Chronic kidney disease identification in a high-risk urban population: does automated eGFR reporting make a difference?
Whether automated estimated glomerular filtration rate (eGFR) reporting for patients is associated with improved provider recognition of chronic kidney disease (CKD), as measured by diagnostic coding of CKD in those with laboratory evidence of the disease, has not been explored in a poor, ethnically diverse, high-risk urban patient population. A retrospective cohort of 237 adult patients (≥ 20 years) with incident CKD (≥ 1 eGFR ≥ 60 ml/min/1.73 m(2), followed by ≥ 2 eGFRs <60 ml/min/1.73 m(2) ≥ 3 months apart)-pre- or post automated eGFR reporting-was identified within the San Francisco Department of Public Health Community Health Network (January 2005-July 2009). Patients were considered coded if any ICD-9-CM diagnostic codes for CKD (585.x), other kidney disease (580.x-581.x, 586.x), or diabetes (250.4) or hypertension (403.x, 404.x) CKD were present in the medical record within 6 months of incident CKD. Multivariable logistic regression was used to obtain adjusted odds ratios (ORs) for CKD coding. We found that, pre-eGFR reporting, 42.5 % of incident CKD patients were coded for CKD. Female gender, increased age, and non-Black race were associated with lower serum creatinine and lower prevalence of coding but comparable eGFR. Prevalence of coding was not statistically significantly higher overall (49.6 %, P = 0.27) or in subgroups after the institution of automated eGFR reporting. However, gaps in coding by age and gender were narrowed post-eGFR, even after adjustment for sociodemographic and clinical characteristics: 47.9 % of those <65 and 30.3 % of those ≥ 65 were coded pre-eGFR, compared to 49.0 % and 52.0 % post-eGFR (OR = 0.43 and 1.16); similarly, 53.2 % of males and 25.4 % of females were coded pre-eGFR compared to 52.8 % and 44.0 % post-eGFR (OR 0.28 vs. 0.64). Blacks were more likely to be coded in the post-eGFR period: OR = 1.08 and 1.43 (P (interaction) > 0.05). Automated eGFR reporting may help improve CKD recognition, but it is not sufficient to resolve under identification of CKD by safety net providers.
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA. laura.plantinga@emory.edu
Laura C
LC
Plantinga
Delphine S
DS
Tuot
Vanessa
V
Grubbs
Chi-yuan
CY
Hsu
Neil R
NR
Powe
D000328:Adult; D000367:Age Factors; D000368:Aged; D059019:Clinical Coding / Q000592:standards* / Q000706:statistics & numerical data; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology*; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000592:standards* / Q000706:statistics & numerical data; D008875:Middle Aged; D015995:Prevalence; D051436:Renal Insufficiency, Chronic / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D012189:Retrospective Studies; D018570:Risk Assessment; D012495:San Francisco / Q000453:epidemiology; D012737:Sex Factors; D014505:Urban Population / Q000706:statistics & numerical data*
D003404:Creatinine