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Date: October 2, 2024

Reference: Paxton et al. Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study. AEM Sept 2024

Guest Skeptic: Dr. Lauren Westafer an Assistant Professor in the Department of Emergency Medicine at the UMass Chan Medical School – Baystate. She is the co-founder of FOAMcast and a pulmonary embolism and implementation science researcher. Dr. Westafer serves as the Social Media Editor and a research methodology editor for the Annals of Emergency Medicine.

Case: The family of a 69-year-old woman activated emergency medical services (EMS) for slurred speech that they noticed when she woke up a couple of hours before. The patient has a history of hypertension, diabetes, gastroesophageal reflux disease (GERD), and dyslipidemia and lives alone but speaks with her son daily. The son reported she seemed fine yesterday evening when they went to dinner, perhaps more tired than usual. The patient had some slurred speech but no obvious facial droop or asymmetric limb weakness.

Background: Stroke remains a leading cause of morbidity and mortality worldwide, with large vessel occlusion (LVO) strokes representing approximately one-third of all acute ischemic strokes (AIS) in the United States. However, LVO strokes disproportionately contribute to stroke-related disability and death, accounting for nearly two-thirds of post-stroke dependence and over 90% of stroke mortality [1,2].

The SGEM has covered LVO strokes several times (SGEM#137, SGEM#292, SGEM#333 and SGEM#349). Rapid identification and transport to an endovascular thrombectomy (EVT)-capable center are important for improving outcomes in these patients, as EVT is the standard treatment for LVO stroke with moderate to severe symptoms​ [3-5].

Prehospital identification of LVO stroke remains a significant challenge for emergency medical services (EMS) [6].  Traditional stroke scales used in the field, such as the Los Angeles Motor Scale (LAMS) [7], the Cincinnati Stroke Triage Assessment Tool (C-STAT) [8,9], and the Rapid Arterial Occlusion Evaluation (RACE) scale [10], have shown varied effectiveness. These scales generally demonstrate high specificity but low sensitivity, often resulting in false negatives where LVO strokes are missed​ [11-13].

Clinical Question: Can a cranial accelerometry (CA) headset device be used by paramedics in the prehospital setting to accurately detect patients with a large vessel occlusion (LVO) stroke?

Reference: Paxton et al. Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study. AEM Sept 2024

  • Population: Consecutive adult patients suspected of acute ischemic stroke (AIS) by prehospital emergency medical services (EMS) in the United States.
  • Intervention: The Harmony 5000 CA headset device (MindRhythm Inc).
  • Comparison: The Los Angeles Motor Scale (LAMS).
  • Outcome:
    • Primary Outcome: The feasibility of prehospital deployment of the device, defined by the proportion of subjects with acceptable ECG and headset data.
    • Secondary Outcome: The device’s diagnostic accuracy in detecting LVO stroke.
  • Type of Study: This was a prospective, multicenter observational diagnostic accuracy study.

Dr. James Paxton

This is an SGEMHOP, and we are pleased to have the lead author, Dr. James Paxton on the show. Dr. Paxton is in the Department of Emergency Medicine, at Wayne State University School of Medicine. 

Authors’ Conclusions: “Obtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone.”

Quality Checklist for A Diagnostic Study:

  1. The clinical problem is well-defined. Yes
  2. The study population represents the target population that would normally be tested for the condition (ie no spectrum bias). Yes
  3. The study population included or focused on those in the ED. Yes 
  4. The study patients were recruited consecutively (i.e. no selection bias). No
  5. The diagnostic evaluation was sufficiently comprehensive and applied equally to all patients (i.e. no evidence of verification bias). Unsure
  6. All diagnostic criteria were explicit, valid and reproducible (i.e. no incorporation bias). Yes
  7. The reference standard was appropriate (i.e. no imperfect gold-standard bias). Yes
  8. All undiagnosed patients underwent sufficiently long and comprehensive follow-up (i.e. no double gold-standard bias). Yes
  9. The likelihood ratio(s) of the test(s) in question is presented or can be calculated from the information provided. Yes 
  10. The precision of the measure of diagnostic performance is satisfactory. Yes
  11. Funding and Conflicts of Interest. The study was funded by MindRhythm, Inc. There were multiple COIs with some authors receiving funding from MindRhythm Inc., three authors were employed by MindRhythm Inc. and two authors reported having ownership interest in MindRhythm Inc. Two authors declared no COIs.

