Evidence map›Paper›PMID 42807417›Full record

ArticleInternational journal of chronic obstructive pulmonary disease2026

Prognostic Analysis of ICU Patients with Chronic Obstructive Pulmonary Disease Based on Blood Phosphate Dynamics: A Retrospective Cohort Study from the MIMIC-IV Database.

Pei Zhou, Chengcheng Liu, Xiaoqin Ju, Du Fan

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Article in International journal of chronic obstructive pulmonary disease, 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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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Pei ZhouDepartment of Respiratory Medicine, The Third Hospital of Changsha (The Affiliated Changsha Hospital of Hunan University), Changsha, Hunan, 410015, People's Republic of China.
Chengcheng LiuDepartment of Emergency Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People's Republic of China.
Xiaoqin JuCardiovascular Disease Center, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi Clinical College of Wuhan University, Enshi, Hubei, 445099, People's Republic of China.
Du FanDepartment of Respiratory Medicine, The Third Hospital of Changsha (The Affiliated Changsha Hospital of Hunan University), Changsha, Hunan, 410015, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: While the relationship between serum phosphorus concentrations and the prognosis of chronic obstructive pulmonary disease (COPD) has been previously investigated, the prognostic significance of dynamic serum phosphorus fluctuations in patients with COPD admitted to intensive care units (ICUs) remains poorly understood. This study aimed to evaluate the association between longitudinal serum phosphorus trajectories during the early ICU stay and 28-day all-cause mortality, and to explore their potential utility as a prognostic indicator. Methods: This retrospective cohort analysis utilized data from COPD patients registered in the Medical Information Mart for Intensive Care IV database (MIMIC-IV, v3.0). Group-based trajectory modeling (GBTM) was applied to delineate distinct serum phosphorus trajectories over the first seven days following ICU admission. The relationship between these trajectory groups and all-cause mortality was examined using Kaplan-Meier survival curves and multivariable Cox proportional hazards regression. Subgroup analyses were performed to verify the robustness of this association across different patient subgroups. Results: A total of 3627 ICU-admitted patients with COPD were enrolled. GBTM distinguished two distinct longitudinal patterns: a high-phosphate trajectory (mean phosphate range: approximately 5.0-6.5 mg/dL; n = 320, 8.8%) and a low/normal-phosphate trajectory (mean phosphate range: approximately 3.2-3.8 mg/dL; n = 3307, 91.2%). Patients in the high-phosphate group exhibited markedly lower 28-day cumulative survival. Multivariable Cox regression confirmed that this trajectory was independently associated with elevated 28-day mortality risk. Furthermore, the trajectory-based model demonstrated superior predictive performance compared with single-time-point phosphate measurements. Conclusion: Longitudinal trends in serum phosphate levels serve as a significant and independent risk factor for short-term mortality in critically ill patients with COPD.

Indexed as

Intensive Care UnitsPhosphatesPulmonary Disease, Chronic ObstructiveAgedBiomarkersDatabases, FactualFemaleHospital MortalityHumansKaplan-Meier EstimateMaleMiddle AgedMultivariate AnalysisPredictive Value of TestsPrognosisProportional Hazards ModelsBiomarkersPhosphateschronic obstructive pulmonary diseasegroup-based trajectory modelingintensive care unitmortalityserum phosphatetrajectory analysis

Identifiers

PMID42807417
PMCPMC13617010

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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.