Evidence map›Paper›PMID 41120899›Full record

ArticleBMC infectious diseases2025

Clinical outcomes and predictive modeling in COVID-19 patients with type 2 diabetes mellitus: a multicenter retrospective cohort study.

Kaiheng Guo, Haini Zhi, Xiaoying Zhou, Shaofeng Huang, Junxu Lin, Jinxin Pang, Lu Xiao, Weiping Sun, Chunping Zeng

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Kaiheng Guo *Department of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, No.621, Gangwan Road, Guangzhou, 540700, China.
Haini Zhi *Department of Endocrinology and Metabolism, Loudi Central Hospital, No. 51 Changqing Street, Louxing District, Loudi, 417099, China.
Xiaoying ZhouDepartment of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, No.621, Gangwan Road, Guangzhou, 540700, China.
Shaofeng HuangDepartment of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, No.621, Gangwan Road, Guangzhou, 540700, China.
Junxu LinDepartment of Endocrinology and Metabolism, Loudi Central Hospital, No. 51 Changqing Street, Louxing District, Loudi, 417099, China.
Jinxin PangDepartment of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, No.621, Gangwan Road, Guangzhou, 540700, China.
Lu XiaoDepartment of Endocrinology and Metabolism, Loudi Central Hospital, No. 51 Changqing Street, Louxing District, Loudi, 417099, China.
Weiping SunDepartment of Endocrinology and Metabolism, Loudi Central Hospital, No. 51 Changqing Street, Louxing District, Loudi, 417099, China. sunwp07@163.com.
Chunping ZengDepartment of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, No.621, Gangwan Road, Guangzhou, 540700, China. zcp193@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to assess the influence of type 2 diabetes mellitus (T2DM) on clinical features and adverse outcomes in COVID-19 patients and to develop a predictive model for adverse outcomes in this population.

methodsA retrospective analysis was conducted from December 2022 to February 2023, involving 1058 COVID-19 inpatients at two hospitals. Patients were divided into T2DM (n = 363) and non-T2DM (n = 695) groups. Demographic and laboratory data were collected, and univariate analyses were performed. Logistic regression analysis was employed to identify risk factors associated with ICU admission, and a predictive model was constructed and validated using ROC curves.

resultsT2DM patients exhibited higher levels of certain inflammatory and biochemical markers and a greater incidence of ICU admission compared to non-T2DM patients. Neutrophil count and lactate dehydrogenase were identified as independent risk factors for ICU admission.

conclusionsT2DM is associated with increased levels of inflammatory and biochemical markers and a higher risk of ICU admission in COVID-19 patients. The predictive model, incorporating neutrophil count and lactate dehydrogenase, offers clinical utility. The study's findings can inform clinical strategies for managing COVID-19 patients with T2DM, particularly in predicting and mitigating adverse outcomes.

Indexed as

COVID-19Diabetes Mellitus, Type 2AdultAgedBiomarkersFemaleHospitalizationHumansIntensive Care UnitsL-Lactate DehydrogenaseMaleMiddle AgedRetrospective StudiesRisk FactorsSARS-CoV-2BiomarkersL-Lactate DehydrogenaseCOVID-19Models, statisticalMulticenter studiesType 2 diabetes mellitus

Identifiers

PMID41120899
PMCPMC12539152

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.