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Frontiers in medicine. 2025 May 16:12:1509220. doi: 10.3389/fmed.2025.1509220 Q13.02024

Data-driven segmentation of type 2 diabetes mellitus patients: an observational study on health care utilisation prior to an emergency department visit in Germany

基于数据二型糖尿病患者细分组:一项关于德国急诊就诊前医疗卫生利用的观察性研究 翻译改进

Mirjam Rupprecht  1, Alessandro Campione  2, Yves Noel Wu  3, Antje Fischer-Rosinský  3, Anna Slagman  3, Dorothee Riedlinger  3, Martin Möckel  3, Thomas Keil  4  5  6, Lukas Reitzle  7, Cornelia Henschke  2  8

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作者单位

  • 1 Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany.
  • 2 Department of Health Care Management, Berlin Centre for Health Economics Research, Technische Universität Berlin, Berlin, Germany.
  • 3 Emergency and Acute Medicine (CVK, CCM), Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • 4 Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • 5 Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
  • 6 State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany.
  • 7 Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany.
  • 8 Faculty of Medicine, Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Eberhard Karls Universität Tübingen, Tübingen, Germany.
  • DOI: 10.3389/fmed.2025.1509220 PMID: 40454153

    摘要 中英对照阅读

    Background: Potentially avoidable hospital admissions (PAHs) due to type 2 diabetes mellitus (T2DM) occur more frequently in Germany than in the rest of Europe. Emergency departments (EDs) play an important role in understanding cross-sectoral health care utilisation resulting in inpatient admissions. Segmenting T2DM patients in homogenous groups according to their health care utilisation may help to understand the population's needs and to allocate limited resources. The aim of this study was to describe ED use and subsequent inpatient admissions among T2DM patients, and to segment the study population into homogenous subgroups based on disease stage, health care utilisation and process quality of outpatient care prior to an ED visit.

    Methods: This study was conducted as part of the INDEED project, comprising data on 56,821 ED visits in 2016 attributable to 40,561 patients with T2DM from 13 German EDs, as well as statutory health insurance claims data from 2014 to 2016 retrospectively linked per patient. Descriptive analyses included patient characteristics, ED admission diagnoses and discharge diagnoses in the case of inpatient admission of T2DM patients to the ED. Latent class analysis was conducted to identify different subgroups of T2DM patients based on disease stage, number of physician contacts and medical examinations prior to the ED visit.

    Results: Almost half of the study population had severe comorbidities (44.3%). In addition to T2DM, multiple cardiovascular diagnoses were among the most frequently documented admission and discharge diagnoses. The proportion of hospitalised ED visits for T2DM patients was higher (59%) than that for the INDEED population (42.8%). We identified three latent classes that were characterised as "early disease stage and high utilisation" (36.5% of the study population), "progressing disease stage and low utilisation" (26.1%) and "progressed disease stage and high utilisation" (37.4%).

    Conclusion: A substantial share of T2DM patients had not received disease monitoring according to guideline recommendations prior to ED presentation. Improving guideline-adherence in the outpatient sector could help reduce potentially avoidable ED visits. Effective interventions that aim at improving continuity and quality of care as well as reducing the share of PAH need to be identified and evaluated per identified class.

    Keywords: avoidable hospital admission; emergency department; health care utilisation; latent class analysis; population segmentation; type II diabetes mellitus.

    Keywords:data-driven segmentation; type 2 diabetes mellitus; health care utilization; emergency department visit; germany

    背景: 由于2型糖尿病(T2DM)而导致的潜在可避免住院在德国比欧洲其他国家更为频繁。急诊科在理解跨部门医疗利用情况方面扮演着重要角色,这种利用情况导致了入院治疗。根据患者的医疗利用情况将T2DM患者划分为同质组可能有助于了解人口需求并分配有限资源。本研究旨在描述T2DM患者的急诊使用情况及随后的住院情况,并基于疾病阶段、医疗利用率和门诊护理质量在急诊访问前对研究人群进行细分。

    方法: 这项研究作为INDEED项目的一部分,涵盖了德国13家急诊科中56,821次就诊(归因于40,561名T2DM患者)在2016年的数据,并且从2014年到2016年间通过法定健康保险索赔数据与每位患者的历史记录进行了回溯链接。描述性分析包括了患者的特征、T2DM患者急诊就诊时的入院诊断和出院诊断(如果住院的话)。利用潜在类别分析来识别基于疾病阶段、就诊次数以及医疗检查数量的不同T2DM患者子群。

    结果: 研究人群中近一半的人患有严重的合并症(44.3%)。除了T2DM之外,多次心血管诊断在入院和出院诊断中也非常常见。对于T2DM患者的急诊住院比例较高(59%),而INDEED总体人口的这一数字为(42.8%)。我们确定了三个潜在类群,分别被描述为“早期疾病阶段且利用度高”(占研究人群的36.5%)、“进展中的疾病阶段且利用率低”(26.1%)以及“已经进展的疾病阶段且利用度高”(37.4%)。

    结论: 很大一部分T2DM患者在急诊科就诊前未按照指南推荐进行疾病监测。提高门诊领域中的指南依从性可能有助于减少潜在可避免的急诊访问次数。需要识别并评估每个已确定类别中有效的干预措施,以改善连续性和护理质量,并且降低PAH的比例。

    关键词: 可避免住院;急诊科;医疗利用率;潜在类别分析;人口细分;2型糖尿病。

    关键词:数据驱动划分; 2型糖尿病; 医疗服务利用率; 急诊就诊; 德国

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