Cohen κ estimate.
In the literature, κ≤0.2 is taken to represent poor agreement, 0.21-0.40 as fair agreement, 0.41-0.60 as moderate agreement, 0.61-0.80 as good agreement, and 0.81-1.0 as very good agreement .
Statistical analyses were performed with Stata statistical software (version 16.1; StataCorp)  and R statistical software (version 4.0.3; R Foundation for Statistical Computing) [ ].
The local data protection office at Oslo University Hospital approved of the handling of the data from the volunteers and consenting paramedics and stroke physicians employed at Oslo University Hospital (approval 19/00667). No institutional review board approval was sought since no actual patients were involved in this study, as outlined by Norwegian guidelines.
This study enrolled all (N=24) paramedics that volunteered and recruited 4 volunteer stroke physicians. The characteristics of the participants are described in.
Time spent on evaluating the videos contained 2 extreme values (196 minutes and 5768 minutes), likely a result of starting a video, pausing, and completing it at a later time point. These outliers were therefore excluded from the analysis.
Comparing the paramedics’ score to the predefined scores in the videos resulted in 480 unique NIHSS assessments. Similarly, the stroke physicians enrolled in the study’s evaluation of the 20 videos resulted in 80 unique NIHSS scores. The paramedics’ scores were on average somewhat higher than the predefined scores (), with a mean difference of –0.38 and the LoA ranging from –4.04 to 3.29 between the paramedics’ scores and the predefined scores ( ). The paramedics scored higher than the stroke physicians, with a mean difference of –0.39 and the LoA ranging from –4.58 to 3.80. The agreements between the paramedics’ scores with the predefined scores and stroke physicians’ scores were both outside the a priori defined acceptable limit of 3.
The stroke physicians were in agreement with the predefined scores (), and the LoA ranged from –2.31 to 2.34 with a mean difference of 0.01, which were well within the limit of 3 ( ).
Differences between the paramedics’ scores and the predefined scores in the videos were considerably smaller for lower NIHSS scores. Calculating the LoA for the 2 clinically different regions, we found the LoA to be from –1.42 to 0.88 for NIHSS scores from 0-5 and from –4.90 to 4.03 for NIHSS scores ≥6 ().
The paramedics’ ability to score patients in the from 0-5 or ≥6 categories showed a Cohen κ of 0.89 as compared to the predefined scores, representing very good agreement. For predefined scores from 0-5, 14 (8.3%) out of 168 paramedics’ scores were overestimated, putting patients in the high-score category. For predefined scores ≥6, the paramedics’ scores were underestimated in 9 (2.9%) out of 312 videos.
|Characteristic||Paramedics (N=24)||Stroke physicians (N=4)|
|Experience (years), median (range)||4 (1-45)||11 (8-14)|
|Time in a stroke unit (years), median (range)||—a||7 (2-10)|
|Level of education, n (%)|
|Trained paramedics||14 (58)||—|
|Apprentice EMT||1 (4)||—|
|Specialist in neurology||—||3 (75)|
|Specialist in geriatric medicine||—||1 (25)|
|Certification in NIHSSc, n (%)||5 (21)||4 (100)|
|Time spent on each case (minutes), median (range)||3 (2-15)||3 (2-4)|
bEMT: emergency medical technician.
cNIHSS: National Institutes of Health Stroke Scale.
Interrater agreement between the stroke physicians’ scores and the predefined scores for the 2 categories was κ=0.92, representing very good agreement. When the predefined scores were from 0-5, the physicians were in complete agreement with predefined scores in 28 (100%) out of 28 videos, and when the predefined scores were ≥6, the stroke physicians’ scores were underestimated in 3 (6%) out of 52 videos.
With 20 predefined scores, 24 paramedics, and 4 stroke physicians, we had 1920 paired NIHSS score comparisons which gave an unadjusted Cohen κ of 0.81 and very good agreement in the direct comparison between the paramedics and stroke physicians. The paramedics scored the simulated patients to be in the ≥6 category while the stroke physicians scored in the from 0-5 category in 128 (17.2%) out of 744 comparisons. The opposite occurred in 36 (3.1%) out of 1176 comparisons.
Our findings indicate that paramedics can achieve very good agreement with stroke physicians when tested after a digital training program for NIHSS in the ParaNASPP model.
