Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure.

Administrative and claims databases may be useful for the study of acute renal failure (ARF) and ARF that requires dialysis (ARF-D), but the validity of the corresponding diagnosis and procedure codes is unknown. The performance characteristics of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for ARF were assessed against serum creatinine-based definitions of ARF in 97,705 adult discharges from three Boston hospitals in 2004. For ARF-D, ICD-9-CM codes were compared with review of medical records in 150 patients with ARF-D and 150 control patients. As compared with a diagnostic standard of a 100% change in serum creatinine, ICD-9-CM codes for ARF had a sensitivity of 35.4%, specificity of 97.7%, positive predictive value of 47.9%, and negative predictive value of 96.1%. As compared with review of medical records, ICD-9-CM codes for ARF-D had positive predictive value of 94.0% and negative predictive value of 90.0%. It is concluded that administrative databases may be a powerful tool for the study of ARF, although the low sensitivity of ARF codes is an important caveat. The excellent performance characteristics of ICD-9-CM codes for ARF-D suggest that administrative data sets may be particularly well suited for research endeavors that involve patients with ARF-D.

[1]  A R Levy,et al.  Coding accuracy of hospital discharge data for elderly survivors of myocardial infarction. , 1999, The Canadian journal of cardiology.

[2]  J. Gerberding,et al.  Incidence of end-stage renal disease among persons with diabetes--United States, 1990-2002. , 2005, MMWR. Morbidity and mortality weekly report.

[3]  P. Roderick,et al.  Trends in adult renal replacement therapy in the UK: 1982-2002. , 2005, QJM : monthly journal of the Association of Physicians.

[4]  C M Ashton,et al.  International Classification of Diseases, 9th Revision, Clinical Modification codes in discharge abstracts are poor measures of complication occurrence in medical inpatients. , 1997, Medical care.

[5]  J. Spinelli,et al.  Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? , 2000, Journal of clinical epidemiology.

[6]  J. Cohen,et al.  Hospital-acquired renal insufficiency: a prospective study. , 1983, The American journal of medicine.

[7]  L. Goldstein Accuracy of ICD-9-CM coding for the identification of patients with acute ischemic stroke: effect of modifier codes. , 1998, Stroke.

[8]  D. K. Williams,et al.  Assessing hospital-associated deaths from discharge data. The role of length of stay and comorbidities. , 1988, JAMA.

[9]  J. Avorn,et al.  Identification of individuals with CKD from Medicare claims data: a validation study. , 2005, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[10]  J P Rissing,et al.  Physician and coding errors in patient records. , 1985, JAMA.

[11]  L I Iezzoni,et al.  Coding of acute myocardial infarction. Clinical and policy implications. , 1988, Annals of internal medicine.

[12]  D. Lezotte,et al.  Uncomplicated acute renal failure and hospital resource utilization: a retrospective multicenter analysis. , 2005, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[13]  D. Mark,et al.  Bias in the coding of hospital discharge data and its implications for quality assessment. , 1994, Medical care.

[14]  E. Fisher,et al.  Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? , 1992, JAMA.

[15]  Katrin Uhlig,et al.  Low rates of testing and diagnostic codes usage in a commercial clinical laboratory: evidence for lack of physician awareness of chronic kidney disease. , 2005, Journal of the American Society of Nephrology : JASN.

[16]  E. McCarthy,et al.  Declining mortality in patients with acute renal failure, 1988 to 2002. , 2006, Journal of the American Society of Nephrology : JASN.

[17]  Joseph V Bonventre,et al.  Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. , 2005, Journal of the American Society of Nephrology : JASN.

[18]  S. Jencks,et al.  Accuracy in recorded diagnoses. , 1992, JAMA.

[19]  Margaret C Fang,et al.  Advanced Age, Anticoagulation Intensity, and Risk for Intracranial Hemorrhage among Patients Taking Warfarin for Atrial Fibrillation , 2004, Annals of Internal Medicine.

[20]  J. Craig,et al.  Long-term survival of children with end-stage renal disease. , 2004, The New England journal of medicine.

[21]  Peter C Austin,et al.  Comparison of Coding of Heart Failure and Comorbidities in Administrative and Clinical Data for Use in Outcomes Research , 2005, Medical care.

[22]  D. Schaubel,et al.  Trends in mortality on peritoneal dialysis: Canada, 1981-1997. , 2000, Journal of the American Society of Nephrology : JASN.

[23]  S. Hou,et al.  Hospital-acquired renal insufficiency. , 2002, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[24]  L. Price,et al.  Epidemiology and outcomes of acute renal failure in hospitalized patients: a national survey. , 2005, Clinical journal of the American Society of Nephrology : CJASN.

[25]  A S Kosinski,et al.  A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. , 1999, Journal of clinical epidemiology.

[26]  H. Quan,et al.  Validity of Procedure Codes in International Classification of Diseases, 9th revision, Clinical Modification Administrative Data , 2004, Medical care.

[27]  C. Bombardier,et al.  Validity of rheumatoid arthritis diagnoses listed in the Saskatchewan Hospital Separations Database. , 1993, Journal of clinical epidemiology.