Dataset Information

Survival prediction among patients with non-cancer-related end-stage liver disease.

ABSTRACT:

Background

Predicting the survival of non-cancer related end-stage-liver-disease patients in general practice has been difficult for physicians because of the extremely variable trajectories due to multiple complex clinical factors, hence it remains a challenging issue to date. This study aimed to develop and validate a specific prognostic scoring system to early recognize the prognosis and improve the quality of end-of life care for non-cancer end-stage-liver-disease population.

Materials and methods

A multicentre, retrospective cohort study was conducted during January 2010 ~ December 2012 and continued follow-up until December 2014. A cox proportional hazard regression analysis was used to derive and validate an optimized model. The main outcome measures were the 28-day, 3-month, 6-month, and 12-month mortality prediction. The performance of the novel model was evaluated, including discrimination and calibration.

Results

A total of 4,080 consecutive subjects were enrolled. The AUROCs for the 3-month survival discrimination in the MELD, MELD-Na and novel model were 0.787, 0.705 and 0.804 (P<0.001); the 6-month survival discrimination were 0.781, 0.702 and 0.797 (P<0.001); the overall survival discrimination were 0.771, 0.694 and 0.785 (P = 0.002) respectively, whereas the novel model showed a significantly higher discrimination power than did the MELD and MELD-Na for the 3-month, 6-month and overall survival prediction. In addition, calibration of external validation cohort showed no statistical difference in all 5 groups compared with the observed groups.

Conclusion

This is a clinically relevant, validated scoring system that can be used sequentially to stratify the prognosis in non-cancer cirrhotic populations, which may help the patients along with medical team in decision making to improve the quality of end-of-life care.

SUBMITTER: Tsai YW

PROVIDER: S-EPMC6150508 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Survival prediction among patients with non-cancer-related end-stage liver disease.

Tsai Yi-Wen YW Tzeng I-Shiang IS Chen Yi-Chuan YC Hsieh Tsung-Han TH Chang Shy-Shin SS

PloS one 20180921 9

<h4>Background</h4>Predicting the survival of non-cancer related end-stage-liver-disease patients in general practice has been difficult for physicians because of the extremely variable trajectories due to multiple complex clinical factors, hence it remains a challenging issue to date. This study aimed to develop and validate a specific prognostic scoring system to early recognize the prognosis and improve the quality of end-of life care for non-cancer end-stage-liver-disease population.<h4>Mate ...[more]

PMID: 30240398

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Project description:BackgroundLow medication adherence is known to contribute to worse health outcomes in the general population.AimWe aimed to evaluate the medication regimen and determine the adherence levels among patients with end-stage liver disease.MethodsWe measured adherence in patients awaiting liver transplantation at a single center using the 8-item Morisky Medication Adherence Scale (MMAS-8), with a score <8 classified as low adherence. Medication regimen complexity was assessed using the Medication Regimen Complexity Index (MRCI) tool. Factors associated with low adherence were identified by logistic regression.ResultsOf 181 patients, 33% were female, median age was 62, and model for end-stage liver disease (MELD) score was 13. The median (IQR) number of medications was 10 (7-13), and the MRCI was 19 (13-27). In total, 54 (30%) were high adherers, and 127 (70%) were low adherers. In total, 42% reported sometimes forgetting to take their medication and 22% reported intermittent adherence within the past 2 weeks. The most common reasons for low adherence were: forgetfulness (27%) and side effects (14%). Compared to high adherence, low adherence was associated with higher number of medications, medication complexity, and diabetes, but lower rates of hepatocellular carcinoma and self-perceived health. In univariable logistic regression, total medication number (OR 1.08), MRCI (OR 1.04), diabetes (OR 2.38), HCC (OR 0.38), and lower self-perceived health (OR 1.37), were statistically significant factors associated with non-adherence. In multivariate analysis, only medication number without supplements (OR 1.14) remained significantly associated with medication non-adherence.ConclusionA majority of patients awaiting liver transplantation demonstrated low medication adherence. Total number of medications and regimen complexity were strong correlates of low adherence. Our data underscores the need for chronic liver disease management programs to improve medication adherence in this vulnerable population.

Project description:BackgroundThe scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients' survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a general-illness severity model (the sequential organ failure assessment score, or SOFA score).Methodology/principal findingsWith retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na(+); presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001) in one-year and 0.88 (P<0.001) in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction.Conclusions/significanceThe posttransplant prognosis is a multidimensional nonlinear problem, and the MLP can achieve significantly high accuracy than SOFA and MELD scores in posttransplant survival prediction. The pattern recognition methodologies like MLP hold promise for solving posttransplant outcome prediction.

Dataset Information

Survival prediction among patients with non-cancer-related end-stage liver disease.

Background

Materials and methods

Results

Conclusion

Publications

Survival prediction among patients with non-cancer-related end-stage liver disease.

Similar Datasets

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