The paramedics scored higher than both the predefined scores and the stroke physicians’ scores when we looked at the scale from 0 to 42 points. Compared to the predefined scores, the paramedics were well within the LoA of 3 in the range of NIHSS scores from 0-5; however, the variation increased with higher scores (≥6). Higher NIHSS scores indicate more complex neurological deficits  and have been associated with greater scoring variance in other settings, and a difference of 4 points is not uncommon in video scoring [ ]. Nevertheless, we had predefined an acceptable difference in scores of 3 points between raters. This is the same predefined limit used in a study to compare the NIHSS scores of remote and bedside vascular neurologist [ ].
In this study, the participants were a heterogenous group, but it was important to test the training model on a group similar to that in the ParaNASPP clinical trial . However, the width of 8.38 on the LoA for the paramedics’ and stroke physicians’ scores found in our study is smaller than seen when compared to experienced neurologists who achieved a width of 10.05 on the LoA [ ]. A grading table for acceptable LoA has been developed, placing the results from our study as Grade A [ ]. Based on this, we accept the LoA in our study in spite of not achieving the predefined limit.
The NIHSS scale ranges from 0 to 42 points where higher scores indicate more severe strokes  and more complex scoring, but a single number on a scale, or a category when it is applied, is never decisive of treatment. However, prehospital triage decisions are to some extent dependent on this scoring. We decided on a cutoff of 6 points for dichotomizing the scale to be in accordance with a cutoff commonly used [ , , ]. In a clinical setting, there is an acceptance for overtriage to ensure the identification of patients eligible for acute treatment [ ]. An overestimation of a NIHSS score or category from paramedics is for that reason less problematic than an underestimation, which in our study also was lower than seen before [ ].
When dichotomized to from 0-5 and ≥6 categories, interrater agreement was very good between the paramedics’ scores and the predefined scores. Although a generalization of Bland and Altman’s  approach for the method comparison of continuous measurements is more than a decade old, when adjusting for replicate measurements and multiple raters, no readily available generalization for Cohen κ exists. However, a crude estimate for comparing categorized NIHSS scores between the paramedics and stroke physicians, combining all value pairs in the same cross table, gave an unadjusted Cohen κ that indicated very good agreement. When not in agreement, the tendency was shifted toward higher NIHSS scores representing the less problematic overtriage from the paramedics.
The duration of evaluating each case referred to the scoring of the simulated symptoms on the videos and does not necessarily reflect the time spent on performing the actual assessment. The stroke physicians scored the videos according to their daily practice with a self-report on case duration, whereas the paramedics were provided with an unfamiliar stroke scale and a new scoring tool that automatically registered case duration. We expected the paramedics to spend more time on scoring the videos based on the novelty, but the time spent did not differ much between the paramedics and stroke physicians. This finding may indicate an instant effect of our training model for the paramedics—an effect that may be sustained . However, the scoring was based on the acting of neurological symptoms that were straight forward and not influenced by confounders seen in a real-world setting. The time spent on patient evaluation may increase for paramedics in a more complex clinical context.
The training of paramedics in acute stroke assessment can easily be converted to a digital format instead of on-site training . Digital solutions have been suggested as an alternative to face-to-face interactions in simulation training [ ], and significant correlation between digital solutions and positive learning outcomes have already been established [ , ]. This knowledge is important when planning for the implementation of new procedures and tools for paramedics. However, the supervision part of digital training is important [ ]. A chat function makes the instructors available and provides a great opportunity for participants to interact despite their remote participation.
Recent publications demonstrate reasonable agreement between prehospital and in-hospital NIHSS scores, in both the modified and full-scale versions [, ]. Importantly, paramedics preferred a hospital-based stroke scale to improve communication with stroke physicians [ ]. The development of stroke triage systems has not focused on the standardization of clinical evaluation and communication between paramedics and the on-call stroke physician. Communication quality between paramedics and the on-call stroke team physician directly influences prehospital on-scene time and is a key component in prenotification and triage [ ]. Introducing a common clinical language through training paramedics may facilitate this communication [ , ]. We believe that a solid training program is the key to standardizing clinical assessment in acute stroke care and that the reliable use of the NIHSS is related to how paramedics are trained rather than the profession itself. A compatible stroke scale will improve prehospital to in-hospital communication and the quality of the prenotification but also holds the potential to improve triage, optimize in-hospital reception, and reduce time to treatment. The ParaNASPP clinical trial [ ] aims to investigate this.
This study was delayed due to organizational issues during the COVID-19 pandemic, and time limits and the pandemic affected our possibilities to engage a larger group.
