A nomogram based on pretreatment clinical parameters for the prediction of inadequate biochemical response in primary biliary cholangitis

Tian, Siyuan; Liu, Yansheng; Sun, Keshuai; Zhou, Xia; Ma, Shuoyi; Zhang, Miao; Zhou, Xinmin; Wang, Lu; Han, Ying

doi:10.1002/jcla.23501

Cited by 8 publications

(16 citation statements)

References 26 publications

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“…The model established by the five risk factors mentioned above had a relatively high predictive ability, with AUC being 0.886 (sensitivity: 82.80%, specificity: 84.40%). Compared to the previous studies that established the predictive model, [14][15][16] our study has some key distinctions. First of all, the independent risk factors that formed the predictor index were different.…”

Section: Discussionmentioning

confidence: 91%

“…Second, we screened more factors that were probably associated with the response to UDCA, including complement 3, complement 4, IgA, IgM, IgG, ESR, ANA, ASMA, AMA-M2, interface hepatitis and so on, among which the IgM showed significant relevance to the UDCA treatment response. Furthermore, we tested the predictive value of the model proposed by previous studies [14][15][16] in our cohort. It turned out that the predictive ability of them was relatively low (Fig.…”

Section: Discussionmentioning

confidence: 99%

“… 15 (B) The model proposed by Tian et al . 16 (C) The model proposed by Carbone et al . 14 …”

Section: Discussionmentioning

confidence: 99%

“…5,6,[9][10][11][12][13] These studies, which are based on 1-or 2-year treatment data, have effectively predicted the long-term outcome but they have not identified the patients earlier. To make up for this deficiency, some criteria have been conducted based upon admission data, [14][15][16] but the findings still need validation. Given that the risk factors that are associated with early biochemical nonresponse have been subject to misidentification, the aims of our study were to accurately identify the independent risk factors of the early biochemical nonresponse and propose a relatively accurate predictor index for insufficient early biochemical response to UDCA treatment before treatment begins, ultimately providing more evidence of relevant aspects in PBC patients.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Model of Ursodeoxycholic Acid Treatment Response in Primary Biliary Cholangitis

Shu¹,

Song²,

Bai³

et al. 2021

Journal of Clinical and Translational Hepatology

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Background and Aims: Although ursodeoxycholic acid (UDCA) treatment in primary biliary cholangitis is effective in many patients, there are still many people who respond poorly to it. Identifying and intervening these patients early is important. Therefore, exploring the risk factors and proposing a predictor index to predict the UDCA treatment nonresponse earlier among primary biliary cholangitis patients were the aims of this research. Methods: A total of 135 primary biliary cholangitis patients treated with UDCA (13-15 mg/kg/d) were enrolled in this retrospective study. The response to treatment was evaluated based on Paris I criteria. The univariate and logistic multivariate regression analyses were adopted to determine the independent risk factors and propose a predictor index. Receiver operating characteristic curve was used to evaluate the predictive ability of the predictor index. Results: Total bilirubin, albumin, globulin, immunoglobin M, and aspartate aminotransferase-to-platelet ratio index were the five independent risk factors associating with early biochemical nonresponse to UDCA treatment. Based on these factors, we established a predictor index with the predictive value being 0.886 (sensitivity: 82.80%, specificity: 84.40%). Conclusions: We developed a predictor index that had an accurate prediction of the early biochemical nonresponse to UDCA treatment, which is expected to provide valuable information for the high-risk group before treatment begins.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

“… 15 (B) The model proposed by Tian et al . 16 (C) The model proposed by Carbone et al . 14 …”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive Model of Ursodeoxycholic Acid Treatment Response in Primary Biliary Cholangitis

Shu¹,

Song²,

Bai³

et al. 2021

Journal of Clinical and Translational Hepatology

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“…Moreover, many studies have evaluated the role of sex in the response to UDCA therapy, although results are conflicting because of small sample size, retrospectivity and different criteria used to determine response. Eleven studies including 9748 patients with PBC were analyzed [ 40 , 48 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. All of these studies, except one [ 59 ], evaluated the impact of sex on UDCA therapy response.…”

Section: Primary Biliary Cholangitismentioning

confidence: 99%

Gender and Autoimmune Liver Diseases: Relevant Aspects in Clinical Practice

Invernizzi

Cilla

Trapani³

et al. 2022

JPM

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Autoimmune liver diseases (AILDs) include autoimmune hepatitis, primary biliary cholangitis and primary sclerosing cholangitis. The etiologies of AILD are not well understood but appear to involve a combination of genetic and environmental factors. AILDs commonly affect young individuals and are characterized by a highly variable clinical course. These diseases significantly influence quality of life and can progress toward liver decompensation or the onset of hepatocellular or cholangiocarcinoma; a significant number of patients eventually progress to end-stage liver disease, requiring liver transplantation. In this review, we focus on the sex characteristics and peculiarities of AILD patients and highlight the relevance of a sex-specific analysis in future studies. Understanding the sex differences underlying AILD immune dysregulation may be critical for developing more effective treatments.

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Machine learning prediction model for treatment responders in patients with primary biliary cholangitis

et al. 2023

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Background and Aim Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data. Methods We conducted a single‐center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out‐of‐sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver‐related deaths were analyzed using Kaplan–Meier analysis. Results Compared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan–Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log‐rank = 0.005 and 0.007). Conclusion ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation.

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A nomogram based on pretreatment clinical parameters for the prediction of inadequate biochemical response in primary biliary cholangitis

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 8 publications

References 26 publications

Predictive Model of Ursodeoxycholic Acid Treatment Response in Primary Biliary Cholangitis

Predictive Model of Ursodeoxycholic Acid Treatment Response in Primary Biliary Cholangitis

Gender and Autoimmune Liver Diseases: Relevant Aspects in Clinical Practice

Machine learning prediction model for treatment responders in patients with primary biliary cholangitis

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