ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2025
[Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].
Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.BMC medical informatics and decision making · 2026Article
- Explainable machine learning for predicting venous thromboembolism in septic shock patients.Frontiers in immunology · 2026Article
- [Interpretable machine learning-based predictive model for assessing abdominal surgery risk and biologic therapy efficacy in CDAI 0 to 1 level Crohn disease patients].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesGlobal epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability.
methodsThis multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV;
resultsFive key predictors, age [odds ratio (
conclusionsBoth the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
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Registered trials
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