P0697IDENTIFYING PATIENTS SUITABLE FOR PRIMARY CARE FOLLOW-UP IN A LARGE NON-DIALYSIS CKD COHORT

Abstract

Abstract Background and Aims Chronic Kidney Disease (CKD) is a growing public health concern, affecting approximately 13.4% of the global population. Due to the rising burden of CKD and its economic cost, there is a pressing need to identify the risk factors which can predict CKD progression and thereby enable us to manage these patients in the community under primary care follow-up. Our study aimed to identify risk factors that can predict outcomes in patients with stable CKD. Method The study was conducted on patients recruited in the Salford Kidney Study, (a large prospective CKD database recruiting patients since 2002). From a total of 2952 recruited between 2002 and the year 2016, 1023 patients with a diagnosis of hypertension, diabetes or pyelonephritis were sampled for this study. Based on the annual rate of progression of estimated glomerular filtration rate (delta eGFR), 140 patients were identified as stable CKD patients (delta eGFR -0.50 and 0.50 ml/min/1.73m2/year). The characteristics of this group was compared with 277 rapid progressors (RP) (delta eGFR<-3) and 212 patients in the improved group (delta eGFR>0.50). Negative predictive value analysis was performed on all patients with an outcome of a GFR < 30ml/min/1.73m2. Results Stable CKD patients had a significantly higher age compared to RP ( 69 vs 62, p<0.001). Further patients in the stable CKD group had a comparatively lower median blood pressure (141.5 vs 137, p<0.001). Other factors which were identified as risk factors for rapid progression included history of diabetes (177 vs 73, p=0.021), low albumin (41 vs 44, p<0.001), raised urine protein creatinine ratio (154.6 vs 18.8, p<0.001) and phosphate (1.24 vs 1.12, p<0.001) (Table-1). Comparing outcomes showed rapid progression reached ESRD (172 vs 18, p<0.001) but no significant difference in mortality (93 vs 55, p=0.25). Characteristics that were statistically different for stable CKD patients in this large cohort were identified: age, systolic blood pressure, diastolic blood pressure, diabetes, albumin, UPCR and phosphate. NPV analysis did not identify reveal any clear predictors for progression in stable CKD patients. Conclusion Several risk factors were identified to distinguish between stable, rapid and improved group. NPV did not identify any predictors in stable group. Further risk prediction models incorporating biomarkers are warranted to identify factors that can guide prognosis thereby stable CKD patients can be managed in the community.