ArticleBMC cardiovascular disorders2021
Prognostic nomogram for 30-day mortality of deep vein thrombosis patients in intensive care unit.
Article in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.JMIR medical informatics · 2026Article
- Investigating risk factors and developing a predictive model for venous thromboembolism in patients with sepsis.Frontiers in medicine · 2026Article
- Blood Urea Nitrogen to Serum Albumin Ratio as A New Prognostic Indicator in Critically Ill Patients with Diabetic Ketoacidosis: A Retrospective Cohort Study.Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association · 2024Article
- A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study.Journal of orthopaedic surgery and research · 2023Article
- Construction and Verification of Risk Predicting Models to Evaluate the Possibility of Venous Thromboembolism After Robot-Assisted Radical Prostatectomy.Annals of surgical oncology · 2022Article
- Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems.International journal of molecular sciences · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundWe aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU).
methodsStepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation.
resultsWe obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI] > 0; Integrated discrimination improvement [IDI] > 0; P < 0.05). The Brier scores of two new models in training set were 0.091 and 0.108. After internal validation, corrected area under the curves for two models were 0.850 and 0.830, while corrected Brier scores were 0.108 and 0.114. The more concise model was chosen to make the nomogram.
conclusionsThe nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.
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