Disclosures. Serial Tes+ng for TB Infec+on with IGRAs: Understanding the Sources of Variability. Stanford Hospital Policy

Transcription

1 Serial Tes+ng for TB Infec+on with IGRAs: Understanding the Sources of Variability Disclosures Banaei: No financial conflicts No industry links or funding Niaz Banaei MD Assistant Professor of Pathology and Medicine Director, Clinical Microbiology Laboratory Stanford University Medical Center Stanford Hospital Policy - Screen new HCW and then annually - >10,000 screenings per year CDC guidelines in 2005 recommended use of IGRAs for HCW screening with: no published data on serial tes+ng no independent, peer- reviewed literature on IGRA reproducibility Simplis+c neg to pos change was defined as conversion (since there were no data) Sensitivity in patients with latent TB using progression to active TB as gold standard CDC guidelines in 2005 recommended use of IGRAs for HCW screening with: no published data on serial tes+ng no independent, peer- reviewed literature on IGRA reproducibility Simplis+c neg to pos change was defined as conversion (since there were no data) Herrera et al CID

2 Since 2006, >50 studies have assessed IGRAs in HCWs Reproducibility of QFT- IT in HCW Thorax 2012 Coversions 2% to 15% Reversions 20 to 40% Largest study of >2000 HCWs (CDC Task Order 18 study): TST = 0.9 % QFT = 6.1% T-SPOT = 8.3% conversion rates Canadian study in HCWs (Zwerling et al. PLoS ONE 2013): TST = 0 % QFT = 5.3% conversion rates Pai and Elwood Can Respir J Trajectory plots of IFN-gamma over 4 test points 960 valid QFT results (HCWs in India tested with QFT every 6 months x 4 times) Early adopters of IGRAs for HCW screening in the US are reporting interesting challenges (and different hospitals are coming up with their own interpretational criteria and cut-offs!) Zwerling A et al. Under review Need to Identify Sources of IGRA Variability Reproducibility studies are now emerging (independent of manufacturers) - Eliminate - Standardize - Account for variability 2

3 Sources of IGRA Variability Quantiferon Gold Intube Assay Pre-analytical Analytical Nil TBAg Mit Manufacturing Immunological Assay cut-off 0.35 IU/ml QFT- GIT Assay Standardiza+on Standardized Not Standardized - Skin disinfection - Blood volume - Shaking of tubes - T cell and APC count - Transportation temp - Diet - Incubation delay - Infection - Incubation temp - Antibiotics - Incubation duration - Plasma separation delay - Plasma storage - Analytical - ELISA QFT- GIT Assay Standardiza+on Standardized Not Standardized - Skin disinfection - Blood volume - Shaking of tubes - T cell and APC count - Transportation temp - Diet - Incubation delay - Infection - Incubation temp - Antibiotics - Incubation duration - Plasma separation delay - Plasma storage Does Blood Volume Matter? - Draw ml of blood - Analytical - ELISA 3

4 Effect of Blood Volume on TB Response Distribution of Blood Volume in QFT Tubes Infected Group (TST+ QFT+) n=13 P = P = Uninfected Group (TST- QFT-) n=18 Median: Range: CV: 3.8% 3.3% 0.8 ml 1.0 ml 1.2 ml <0.35 2(15%) 5 (39%) 7 (54%) ml 1.0 ml 1.2 ml < Gaur et al unpublished Effect of Shaking on TB Response Does Shaking Matter? P=0.058 Nil P=0.39 TB Ag Nil TBAg Mit P=0.5 Vigorous n=10 n=10 Vigorous Assay cut-off 0.35 IU/ml Vigorous n=10 Does Incubation Delay Matter? Effect of Incubation Delay on IGRA - Transport to incubator ( 16h) Hanekom et al 2004 J. Immunol. Methods 4

