The Positive Predictive Value of a Single Assessment of Glomerular Filtration Rate under 60mL/min in the Diagnosis of Chronic Kidney Disease

Transcription

1 The Positive Predictive Value of a Single Assessment of Glomerular Filtration Rate under 60mL/min in the Diagnosis of Chronic Kidney Disease Nikhil Satchidanand PhD 1, Matthew Withiam-Leitch MD PhD 1, Miriam Dickinson PhD 2, Wilson Pace MD 2, Caroline Bublitz-Emsermann PhD 2, Geoffrey M. Allen 1, Min Yang MD PhD 1, Joseph Vassalotti MD 3, Pradeep Arora MD 1, Patrick Glasgow MD 1, Chester Fox MD 1 1 University at Buffalo State University of New York 2 University of Colorado, Denver 3 Mount Sinai Hospital Corresponding Author: Chester Fox, MD University at Buffalo, SUNY Department of Family Medicine 77 Goodell St. Suite 220 Buffalo, NY Cfox@buffalo.edu Telephone: (716) Abstract Word Count: 244 Manuscript Body Word Count:

2 SUPPORT TRANSLATE CKD is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (1R01DK090407) (1R01DK A1). FINANCIAL DISCLOSURES: Nikhil Satchidanand: NONE Matthew Withiam-Leitch: NONE Miriam Dickinson: NONE Wilson Pace: NONE Caroline Bublitz-Emsermann: NONE Geoffrey M. Allen: NONE Min Yang: NONE Joseph Vassalotti: NONE Pradeep Arora: NONE Patrick Glasgow: NONE Chester Fox: NONE 2

3 ABSTRACT The positive predictive value (PPV) of a single assessment of estimated glomerular filtration rate (egfr) in the diagnosis of chronic kidney disease (CKD) is not known. We conducted a descriptive analysis to determine the PPV of a single assessment of egfr among adults with at least one egfr below 60 ml/min in their lifetime, utilizing the Distributed Area Research and Therapeutics Network (DARTNet) CKD natural history dataset. In all 47,104 adults, representing 113 practices in the U.S. were included. Proportions of patients in egfr categories at baseline were calculated using the following categories: (1) ml/min; (2) ml/min; (3) ml/min; (4) ml/min; (5) ml/min. Comparisons were then made between baseline and endpoint to identify patients who had a follow-up egfr that remained below 60 ml/min. The proportions of patients in each egfr category were compared baseline to endpoint using crosstabulations. To test the proposed cut-point, a visual inspection of the proportions of patients who had an egfr that remained below 60 ml/min was conducted, utilizing the cutpoints that included the highest cumulative proportion of patients. The sensitivity and specificity of that cut-point were calculated. A cut-point of 45 ml/min was identified yielding a PPV of 95% with a sensitivity of 34% and a specificity of 90%. A valid cut-point to screen for CKD was identified. This cut-point may prove important to screening early for CKD while reducing the burden on the healthcare system and patients suspected of having CKD. 3

4 INTRODUCTION The U.S. Preventive Services Task Force (USPSTF) Recommendation Statement for Screening of CKD states that adequate evidence does not exist to support screening of asymptomatic adults for CKD. 1 The American College of Physicians Guideline on CKD stages 1-3 agrees that the validity of a single assessment of egfr to screen for CKD is not known.(1, 2) The question remains whether a single egfr below 60 ml/min alone, independent of data on other risk factors, can accurately predict CKD in adults. In other words, the PPV of a single low egfr for CKD at present is not known.(2) Importantly, targeted screening is recommended for patients at higher risk for CKD, such as those with diabetes, hypertension, older age and obesity.(3-5) Given the aging population and increasing prevalence of obesity-related disease, the future of screening for CKD will expand to populations not currently recommended. The serum creatinine test is widely preformed in primary care, making the predictive value of a single low egfr clinically relevant and manageable. Using a large, longitudinal national primary care dataset, this study addresses the impact of opportunistic testing in primary care for detecting CKD by egfr. We anticipated that among older patients (> 60 years), a viable cut-point could be assessed in a future prospective study. This finding is important to how we identify and classify patients, while minimizing the burden when diagnosing CKD. This is important to reducing the cost of care by more precisely identifying the most appropriate patients who would benefit, while minimizing use of unnecessary laboratory tests. Additionally, understanding the utility of this assessment may influence the adoption of the 2012 Kidney Disease Improving Global Outcomes (KDIGO) Guidelines. 4

