ArticleBMC medical informatics and decision making2016
Logistic regression model can reduce unnecessary artificial liver support in hepatitis B virus-associated acute-on-chronic liver failure: decision curve analysis.
Article in BMC medical informatics and decision making, 2016. 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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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
6 citing papers in PubMed, 6 citations in OpenAlex.
- Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.Frontiers in immunology · 2024Article
- Coagulation Dysfunction in Patients with Liver Cirrhosis and Splenomegaly and Its Countermeasures: A Retrospective Study of 1522 Patients.Disease markers · 2023Article
- A novel prognostic model to predict outcome of artificial liver support system treatment.Scientific reports · 2021Article
- Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.Scientific reports · 2019Article
- The Model of Mortality with Incident Cirrhosis (MoMIC) and the model of Long-term Outlook of Mortality in Cirrhosis (LOMiC).PloS one · 2019Article
- [Red blood cell distribution width is a independent prognostic indicator for mortality in patients with HBV related acute-on-chronic liver failure].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment. We aimed to determine whether better decisions for artificial liver support system (ALSS) treatment could be made with a model than without, through decision curve analysis (DCA).
methodsThe medical profiles of a cohort of 232 patients with hepatitis B virus (HBV)-associated ACLF were retrospectively analyzed to explore the role of plasma prothrombin activity (PTA), model for end-stage liver disease (MELD) and logistic regression model (LRM) in identifying patients who could benefit from ALSS. The accuracy and reliability of PTA, MELD and LRM were evaluated with previously reported cutoffs. DCA was performed to evaluate the clinical role of these models in predicting the treatment outcome.
resultsWith the cut-off value of 0.2, LRM had sensitivity of 92.6 %, specificity of 42.3 % and an area under the receiving operating characteristic curve (AUC) of 0.68, which showed superior discrimination over PTA and MELD. DCA revealed that the LRM-guided ALSS treatment was superior over other strategies including "treating all" and MELD-guided therapy, for the midrange threshold probabilities of 16 to 64 %.
conclusionsThe use of LRM-guided ALSS treatment could increase both the accuracy and efficiency of this procedure, allowing the avoidance of unnecessary ALSS.
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