Results: They enrolled 594 subjects in the study with 183 receiving the second-generation device, and usable data captured in 158 patients (86%). Study subjects were 53% female and 56% Black/African American, with a median age of 69 years. Among the patients, 26 (16%) had LVO, 33 had AIS without LVO, 9 had intracerebral hemorrhage, and 91 (58%) had stroke mimics.

  • Among the 26 LVO strokes:
    • Seven (27%) received intravenous thrombolysis (IVT)
    • Fifteen (58%) ultimately received EVT
    • One subject with an LVO received both IVT and attempted EVT
  • Among the 33 AIS without LVO
    • Five (15%) received IVT
    • Two (6%) received attempted EVT
    • Two (2%) subjects who were ultimately deemed to be stroke mimics received IVT

Key Result: The Harmony 5000 CA headset device had much better sensitivity without sacrificing specificity compared to the LAMS tool.

  • Primary Outcome: The CA headset device had a sensitivity of 84.6% (95% CI; 70.7% to 98.5%) and specificity of 82.6% (95% CI; 76.1% to 89.1%) compared to LAMS sensitivity of 38.5% and specificity of 82.7%.
  • Secondary Outcomes: Diagnostic accuracy was a Positive Likelihood Ratio (LR+) 4.86 (95% CI; 24 to 7.30) and a Negative Likelihood Ratio (LR-) 0.19 (95% CI; 0.08 to 0.46).

Listen to the SGEM podcast to hear James respond to our five nerdy questions.

1. Funding Source: Potential bias due to funding from MindRhythm, Inc., which could influence the study’s reporting. This does not invalidate the research but should make us more skeptical. There is a body of evidence supporting the position that financial conflicts of interest can bias publications. Did you take any steps to mitigate/minimize this potential bias?

2. Harmony 4000 vs Harmony 5000: There were technical problems with the original device, the Harmony 4000. Is that why 594 subjects were enrolled but only 183 received the Harmony 5000 to analyze? Why was there still a 14% (25/183) device failure rate with the Harmony 5000? Are there plans for a Harmony 6000? In the final cohort of 158, more than half (58%) were determined to be stroke mimics and two of those mimics received IVT. That is a larger number of mimics than we see is some of the stroke literature.

Lambs

3. LAMS Tool: The LAMS tool has a reported IRR for paramedics of 0.81 which is good [14].  However, the device placement depended on the prehospital EMS provider’s decision based on their suspicion of acute ischemic stroke, which introduces some subjectivity. Not all the potential patients were included which could have introduced some selection bias. Speaking of selection bias, it would have been helpful to have information on the patients with a suspected stroke who did not receive attempted headset placement by EMS personnel.

4. Over-Fitted Data: The algorithm to predict LVO stroke was derived from a data set without a separate validation subset. This can result in overfitting the data which may increase the reported performance of the Harmony 5000.

5. Mobile CT Scanner: We have looked at the data supporting mobile CT scanners and remain skeptical. The Harmony 5000 represents a less costly device that could direct patients to an appropriate stroke center that can perform endovascular therapy. Have you considered doing a cost-effectiveness analysis comparing the two pre-hospital systems (mobile CT and Harmony 5000)?

Comment on Authors’ Conclusion Compared to SGEM Conclusion: We made a friendly amendment to the conclusion…Obtaining adequate recordings with a CA headset is feasible in the prehospital environment. The device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the LAMS alone, but the data could have been overfitted. Despite the better diagnostic accuracy for LVOs, more than half of the suspected stroke patients ultimately turned out to be stroke mimics.

SGEM Bottom Line: Early stroke identification is important but can be difficult in the pre-hospital setting even with the existing tools.

 Case Resolution: Based on the patient’s symptoms, EMS activates a stroke alert and transports the patient to the nearest stroke center. The patient undergoes a CT and CT angiogram of the head which reveals a small acute lacunar infarct but no large vessel occlusion or hemorrhage. The patient, family, neurologist, and emergency physician engage in shared decision-making.

Dr. Lauren Westafer

Clinical Application: Diagnosis of an acute stroke and differentiation between a stroke and a stroke mimics can be difficult. Further investigation is needed to understand if the Harmony 5000 can improve diagnostic accuracy for stroke in the prehospital setting to triage their transportation to an appropriate receiving hospital.

What Do I Tell the Patient? We are worried you are having an acute stroke based on our evaluation. Definitive diagnosis is tricky, so we are taking you to an emergency department for additional testing. 

Keener Kontest: Last week’s winner was…there was none. The other classification for aortic dissection is called the Svensson Classification. 

