Evidence map›Paper›PMID 42698090›Full record

ArticleBMC emergency medicine2026

Impact of a high-profile sudden cardiac death on emergency department utilization: a multi-campus interrupted time series and exploratory phenotyping study.

Chenxi Wang, Han Cai, Xiujuan Hu, Jiayu Liu, Guanhua Li, Li Zhang

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Article in BMC emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chenxi WangRespiratory Intensive Care Unit, Tianjin Chest Hospital, Tianjin, China. 18622389954@163.com.
Han CaiHospital Human Resources Department, Tianjin Chest Hospital, Tianjin, China.
Xiujuan HuRespiratory Intensive Care Unit, Tianjin Chest Hospital, Tianjin, China.
Jiayu LiuRespiratory Intensive Care Unit, Tianjin Chest Hospital, Tianjin, China.
Guanhua LiRespiratory Intensive Care Unit, Tianjin Chest Hospital, Tianjin, China.
Li ZhangRespiratory Intensive Care Unit, Tianjin Chest Hospital, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-profile public health events can be temporally associated with significant shifts in healthcare-seeking behavior. We aimed to quantify the surge in emergency department (ED) volume and characterize the demographic shifts following a high-profile sudden cardiac death (SCD) event of a nationally prominent public figure.

methodsA retrospective study was conducted across two major hospital cohorts (N = 15,981). We employed interrupted time series (ITS) analysis to estimate the immediate level shift in daily ED visits. An XGBoost model with SHAP (SHapley Additive exPlanations) interpretability was developed as an exploratory phenotyping tool to characterize the surge based on clinical and behavioral features. Findings were confirmed using an internal validation cohort.

resultsFollowing the SCD event, daily ED volume exhibited a significant immediate level shift (Main: +24.4%; Internal Validation: +34.7%,p < 0.001). A subtle but robust demographic shift toward younger populations was observed (Median age decrease: 2.2 years,p < 0.001). Machine learning identified Age, Nighttime Presentation, and Cardiovascular Department attendance as the features most strongly associated with the surge phenotype. Notably, the SHAP dependence analysis revealed a sharp, non-linear escalation in surge probability for individuals aged < 45 years, particularly during nocturnal hours. Despite the volume surge, no significant increase in mortality or acute myocardial infarction diagnoses was observed, indicating that the surge was predominantly associated with low-risk individuals exhibiting reassurance-seeking behavior.

conclusionMedia-driven events can be followed by a structured, large-scale surge in emergency department utilization, predominantly among younger, low-risk individuals seeking reassurance. These demographic and behavioral findings are consistent with the cyberchondria phenomenon, which may produce a dilution of clinical severity without increasing hard endpoints, potentially straining acute care resources. Targeted public health communication and adaptive triage strategies are essential to preserve emergency capacity during periods of heightened media-associated health concerns.

Indexed as

Death, Sudden, CardiacEmergency Service, HospitalPatient Acceptance of Health CareAdultAgedEmergency Room VisitsFemaleHumansInterrupted Time Series AnalysisMachine LearningMaleMiddle AgedPhenotypeRetrospective StudiesEmergency department crowdingHealth-seeking behaviorInterrupted time series analysisMachine learningPublic health surveillance

Identifiers

PMID42698090
PMCPMC13545800

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.