We decided to use a stroke physician to perform as the patient in the videos to achieve a trustworthy acting of neurological findings. We realize that this is also a limitation as neurological findings in a real-world setting may be influenced by comorbidities, complicating the patient assessment. The results on the interrater agreement achieved in this study may therefore not be directly transferrable to a clinical setting.
The study was performed using a convenience sample, and an a priori power analysis was not performed. The low number of assessments between neurologists and video or paramedics might thus make the Bland-Altman analysis underpowered, with the accompanying increased uncertainty in the LoA estimates.
Only the total NIHSS score, and not the specific NIHSS score for each of the 11 score items, were available for analysis for the paramedics, and as a consequence, we were not able to identify if there were specific items that affected the agreement.
Failing to stay inside the predefined LoA of 3 is fundamentally different depending on if we are evaluating the lower or higher range of the NIHSS score. For future studies, it would be interesting to investigate if a shifting LoA acceptability and different cutoffs for dichotomizing the scale would alter the interrater agreement.
There were 5 paramedics who reported that they had an international certification in NIHSS. The NIHSS was not a part of standard protocol for paramedics, and the rather high proportion of paramedics with extracurricular knowledge may have contributed to a selection bias, since paramedics already interested in the topic were more likely to respond to the advertisement.
The paramedics scored higher than both the predefined scores and the stroke physicians’ scores, hence the predefined LoA were not met. However, the width of LoA was smaller than seen when experienced neurologists are compared. When the NIHSS scores were dichotomized, the paramedics achieved very good agreement with both the predefined scores and the stroke physicians’ scores. This study demonstrates possibilities for the transfer of clinical competence in digital simulation training. It may facilitate training and implementation in greater scales in different prehospital services and improve the efficacy of training in the future.
The study and fee for open access publication were funded by the Norwegian Air Ambulance Foundation. The funder had no influence on study design; the collection, management, analysis, interpretation of data; and the writing and publishing of the report.
We would like to thank Sindre Nilo and Ole Kristian Andreassen for their contribution to the videos; the stroke physicians at Oslo University Hospital, Stroke Unit Ullevaal, and the paramedics at Oslo University Hospital, Prehospital Divisions Lørenskog, Bærum, and Aurskog/Høland for participating in the study; and Fredrik Kaupang at NLA technology for the contribution of the ParaNASPP app.
MG, HFB, and MRH contributed to the conception and design of the study. MG organized the database and performed the statistical analysis with JR. MG wrote the first draft of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.
Conflicts of Interest
National Institutes of Health Stroke Scale, English and Norwegian versions.PDF File (Adobe PDF File), 156 KB
The Paramedic Norwegian Acute Stroke Prehospital Project (ParaNASPP) app with pictograms.PNG File , 4561 KB
Example video.MP4 File (MP4 Video), 76542 KB
- Berglund A, Svensson L, Sjöstrand C, von Arbin M, von Euler M, Wahlgren N, HASTA Collaborators, et al. Higher prehospital priority level of stroke improves thrombolysis frequency and time to stroke unit: the Hyper Acute STroke Alarm (HASTA) study. Stroke 2012 Oct;43(10):2666-2670. [CrossRef] [Medline]
- Kobayashi A, Czlonkowska A, Ford GA, Fonseca AC, Luijckx GJ, Korv J, et al. European Academy of Neurology and European Stroke Organization consensus statement and practical guidance for pre-hospital management of stroke. Eur J Neurol 2018 Mar;25(3):425-433. [CrossRef] [Medline]
