Evidence map›Paper›PMID 40668489›Full record

ArticleCurrent medical science2025

Development and Validation of a Nomogram Prediction Model for Sepsis-Induced Coagulopathy: A Multicenter Retrospective Study.

Wen-Hao Ma, Ze-Yu Yang, Xing-Xing Fan, Lei Tian, Tuo Zhang, Ming-da Wang, Ji-Yuan Gao, Jian-le Xu, Wei Fang, Hui-Min Hou and 1 more

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Current medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

11 authors.

Wen-Hao Ma *Department of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Ze-Yu Yang *Department of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Xing-Xing FanDepartment of Critical Care Medicine, Shenxian People's Hospital, Liaocheng, 252400, China.
Lei TianDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Tuo ZhangDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Ming-da WangDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Ji-Yuan GaoDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Jian-le XuDepartment of Statistics and Medical Records Management, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Wei FangDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Hui-Min HouDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China. houhuimin92@163.com.
Man ChenDepartment of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China. chenman_slyy@163.com.

Funding

Beijing iGandan Foundation iGandanF-1082022-RGG007Natural Science Foundation of Shandong Province ZR2024MH008Postdoctoral Innovation Project of Shandong Province SDCX-ZG-202400043
6 · The paper itself

Abstract

objectiveThis study aimed to develop a prediction model to assess the risk of sepsis-induced coagulopathy (SIC) in sepsis patients.

methodsWe conducted a retrospective study of septic patients admitted to the Intensive Care Units of Shandong Provincial Hospital (Central Campus and East Campus), and Shenxian People's Hospital from January 2019 to September 2024. We used Kaplan-Meier analysis to assess survival outcomes. LASSO regression identified predictive variables, and logistic regression was employed to analyze risk factors for pre-SIC. A nomogram prediction model was developed via R software and evaluated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

resultsAmong 309 patients, 236 were in the training set, and 73 were in the test set. The pre-SIC group had higher mortality (44.8% vs. 21.3%) and disseminated intravascular coagulation (DIC) incidence (56.3% vs. 29.1%) than the non-SIC group. LASSO regression identified lactate, coagulation index, creatinine, and SIC scores as predictors of pre-SIC. The nomogram model demonstrated good calibration, with an AUC of 0.766 in the development cohort and 0.776 in the validation cohort. DCA confirmed the model's clinical utility.

conclusionSIC is associated with increased mortality, with pre-SIC further increasing the risk of death. The nomogram-based prediction model provides a reliable tool for early SIC identification, potentially improving sepsis management and outcomes.

Indexed as

Blood Coagulation DisordersDisseminated Intravascular CoagulationNomogramsSepsisAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveEarly diagnosisIntensive care unit (ICU)NomogramPrediction modelRetrospective studySepsisSepsis-induced coagulopathyThromboelastography

Identifiers

PMID40668489

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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.