Development of Risk Prediction Equations for Incident Chronic Kidney Disease

IMPORTANCE ‐ Early identification of individuals at elevated risk of developing chronic kidney disease  could improve clinical care through enhanced surveillance and better management of underlying health  conditions.  OBJECTIVE – To develop assessment tools to identify individuals at increased risk of chronic kidney  disease, defined by reduced estimated glomerular filtration rate (eGFR).  DESIGN, SETTING, AND PARTICIPANTS – Individual level data analysis of 34 multinational cohorts from  the CKD Prognosis Consortium including 5,222,711 individuals from 28 countries. Data were collected  from April, 1970 through January, 2017. A two‐stage analysis was performed, with each study first  analyzed individually and summarized overall using a weighted average. Since clinical variables were  often differentially available by diabetes status, models were developed separately within participants  with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external  cohorts (N=2,253,540). EXPOSURE Demographic and clinical factors.  MAIN OUTCOMES AND MEASURES – Incident eGFR <60 ml/min/1.73 m2.  RESULTS – In 4,441,084 participants without diabetes (mean age, 54 years, 38% female), there were  660,856 incident cases of reduced eGFR during a mean follow‐up of 4.2 years. In 781,627 participants  with diabetes (mean age, 62 years, 13% female), there were 313,646 incident cases during a mean follow‐up of 3.9 years. Equations for the 5‐year risk of reduced eGFR included age, sex, ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, BMI, and albuminuria. For participants  with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction  between the two. The risk equations had a median C statistic for the 5‐year predicted probability of  0.845 (25th – 75th percentile, 0.789‐0.890) in the cohorts without diabetes and 0.801 (25th – 75th percentile, 0.750‐0.819) in the cohorts with diabetes. Calibration analysis showed that 9 out of 13 (69%) study populations had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was  similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 out of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. CONCLUSIONS AND RELEVANCE – Equations for predicting risk of incident chronic kidney disease developed in over 5 million people from 34 multinational cohorts demonstrated high discrimination and  variable calibration in diverse populations

THE IMPACT OF ESTIMATED GLOMERULAR FILTRATION RATEREPORTING ON NEPHROLOGY REFERRAL PATTERN, PATIENT CHARACTERISTICS AND OUTCOME

Background: Chronic kidney disease (CKD) is a growing public health problem worldwide. The key to reducing the burden of CKD is earlydetection and delay of disease progression. An elevated serum creatinine concentration, [Cr], is acommon indicator of CKD. However, even with advanced CKD, [Cr] may not be highamong patients with low muscle mass (particularly the elderly). Thus, the use of the estimated glomerular filtration rate (eGFR) has been advocated for identifying severe occult kidney disease. In January 2006, community laboratories in Ontario, Canada, began to report an eGFR value along with every [Cr] result. The present study sought to investigate the impact of eGFR reporting on nephrology referrals and subsequent patient outcome. Methods: The current study consisted of a retrospective analysis of all referrals to the adult general nephrology clinics at Sunnybrook Health Sciences Centre 15 months before and after eGFR reporting took effect on January 1, 2006. Results: eGFR reporting was associated with a significant rise in the number of referrals (971 to 1101, p=0.03), a 51% rise in patient wait-time (from 73 days to 110days, p < 0.001) and an increase in nephrologists’ workload. Patients referred after eGFR reporting were older, but suffered from fewer comorbidities such as hypertension, vascular disease, and dementia. There was an increase in the number of patients referred with stage 3 CKD, but no change in the proportion of stage 4 and 5 CKD referrals or the number of patients initiating dialysis. Conclusion: Laboratory reporting of eGFR increased nephrology referral volume, patient waiting times, and nephrologists’ workload, without a demonstrable benefit in terms of detection and referral of severe (stage 4 and 5) CKD nor in the reduction of ESRD frequency. Screening programs may need to be implemented along with knowledge translation strategies in order to achievethe goal of preventing and delaying progression of CKD. Reference: Canadian Society of Nephrology, September 2006. Careand referral of adult patients with reduced kidney function- Position paperfrom the Canadian Society of Nephrology.Available at: http://www.csnscn.ca/local/files/CSN-Documents/CSN%20Postion%20Paper%20Sept2006.pd

Past Decline Versus Current eGFR and Subsequent ESRD Risk

eGFR is a robust predictor of ESR Drisk. However, the prognostic information gained from the past trajectory (slope) beyond that of the current eGFR is unclear. We examined 22 cohorts to determine the association of past slopes and current eGFR level with subsequent ESRD. We modeled hazard ratios as a spline function of slopes, adjusting for demographic variables, eGFR, and comorbidities. We used random effects meta-analyses to combine results across studies stratified by cohort type. We calculated the absolute risk of ESRD at 5 years after the last eGFR using the weighted average baseline risk. Overall, 1,080,223 participants experienced 5163 ESRD events during a mean follow-up of 2.0 years. In CKD cohorts, a slope of 26 versus 0 ml/min per 1.73 m(2) per year over the previous 3 years (a decline of 18 ml/min per 1.73 m(2) versus no decline) associated with an adjusted hazard ratio of ESRD of 2.28 (95% confidence interval, 1.88 to 2.76). In contrast, a current eGFR of 30 versus 50 ml/min per 1.73 m(2) (a difference of 20 ml/min per 1.73 m(2)) associated with an adjusted hazard ratio of 19.9 (95% confidence interval, 13.6 to 29.1). Past decline contributed more to the absolute risk of ESRD at lower than higher levels of current eGFR. In conclusion, during a follow-up of 2 years, current eGFR associates more strongly with future ESRD risk than the magnitude of past eGFR decline, but both contribute substantially to the risk of ESRD, especially at eGF

Conversion of Urine Protein–Creatinine Ratio or Urine Dipstick Protein to Urine Albumin–Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis

Background: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead.Objective: To develop equations for converting urine protein creatinine ratio (PCR) and dipstick protein to urine albumin creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging.Design: Individual participant–based meta-analysis.Setting: 12 research and 21 clinical cohorts.Participants: 919 383 adults with same-day measures of ACR and PCR or dipstick protein.Measurements: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g).Results: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR.Limitation: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample.Conclusion: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis.</div

Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets

Background: Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. “CKD Patch” is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. Methods: Utilizing data from 4,143,535 adults from 35 datasets, we developed several “CKD Patches” incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Dc-statistic 0.027 [95% CI 0.018 0.036] and 0.010 [0.007 0.013] and categorical net reclassification improvement 0.080 [0.032 0.127] and 0.056 [0.044 0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89 3.40) in very high-risk CKD (e.g., eGFR 30 44 ml/min/ 1.73m2 with albuminuria 30 mg/g), 1.86 (1.48 2.44) in high-risk CKD (e.g., eGFR 45 59 ml/min/1.73m2 with albuminuria 30 299 mg/g), and 1.37 (1.14 1.69) in moderate risk CKD (e.g., eGFR 60 89 ml/min/ 1.73m2 with albuminuria 30 299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37 1.81), 1.24 (1.10 1.54), and 1.21 (0.98 1.46). Interpretation: The “CKD Patch” can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available

Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes.

ObjectiveTo predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.Research design and methodsIn this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or ResultsThere were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts.ConclusionsNovel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR