Evidence map›Paper›PMID 42343228›Full record

ArticleBMC anesthesiology2026

How glycemic variability trajectories impact clinical outcomes in critically ill patients with severe pneumonia: a prospective cohort study.

Xiang-Yu Zhang, Ying-Qian Zhu, Yuan-Qiu Guo, Yi-Yang Wang, Jing-Chao Luo, Huan Wang

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Article in BMC anesthesiology, 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiang-Yu Zhang *Department of Critical Care Medicine, Taicang Hospital of Nanjing University of Chinese Medicine, Suzhou, 215400, China.
Ying-Qian Zhu *Department of Critical Care Medicine, Taicang Hospital of Nanjing University of Chinese Medicine, Suzhou, 215400, China.
Yuan-Qiu Guo *Department of Critical Care Medicine, PeiXian People's Hospital, Xuzhou, Jiangsu, China.
Yi-Yang WangDepartment of Critical Care Medicine, Taicang Hospital of Nanjing University of Chinese Medicine, Suzhou, 215400, China.
Jing-Chao LuoDepartment of Critical Care Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China. sucapter@gmail.com.
Huan WangDepartment of Critical Care Medicine, Taicang Hospital of Nanjing University of Chinese Medicine, Suzhou, 215400, China. lassonberg@163.com.

Funding

Basic Research Program of Taicang City TC2024JCYL09
6 · The paper itself

Abstract

backgroundWhile glycemic variability (GV) affects outcomes in critically ill patients, its temporal patterns and prognostic implications in severe pneumonia remain unclear. This study aimed to identify distinct GV trajectory patterns and their associations with clinical outcomes.

methodsThis prospective cohort study enrolled 315 patients with severe pneumonia admitted to our intensive care unit (ICU) at Taicang Hospital of Nanjing University of Chinese Medicine, from January 2021 to December 2024. Using group-based trajectory modeling (GBTM), we analyzed the coefficient of variation (CV) trajectories during the first 5 ICU days. We compared clinical characteristics between trajectory groups, conducted exploratory landmark survival analysis, and assessed the performance of clinical parameters for identifying trajectory classification.

resultsGBTM identified two distinct GV trajectories: Dynamic Variant (DV, 12.1%) characterized by initially high variability that gradually decreased but remained persistently elevated, and Stable Control (SC, 87.9%) with consistently low variability. While early outcomes were comparable, after day 14, the DV group demonstrated significantly higher mortality (70.3% vs. 60.0%, p = 0.034). DV patients had higher procalcitonin (PCT, 2.35 vs. 0.76 ng/ml, p = 0.030) and glycated hemoglobin A1c (HbA1c, 7.1% vs. 6.3%, p = 0.002) levels on ICU admission, despite similar disease severity scores and other clinical parameters. Combined prediction using PCT and HbA1c showed good performance in identifying trajectories (Area Under the Receiver Operating Characteristic curve: 0.87, 95% Confidence Interval: 0.80-0.94).

conclusionDistinct GV trajectories in severe pneumonia patients are associated with differential subsequent mortality risks, with admission PCT and HbA1c levels serving as predictors of trajectory group classification. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Blood GlucoseCritical IllnessPneumoniaAgedCohort StudiesFemaleGlycated HemoglobinHumansIntensive Care UnitsMaleMiddle AgedProcalcitoninPrognosisProspective StudiesSeverity of Illness IndexBlood GlucoseGlycated HemoglobinProcalcitoninGlycemic variabilityGroup-based trajectory modelingSevere pneumoniaSurvival analysis

Identifiers

PMID42343228
PMCPMC13573460

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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 Socratic graph.