Dataset Information

Circulating cell death biomarker: good candidates of prognostic indicator for patients with hepatitis B virus related acute-on-chronic liver failure.

ABSTRACT: Investigations on survival of patients with hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF) are sparse and urgently needed. The current study aimed to evaluate the prognostic value of circulating cell death biomarkers (M30-anigen, M65-antigen and HMGB1) for HBV ACLF. In this prospective study (2/2013-8/2014), 94 patients including 54 HBV-ACLF and 40 chronic hepatitis B (CHB) patients were recruited. 40 healthy controls (HC) were also recruited. HBV-ACLF were followed up for 3 months for short-term mortality. All three biomarkers were significantly elevated in HBV-ACLF compared with CHB or HC. M30- and M65-antigens could significantly discriminate between non-survivors and survivors in HBV-ACLF. However, HMGB1 showed no prognostic value. By Cox regression analysis, M30- and M65-antigens and MELD were identified as independent predictors for short-term mortality. A novel prognostic model, MELD-CD (MELD-cell death) was established based on the multivariate results. The adjusted Harrell's C-index of MELD-CD was 0.86 (P < 0.001) and was significantly higher (P < 0.001 for all) than the currently used models, MELD (C-index, 0.71, P < 0.001), MELD-NA (0.67, P < 0.001), CTPs (0.61, P < 0.05). Dynamic analyses further confirmed the prognostic utility of M30- and M65-antigen. Future studies are warranted to validate the results.

SUBMITTER: Cao Z

PROVIDER: S-EPMC4585557 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Background Circulating microRNAs are emerging biomarkers for heart failure (HF). Our study aimed to assess the prognostic value of microRNA signature that is differentially expressed in patients with acute HF. Methods and Results Our study comprised a screening cohort of 15 patients with AHF and 5 controls, a PCR-discovery cohort of 50 patients with AHF and 26 controls and a validation cohort of 564 patients with AHF from registered study DRAGON-HF (Diagnostic, Risk Stratification and Prognostic Value of Novel Biomarkers in Patients With Heart Failure). Through screening by RNA-sequencing and verification by reverse-transcription quantitative polymerase chain reaction, 9 differentially expressed microRNAs were verified (miR-939-5p, miR-1908-5p, miR-7706, miR-101-3p, miR-144-3p, miR-4732-3p, miR-3615, miR-484 and miR-19b-3p). Among them, miR-19b-3p was identified as the microRNA signature with the highest fold-change of 8.4 and the strongest prognostic potential (area under curve with 95% CI, 0.791, 0.654-0.927). To further validate its prognostic value, in the validation cohort, the baseline level of miR-19b-3p was measured. During a follow-up period of 19.1 (17.7, 20.7) months, primary end point comprising of all-cause mortality or readmission due to HF occurred in 48.9% patients, while patients in the highest quartile of miR-19b-3p level presented the worst survival (Log-rank P<0.001). Multivariate Cox model showed that the level of miR-19b-3p could independently predict the occurrence of primary end point (adjusted hazard ratio,1.39; 95% CI, 1.18-1.64). In addition, miR-19b-3p positively correlated with soluble suppression of tumorigenicity 2 and echocardiographic indexes of left ventricular hypertrophy. Conclusions Circulating miR-19b-3p could be a valuable prognostic biomarker for AHF. In addition, a high level of circulating miR-19b-3p might indicate ventricular hypertrophy in AHF subjects. Registration URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03727828.

Project description:Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF. Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771-0.895) and validation set (AUC 0.798, 95% CI: 0.719-0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05). Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

Dataset Information

Circulating cell death biomarker: good candidates of prognostic indicator for patients with hepatitis B virus related acute-on-chronic liver failure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets