1. Studying Health Care: Some ICD-10 Tools Hude Quan, Nicole Fehr, Leslie Roos University of Calgary and Manitoba Centre for Health Policy

2. Purpose To highlight and provide an overview of currently available ICD-10 tools

3. ICD-10 Background (International Classification of Diseases, 10 th Revision) International coding guidelines for health problemsand procedures Released by the World Health Organization (WHO) in 1992, replacing ICD-9 Introduced alphanumeric categorization

4. Developing and Validating ICD translation tools 1.Quan H et al. (2005): Translated the Charlson and Elixhauser comorbidity indexed into ICD-10Quan H et al 2. Halfon P et al. (2002): Developed a measure of potentially avoidable readmissionsHalfon P et al 3. Kokotailo et al. (2005): Compared the performance of ICD-9 and ICD-10 stroke and stroke risk factor codesKokotailo et al

5. 1.Adjusting for Comorbidity Quan et al.(2005) Quan et al Objectives: Develop and assess ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data.

6. Data Used Calgary Health Region hospital discharge data Up to 16 diagnostic coding fields First admission only 18 years of age and older April 2001 to March 2003

7. Algorithms Developed and Evaluated Charlson Deyo’s ICD-9-CM ICD-10 Enhanced ICD-9-CM Elixhauser Elixhauser’s Original ICD-9-CM Elixhauser AHRQ-Web ICD-9-CM ICD-10 Enhanced ICD-9-CM

8. Halfon et alHalfon et al.Sundararajan et alSundararajan et al. ICD-10 codes List 1: Combined list of ICD-10 codes from previous research Developing Charlson Algorithms

9. Coder ACoder B ICD-10 codes List 2: A consensus between 2 coders of all related ICD-10 codes Developing Charlson Algorithms

10. List 3: Re-coded Comorbidities Coder ACoder B ICD-10 codes

11. List 1 Combined list of ICD-10 codes ICD-10 codes descriptions Physician review Final codes List 2List 3 Developing Charlson Algorithms

12. Charlson Comorbidities Charlson Comorbidity ICD-10 Codes Congestive heart failure I09.9, I11.0, I13.0, I13.2, I25.5, I42.0, I42.5–I42.9, I43.x, I50.x, P29.0 Rheumatic disease M05.x, M06.x, M31.5, M32.x–M34.x, M35.1, M35.3, M36.0 AIDS/HIVB20.x–B22.x, B24.x

13. Developing Elixhauser Algorithms List 1: Coded using ICD-10-CA computerize d code finder List 2: Coded clinical terms taken from ICD-9-CM manual List 3: Coded using cross-table mapping algorithm Combined list of ICD-10 codes Physician review Final codes

14. Elixhauser Comorbidities Elixhauser Comorbidites ICD-10 Codes Hypertension, complicated I11.x–I13.x, I15.x Renal failure I12.0, I13.1, N18.x, N19.x, N25.0, Z49.0– Z49.2, Z94.0, Z99.2 Drug abuseF11.x–F16.x, F18.x, F19.x, Z71.5, Z72.2

15. Performance of Different Coding Algorithms Assessed 1.Conditions present only at admission 2.Conditions present at admission or after admission

16. C-Statistic 1.Compares each coding algorithm’s performance in in- hospital mortality prediction. 2.Measures a model’s ability to discriminate those who die from those who do not die in hospital.

17. Algorithm Performance Charlson Comorbidities

18. Algorithm Performance Elixhauser Comorbidities

19. 2.Readmissions and Adverse Events: Halfon et al.(2002)Halfon et al Objectives: Develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix.

20. Data Used Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland hospital information system data Up to 15 diagnosis codes (ICD-10) Up to 12 interventions (ICD-9-CM) January 1997 to December 1997

21. no yes Potentially avoidable readmissions if within one month after the previous discharge no Algorithm Used to Classify Readmissions

22. The Measure of Potentially Avoidable Readmissions Reliable: Medical record review as gold standard; the algorithmic process has been explicit Relevant: Makes use of a priori medical criteria

23. The Measure of Potentially Avoidable Readmissions Valid: Supported by a relatively high pseudo-R 2 and adjusts for most potential confounding factors. Affordable: Uses routinely collected data

24. Causes of Unforeseen Readmissions for a Previously Known Affection I 1.Complication of surgical care 2.Complication of non-surgical care 3.Drug-related adverse event 4.Premature discharge 5.Discharge with a missing or erroneous diagnosis or therapy

25. Causes of Unforeseen Readmissions for a Previously Known Affection II 6.Other inadequate discharge 7.Failure of post-discharge follow-up care 8.Inadequate patient behavior 9.Relapse or aggravation of a previously known affection 10.Social readmission

26. Complications for which ICD-10 Codes Available in Halfon et al.Halfon et al 1.Related to surgical care 2.Related to a delivery or an abortion 3.Some infections of a surgical site classified elsewhere 4.Drug or radiation-induced disorders 5.Conditions generally resulting from a preexisting disease with multiple accompanying diseases 6.Deep vein thrombosis, pulmonary embolism, and decubitus ulcer

27. 3. Coding Stroke Kokotailo et al. Kokotailo et al Stroke Coding 0.86 Kappa and 90% Sensitivity (ICD-9) vs. 0.89 Kappa and 92% Sensitivity (ICD-10) Code Validation: Patient chart review

28. Cont… Stroke Risk Factor Coding 67% Sensitivity (ICD-9) vs. 58% Sensitivity (ICD-10) Code Validation: Patient chart review

29. Working Productively… Additional material on these indices, as well as information on other research tools, is available through the Manitoba Centre for Health Policy’s Glossary and Concept Dictionary.GlossaryConcept Dictionary

30. Final Thoughts… Taken together, these two papers: 1.Present reliable ICD-10 codes for diagnoses and complications frequently used in health services research 2.Use different methods to validate their work 3.Update popular research tools