5 METHODS The source for this analysis is the Distributed Area Research and Therapeutics Network (DARTNet) CKD natural history dataset. This was a data extract of electronic health record (EHR) data from 113 practices from over 20 different States from New York to California. It included a number federally qualified health Centers and some predominantly African American and Hispanic practices. No Native American practices were part of this study. This study was deemed exempt by the Institutional Review Board (IRB) of the State University of New York at Buffalo. Completion of data use agreements were obtained from the DARTNet Institute, which maintains business associate agreements with all sites included in this dataset. Then, the extracted data from each clinical organization were entered into a clinical data repository (CDR) maintained at each site for research and clinical support activities. These data were moved to a central location and merged into a single dataset. At this time final local personal health identifiers were removed and replaced. For instance, the local patient identifier (in this case the local CDR patient identification number) was converted to a random global universal identifier (GUID). The data underwent a series of data checks to ensure no personal health identifiers were retained in data fields and the final limited dataset was securely transferred to the researcher team. The dataset included all periods of time included in the local organizations EHRs, which varied from several years to over 10 years. The study data were captured 11/1/2011 and had clinical data going as far back as 2003 with an average of 3.5 years of EHR data. The medical organizations included 19 different EHR products with all data from each system standardized across sites at the time of local extraction from the EHR to the local CDR. Patients were included in the dataset if they had one egfr in their lifetime under 60 ml/min. Data elements 5

6 included in the data extraction are detailed in Table 1. For this analysis, creatinine, along with age and sex, (race was not available in this dataset), were used to calculate the egfr. Since most of the creatinines were not standardized in this data extract, the Modification of Diet in Renal Disease (MDRD) equation for non-standardized creatinine measures was utilized(6). Table 1: Data Elements included in the CKD Natural History Dataset Alanine Aminotransferase Aspartate Aminotransferase Gender Age Smoking Status Height Weight BMI Total # Physician Visits Hemoglobin Urine/Albumin Creatinine Ratio HbA1c 25 OH Vitamin D Serum Phosphorus PTH Intact All Medications Diagnoses Active Diagnoses Inactive Blood Pressure Estimated GFR Creatinine HDL LDL Patient Inclusion Criteria Patient inclusion criteria were if they had one egfr in their lifetime below 60 ml/min alone; yielding 69,817 patients. Patients without a valid gender and birth year were excluding, yielding 63,215 patients. Further, only patients years were included, resulting in a total of 62,950. Estimated GFR was then calculated from creatinine. Only those patients having a baseline egfr under 60 ml/min were included. Finally a follow-up egfr was calculated at least 90 days later, yielding a total of 47,104 patients (Figure 1). 6

7 Figure 1: Flow of Patient Inclusion Criteria 69,817 Patients with one egfr < Patients excluded 63,215 Patients with valid gender and birth year 62,950 Patients with age years 265 Patients excluded 47,104 Patients with two valid egfrs under 60, at least 90 days apart 15,846 patients excluded To calculate egfr from creatinine tests, the following procedure was utilized: Modified Diet in Renal Disease (MDRD) Study equation (unstandardized): 186 x (Creat in mg/dl) x (Age) x (0.742 if female) x (1.210 if black) 7

8 Analysis Descriptive data were assessed by mean + SD for continuous variables and crosstabulation for categorical variables. Patients were categorized by egfr cut-points; 1) ml/min; 2) ml/min; 3) ml/min; 4) ml/min; 5) ml/min. The proportion of patients in each category was compared to the proportion of patients having an egfr that remained below 60 ml/min at endpoint. Subgroup analyses by gender and age, and diabetes (DM) and hypertension (HTN) status were then performed using the same egfr categories, comparing baseline to endpoint. To test the cut-point a visual inspection of the proportions of patients who had an egfr that remained below 60 ml/min at endpoint was conducted, utilizing the cut-points that cumulatively involved the highest proportion of patients. To assess the validity of this cut-point, the sensitivity, specificity, and PPV were calculated. All analyses were conducted using IBM SPSS Version 22 (Armonk, NY). RESULTS In all, 61% of patients were female (mean age: 72 yrs.). Additionally, 31% had DM, 77% HTN, and 28% had DM and HTN. Among patients with a baseline egfr between 15 and 30 ml/min, 96.1% had an endpoint egfr that remained below 60 ml/min. Among patients with a baseline egfr between 31 and 45 ml/min, 94.2% had an endpoint egfr below 60 ml/min. (Table 2). Very similar results were discovered when comparing males and females (Table 3). The results of the subgroup analyses by age (< 45 yrs., yrs., > 60 yrs.) reveal that among patients who were < 45 years of age and in the ml/min and ml/min categories had the highest PPV of 83.0% and 83.6%, respectively. In the same age group, 8

