Cargando…

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

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 progr...

Descripción completa

Detalles Bibliográficos
Autores principales: McEwan, Phil, Bennett Wilton, Hayley, Ong, Albert C. M., Ørskov, Bjarne, Sandford, Richard, Scolari, Francesco, Cabrera, Maria-Cristina V., Walz, Gerd, O’Reilly, Karl, Robinson, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810027/
https://www.ncbi.nlm.nih.gov/pubmed/29439650
http://dx.doi.org/10.1186/s12882-017-0804-2
_version_ 1783299669974056960
author McEwan, Phil
Bennett Wilton, Hayley
Ong, Albert C. M.
Ørskov, Bjarne
Sandford, Richard
Scolari, Francesco
Cabrera, Maria-Cristina V.
Walz, Gerd
O’Reilly, Karl
Robinson, Paul
author_facet McEwan, Phil
Bennett Wilton, Hayley
Ong, Albert C. M.
Ørskov, Bjarne
Sandford, Richard
Scolari, Francesco
Cabrera, Maria-Cristina V.
Walz, Gerd
O’Reilly, Karl
Robinson, Paul
author_sort McEwan, Phil
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-017-0804-2) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5810027
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-58100272018-02-16 A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model McEwan, Phil Bennett Wilton, Hayley Ong, Albert C. M. Ørskov, Bjarne Sandford, Richard Scolari, Francesco Cabrera, Maria-Cristina V. Walz, Gerd O’Reilly, Karl Robinson, Paul BMC Nephrol Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-017-0804-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-13 /pmc/articles/PMC5810027/ /pubmed/29439650 http://dx.doi.org/10.1186/s12882-017-0804-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
McEwan, Phil
Bennett Wilton, Hayley
Ong, Albert C. M.
Ørskov, Bjarne
Sandford, Richard
Scolari, Francesco
Cabrera, Maria-Cristina V.
Walz, Gerd
O’Reilly, Karl
Robinson, Paul
A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title_full A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title_fullStr A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title_full_unstemmed A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title_short A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model
title_sort model to predict disease progression in patients with autosomal dominant polycystic kidney disease (adpkd): the adpkd outcomes model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810027/
https://www.ncbi.nlm.nih.gov/pubmed/29439650
http://dx.doi.org/10.1186/s12882-017-0804-2
work_keys_str_mv AT mcewanphil amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT bennettwiltonhayley amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT ongalbertcm amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT ørskovbjarne amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT sandfordrichard amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT scolarifrancesco amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT cabreramariacristinav amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT walzgerd amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT oreillykarl amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT robinsonpaul amodeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT mcewanphil modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT bennettwiltonhayley modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT ongalbertcm modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT ørskovbjarne modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT sandfordrichard modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT scolarifrancesco modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT cabreramariacristinav modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT walzgerd modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT oreillykarl modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel
AT robinsonpaul modeltopredictdiseaseprogressioninpatientswithautosomaldominantpolycystickidneydiseaseadpkdtheadpkdoutcomesmodel