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Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.

ABSTRACT: Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2-39.5%) and high risk (63.8-91.1%). The CART model showed that patients lacking HE and with a PT???27.8?s and a TBil level ?455??mol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT?>?27.8?s, mortality remained high after such therapy. Patients lacking HE with a PT???27.8?s and TBil level???455??mol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

SUBMITTER: Huang K

PROVIDER: S-EPMC6848208 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.

Huang Kaizhou K Ji Feiyang F Xie Zhongyang Z Wu Daxian D Xu Xiaowei X Gao Hainv H Ouyang Xiaoxi X Xiao Lanlan L Zhou Menghao M Zhu Danhua D Li Lanjuan L

Scientific reports 20191111 1

Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three ...[more]

PMID: 31712684

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Project description:Artificial liver support system (ALSS) therapy is widely used in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). We aimed to develop a predictive score to identify the subgroups who may benefit from plasma exchange (PE)-centered ALSS therapy. A total of 601 patients were retrospectively enrolled and randomly divided into a derivation cohort of 303 patients and a validation cohort of 298 patients for logistic regression analysis, respectively. Five baseline variables, including liver cirrhosis, total bilirubin, international normalized ratio of prothrombin time, infection and hepatic encephalopathy, were found independently associated with 3-month mortality. A predictive PALS model and the simplified PALS score were developed. The predicative value of PALS score (AUROC = 0.818) to 3-month prognosis was as capable as PALS model (AUROC = 0.839), R score (AUROC = 0.824) and Yue-Meng' score (AUROC = 0.810) (all p > 0.05), and superior to CART model (AUROC = 0.760) and MELD score (AUROC = 0.765) (all p < 0.05). The PALS score had significant linear correlation with 3-month mortality (R2 = 0.970, p = 0.000). PALS score of 0-2 had both sensitivity and negative predictive value of > 90% for 3-month mortality, while PALS score of 6-9 had both specificity and positive predictive value of > 90%. Patients with PALS score of 3-5 who received 3-5 sessions of ALSS therapy had much lower 3-month mortality than those who received 1-2 sessions (32.8% vs. 59.2%, p < 0.05). The more severe patients with PALS score of 6-9 could still benefit from ≥ 6 sessions of ALSS therapy compared to ≤ 2 sessions (63.6% vs. 97.0%, p < 0.05). The PALS score could predict prognosis reliably and conveniently. It could identify the subgroups who could benefit from PE-centered ALSS therapy, and suggest the reasonable sessions.Trial registration: Chinese Clinical Trial Registry, ChiCTR2000032055. Registered 19th April 2020, http://www.chictr.org.cn/showproj.aspx?proj=52471 .

Project description:Background/aimAssessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King's College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.MethodsCART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).ResultsTraditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease), lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade) offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.ConclusionCARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.Key points• Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.

Project description:Background and aims: Hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is a complicated syndrome with extremely high short-term mortality. Whether plasma exchange (PE) improves HBV-ACLF outcomes remains controversial. Here, PE-based non-bioartificial liver support system (NB-ALSS) effects on short-term HBV-ACLF patient outcomes were investigated. Materials and methods: HBV-ACLF patients from Chinese Acute-on-chronic Liver Failure (CATCH-LIFE) cohort receiving standard medical therapy (SMT) alone or PE-based NB-ALSS in addition to SMT were allocated to SMT and SMT+PE groups, respectively; propensity score matching (PSM) was used to eliminate confounding bias. Short-term (28/90-day and 1-year) survival rates were calculated (Kaplan-Meier). Results: In total, 524 patients with HBV-ACLF were enrolled in this study; 358 received SMT alone (SMT group), and the remaining 166 received PE-based NB-ALSS in addition to SMT (SMT+PE group). PSM generated 166 pairs of cases. In the SMT+PE group, 28-day, 90-day, and 1-year survival rates were 11.90, 8.00, and 10.90%, respectively, higher than those in the SMT group. Subgroup analysis revealed that PE-based NB-ALSS had the best efficacy in patients with ACLF grade 2 or MELD scores of 30-40 (MELD grade 3). In MELD grade 3 patients who received SMT+PE, 28-day, 90-day, and 1-year survival rates were improved by 18.60, 14.20, and 20.10%, respectively. According to multivariate Cox regression analysis, PE-based NB-ALSS was the only independent protective factor for HBV-ACLF patient prognosis at 28 days, 90 days, and 1 year (28 days, HR = 0.516, p = 0.001; 90 days, HR = 0.663, p = 0.010; 1 year, HR = 0.610, p = 0.051). For those who received SMT+PE therapy, PE-based NB-ALSS therapy frequency was the only independent protective factor for short-term prognosis (28-day, HR = 0.597, p = 0.001; 90-day, HR = 0.772, p = 0.018). Conclusions: This multicenter prospective study showed that the addition of PE-based NB-ALSS to SMT improves short-term (28/90 days and 1-year) outcomes in patients with HBV-ACLF, especially in MELD grade 3 patients. Optimization of PE-based NB-ALSS may improve prognosis or even save lives among HBV-ACLF patients.

