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Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases Dionissios Neofytos, MD, MPH Transplant & Oncology.

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Presentation on theme: "Epidemiology and Outcomes of IA in the 21st Century: Strengths and Weaknesses of Surveillance Databases Dionissios Neofytos, MD, MPH Transplant & Oncology."— Presentation transcript:

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: 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) –Kids 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 records 1 –2003: 28 states, 994 hospitals, 7.5 million records 2 1 Dasbach E, Clin Infect Dis, Tong K, Int J Infect Dis, N=10, N=10,400 Age (Mean), years Incidence (per million) IA- IA+ IA- IA LOS, days Cost Mortality, in hospital 19.3%2.5%17.1%NR

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 Chang DC, Inf Control Hosp Epid, 2008 IDC-9 codes triggered MR review for 64 pts: 16 (25%) with IA

13 Multicenter databases Transplant Associated Infections Surveillance Network (TRANSNET) – –Prospective surveillance data on transplant recipients with IFIs –23 transplant centers in the US Prospective Antifungal Therapy (PATH) Alliance ® – –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 Marr KA, Blood, 2002 Upton A, Clin Infect Dis, 2008 Kontoyiannis D, in press Neofytos D, Clin Infect Dis, 2009 Decreased incidence 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 Pappas P, In Press SOT - TRANSNET –Overall by site 12-month cumulative incidence of IFIs: % –SOT specific 12-month cumulative incidence of IFIs: –Liver: % –Pancreas: % –Lung/Heart-lung: % HSCT - TRANSNET –12-month cumulative incidence of IFIs: 3.4% (range, %) –6 of 21 sites: 80% of IFIs in MMR HSCT (range, %)

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? HSCT TypeIFI FrequencyAspergillus spp.Candida spp. AutologousAllogeneicIAICZygoA. fumUnknownC. albC. glaC. par TRANSET21%78%43%28%8%44%26%20%33%14% PATH Alliance31.2%68.8%59.2%24.8%7.2%37.2%52.7%24.2%43.5%11.3% 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 Therapeutics IFI definitions GMA EIA VoriconazoleCombination therapy PCR Prophylaxis with anti-mould agents

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


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