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Improving Surveillance of Surgical Site Infections Using Administrative Claims Data

Improving Surveillance of Surgical Site Infections Using Administrative Claims Data. Susan S. Huang, MD MPH Associate Professor and Hospital Epidemiologist Division of Infectious Diseases Health Policy Research Institute University of California Irvine School of Medicine. Disclosures: None.

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Improving Surveillance of Surgical Site Infections Using Administrative Claims Data

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  1. Improving Surveillance of Surgical Site Infections Using Administrative Claims Data Susan S. Huang, MD MPH Associate Professor and Hospital Epidemiologist Division of Infectious Diseases Health Policy Research Institute University of California Irvine School of Medicine Disclosures: None

  2. Agenda • Describe limitations of SSI surveillance • Review data supporting claims-based detection of SSI • Use of claims in SSI validation by states • Considerations to change NHSN SSI definitions

  3. Surgical Site Infections • Healthcare-associated infections presumably due to bacterial entry at the time of surgery • 300,000-500,000 SSIs each year in the US • Several billion dollars • High volume, high cost procedures

  4. Surgical Care Improvement Project (SCIP) • High volume, high cost procedures

  5. Surgical Site Infections • 30% states mandate SSI reporting • Texas mandates

  6. CMS Mandates

  7. Partnership for Patients • Goals for 2013 • 40% reduction in healthcare acquired conditions • 20% reduction in readmissions • 9 Harms CLABSI Adverse drug events CAUTI Fall injuries SSI Obstetrical events VAP Pressure ulcers VTE

  8. Value of SSI Surveillance • Patient safety and real-time response • Internal tracking and response • External benchmarking • Inter-hospital comparison • Validity dependent on: • standardized definitions • uniform surveillance methods • case mix adjustment

  9. Issues with SSI Surveillance • Prolonged follow up (30 days/1 year) • Variable dedication of resources by hospitals • More you look, more you find • Non-standardized methodology • Microbiology cultures most common surveillance • Surgeons do not always send cultures • Post discharge – surgeon surveys • Post discharge – patient calls • Unreliable, not sensitive or specific

  10. Options for SSI Surveillance Surveillance Method ? Reasonable Frequency Microbiology cultures Daily – Weekly Re-admissions Weekly – Monthly Re-operations Weekly – Monthly ICD-9/CPT codes Weekly – Monthly Antibiotic usage ? • Goal is to capture SSI issues in time to address them • Timely NHSN reporting

  11. NHSN Spring 2012 Newsletter

  12. Common SSI SurveillanceWashington State 65 Hospital Survey Zarate R, Birnbaum D. ICHE 2012;33(1):87-89

  13. Improving SSI Surveillance • Surveillance improvements needed • Labor intensive • Increasing number of surgeries monitored • Capitalize on electronic health records • Continued limitations • Tracking only returns to index surgery hospital • 20% SSIs occur at other hospitals 1 • Readmissions range from 0-100% across hospitals 1 1 Yokoe DS et al. SHEA 2011 (Dallas, Tx)

  14. The Case for Claims Datafor SSI Surveillance

  15. Improving Capture Part 1Evaluating multiple data streams

  16. Improving Case Finding • SSIs often do not have associated cultures • Evaluate data streams other than micro • Readmissions • ICD-9 codes suggesting SSI • Antibiotic therapy beyond prophylaxis • Data streams trigger chart review • Compare SSI rates to those currently used by infection prevention programs Yokoe DS, et al. Emerg Infect Dis 2004; 10:1924-1930 Bolon MK, et al. Clin Infect Dis 2009; 48:1223-1229

  17. Methods • Academic and community hospitals affiliated with the CDC Prevention Epicenters Program • For each SCIP procedure, subjects included • All SSI identified by routine surveillance • Random sample of 200 non-SSI cases at each hospital • Medical records were retrospectively reviewed for: • Readmission, ICD-9 SSI codes, antibiotic exposure • SSI classification using NHSN definitions • SSI rates were extrapolated back to the entire population

  18. Initial Diagnosis Codes for SSI Yokoe DS, et al. Emerg Infect Dis 2004; 10:1924-1930 Bolon MK, et al. Clin Infect Dis 2009; 48:1223-1229

  19. SSI Studies • CDC Prevention Epicenters: 14 teaching/community hospitals • CABG 8,379 procedures, 9 hospitals • Cesarean deliveries 7,399 procedures, 8 hospitals • Breast procedures 6,175 procedures, 11 hospitals • Total hip arthroplasty 2,128 procedures, 4 hospitals • Total knee arthroplasty 4,194 procedures, 5 hospitals • Hysterectomy 3,079 procedures, 4 hospitals • Colorectal 4,748 procedures, 4 hospitals • Vascular 3,332 procedures, 4 hospitals Yokoe DS, et al. Emerg Infect Dis 2004; 10:1924-1930 Bolon MK, et al. Clin Infect Dis 2009; 48:1223-1229

  20. SSI Rates by Routine Surveillance vs. Enhanced Surveillance Yokoe DS, et al. Emerg Infect Dis 2004; 10:1924-1930 Bolon MK, et al. Clin Infect Dis 2009; 48:1223-1229