Project description:BackgroundHepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is an important type of liver failure in Asia. There is a direct relationship between HBV-ACLF and gastrointestinal barrier function. However, the nutritional status of HBV-ACLF patients has been poorly studied.AimTo investigate the nutritional risk and nutritional status of HBV-ACLF patients and evaluated the impact of nutritional support on the gastrointestinal barrier and 28-d mortality.MethodsNutritional risk screening assessment and gastrointestinal barrier biomarkers of patients with HBV-ACLF (n = 234) and patients in the compensatory period of liver cirrhosis (the control group) (n = 234) were compared during the period between 2016 and 2018. Changes were analyzed after nutritional support in HBV-ACLF patients. Valuable biomarkers have been explored to predict 28-d death. The 28-d survival between HBV-ACLF patients with nutritional support (n = 234) or no nutritional support (2014-2016) (n = 207) was compared.ResultsThe nutritional risk of the HBV-ACLF patients was significantly higher than that of the control group. The nutritional intake of the patients with HBV-ACLF was lower than that of the control group. The decrease in skeletal muscle and fat content and the deficiency of fat intake were more obvious (P < 0.001). The coccus-bacillus ratio, secretory immunoglobulin A, and serum D-lactate were significantly increased in HBV-ACLF patients. The survival group had a lower nutritional risk, lower D-lactate, and cytokine levels (endotoxin, tumor necrosis factor alpha, interleukin-10, and interleukin-1). Interleukin-10 may be a potential predictor of death in HBV-ACLF patients. The 28-d survival of the nutritional support group was better than that of the non-nutritional support group (P = 0.016).ConclusionPatients with HBV-ACLF have insufficient nutritional intake and high nutritional risk, and their intestinal barrier function is impaired. Individualized and dynamic nutritional support is associated with a better prognosis of 28-d mortality in HBV-ACLF patients.

Project description:BackgroundIn the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension p > 40,000). This is in contrast to the very sophisticated variable selection methods that are computationally expensive, need pre-processing routines, and often require calibration of priors.ResultsWe present a tree-based sequential CART (S-CART) approach to variable selection in the binary classification setting and compare it against the more sophisticated procedures using simulated and real biological data. In simulated data, we analyze S-CART performance versus (i) a random forest (RF), (ii) a fully-parametric Bayesian stochastic search variable selection (SSVS), and (iii) the moderated t-test statistic from the LIMMA package in R. The simulation study is based on a hierarchical Bayesian model, where dataset dimensionality, percentage of significant variables, and substructure via dependency vary. Selection efficacy is measured through false-discovery and missed-discovery rates. In all scenarios, the S-CART method is seen to consistently outperform SSVS and RF in both speed and detection accuracy. We demonstrate the utility of the S-CART technique both on simulated data and in a control-treatment mouse study. We show that the network analysis based on the S-CART-selected gene subset in essence recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.ConclusionsThe relatively simple-minded gene selection algorithms like S-CART may often in practical circumstances be preferred over much more sophisticated ones. The advantage of the "greedy" selection methods utilized by S-CART and the likes is that they scale well with the problem size and require virtually no tuning or training while remaining efficient in extracting the relevant information from microarray-like datasets containing large number of redundant or irrelevant variables.AvailabilityThe MATLAB 7.4b code for the S-CART implementation is available for download from https://neyman.mcg.edu/posts/scart.zip.

Dataset Information

Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.

Publications

Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.

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