Early dynamic changes in platelet counts and 28-day mortality in sepsis patients: a retrospective cohort study using dynamic latent class model and generalized additive mixture model analysis.

IF 3.1 3区医学 Q1 MEDICINE, GENERAL & INTERNAL

Frontiers in Medicine Pub Date : 2025-07-09 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1596134

Yong Han, Jie Liu, Zhenhua Huang, Haofei Hu, Haiyan Yin

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Abstract

Objective: This study investigates the relationship between early dynamic changes in platelet (PLT) counts and 28-day mortality in Chinese patients with sepsis, addressing the limitations of previous studies that focused on single baseline measurements.

Methods: In this retrospective cohort study, 266 sepsis patients admitted to Shenzhen Second People's Hospital from January 2023 to December 2024 were included. A dynamic latent class model analyzed the patterns of PLT count changes during the first week of hospitalization. The Cox proportional hazards regression model assessed the link between these dynamic changes and 28-day mortality, supported by sensitivity and subgroup analyses for robustness. The GAMM model compared PLT change trajectories over 7 days between the mortality and survival groups.

Results: After adjusting for various variables, participants with gradually increasing PLT counts (class 2), decreasing counts (class 3), and persistently low counts (class 4) had hazard ratios (HRs) for 28-day mortality of 1.687 (95% CI:0.380, 7.494), 3.710 (95% CI:1.124, 12.251), and 4.258 (95% CI:1.435, 12.636) respectively, compared to those with persistently high PLT counts (class 1). The GAMM model revealed that PLT counts for patients who died were significantly lower and had a downward trend, while the survival group's counts trended upward; the difference between the two groups generally exhibited an upward trend after admission, with a calculated average daily increase of 12.919 × 10⁹/L.

Conclusion: Early dynamic changes in PLT counts (1-7 days) are independently associated with 28-day mortality in sepsis patients. Those with low and declining PLT counts are at a higher risk. By dynamically monitoring early changes in PLT may help identify high-risk patients and inform personalized treatment strategies, improving outcomes.

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脓毒症患者血小板计数和28天死亡率的早期动态变化：采用动态潜类模型和广义加性混合模型分析的回顾性队列研究

目的：本研究探讨了中国脓毒症患者血小板（PLT）计数早期动态变化与28天死亡率之间的关系，解决了以往研究集中于单一基线测量的局限性。方法：采用回顾性队列研究方法，选取2023年1月至2024年12月在深圳市第二人民医院住院的266例脓毒症患者。动态潜类模型分析了住院第一周PLT计数变化的模式。Cox比例风险回归模型评估了这些动态变化与28天死亡率之间的联系，并通过敏感性和亚组分析来支持稳健性。GAMM模型比较了死亡组和生存组在7 天内的PLT变化轨迹。结果：在调整各种变量后，与PLT计数持续高（1类）的参与者相比，PLT计数逐渐增加（2类），计数减少（3类）和持续低计数（4类）的参与者28天死亡率的风险比（hr）分别为1.687 (95% CI:0.380, 7.494), 3.710 （95% CI:1.124, 12.251）和4.258 （95% CI:1.435, 12.636）。GAMM模型显示，死亡患者的PLT计数明显降低并呈下降趋势，而生存组的PLT计数呈上升趋势；入院后两组差异总体呈上升趋势，计算平均每日增加12.919 × 109/L。结论：早期PLT计数的动态变化（1-7 天）与败血症患者28天死亡率独立相关。血小板计数低且不断下降的患者风险更高。通过动态监测PLT的早期变化可能有助于识别高危患者，并告知个性化的治疗策略，改善结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Medicine Medicine-General Medicine

CiteScore

5.10

自引率

5.10%

发文量

3710

审稿时长

12 weeks

期刊介绍： Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world