ArticleAnnals of medicine2024
Establishing a predictive nomogram for 21‑day transplant-free survival in drug-induced liver failure.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Association between hemoglobin-to-red blood cell distribution width ratio trajectory patterns and in-hospital mortality in critically ill patients with cirrhosis: a retrospective cohort study based on MIMIC-IV and eICU-CRD.BMC medical informatics and decision making · 2026Article
- Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study.Frontiers in medicine · 2025Article
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6 authors.
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Abstract
backgroundThe high prevalence of drug-induced liver failure (DILF) have drawn great attention from clinicians.
aimTo further delineate the clinical features of DILF and develop an easily applicable nomogram, based on readily-discernable clinical data, to predict transplant-free survival (TFS) at different time points.
methods202 DILF patients were enrolled between January 2016 and December 2022, and were followed up from DILF diagnosis to death, liver transplantation, or 91 days afterward, whichever came first. The primary endpoint, though, was 21-day TFS. Clinical data was collected from all patients, and independent risk factors associated with death/liver transplantation was identified using both uni- and multi-variate Cox regression analyses.
resultsIndependent risk factors incorporated into the predictive nomogram are neutrophils (HR = 1.148, 95% CI = 1.048-1.257), prothrombin time (HR = 1.048, 95% CI = 1.017-1.080), albumin (HR = 0.880, 95% CI = 0.823-0.941), acute kidney injury (HR = 2.487, 95% CI = 1.134-5.452), and hepatic encephalopathy (HR = 3.378, 95% CI = 1.744-6.543). The resulting nomogram was highly predictive, with an area under the curve of 0.947 for 21-day TFS.
conclusionsCompared to existing models, such as the Model for End-Stage Liver Disease score, the predictive nomogram is more accurate, only requires easily-measurable clinical and laboratory metrics, as well as being able to directly calculate TFS at various time points.
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