9 patients in the baseline egfr categories of 46-50, 51 55, and ml/min, demonstrated a PPV of 55.1%, 42.1%, and 60.6%, respectively. Among patients in the years age group, similar results were discovered, with PPVs equal to 89.5%, 88.2%, 62.3%, 65.5% and 57.1%. Finally, for patients within the > 60 years age group, the PPV ranged from a high of 97.3% to a low of 62.4%, from the ml/min category to the ml/min category. Table 2: Proportion of Patients with a Baseline egfr in each category who remained below 60 ml/min/1.73 m 2 at Endpoint GFR Range n % ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m Table 3: Proportion of Patients with a Baseline egfr in each category who remained below 60 ml/min/1.73 m 2 at Endpoint by Sex Males Females GFR Range n % ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m ml/min/1.73 m

10 Analysis by Diabetes and Hypertension Status Sub-group analysis by DM status (n=14,511, 31%) revealed similar results with PPVs ranging from a high of 98.5% among patients with a beginning egfr of ml/min, and a low of 69.4% for the egfr range of ml/min. Sub-group analysis by HTN status (n=36,436, 77%) revealed very similar results with PPVs ranging from a high of 97.5% among patients with a beginning egfr of ml/min, and a low of 68.7% for the egfr range of ml/min. Among patients with DM and HTN (n = 13,108, 28%) results were similar with PPVs ranging from a high of 98.5% among patients with a beginning egfr of ml/min, and a low of 69.4% for the egfr range of ml/min. Identification of an egfr Cut-point Upon visual dtermination of the proportion of patients with an egfr remaining below 60 ml/min, within each beginning egfr category, a cut-point of < 45 ml/min was identified. This cut-point was then tested by calculating the PPV, specificity and sensitivity. The PPV of a single assessment of egfr below 45 ml/min was equal to 95%, with a specificity of 90% and sensitivity equal to 34%. Further analyses by age identified the same cut-point of < 45 ml/min, with a PPV equal to 95%, a specificity of 94%, and a sensitivity of 32%. DISCUSSION egfr is the most widely used assessment of kidney function, globally adopted to define and stratify CKD into stages by severity. The egfr is also used to monitor disease progression.(7) Adoption of the 2002 National Kidney Foundation (NKF) KDOQI guidelines sparked major changes to routine laboratory reporting to include egfr with decision supports regarding CKD 10

11 classification.(8) This new definition of generic disease resulted in increases in nephrology referral for evaluation of presumed CKD whenever an egfr of less than 60 ml/min was reported.(7, 9) However, the validity and utility of this stand alone measure independent of data on other risk factors, has not been extensively studied. Critics of the 2002 guidelines also raise concerns about misdiagnosis and over-diagnosis, and misclassification of stage based on egfr.(9-14) For this reason we sought to examine the utility of a single assessment of egfr to predict CKD. Using a sufficiently large dataset we identified a cut-point of < 45 ml/min for egfr that could be proposed in the prediction of CKD patients. We calculated a PPV equal to 95%. Patients with an egfr less than 45 ml/min should then be prioritized and monitored more closely for progression. In a time when financial resources are limited and there is a focus on reducing the cost of care while maintaining quality, this finding will inform clinical decision making with regard to diagnosis and monitoring of CKD. Adoption of the 2002 guidelines has had a profound impact on awareness, population health management, patient care, and public health policy surrounding CKD.(10) Clinical research on kidney disease has grown exponentially and the adoption of a unified nomenclature; chronic kidney disease, allows for comparison of outcomes across studies.(10, 15) One of the major aims of the 2002 guidelines was to facilitate diagnosis of patients with early signs of CKD.(9, 13, 16-18) The ability to detect CKD early can have important implications on disease prognosis. Thus, early detection remains of critical importance.(2, 9-11, 19-21) Our findings suggest a valid egfr cut-point to be used for predicting CKD. It is currently unclear if the PPV of a single assessment of egfr can be effective in diagnosing CKD.(10) Among more than 40,000 patients, it was found that within each egfr category at 11

