Dhand Amar, Mangipudi Rama, Varshney Anubodh S, Crowe Jonathan R, Ford Andria L, Sweitzer Nancy K, Shin Min, Tate Samuel, Haddad Haissam, Kelly Michael E, Muller James, Shavadia Jay S
Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, United States, 1 617 732 5330.
Network Science Institute, Northeastern University, Boston, MA, United States.
JMIR Form Res. 2025 Mar 3;9:e60465. doi: 10.2196/60465.
Most people do not recognize symptoms of neurological and cardiac emergencies in a timely manner. This leads to delays in hospital arrival and reduced access to therapies that can open arteries. We created a smartphone app to help patients and families evaluate if symptoms may be high risk for stroke or heart attack (myocardial infarction, MI). The ECHAS (Emergency Call for Heart Attack and Stroke) app guides users to assess their risk through evidence-based questions and a test of weakness in one arm by evaluating finger-tapping on the smartphone.
This study is an initial step in the accuracy evaluation of the app focused on sensitivity. We evaluated whether the app provides appropriate triage advice for patients with known stroke or MI symptoms in the Emergency Department. We designed this study to evaluate the sensitivity of the app, since the most dangerous output of the app would be failure to recognize the need for emergency evaluation. Specificity is also important, but the consequences of low specificity are less dangerous than those of low sensitivity.
In this single-center cross-sectional study, we enrolled patients presenting with symptoms of possible stroke or MI. The ECHAS app assessment consisted of a series of evidence-based questions regarding symptoms and a test of finger-tapping speed and accuracy on the phone's screen to detect unilateral arm weakness. The primary outcome was the sensitivity of the ECHAS app in detecting the need for ED evaluation. The secondary outcome was the sensitivity of the ECHAS app in detecting the need for hospital admission. Two independent and blinded board-certified physicians reviewed the medical record and adjudicated the appropriateness of the ED visit based on a 5-point score (ground truth). Finally, we asked patients semistructured questions about the app's ease of use, drawbacks, and benefits.
We enrolled 202 patients (57 with stroke and 145 with MI). The ECHAS score was strongly correlated with the ground truth appropriateness score (Spearman correlation 0.41, P<.001). The ECHAS app had a sensitivity of 0.98 for identifying patients in whom ED evaluation was appropriate. The app had a sensitivity of 1.0 for identifying patients who were admitted to the hospital because of their ED evaluation. Patients completed an app session in an average of 111 (SD 60) seconds for the stroke pathway and 60 (SD 33) seconds for the MI pathway. Patients reported that the app was easy to use and valuable for personal emergency situations at home.
The ECHAS app demonstrated a high sensitivity for the detection of patients who required emergency evaluation for symptoms of stroke or MI. This study supports the need for a study of specificity of the app, and then a prospective trial of the app in patients at increased risk of MI and stroke.
大多数人不能及时识别神经和心脏急症的症状。这导致患者延迟到达医院,并减少了获得能够疏通血管治疗的机会。我们开发了一款智能手机应用程序,以帮助患者及其家属评估症状是否可能是中风或心脏病发作(心肌梗死,MI)的高风险症状。ECHAS(心脏病发作和中风紧急呼叫)应用程序通过基于证据的问题以及通过评估在智能手机上点击手指来测试单臂无力,指导用户评估自身风险。
本研究是专注于灵敏度的应用程序准确性评估的第一步。我们评估了该应用程序是否能为急诊科有已知中风或心肌梗死症状的患者提供适当的分诊建议。我们设计本研究以评估该应用程序的灵敏度,因为该应用程序最危险的输出结果将是未能识别紧急评估的必要性。特异性也很重要,但低特异性的后果不如低灵敏度那么危险。
在这项单中心横断面研究中,我们纳入了有中风或心肌梗死可能症状的患者。ECHAS应用程序评估包括一系列关于症状的基于证据的问题,以及在手机屏幕上测试点击手指的速度和准确性以检测单侧手臂无力。主要结局是ECHAS应用程序检测急诊科评估必要性的灵敏度。次要结局是ECHAS应用程序检测住院必要性的灵敏度。两名独立且不知情的获得委员会认证的医生审查了病历,并根据5分制评分(真实情况)判定急诊科就诊的适宜性。最后,我们向患者询问了关于该应用程序易用性、缺点和益处的半结构化问题。
我们纳入了202例患者(57例中风患者和145例心肌梗死患者)。ECHAS评分与真实情况适宜性评分密切相关(Spearman相关性为0.41,P<0.001)。ECHAS应用程序识别适合急诊科评估患者的灵敏度为0.98。该应用程序识别因急诊科评估而住院患者的灵敏度为1.0。对于中风路径,患者完成一次应用程序会话平均用时111(标准差60)秒,对于心肌梗死路径平均用时60(标准差33)秒。患者报告该应用程序易于使用,对家庭中的个人紧急情况很有价值。
ECHAS应用程序在检测因中风或心肌梗死症状需要紧急评估的患者方面显示出高灵敏度。本研究支持有必要对该应用程序的特异性进行研究,然后在心肌梗死和中风风险增加的患者中对该应用程序进行前瞻性试验。