Evidence map›Paper›PMID 41013334›Full record

ArticleBMC infectious diseases2025

Association between the changing trends of platelet distribution width and in-hospital mortality in critically ill patients with sepsis: a multicenter study based on machine learning.

Yinjing Xie, Xinxing Lei, Hao Deng, Jing Zhang, Shaorong Qiu, Dehua Zhuang, Hao Wu, Tianjing Wei, Shijie Su, Xiaoning Zhang and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Yinjing Xie *Department of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Xinxing Lei *Department of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Hao Deng *Laboratory Medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Jing ZhangDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Shaorong QiuDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Dehua ZhuangLaboratory Medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Hao WuDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Tianjing WeiDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Shijie SuDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Xiaoning ZhangDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Bin WangDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Lian YuDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Yuzhong XuDepartment of Clinical Laboratory, Shenzhen Baoan People's Hospital, Shenzhen University, 518101, Shenzhen, China. 254501651@qq.com.
Dayong GuLaboratory Medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China. wanhood@163.com.
Xiaopeng YuanDepartment of Laboratory Medicine, Shenzhen People's Hospital (The First Affliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China. yuanxp2001@126.com.

Funding

Guangdong Basic and Applied BasicResearch Foundation 2024A1515220092Science and Technology Planning Project of Shenzen Municipality JCYJ20240813104213017
6 · The paper itself

Abstract

backgroundSepsis remains the leading cause of in-hospital mortality in critically ill patients. Platelet distribution width (PDW), an indicator of platelet activation and variability, is associated with inflammation and coagulation dysfunction during sepsis. However, dynamic changes in PDW and their association with patient outcomes remain unexplored. This study investigated the relationship between changing PDW trends and in-hospital mortality in critically ill patients with sepsis using machine learning techniques for robust analysis.

methodsIn the model development cohort, inpatient admissions fulfilling the sepsis 3.0 criteria in the Intensive Care Unit of Shenzhen People's Hospital were analyzed. PDW measurements were obtained at six-time points: First Day (D1), Second Day (D2), Third Day (D3), and the last three days before discharge (LD-3, LD-2, and LD-1). PDW was compared between survivors and non-survivors. Group-based trajectory modeling identified distinct PDW trajectory groups, and patient characteristics and outcomes were analyzed. The model was externally validated at a second hospital using identical inclusion criteria.

resultsA total of 1,090 and 429 patients with sepsis were included in the development and validation cohorts, respectively. Four distinct PDW trajectory groups emerged in the development cohort: "PDW Rapidly Increasing Group" (n = 174; 15.96%), "Low PDW Stable Group" (n = 416; 38.17%), "Moderate PDW Stable Group" (n = 421; 38.62%), and "High PDW Group" (n = 79; 7.25%). Subjects in the "PDW Rapidly Increasing Group" were the oldest, exhibiting the highest levels of inflammatory markers, including Interleukin 6 (IL-6), Procalcitonin, and C-reactive protein, and the highest hospital mortality rate of 55.2%. Conversely, the "Low PDW Stable Group" included the youngest patients, with the lowest inflammatory marker levels and a 16.6% mortality rate. Comparable trajectory groups, patient characteristics, and outcomes were observed in the validation cohorts.

conclusionsBased on PDW trajectories, we identified and validated four distinct sepsis subphenotypes, each characterized by significant variations in inflammatory marker levels and clinical outcomes.

Indexed as

Blood PlateletsCritical IllnessHospital MortalityMachine LearningSepsisAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedGroup-based trajectory modelingPDWSepsis

Identifiers

PMID41013334
PMCPMC12465830

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.