Evidence map›Paper›PMID 40922986›Full record

ArticleInfection and drug resistance2025

A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.

Chaochao Chen, Zhengxian Su, Yuwei Zheng, Minya Jin, Xiaojie Bi

Abstract read
In one paragraph

Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Recent advances in early screening and diagnosis of sepsis: a narrative review.Frontiers in cellular and infection microbiology · 2026
    Review
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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

5 authors.

Chaochao Chen *Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.
Zhengxian Su *Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.
Yuwei ZhengDepartment of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.
Minya JinDepartment of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.
Xiaojie BiDepartment of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Sepsis has high mortality and progresses rapidly, requiring early diagnosis; traditional scoring and lab parameters are limited in non-ICU settings, highlighting the need for biomarker integration and continuous monitoring to enhance diagnostic accuracy. Patients and Methods: A retrospective analysis of 1,098 patients at Taizhou Hospital of Zhejiang Province identified sepsis and non-sepsis groups per Sepsis 3.0 criteria, Logistic regression analyses were used to identify the risk factors. A dynamic nomogram was built, and predictive accuracy was evaluated using calibration and decision curves. External validation for 94 patients occurred from January to March 2024, using Receiver operating characteristic (ROC) curve analysis for diagnostic evaluation. Results: Multivariate logistic regression analysis revealed eight independent risk factors significantly associated with sepsis development: hypertension (odds ratio [OR] = 1.6278, 95% confidence interval [CI], 1.2079-2.1937), renal insufficiency (OR=1.7002, 95% CI, 1.2840-2.2513), cardiac insufficiency (OR=1.8927, 95% CI, 1.2979-2.7599), interleukin-6 levels (OR=1.0003 95% CI, 1.0002-1.0005), basophil percentage (OR=0.4319, 95% CI, 0.2353-0.7926), platelet-to-lymphocyte ratio (PLR) (OR=1.0025, 95% CI, 1.0011-1.0040), platelet count (PLT) (OR=0.9939, 95% CI, 0.9912-0.9959) and D-dimer levels (OR=1.0796, 95% CI, 1.0273-1.1347). The prognostic nomogram showed significant discriminative power, with a concordance index of 0.746 (95% CI 0.709-0.772). ROC analysis further revealed a negative predictive value (NPV) of 0.832 and a positive predictive value (PPV) of 0.511. Decision curve analysis validated the clinical utility of the model, demonstrating a substantial net benefit for predicting disease progression within a clinically relevant probability threshold range of 30% - 70%. The model maintained satisfactory discriminative performance in external validation, demonstrating an area under the curve (AUC) of 0.663 (95% CI, 0.549-0.776). The interactive web-based nomogram is available at https://bixiaojie-1987.shinyapps.io/DynNomapp/. Conclusion: This web-based dynamic nomogram incorporating eight clinically readily available predictors demonstrates robust diagnostic performance for sepsis, which helps doctors make quicker decisions by providing real-time risk assessments for each patient in non-ICU departments.

Indexed as

clinical biomarkersmodel validationnon-ICU settingssepsis diagnosisweb-based dynamic nomogram

Identifiers

PMID40922986
PMCPMC12414447

What Socratic holds

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Registered trials

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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.