Listen to the SGEM podcast for this week’s question. If you know, then send an email to thesgem@gmail.com with “keener” in the subject line. The first correct answer will receive a shoutout on the next episode.

SGEMHOP: Now it is your turn SGEMers. What do you think of this episode on the Harmony 5000? Tweet your comments using #SGEMHOP.  What questions do you have for James and his team, ask them on the SGEM blog. The best social media feedback will be published in AEM.

REMEMBER TO BE SKEPTICAL OF ANYTHING YOU LEARN, EVEN IF YOU HEARD IT ON THE SKEPTICS’ GUIDE TO EMERGENCY MEDICINE.

References:

  1. Malhotra K, Gornbein J, Saver JL. Ischemic strokes due to large-vessel occlusions contribute disproportionately to stroke-related dependence and death: a review. Front Neurol. 2017;8:651. doi:10.3389/fneur.2017.00651
  2. Lakomkin N, Dhamoon M, Carroll K, et al. Prevalence of large vessel occlusion in patients presenting with acute ischemic stroke: a 10-year systematic review of the literature. J Neurointerv Surg. 2019;11(3):241-245. doi:10.1136/neurintsurg-2018- 014239
  3. Christou I, Burgin WS, Alexandrov AV, Grotta JC. Arterial status after intravenous TPA therapy for ischaemic stroke. A need for further interventions. Int Angiol. 2001;20(3):208-213.
  4. Goyal M, Menon BK, van Zwam WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomized trials. Lancet. 2016;387(10029):1723-1731. doi:10.1016/S0140-6 736(16)00163-X
  5. Haussen DC, Bouslama M, Grossberg JA, et al. Too good to intervene? Thrombectomy for large vessel occlusion strokes with minimal symptoms: an intention-to- treat analysis. J Neurointerv Surg. 2017;9(10):917-921. doi:10.1136/neurintsurg-2016- 012633
  6. Sun C-H, Zaidat OO, Castonguay AC, et al. A decade of improvement in door-to- puncture times for mechanical thrombectomy but ongoing stagnation in prehospital care. Stroke Vasc Interv Neurol. 2022;3:e000561. doi:10.1161/SVIN.122.000561
  7. 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(1):46- 50. doi:10.1080/312703002806
  8. Katz BS, McMullan JT, Sucharew H, Adeoye O, Broderick JP. Design and validation of a prehospital scale to predict stroke severity: Cincinnati prehospital stroke severity scale. Stroke. 2015;46(6):1508-1512. doi:10.1161/STROKEAHA. 115.008804
  9. Li JL, McMullan JT, Sucharew H, et al. Potential impact of C-STAT for prehospital stroke triage up to 24 hours on a regional stroke system. Prehosp Emerg Care. 2020;24(4):500-504. doi:10.1080/109 03127.2019.1676343
  10. de la Ossa NP, Carrera D, Gorchs M, et al. Design and validation of a prehospital stroke scale to predict large arterial occlusion. Stroke. 2014;45:87-91. doi:10.1161/STROKEAHA.113.003071
  11. Anadani M, Almallouhi E, Wahlquist AE, Debenham E, Holmstedt CA. The accuracy of large vessel occlusion recognition scales in telestroke setting. Telemed J E Health. 2019;25(11):1071-1076. doi:10.1089/tmj.2018.0232
  12. Duvekot MHC, Venema E, Rozeman AD, et al. Comparison of eight prehospital stroke scales to detect intracranial large-vessel occlusion in suspected stroke (PRESTO): a prospective observational study. Lancet Neurol. 2021;20(3):213-221. doi:10.1016/ S1474-4422(20)30439-7
  13. Keenan KJ, Smith WS, Cole SB, Martin C, Hemphill JC, Madhok DY. Large vessel occlusion prediction scales provide high negative but low positive predictive values in prehospital suspected stroke patients. BMJ Neurol Open. 2022;4(2):e000272. doi:10.1136/ bmjno-2022- 000272
  14. Ferguson KN, Kidwell CS, Starkman S, Saver JL, Duckwiler G, Jahan R, Alger JR, Ovbiagele B, Fredieu A, Villablanca P, Vespa P, Niimi Y, Rajajee V, Frazee J, Hovsepian D, Stark R, Vinuela F, Lavrov I, Schwamm L, Yonas H, Johnson E, Lev MH, Koroshetz WJ, Latchaw R, Mugler JP, “Inter-rater and intra-rater reliability of the Los Angeles Motor Scale (LAMS), a prehospital measure of stroke severity,” Stroke 2002; 33(1): 384-384.