Abstract: TH-PO470
Design of a Clinical Risk Calculator for Atherosclerotic Renovascular Disease
Session Information
- CKD: Epidemiology, Outcomes - Cardiovascular - I
November 02, 2017 | Location: Hall H, Morial Convention Center
Abstract Time: 10:00 AM - 10:00 AM
Category: Chronic Kidney Disease (Non-Dialysis)
- 303 CKD: Epidemiology, Outcomes - Cardiovascular
Authors
- Vassallo, Diana, Salford Royal Hospital NHS Foundation Trust, Salford, United Kingdom
- Foley, Robert N., University of Minnesota, Minneapolis, Minnesota, United States
- Kalra, Philip A., Salford Royal Hospital NHS Foundation Trust, Salford, United Kingdom
Background
Risk stratification in atherosclerotic renovascular disease (ARVD) can influence treatment decisions and facilitate patient selection for revascularization. In this study, we aim to use variables with the best predictive value to design a risk calculator that can assist clinicians with risk stratification and outcome prediction.
Methods
Patients with a radiological diagnosis of ARVD were recruited into a single-center prospective cohort study between 1986 and 2014. Primary clinical end-points included: death, end-stage kidney disease (ESKD) and cardiovascular events (CVE). A stepwise regression model was used to select variables with the most significant hazard ratio for each end-point. The risk calculator was designed using HyperText Markup language (HTML). Survival and CVE-free survival were estimated at 1, 5 and 10 years.
Results
In total 872 patients were recruited into this study with a median follow-up of 54.9 months (20.2-96.0). Only models predicting death and CVE showed good performance (c-index >0.80). The risk calculator (figure 1) showed that while all patients with ARVD had poor longterm survival, revascularization may improve outcomes in patients with better preserved eGFR and lower baseline proteinuria (table 1).
Conclusion
Although this risk calculator requires further independent validation in other ARVD cohorts, this study shows that a small number of easily obtained variables can help predict clinical outcomes and encourage a patient-specific therapeutic approach.
Table 1 Predicted survival probabilities for patients with different clinical phenotypes.
| Overall Survival | Patient A-non-revascularized | Patient A-revascularized | Patient B-non-revascularized | Patient B-revascularized |
| 1-year | 0.75 | 0.89 | 0.75 | 0.75 |
| 5-year | 0.36 | 0.48 | 0.36 | 0.36 |
| 10-year | 0.08 | 0.17 | 0.08 | 0.08 |
Patient A – 70 years old, eGFR 30ml/min/1.73m2, proteinuria 0.5g/day, previous myocardial infarction, congestive heart failure and peripheral vascular disease. Patient B – 70 years old, eGFR 20ml/min/1.73m2, proteinuria 1.0g/day, previous myocardial infarction, congestive heart failure and peripheral vascular disease.
Risk Calculator