- Meretoja A, Keshtkaran M, Saver JL, Tatlisumak T, Parsons MW, Kaste M, et al. Stroke thrombolysis: save a minute, save a day. Stroke 2014 Apr;45(4):1053-1058. [CrossRef] [Medline]
- Patel MD, Rose KM, O'Brien EC, Rosamond WD. Prehospital notification by emergency medical services reduces delays in stroke evaluation: findings from the North Carolina stroke care collaborative. Stroke 2011 Aug;42(8):2263-2268 [FREE Full text] [CrossRef] [Medline]
- Lyden P. Using the National Institutes of Health Stroke Scale: a cautionary tale. Stroke 2017 Feb;48(2):513-519. [CrossRef] [Medline]
- Kesinger MR, Sequeira DJ, Buffalini S, Guyette FX. Comparing National Institutes of Health Stroke Scale among a stroke team and helicopter emergency medical service providers. Stroke 2015 Mar;46(2):575-578. [CrossRef] [Medline]
- Smith EE, Kent DM, Bulsara KR, Leung LY, Lichtman JH, Reeves MJ, American Heart Association Stroke Council. Accuracy of prediction instruments for diagnosing large vessel occlusion in individuals with suspected stroke: a systematic review for the 2018 guidelines for the early management of patients with acute ischemic stroke. Stroke 2018 Mar;49(3):e111-e122 [FREE Full text] [CrossRef] [Medline]
- Zhelev Z, Walker G, Henschke N, Fridhandler J, Yip S. Prehospital stroke scales as screening tools for early identification of stroke and transient ischemic attack. Cochrane Database Syst Rev 2019 Apr 09;4:CD011427 [FREE Full text] [CrossRef] [Medline]
- Brandler ES, Sharma M, Sinert RH, Levine SR. Prehospital stroke scales in urban environments: a systematic review. Neurology 2014 Jun 17;82(24):2241-2249 [FREE Full text] [CrossRef] [Medline]
- 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(6):323-327. [CrossRef] [Medline]
- Hov MR, Røislien J, Lindner T, Zakariassen E, Bache KCG, Solyga VM, et al. Stroke severity quantification by critical care physicians in a mobile stroke unit. Eur J Emerg Med 2019 Jun;26(3):194-198 [FREE Full text] [CrossRef] [Medline]
- Goldstein LB, Samsa GP. Reliability of the National Institutes of Health Stroke Scale: extension to non-neurologists in the context of a clinical trial. Stroke 1997 Feb;28(2):307-310. [CrossRef] [Medline]
- Liang ZC, Ooi SBS, Wang W. Pandemics and their impact on medical training: lessons from Singapore. Acad Med 2020 Sep;95(9):1359-1361 [FREE Full text] [CrossRef] [Medline]
- 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 Dec;43(12):3271-3277. [CrossRef] [Medline]
- Lyden P, Raman R, Liu L, Grotta J, Broderick J, Olson S, et al. NIHSS training and certification using a new digital video disk is reliable. Stroke 2005 Nov;36(11):2446-2449. [CrossRef] [Medline]
- NIH Stroke Scale. URL: http://www.nihstrokescale.org/ [accessed 2022-05-01]
- Koka A, Suppan L, Cottet P, Carrera E, Stuby L, Suppan M. Teaching the National Institutes of Health Stroke Scale to paramedics (e-learning vs video): randomized controlled trial. J Med Internet Res 2020 Jun 09;22(6):e18358 [FREE Full text] [CrossRef] [Medline]
- Bugge HF, Guterud M, Bache KCG, Braarud A, Eriksen E, Fremstad KO, et al. Paramedic Norwegian Acute Stroke Prehospital Project (ParaNASPP) study protocol: a stepped wedge randomised trial of stroke screening using the National Institutes of Health Stroke Scale in the ambulance. Trials 2022 Mar 04;23(1):113 [FREE Full text] [CrossRef] [Medline]
- Bugge HF, Guterud MM, Røislien J, Larsen K, Ihle-Hansen H, Toft M, et al. National Institutes of Health Stroke Scale scores obtained using a mobile application compared to the conventional paper form: a randomised controlled validation study. BMJ Innov 2022 Jun 16:bmjinnov-2022-000968. [CrossRef]
- Fjærtoft H, Skogseth-Stephani R, Indredavik B, Bjerkvik TF, Varmdal T. Norwegian Stroke Registry: annual report 2019. Article in Norwegian. Trondheim. 2020 Oct 1. URL: https://www.kvalitetsregistre.no/sites/default/files/1_arsrapport_2019_norsk_hjerneslagregister_justert_21.10.2020.pdf [accessed 2022-07-29]
- Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986 Mar 08;1(8476):307-310. [Medline]
- Giavarina D. Understanding Bland Altman analysis. Biochem Med (Zagreb) 2015;25(2):141-151 [FREE Full text] [CrossRef] [Medline]
