Impact of a Prehospital Chest Pain Alert App-mediated Prehospital-In-Hospital Coordination Model on Treatment Delays and Clinical Outcomes in STEMI Patients: Protocol for a Four-Year Retrospective Real-World Cohort Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> The effectiveness of ST-elevation myocardial infarction (STEMI) treatment is highly time-dependent, and the information barrier between prehospital and in-hospital settings remains a key factor leading to treatment delays. Existing digital coordination tools either have a single function or lack long-term real-world evidence support, making it difficult to meet clinical needs. This study adopts a self-developed prehospital chest pain alert app (hereafter referred to as the App) by Fengxian District Medical Emergency Center. Mediated through a WeChat-based chest pain center group, the App enables prehospital information synchronization, real-time alerts, multidisciplinary coordination, and feedback on treatment outcome parameters to form a closed-loop communication model, providing a solution to break the information barrier. </sec> <sec> <title>OBJECTIVE</title> To evaluate the impact of the App-mediated prehospital-in-hospital coordination model on treatment delays (e.g., time from first ECG to catheterization laboratory preactivation, door-to-wire time) and clinical outcomes (e.g., 30-day major adverse cardiovascular events, 1-year and 4-year all-cause mortality) in STEMI patients, and to assess its generalizability in high-risk subgroups. </sec> <sec> <title>METHODS</title> This is a single-center retrospective cohort study. STEMI patients admitted to Fengxian District Central Hospital from January 1, 2019, to December 31, 2024, will be enrolled and categorized into three groups: baseline group (January 1, 2019-December 31, 2020, without App use), intervention group (January 1, 2021-December 31, 2024, with App-mediated coordination), and concurrent control group (STEMI patients who came to the hospital independently without calling an ambulance or were transported by ambulance but not reported via the App during the same period). The primary outcome is door-to-wire time (D2W). Secondary outcomes include other treatment delay indicators, clinical prognosis, and App operational efficiency. We will use propensity score matching (PSM) to control for baseline confounding, segmented linear regression to analyze intervention trend effects, and subgroup analysis to assess generalizability in high-risk populations. </sec> <sec> <title>RESULTS</title> This study is based on four-year real-world data from the Department of Cardiology and STEMI database of Fengxian District Central Hospital. Baseline data and intervention-related data are derived from the hospital’s electronic medical record system and App backend logs. A total sample size of ≥944 is expected. Data extraction and statistical analysis are scheduled from January to April 2026. Results will focus on the App-mediated model’s effect on reducing treatment delays and improving clinical outcomes. </sec> <sec> <title>CONCLUSIONS</title> Using four-year real-world data combined with PSM and interrupted time series analysis, this study will provide high-quality evidence for the App-mediated coordination model, which is expected to optimize the regional STEMI care system and offer references for the application of digital health technologies in acute coronary syndrome treatment. </sec> <sec> <title>CLINICALTRIAL</title> Planned registration; https://www.chictr.org.cn/ </sec>

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