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.

Keywords: Disease modelling; ESRD; End-stage renal disease; Kidney volume; Renal function decline; Renal progression.

Conflict of interest statement

Ethics approval and consent to participate

This article is based on previously conducted research, and does not involve any new studies of human or animal subjects performed by any of the authors.

Consent for publication

Not applicable.

Competing interests

ACMO, BØ, RS, FS, M-CVC, and GW have received honoraria from Otsuka Pharmaceutical Europe Ltd. for serving on advisory board meetings and/or lectures. PM and HBW are employees of HEOR Ltd., a company sponsored by Otsuka Pharmaceutical Europe Ltd. to build the model. KO and PR are employees of Otsuka Pharmaceutical Europe Ltd.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1

Flow diagram of patient simulation through the ADPKD Outcomes Model. CKD: chronic kidney disease; eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; TKV: total kidney volume

Fig. 2

Validation of the TEMPO 3:4 disease progression equations implemented within the ADPKD Outcomes Model. Trajectories of eGFR progression were consistent with predictions derived from equations fitted to CRISP I data (panel a), while model-predicted eGFR progression was consistent with observed data from HALT-PKD Study A (panel b), HALT-PKD Study B (panel c), and THIN database (panel d). Shaded regions depict 95% prediction intervals; error bars depict 95% confidence intervals. ADPKD-OM: autosomal dominant polycystic kidney disease Outcomes Model; CRISP: Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study; eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; HALT-PKD: Halt Progression of Polycystic Kidney Disease trials; THIN: The Health Improvement Network; TKV: total kidney volume

Fig. 3

Age at ESRD onset as a function of baseline patient characteristics: eGFR (60–110 mL/min/1.73 m2); age (25–45 years); TKV (1000–2000 mL). Midline represents median; upper and lower hinges represent 25th and 75th percentiles; upper and lower whiskers represent highest and lowest values within 1.5 times the interquartile range; data beyond the whiskers are plotted as outliers. eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; TKV: total kidney volume

Fig. 4

Univariate sensitivity analyses demonstrating the influence of baseline patient characteristics on lifetime ESRD risk and predicted age at ESRD onset. Profiles 1–3 represent hypothetical patient cohorts with increasingly advanced stages of ADPKD progression at baseline. Vertical lines represent base-case values. eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; TKV: total kidney volume

Fig. 5

Prediction of disease progression for three illustrative ADPKD patient profiles. Upper sections of patient characteristics describe modelled drivers of progression. CKD: chronic kidney disease; eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; TKV: total kidney volume; PI: prediction interval