Kidney Week

Abstract: PO0515

Classification of Cause of CKD Using ICD-9 and ICD-10 Codes

Session Information

• CKD Health Services Research
  October 22, 2020 | Location: On-Demand
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Chen, Jennifer, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States

Jennifer Chen,

• Blum, Matthew F., Johns Hopkins University School of Medicine, Baltimore, Maryland, United States

Matthew F. Blum,

• Surapaneni, Aditya L., Johns Hopkins School of Public Health, Baltimore, Maryland, United States

Aditya L. Surapaneni,

• Sang, Yingying, Johns Hopkins School of Public Health, Baltimore, Maryland, United States

Yingying Sang,

• Chang, Alex R., Geisinger Health, Danville, Pennsylvania, United States

Alex R. Chang,

• Coresh, Josef, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States

Josef Coresh,

• Grams, Morgan, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States

Morgan Grams,

Background

Current KDIGO guidelines classify CKD using three parameters: glomerular filtration rate (GFR), albuminuria, and cause of disease. While prognosis based on estimated GFR and albuminuria have been studied, less is known about the prevalence of disease etiology in CKD patients. We sought to classify various causes of CKD using billing codes for better assessment of the prevalence and risk implications of disease etiology in CKD staging.

Methods

We categorized cause of CKD with 18 potential etiologies and assigned relevant Internal Classification of Diseases (ICD) 9^th and 10^th revision Clinical Modification codes pertaining to each etiology. We applied the algorithm to two study populations, Johns Hopkins Medicine and Geisinger Health, to assess the prevalence of different etiologies of CKD in large health systems. To validate our CKD classification system, we determined CKD cause among 101 outpatients treated within Johns Hopkins Medicine through internal chart reviews and compared our findings to the classification algorithm generated CKD etiology.

Results

43.3% and 26.4% of patients with eGFR <60 ml/min/1.73 m² in 2016 in the Geisinger and Johns Hopkins study population, respectively, had a billing code used in our classification algorithm. The most prevalent etiologies of CKD in patients with available billing codes at Geisinger were hypertensive nephrosclerosis (27%), diabetic nephropathy (13.6%), obstructive nephropathy (5.2%), and nephritic syndrome (4.9%). In contrast, the most common causes of CKD in the Johns Hopkins cohort were miscellaneous (12%), obstructive nephropathy (6.3%), and non-PKD hereditary disease (3.2%). Chart review revealed 56% concordance between cause of CKD determined by chart review and that by billing code, with higher agreement for polycystic kidney disease, kidney transplant, autoimmune disease, diabetic nephropathy, neoplasm, hypertensive nephrosclerosis, and solitary kidney.

Conclusion

We developed an algorithm for classifying CKD cause by ICD-9 and ICD-10 codes using electronic medical record data; however, validation suggests varying degrees of accuracy across different CKD etiologies.