  21. Routine vs EnhancedSSI surveillance Yokoe et al. Emerg Infect Dis 2004;10(11): 1924-30 Bolon M et al. Clin Infect Dis 2009:48 (49):1223-9

  22. Number of Charts Reviewed by EnhancedSSI surveillance (readmit, select few ICD9 codes, antibiotics)

  23. Enhanced Surveillance • Detected 2x-4x as many SSIs • Allows automated surveillance triggers • Efficient high yield chart review • Enables standardized capture • Limitations • Limited to SSIs returning to index hospital • Dependent on antibiotic capture • May allow gaming by selecting a few ICD9 codes • Next Steps • Focus on claims based coding triggers

  24. Improving Capture Part 2Expanding Assessment of Claims Codes

  25. SSI Risk by Claims Codes AloneInitial demonstration projects Platt R et al. Emerg Infect Dis 2002;8(12):143-41. Huang SS et al. BMC Med Res Methodol 2007;7:20 Huang SS et al. ICHE 2011;32(8):775-783 Calderwood et al. ICHE 2012; 33(1):40-49

  26. Assessing Claims Codes • CDC Prevention Epicenter hospitals • Retrospective cohort studies using Medicare claims • Compare claims triggers for chart review to SSI by routine surveillance • Gold standard: any NHSN confirmed SSI identified by either routine surveillance or claims triggered chart review • Goal • Determine if codes detect more SSI • Identify codes with high sensitivity and reasonable specificity for SSI detection • Begin with SCIP procedures

  27. Selecting Claims Codes • Selection of comprehensive claims codes for SSI • Broad codes to account for variations in coding • Broad codes to prevent gaming the system • Includes codes infrequently used by most hospitals • Goal • High capture of SSI (high sensitivity) • Tolerable # chart reviews (reasonable specificity) • Ability to accurately identify high vs low SSI rates • Begin with SCIP procedures

  28. Hip Arthroplasty

  29. SSI Codes * Preliminary

  30. Claims vs Routine SSI Surveillance

  31. Claims vs Routine Surveillance Deep and Organ Space SSI

  32. Validating Capture Part 3National Validation of Claims Codes

  33. National ValidationClaims identifying high vs low SSI rates Huang SS et al. ICHE 2011;32(8):775-783 Calderwood et al. IDWeek Abstract 2012, submitted

  34. Further Validation • National Medicare Claims • Retrospective cohort study of US hospitals • Hospitals SSI ranking by claims codes • Hospitals ranked in top and bottom SSI deciles had charts randomly selected for chart review • Goal • Confirm codes can identify SSI • Confirm codes can distinguish high and low SSI rates • Begin with SCIP procedures Huang SS et al. ICHE 2011;32(8):775-783

  35. Characteristics of Patients Undergoing CABG in Top and Bottom Decile Hospitals Huang SS et al. ICHE 2011;32(8):775-783

  36. Chart-Confirmed SSI • Charts with CABG SSI Codes • Review of 2.5 charts yielded 1 SSI • Consistent across deciles • Chart Confirmed SSI Rates • Top Decile: 2.4% • Bottom Decile: 6.3% • P<0.0001 Huang SS et al. ICHE 2011;32(8):775-783

  37. Predictors of CABG SSI Huang SS et al. ICHE 2011;32(8):775-783

  38. Hip Arthroplasty SSI After adjusting for age, gender, comorbidities, patients undergoing hip arthroplasty in a worst decilevs best decile hospital had a 2.9-fold higher risk for any SSI (p<0.001) and a 3.0-fold higher risk for deep and organ space SSI (p<0.001) Calderwood M et al. IDWeek 2012

  39. Hip - Deep and Organ Space Bolon M et al. Clin Infect Dis 2009:48 (49):1223-9

  40. NY State SSI Validation • Validation technique for CABG, hip, colon • Review of 18 charts • Identified common codes found in NHSN reported SSIs and not in unreported SSIs • Chart Sampling at Hospitals • # Sampled depended on # performed (N = 9-18) • Up to 6 charts among SSIs reported to NHSN • Sampled charts with codes but not reported to NHSN • Sampled wrong surgery codes • Sampled remainder Haley VB et al. ICHE 2012;33(6):565-571

  41. NY State SSI Validation • 176 Hospitals • Review for CABG, hip, colon • 7,059 charts reviewed (6% of total) • Overall, among cases reported to NHSN: 7% not SSI Compared to 2.1X 1.8x ? Haley VB et al. ICHE 2012;33(6):565-571

  42. Ideal Codes • Improve NHSN capture • Efficient in targeting missed cases • Easy electronic reports for review • Prevent gaming • Validated • Potential for future assessment across hospitals

  43. Claims Perspectives • Current SSI surveillance underestimates truth • Variable capture across hospitals • problem for benchmarking, inter-hospital comparisons • Coded data helps with standardization • Successful in triggering chart reviews • Efficient capture of additional SSI • Effective method to identify high/low SSI rates • Claims data assist with validation • Excellent way to determine improvement

  44. HICPAC Deliberations • Closure • Inclusion of lack of closure • Accounting for lack of closure • Implants • Tracking implants • Duration of surveillance • Post-Discharge surveillance methods

  45. Questions?

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