- Carstensen B, Simpson J, Gurrin LC. Statistical models for assessing agreement in method comparison studies with replicate measurements. Int J Biostat 2008;4(1):Article 16 [FREE Full text] [CrossRef] [Medline]
- Heldner MR, Zubler C, Mattle HP, Schroth G, Weck A, Mono M, et al. National Institutes of Health Stroke Scale score and vessel occlusion in 2152 patients with acute ischemic stroke. Stroke 2013 Apr;44(4):1153-1157. [CrossRef] [Medline]
- Dobrocky T, Piechowiak EI, Volbers B, Slavova N, Kaesmacher J, Meinel TR, et al. Treatment and outcome in stroke patients with acute M2 occlusion and minor neurological deficits. Stroke 2021 Mar;52(3):802-810. [CrossRef] [Medline]
- Brennan P, Silman A. Statistical methods for assessing observer variability in clinical measures. BMJ 1992 Jun 06;304(6840):1491-1494 [FREE Full text] [CrossRef] [Medline]
- StataCorp. Stata Statistical Software: Release 16. Stata. 2019. URL: https://www.stata.com/ [accessed 2022-07-29]
- R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2014. URL: https://www.r-project.org/ [accessed 2022-07-29]
- Josephson SA, Hills NK, Johnston SC. NIH Stroke Scale reliability in ratings from a large sample of clinicians. Cerebrovasc Dis 2006;22(5-6):389-395. [CrossRef] [Medline]
- Larsen K, Jæger HS, Hov MR, Thorsen K, Solyga V, Lund CG, et al. Streamlining acute stroke care by introducing National Institutes of Health Stroke Scale in the emergency medical services: a prospective cohort study. Stroke 2022 Jun;53(6):2050-2057 [FREE Full text] [CrossRef] [Medline]
- Scheitz JF, Abdul-Rahim AH, MacIsaac RL, Cooray C, Sucharew H, Kleindorfer D, SITS Scientific Committee. Clinical selection strategies to identify ischemic stroke patients with large anterior vessel occlusion: results from SITS-ISTR (Safe Implementation of Thrombolysis in Stroke International Stroke Thrombolysis Registry). Stroke 2017 Feb;48(2):290-297. [CrossRef] [Medline]
- Hansen CK, Christensen A, Ovesen C, Havsteen I, Christensen H. Stroke severity and incidence of acute large vessel occlusions in patients with hyper-acute cerebral ischemia: results from a prospective cohort study based on CT-angiography (CTA). Int J Stroke 2015 Apr;10(3):336-342. [CrossRef] [Medline]
- Anderson A, Klein J, White B, Bourgeois M, Leonard A, Pacino A, et al. Training and certifying users of the National Institutes of Health Stroke Scale. Stroke 2020 Mar;51(3):990-993 [FREE Full text] [CrossRef] [Medline]
- Mulkerin WD, Spokoyny I, Francisco JT, Lima B, Corry MD, Nudelman MJR, et al. Prehospital identification of large vessel occlusions using modified National Institutes of Health Stroke Scale: a pilot study. Front Neurol 2021;12:643356 [FREE Full text] [CrossRef] [Medline]
- Chung C, Cooper SJ, Cant RP, Connell C, McKay A, Kinsman L, et al. The educational impact of web-based and face-to-face patient deterioration simulation programs: an interventional trial. Nurse Educ Today 2018 May;64:93-98. [CrossRef] [Medline]
- Austlid I, Arkestaal KL, Kibsgaard HP. Skill learning and retention. J Trauma Acute Care Surg 2017 Jun;82(6S):S103-S106. [CrossRef]
- Drenck N, Viereck S, Bækgaard JS, Christensen KB, Lippert F, Folke F. Pre-hospital management of acute stroke patients eligible for thrombolysis - an evaluation of ambulance on-scene time. Scand J Trauma Resusc Emerg Med 2019 Jan 09;27(1):3 [FREE Full text] [CrossRef] [Medline]
|e-Learning: electronic learning|
|LoA: limits of agreement|
|NIHSS: National Institutes of Health Stroke Scale|
|ParaNASPP: Paramedic Norwegian Acute Stroke Prehospital Project|
Edited by P Kubben; submitted 10.05.22; peer-reviewed by H Zhao, PV Eswaradass; comments to author 22.06.22; revised version received 02.07.22; accepted 29.07.22; published 11.08.22Copyright
©Mona Guterud, Helge Fagerheim Bugge, Jo Røislien, Karianne Larsen, Erik Eriksen, Svein Håkon Ingebretsen, Martin Lerstang Mikkelsen, Jo Kramer-Johansen, Kristi G Bache, Else Charlotte Sandset, Maren Ranhoff Hov. Originally published in JMIR Neurotechnology (https://neuro.jmir.org), 11.08.2022.
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