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Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases

Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases. Dionissios Neofytos, MD, MPH Transplant & Oncology Infectious Diseases The Johns Hopkins University School of Medicine. Disclosures. Consultant (Pfizer, LifeCell)

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Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases

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  1. Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases Dionissios Neofytos, MD, MPH Transplant & Oncology Infectious Diseases The Johns Hopkins University School of Medicine

  2. Disclosures • Consultant (Pfizer, LifeCell) • Research grant (Pfizer)

  3. Overview • Compare and contrast the data from single and multicenter databases • What we learned and not • Specific problems • Number of patients • Case definition • Case capture • Suggestions

  4. Incidence of IA: 1987-1998 Wald A, Clin Infect Dis, 1996 Marr KA, Blood, 2002

  5. Timing and risk factors of IA post HSCT Wald A, Clin Infect Dis, 1996 Kontoyiannis D, Med Myc, 2008

  6. Combination therapy for IA Marr KA, Clin Infect Dis, 2004 Garcia-Vidal C, Clin Infect Dis, 2009

  7. Single-center databases • Benefits: • Clinical data availability • Homogeneity in: • Case capture • Case definitions • Clinical practices • Long follow-up • Deficiencies: • Small numbers of patients • Decreased patient and practice variability • Are results generalizable? • Prophylaxis, diagnosis, and treatment practices • Case capture rates

  8. Variability in attack rates

  9. National databases • Administrative healthcare surveillance databases • National Hospital Discharge Survey (NHDS) • National Inpatient Sample (NIS) • Kid’s Inpatient Database (KID) • Excellent tools for: • Inter-institutional comparison • Clinical research • Benefits: • Assess the magnitude & temporal aspects of IA on population basis • Big numbers of patients

  10. Nationwide Inpatient Sample • NIS • 20% discharges from US community hospitals • Data: demographics, diagnosis (ICD-9), procedures, length of stay (LOS), charges, payer type, patient disposition • 1996: 19 states, 906 hospitals, 6.5 million records1 • 2003: 28 states, 994 hospitals, 7.5 million records2 1Dasbach E, Clin Infect Dis, 2000 2 Tong K, Int J Infect Dis, 2008

  11. National database problems • Lack of clinical data • Inability to adjust for severity of disease differences • Differences at individual and institutional level • Lack of longitudinal follow-up • Patients not individually identified • Re-admission vs. transfer • Under-representation of tertiary care centers • NIS • ICD-9 coding • Designed for financial & administrative purposes • Incentives to maximize payments • Experience of billing staff & coding verification

  12. ICD-9 coding and accuracy of IA diagnosis IDC-9 codes triggered MR review for 64 pts: 16 (25%) with IA Chang DC, Inf Control Hosp Epid, 2008

  13. Multicenter databases • Transplant Associated Infections Surveillance Network (TRANSNET) • 2001-2006 • Prospective surveillance data on transplant recipients with IFIs • 23 transplant centers in the US • Prospective Antifungal Therapy (PATH) Alliance® • 2003-2008 • Prospectively collected data on patients with IFIs • 23 centers in North America Horn D, DMID, 2007 Pappas P, In Press

  14. Epidemiology & outcomes of IA in HSCT Decreased incidence Marr KA, Blood, 2002 Upton A, Clin Infect Dis, 2008 Kontoyiannis D, in press Neofytos D, Clin Infect Dis, 2009 Better survival

  15. Multicenter database challenges • Can we predict which data we need to capture? • Disease / Clinical practice-related data • New diagnostic methods • New clinical practices • Can we always capture the data we need? • Total number of at risk patient population • Antifungal prophylaxis • Can we always effectively translate data? • Antifungal therapies with >1 agent • Sequential vs. concomitant treatment

  16. How complete is case capture? • Case identification • Microbiology & pathology databases • Attending physicians’ reporting • Consultation records • Medical & pharmacy records • TRANSNET internal audit • Medical record review of randomly selected patients • HSCT: 20-30% highest risk group • SOT: lung transplant recipients • <5% of total cases identified Horn D, DMID, 2007 Pappas P, In Press Chang DC, Inf Control Hosp Epid, 2008

  17. Case reporting • SOT - TRANSNET • Overall by site 12-month cumulative incidence of IFIs: 1.2-6.1% • SOT specific 12-month cumulative incidence of IFIs: • Liver: 0-15.5% • Pancreas: 0-20.0% • Lung/Heart-lung: 0-25.9% • HSCT - TRANSNET • 12-month cumulative incidence of IFIs: 3.4% (range, 0.9-13.2%) • 6 of 21 sites: 80% of IFIs in MMR HSCT (range, 3.1-20.6%) Pappas P, In Press

  18. The diagnosis challenge • Patients at risk: • Center based clinical practices • Geography • Diagnosis based on: • Diagnostic practices vary • By center • By patient population • Interaction between Infectious Disease service with other services • Hematology, BMT, Surgery, Pulmonary, Microbiology • Availability of diagnostic assays on site Neofytos D, Clin Infect Dis, 2009 Neofytos D, Tran Infect Dis, 2010

  19. Are multicenter database results comparable? Kontoyiannis D, In Press Neofytos D, Clin Infect Dis, 2009

  20. Multi-Center Databases • Benefits: • High numbers of patients • Patient and practice diversity • Deficiencies: • Heterogeneity in: • Case capture • Case definitions • Clinical practices • Differences in endemicity • Limited clinical data • Inadequate follow-up • Inability to capture “late events” related to: • Transplant-associated complications • Underlying disease relapse • Infections

  21. How are data affected by changes in practice? Diagnostics IFI definitions GMA EIA PCR 1980 1990 2000 2010 Voriconazole Combination therapy Therapeutics Prophylaxis with anti-mould agents

  22. Acknowledgments • Acknowledgments • Peter Pappas, MD • David Horn, MD • Kieren Marr, MD • Thanks for your attention

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