Kidney Week

Abstract: SA-PO762

Estimating Short-Term Risk of Disease-Related Outcomes in ADPKD: A Prediction Model Based on Longitudinal Data from the OVERTURE Study

Session Information

• CKD: Epidemiology, Risk Factors, Prevention - III
  October 27, 2018 | Location: Exhibit Hall, San Diego Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 1901 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Perrone, Ronald D., Tufts Medical Center, Boston, Massachusetts, United States

Ronald D. Perrone,

• Gobburu, Jogarao, Dr., Baltimore, Maryland, United States

Jogarao Gobburu,

• Czerwiec, Frank S., Otsuka Pharma. Devel. & Comm., Inc., Rockville, Maryland, United States

Frank S. Czerwiec,

• Pao, Christina, Otsuka Pharmaceutical Development and Commercialization, Princeton, New Jersey, United States

Christina Pao,

• Ouyang, John, Otsuka Pharm. Dev. & Comm., Rockville, Maryland, United States

John Ouyang,

• Sergeyeva, Olga, Otsuka Pharmaceuticals, Princeton, New Jersey, United States

Olga Sergeyeva,

• Ivaturi, Vijay, University of Maryland, Baltimore, Baltimore, Maryland, United States

Vijay Ivaturi,

Background

Models are available for predicting rapid decline in kidney function in autosomal dominant polycystic kidney disease (ADPKD), but there is a need for a clinical tool that estimates the short-term risk of other ADPKD-related complications. We analyzed observational data to identify patient baseline variables most useful for estimating risk of complications and to construct a patient risk calculator.

Methods

OVERTURE was a prospective study with follow-up at 6-month intervals to assess for 17 types of ADPKD-related manifestations that included hematuria, kidney pain, albuminuria, urinary tract infection (UTI) and nephrolithiasis. In our analysis, occurrence of each type of manifestations in a patient was assigned a score of 1, yielding a maximum ADPKD severity score of 17. Item response analysis¹ was used to determine the complications that contributed most to overall disease severity across the population, and multivariate logistic regression was performed to ascertain which patient baseline risk factors best predicted experiencing ≥1 of the identified complications in the follow-up 6–18 months.

Results

Among 1880 subjects with follow-up data over the analysis period, the manifestations that contributed most to overall ADPKD severity were hematuria, kidney pain, albuminuria, UTI, and nephrolithiasis. In the regression model, baseline risk factors most predictive of occurrence of ≥1 of these manifestations over 6–18 months were: age ≤35, male sex, non-Hispanic ethnicity, higher baseline total kidney volume growth rate, and higher risk class in the Mayo ADPKD classification system.² These predictive variables were incorporated into a simple nomogram for patient risk scoring.

Conclusion

Patient baseline risk factors can be used to estimate the risk of experiencing ≥1 of 5 important ADPKD-related manifestations over 6–18 months. A prediction nomogram that assigns point scores for each predictive variable enables rapid risk estimation in the clinic.

Funding

• Commercial Support – Otuska Pharmaceutical Research and Development, Inc