Dataset Information

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

ABSTRACT: BACKGROUND:Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics. METHODS:The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from the placebo arm of the Tolvaptan Efficacy and Safety in Management of ADPKD and its Outcomes Study (TEMPO 3:4; ClinicalTrials.gov identifier NCT00428948). Multivariable regression equations estimating annual rates of ADPKD progression, in terms of total kidney volume (TKV) and estimated glomerular filtration rate, formed the basis of the lifetime patient-level simulation model. Outputs of the ADPKD-OM were compared against external data sources to validate model accuracy and generalisability to other ADPKD patient populations, then used to predict long-term outcomes in a cohort matched to the overall TEMPO 3:4 study population. RESULTS:A cohort with baseline patient characteristics consistent with TEMPO 3:4 was predicted to reach ESRD at a mean age of 52 years. Most patients (85%) were predicted to reach ESRD by the age of 65 years, with many progressing to ESRD earlier in life (18, 36 and 56% by the age of 45, 50 and 55 years, respectively). Consistent with previous research and clinical opinion, analyses supported the selection of baseline TKV as a prognostic factor for ADPKD progression, and demonstrated its value as a strong predictor of future ESRD risk. Validation exercises and illustrative analyses confirmed the ability of the ADPKD-OM to accurately predict disease progression towards ESRD across a range of clinically-relevant patient profiles. CONCLUSIONS:The ADPKD-OM represents a robust tool to predict natural disease progression and long-term outcomes in ADPKD patients, based on readily available and/or measurable clinical characteristics. In conjunction with clinical judgement, it has the potential to support decision-making in research and clinical practice.

SUBMITTER: McEwan P

PROVIDER: S-EPMC5810027 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

McEwan Phil P Bennett Wilton Hayley H Ong Albert C M ACM Ørskov Bjarne B Sandford Richard R Scolari Francesco F Cabrera Maria-Cristina V MV Walz Gerd G O'Reilly Karl K Robinson Paul P

BMC nephrology 20180213 1

<h4>Background</h4>Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics.<h4>Methods</h4>The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from ...[more]

PMID: 29439650

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Project description:BACKGROUND:Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease-1 (PKD1) or PKD2 genes deficiency. In this study, we have re-analyzed a microarray dataset to generate a holistic view of this disease. METHODOLOGY:GSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p-value???0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein-protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified. RESULTS:In this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re-analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity. CONCLUSION:We have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.

Project description:Background and objectivesAutosomal dominant polycystic kidney disease (ADPKD) occurs at conception and is often diagnosed decades prior to kidney failure. Nephrology care and transplantation access should be independent of race and ethnicity. However, institutional racism and barriers to health care may affect patient outcomes in ADPKD. We sought to ascertain the effect of health disparities on outcomes in ADPKD by examining age at onset of kidney failure and access to preemptive transplantation and transplantation after dialysis initiation.Design, setting, participants, & measurementsRetrospective cohort analyses of adults with ADPKD in the United States Renal Data System from January 2000 to June 2018 were merged to US Census income data and evaluated by self-reported race and ethnicity. Age at kidney failure was analyzed in a linear model, and transplant rates before and after dialysis initiation were analyzed in logistic and proportional hazards models in Black and Hispanic patients with ADPKD compared with White patients with ADPKD.ResultsA total of 41,485 patients with ADPKD were followed for a median of 25 (interquartile range, 5-54) months. Mean age was 56±12 years; 46% were women, 13% were Black, and 10% were Hispanic. Mean ages at kidney failure were 55±13, 53±12, and 57±12 years for Black patients, Hispanic patients, and White patients, respectively. Odds ratios for preemptive transplant were 0.33 (95% confidence interval, 0.29 to 0.38) for Black patients and 0.50 (95% confidence interval, 0.44 to 0.56) for Hispanic patients compared with White patients. Transplant after dialysis initiation was 0.61 (95% confidence interval, 0.58 to 0.64) for Black patients and 0.78 (95% confidence interval, 0.74 to 0.83) for Hispanic patients.ConclusionsBlack and Hispanic patients with ADPKD reach kidney failure earlier and are less likely to receive a kidney transplant preemptively and after initiating dialysis compared with White patients with ADPKD.

Project description:BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disease and shows a wide phenotype. Only patients with rapid progression (RP) are included in clinical trials or are approved to receive disease-modifying drugs. This study aims at comparing different available predictive tools in ADPKD with the Mayo classification (MC) identification of rapid progressors based on high total kidney volume (TKV) according to age.MethodsA total of 164 ADPKD patients were recruited retrospectively from a single centre. The performance of diverse tools to identify RP defined as being in MC categories 1C-1E was assessed.ResultsA total of 118 patients were MC 1C-1E. The algorithm developed by the European Renal Association-European Dialysis and Transplant Association Working Group on Inherited Kidney Disorders/European Renal Best Practice had a low sensitivity in identifying MC 1C-1E. The sensitivity and specificity of TKV to predict RP depend on the cut-off used. A kidney length of >16.5 cm before age 45 years has high specificity but low sensitivity. Assessing the MC by ultrasonography had high levels of agreement with magnetic resonance imaging (MRI) data, especially for 1A, 1D and 1E. The estimated glomerular filtration rate (eGFR) decline was very sensitive but had low specificity. In contrast, the Predicting Renal Outcome in Polycystic Kidney Disease (PROPKD) score was very specific but had poor sensitivity. Having hypertension before 35 years of age is a good clinical predictor of MC 1C-1E. Family history can be of help in suggesting RP, but by itself it lacks sufficient sensitivity and specificity.ConclusionsThe MC by ultrasonography could be an option in hospitals with limited access to MRI as it performs well generally, and especially at the extremes of the MC, i.e. classes 1A, 1D and 1E. The eGFR decline is sensitive but not very specific when compared with the MC, whereas the PROPKD score is very specific but has low sensitivity. Integrating the different tools currently available to determine RP should facilitate the identification of rapid progressors among patients with ADPKD.

Dataset Information

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

Publications

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

Similar Datasets

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