12 baseline, between 61% and 96% of patients had an endpoint egfr that remained below 60 ml/min. Similar results were found for gender, indicating that effect of gender is minimal, likely allowing for a uniform cut-point between genders. It is important to note that this cutpoint has clinical significance reflected in a meta-analysis by Sharma et al. (2010) indicating that patients with an egfr below 45 ml/min demonstrated higher overall and cardiovascular mortality rates than patients with an egfr between 45 and 60 ml/min.(22) Analysis by age group (< 45 yrs., yrs., > 60 yrs.) reveals similar findings among the oldest patient sub-group with a beginning egfr of ml/min, with reduced PPVs in the yrs. and < 45 age groups. Overall, that older patients with stage 3A, 3B, and 4 demonstrated higher PPVs is consistent with previous findings of significant differences in CKD progression and rates of end-stage renal disease (ESRD) by age. O Hare and colleagues (2007) found that among those with comparable levels of egfr, older patients had higher rates of death and lower rates of ESRD than younger patients. Further, the level of egfr below which the risk of ESRD exceeded the risk of death varied by age, ranging from 45 ml/min for 18 to 44 year old patients, to 15 ml/min for 65 to 84 year old patients. In the O Hare study, for patients 85 years or older, the risk of death always exceeded the risk of ESRD. They hypothesized that this may show that older patients are living with a slowly progressive CKD, perhaps indicative of a more stage-stable disease(23). In our study, a higher proportion of the oldest patients had a follow-up egfr below 60 ml/min. These findings confirm that age has a significant impact on disease progression but our results also demonstrate that this does not significantly impact ability to screen for CKD using a single assessment of egfr. In our analysis, while the highest PPV was demonstrated in the > 60 yrs. age group, patients across all age groups had a mean PPV equal to 90%, when examining patients with stage 3A, 3B, and 4. 12

13 When examined in totality, our findings reveal a cut-point of < 45 ml/min for baseline estimation of egfr to predict CKD. Fewer patients with baseline egfrs above 45 but below 60 ml/min progress into CKD. This cut-point is consistent with stages 3A, 3B, and 4 CKD. As stated by Eckardt et al. (2009), several critics of the 2002 KDOQI guidelines question whether a single egfr below 60 ml/min alone, in the absence of other markers of kidney disease is sufficient to define CKD.(9, 11-13, 17, 24) This criticism has resulted in efforts to revise the classification system. Based upon the examination of the PPV of a single assessment of GFR alone, our results suggest that patients with a beginning egfr less than 45 ml/min and who are 60 years of age or older, can be successfully screened for CKD with a single assessment of egfr, necessitating the need for close follow-up to monitor progression. Our current analysis continues the discussion to better understand the utility of screening for CKD using a single assessment of egfr. The similar results revealed by sub-group analysis by DM and HTN status indicate that presence of DM and/or HTN does not exert a differential effect in predicting CKD progression. While the DARTNet dataset allowed for a robust sample size within a realistic context of the practice of primary care, there are some limitations that must be recognized. This analysis sought to determine the PPV of a single assessment of egfr for use in screening for CKD. However, the DARTNet dataset contains only patients having an egfr below 60 ml/min at some point in their lifetime as the baseline assessment. Therefore, a true control group is not present in the dataset. This analysis proposed a cut-point among adult patients for egfr that might be tested. However, patient demographic data that might impact CKD progression were limited to patient age and gender, and co-morbidity data were limited to diabetes and hypertension. Other more complex patient contextual factors were not included in this national 13

14 dataset, and therefore could not be investigated. However, an effective screening for egfr cutpoint should not necessarily rely upon more complex patient contextual factors, thus maximizing generalizability and ease of use. The data collected in the DARTNet national dataset is sufficient to identify this cut-point for screening purposes. Our results indicate that a single cut-point was identified that might inform future efforts to screen for CKD. Another issue that must be recognized is that of missing data. The nature of the DARTNet national dataset, merging EMR data from many primary care practices throughout the nation, highlights the relatively high proportion of missing values for variables such as race and ethnicity. Therefore, such factors could not be included in our analysis. The lack of race data clearly affects the egfr but since this was a comparison of one test to a subsequent one in the same person, that should not have affected our findings. The DARTNet dataset provides a large population of over 40,000 patients cared for in a geographically diverse national primary care practice environment. This setting is probably more generalizable to routine practice than large university academic clinical research settings. Additionally, the clinical implications of our findings are significant and important to highlight given the ubiquitous application of the 2002 KDOQI Guidelines. One of the major objectives of the 2002 KDOQI Guidelines was to facilitate easy, non-intrusive early screening for CKD, prior to the need for renal replacement therapy. Our findings suggest a valid cut-point to screen for CKD with a PPV of 95%. This cut-point may prove important to the efforts to screen early for CKD while reducing the burden on the healthcare system and patients suspected of having CKD. 14

