Chedid, Maroun

Key Points Data were collected from a cohort of 759 patients with autosomal dominant polycystic kidney disease that were followed for a median of 10.2 years. Prognostic models were developed to predict kidney failure using routinely obtained clinical information and externally validated. Risk predictions had good discrimination and calibration over a time horizon of 15 years. Background Autosomal dominant polycystic kidney disease (ADPKD) is a common cause of kidney failure. Progression is highly variable, and accurate prognostic information is needed to guide early treatment decisions. The objective of this study was to develop a multivariable predictive model for progression to kidney failure. Methods We developed prognostic models using Cox regression for the outcome of kidney failure, defined as eGFR <15 ml/min per 1.73 m 2 , dialysis, or kidney transplantation. The development dataset consisted of participants in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) and Halt Progression of Polycystic Kidney Disease study A (HALT-A) studies. The validation dataset consisted of patients with ADPKD in the Mayo clinical registry aged 15–49 years, with eGFR ≥60 ml/min per 1.73 m 2 . Predictor variables in the base model were age, sex, creatinine or eGFR, ADPKD genotype, and Mayo Imaging Class. Clinical and laboratory data were evaluated in the full model. Results The development cohort included 759 patients with baseline eGFR of 91±29 ml/min per 1.73 m 2 (mean±SD), of whom 16% reached end point after median (interquartile range) follow-up of 10.2 (5.5–16.7) years. The validation cohort included 535 patients with baseline eGFR of 90±31 ml/min per 1.73 m 2 (mean±SD), of whom 11% reached end point after median (interquartile range) follow-up of 5.5 (1.5–13.7) years. The full model, including age, sex, serum creatinine, Mayo Imaging Class, total carbon dioxide, hemoglobin, diastolic BP, and body mass index, had a C-index of 0.81 (95% confidence interval, 0.72 to 0.90) in the validation cohort and 0.75 (95% confidence interval, 0.62 to 0.88) when ADPKD genotype was also included. Risk predictions from base and full models were well-calibrated out to 15 years. Conclusions In persons with early ADPKD and preserved eGFR, a model using routinely obtained clinical information quantified risk of kidney failure over 15 years.

Abstract Background and Aims Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disease and is characterized by genetic complexity and phenotypic variability. The age of reaching kidney failure (KF) is variable and covers the complete age-spectrum. There is an unmet need for early biomarkers to differentiate between rapid and slow progressors. The PROPKD score identified hypertension before the age of 35 years as a risk factor for rapid kidney function decline. We aim to identify earlier risk factors for rapid disease progression by studying a population of ADPKD patients who reached kidney failure (KF) before the age of 40y. Method This multicentric retrospective study focusses on a unique population (n = 200) of ADPKD patients who reached KF before 40y. Kidney failure (KF) was defined as CKD5 or start of Kidney Replacement Therapy (KRT), whichever came first. Longitudinal data on childhood history, comorbidities and kidney function were collected. Life table and Proportional Hazards analysis were used to assess associations between clinical parameters and time to KF. Results Median age of ADPKD diagnosis was 22.3 (16.5 – 28.6) and median age of KF onset was 36.2 years (32.9-38.7 years). Fourty-seven patients were genotyped (23.5%) of which 38 patients (81.0%) were PKD1T and 8 (17.0%) were PKD1NTand only 1 patient (2.1%) was PKD2. Median age at first urological event was 27.0 (20.7 – 32.0) years. 71 patients (35.5%) had history of UTI's, 67 patients (33.5%) had hemorrhagic cysts on abdominal imaging, 66 patients (33.0%) presented with gross hematuria and 40 patients (25.0%) presented with kidney stones. There was a high prevalence of hypertension (N = 128, 64.0%). Four patients (N = 4/128, 3.1%) had a very early diagnosis of hypertension before the age of 10 years. Hypertension-onset before the age of 18 years correlated with a significantly faster progression (UV HR: 2.07 (1.32 – 3.25)). Conclusion This study describes a unique cohort of ADPKD patients with rapid disease progression. Hypertension at young age (&lt;18) correlated with rapid disease progression, suggesting that ambulatory blood pressure in children might be useful to identify patients at risk for rapidly progressive ADPKD.

ABSTRACT Background Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups. Methods This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (&lt;46, 46–56, &gt;56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD). Results Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly. Conclusion Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.