Thromb Haemost 2010; 103(06): 1203-1209
DOI: 10.1160/TH09-08-0595
Platelets and Blood Cells
Schattauer GmbH

Validation of claims-based diagnostic codes for idiopathic thrombotic thrombocytopenic purpura in a commercially-insured population

Peter M. Wahl
1   HealthCore, Inc., Wilmington, Delaware, USA
,
Deirdra R. Terrell
2   Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
,
James N. George
2   Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
,
J. Keith Rodgers
1   HealthCore, Inc., Wilmington, Delaware, USA
,
Lynn Uhl
3   Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
,
Spero Cataland
4   Ohio State University, Columbus, Ohio, USA
,
Rhonda L. Bohn
1   HealthCore, Inc., Wilmington, Delaware, USA
› Author Affiliations
Financial support: Funding for this manuscript was provided by Baxter Healthcare International.
Further Information

Publication History

Received: 26 August 2009

Accepted after major revision: 29 January 2010

Publication Date:
22 November 2017 (online)

Summary

It was the purpose of the present study to validate administrative claims codes for idiopathic thrombotic thrombocytopenic purpura (TTP) in a commercially-insured US population. Patients with at least one medical claim with ICD-9 code 446.6X between 1/1/2001 and 5/31/2008 were identified in the HealthCore Integrated Research Database™ (HIRD). A chart abstraction form was developed to enable case determination for patients identified by the claims code. Two clinical experts, not involved in the design of the study, reviewed the abstracted medical record data and determined whether definite evidence supporting the diagnosis of TTP was present. The positive predictive value (PPV) of the claims coding algorithm for cases assessed by both reviewers was computed. The claims algorithm was further refined and the PPV of the refined algorithm was computed. One hundred eighty-nine abstracted charts were reviewed by two clinical experts; 86 were assessed to have definite evidence supporting the diagnosis of TTP (PPV 45.5% [86/189; 95% confidence interval (CI), 38.3–52.9%]). Refinement of the claims algorithm first included the use of plasma exchange treatment, resulting in 103 potential cases, of which 67 were assessed to have definite evidence supporting the diagnosis of TTP (PPV 65.0%; 95% CI, 55.0–74.2%). Further refinement of the claims algorithm ruled out alternative diagnoses that may mimic TTP; 34 were assessed to have definite evidence supporting the diagnosis of TTP (PPV 72.3% [34/47; 95% CI, 57.4–84.4%]).Our findings demonstrate the difficulty of confirming the diagnosis of rare disorders that lack definite diagnostic criteria, and indicate that more complex claims coding algorithms are necessary for identifying these disorders.

 
  • References

  • 1 George JN. Thrombotic thrombocytopenic purpura. N Engl J Med 2006; 354: 1927-1935.
  • 2 Sadler JE. Von Willebrand factor, ADAMTS13, and thrombotic thrombocytopenic purpura. Blood 2008; 112: 11-18.
  • 3 Hawkins BM, Abu-Fadel M, Vesely SK. et al. Clinical cardiac involvement in thrombotic thrombocytopenic purpura: a systematic review. Transfusion 2008; 48: 382-392.
  • 4 Kennedy AS, Lewis QF, Scott JG. et al. Cognitive deficits after recovery from thrombotic thrombocytopenic purpura. Transfusion 2009; 49: 1092-1101.
  • 5 Terrell DR, Williams LA, Vesely SK. et al. The incidence of thrombotic thrombocytopenic purpura-hemolytic uremic syndrome: all patients, idiopathic patients, and patients with severe ADAMTS-13 deficiency. J Thromb Haemost 2005; 03: 1432-1436.
  • 6 Raiford DS, Pérez Gutthann S, García Rodríguez LA. Positive predictive value of ICD-9 codes in the identification of cases of complicated peptic ulcer disease in the Saskatchewan hospital automated database. Epidemiology 1996; 07: 101-104.
  • 7 Tirschwell DL, Longstreth WT. Validating administrative data in stroke research. Stroke 2002; 33: 2465-2470.
  • 8 Kiyota Y, Schneeweiss S, Glynn RJ. et al. Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J 2004; 148: 99-104.
  • 9 Birman-Deych E, Waterman AD, Yan Y. et al. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care 2005; 43: 480-485.
  • 10 Roumie CL, Mitchel E, Gideon PS. et al. Validation of ICD-9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data. Pharmacoepidemiol Drug Saf 2008; 17: 20-26.
  • 11 Varas-Lorenzo C, Castellsague J, Stang MR. et al. Positive predictive value of ICD-9 codes 410 and 411 in the identification of cases of acute coronary syndromes in the Saskatchewan Hospital automated database. Pharmacoepidemiol Drug Saf 2008; 17: 842-852.
  • 12 Segal S, Powe NR. Accuracy of identification of patients with immune thrombocytopenic purpura through administrative records: A data validation study. Amer J Hematol 2004; 75: 12-17.
  • 13 Fleiss JL. The Design and Analysis of Clinical Experiments. Wiley, John & Sons, Inc.; New York: 1986
  • 14 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-174.
  • 15 Newton HJ. Stata Technical Bulletin. Stata Corporation; ISSN 1097–8879, STB-59; January 2001. pp. 9-11.