15 Acknowledgments This study was funded by National Kidney Foundation and the National Institutes of Health (NIDDK R-01 DK 90407). The findings from this study were presented as a poster for Translational Science 2015, Washington D.C. 15

16 REFERENCES 1. Moyer VA. Screening for chronic kidney disease: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2012;157(8): Qaseem A, Hopkins JRH, Sweet DE, Starkey M, Shekelle P. Screening, Monitoring, and Treatment of Stage 1 to 3 Chronic Kidney Disease: A Clinical Practice Guideline From the American College of Physicians. Annals of Internal Medicine. 2013;159(12): doi: / Inker LA, Astor BC, Fox CH, Isakova T, Lash JP, Peralta CA, et al. KDOQI US Commentary on the 2012 KDIGO Clinical Practice Guideline for the Evaluation and Management of CKD. American Journal of Kidney Diseases. 2014;63(5): doi: 4. Carville S, Wonderling D, Stevens P. Early identification and management of chronic kidney disease in adults: summary of updated NICE guidance :15: National Guideline C. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease Rockville MD: Agency for Healthcare Research and Quality (AHRQ); [10/16/2014]. Available from: 6. Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the Modification of Diet in Renal Disease and Cockcroft Gault Equations in the Estimation of GFR in Health and in Chronic Kidney Disease. Journal of the American Society of Nephrology. 2005;16(2): doi: /asn Glassock RJ. Referrals for chronic kidney disease: Real problem or nuisance? JAMA. 2010;303(12): doi: /jama Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro IIIAF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate. Annals of Internal Medicine. 2009;150(9): doi: / Glassock RJ, Winearls C. Screening for CKD with egfr: Doubts and Dangers. Clinical Journal of the American Society of Nephrology. 2008;3(5): doi: /cjn Eckardt KU, Berns JS, Rocco MV, Kasiske BL. Definition and classification of CKD: the debate should be about patient prognosis a position statement from KDOQI and KDIGO. Am J Kidney Dis. 2009;53(6): Eknoyan G. Kidney disease: Wherefore, whence, and whereto? Kidney Int. 0000;71(6): Eknoyan G, Lameire N, Barsoum R, Eckardt K U, Levin A, Levin N, et al. The burden of kidney disease: Improving global outcomes. Kidney Int. 2004;66(4): Glassock RJ, Winearls C. An epidemic of chronic kidney disease: fact or fiction? Nephrology Dialysis Transplantation. 2008;23(4): doi: /ndt/gfn Rule AD, Rodeheffer RJ, Larson TS, Burnett JC, Jr., Cosio FG, Turner ST, et al. Limitations of estimating glomerular filtration rate from serum creatinine in the general population. Mayo Clin Proc. 2006;81(11): K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 Suppl 1):S Bauer C, Melamed ML, Hostetter TH. Staging of chronic kidney disease: time for a course correction. J Am Soc Nephrol. 2008;19(5): Glassock RJ, Winearls C. The global burden of chronic kidney disease: how valid are the estimates? Nephron Clin Pract. 2008;110(1): Moynihan R, Glassock R, Doust J. Chronic kidney disease controversy: how expanding definitions are unnecessarily labelling many people as diseased. BMJ. 2013;347. doi: /bmj.f

17 19. Brown WW, Peters RM, Ohmit SE, Keane WF, Collins A, Chen S C, et al. Early detection of kidney disease in community settings: the kidney early evaluation program (KEEP). American Journal of Kidney Diseases. 2003;42(1): doi: (03) de Lusignan S, Chan T, Stevens P, O'Donoghue D, Hague N, Dzregah B, et al. Identifying patients with chronic kidney disease from general practice computer records. Family Practice. 2005;22(3): doi: /fampra/cmi Locatelli F, Vecchio LD, Pozzoni P. The importance of early detection of chronic kidney disease. Nephrology Dialysis Transplantation. 2002;17(suppl 11):2 7. doi: /ndt/17.suppl_ Sharma P, McCullough K, Scotland G, McNamee P, Prescott G, MacLeod A, et al. Does stage 3 chronic kidney disease matter?: A systematic literature review. The British Journal of General Practice. 2010;60(575):e266 e76. doi: /bjgp10X PubMed PMID: PMC O'Hare AM, Choi AI, Bertenthal D, Bacchetti P, Garg AX, Kaufman JS, et al. Age affects outcomes in chronic kidney disease. J Am Soc Nephrol. 2007;18(10): Poggio ED, Rule AD. Can we do better than a single estimated GFR threshold when screening for chronic kidney disease? Kidney Int. 2007;72(5):