5 Effect of Incubation Delay on the Accuracy of QFT-IT Results TB Response Following Immediate and Delayed Incubation 128 study participants 3 QFT-GIT sets collected Low risk & - TST&orQFT High risk & + TST&orQFT Incubation Delay 0 h 6 h 12 h Doberne et al JCM 2011 n=128 Doberne et al JCM 2011 TB Response for Subjects with Discordant Results Mitogen Results Following Incubation Delay Reversion rate: 19% (5/26) with 6 h delay 22% (5/23) with 12 h delay Doberne et al JCM 2011 Doberne et al JCM 2011 Effect of Incubation Delay on Determinate Results Mitogen Results Following Incubation Delay Herrera et al JCM 2010 Herrera et al JCM

6 Incubation Delay Increases Indeterminate Results Does Incubation Duration Matter? C h Herrera et al JCM 2010 Effect of Incubation Duration on TB Response QFT- GIT Assay Standardiza+on 16 hr 20 hr 24 hr < Infected Group (TST+ QFT+) n=13 Uninfected Group (TST- QFT-) n=18 16 hr 20 hr 24 hr < Gaur et al unpublished Standardized Not Standardized - Skin disinfection - Blood volume - Shaking of tubes - T cell and APC count - Transportation temp - Diet - Incubation delay - Infection - Incubation temp - Antibiotics - Incubation duration - Plasma separation delay - Plasma storage - Analytical - ELISA Analy+cal Precision of QFT- GIT Assay QFT- GIT Assay Standardiza+on Standardized Not Standardized - Skin disinfection - Blood volume - Shaking of tubes - T cell and APC count - Transportation temp - Diet - Incubation delay - Infection - Incubation temp - Antibiotics - Incubation duration - Plasma separation delay - Plasma storage Detjen et al Clin Vaccine Immunol Analytical - ELISA 6

7 Analy+cal Precision of QFT- GIT Assay Analy+cal Precision of QFT- GIT Assay Detjen et al Clin Vaccine Immunol 2009 Metcalfe et al AJRCCM 2012 Whitworth et al JCM Qualitative results: High agreement (kappa 0.84) - Quantitative results: Considerable between-run variability (CV 14% for all and CV 27% for borderline ( IU/ml) - 86% (24/28) of discordants had borderline result ( IU/ml) Metcalfe et al AJRCCM 2012 Analy+cal Imprecision of QFT- GIT Assay: Between- Run Variability (n=20 ELISA runs) Sources of IGRA Variability Borderline Negative Sample Borderline Positive Sample Pre-analytical Analytical Manufacturing Immunological CV 14% CV 11% Conversion 10% (2/20) Reversion 20% (4/20) The QFT-GIT Surveillance Graph: Daily Positive Rate Mar-Oct 2010 The QFT-GIT Surveillance Graph Showing Daily Positive Rate at Stanford Positive Rate Mar 3-Mar 5-Mar 10-Mar 7-Mar 12-Mar 15-Mar 18-Mar 20-Mar 23-Mar 25-Mar 27-Mar 30-Mar 1-Apr 3-Apr 7-Apr 11-Apr 9-Apr 13-Apr 15-Apr 18-Apr 20-Apr 22-Apr 25-Apr 27-Apr 29-Apr 2-May 5-May 10-May 7-May 12-May 15-May 18-May 20-May 23-May 25-May 27-May 29-May 31-May 2-Jun 4-Jun 6-Jun 11-Jun 9-Jun 13-Jun 16-Jun 18-Jun 20-Jun 22-Jun 25-Jun 27-Jun 29-Jun 1-Jul 3-Jul 5-Jul 7-Jul 13-Jul 9-Jul 15-Jul 18-Jul 20-Jul 22-Jul 24-Jul 26-Jul 28-Jul 1-Aug 3-Aug 5-Aug 11-Aug 8-Aug 13-Aug 15-Aug 18-Aug 20-Aug 23-Aug 26-Aug 28-Aug 31-Aug 2-Sep 5-Sep 12-Sep 9-Sep 15-Sep 17-Sep 19-Sep 21-Sep 23-Sep 26-Sep 28-Sep 30-Sep 2-Oct 4-Oct 6-Oct 8-Oct Positive rate cot-11 4-Oct-11 5-Oct-11 6-Oct-11 Elevated rate noted 7-Oct-11 9-Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct-11 1-Nov-11 2-Nov-11 3-Nov-11 4-Nov-11 6-Nov-11 8-Nov-11 9-Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov Nov-11 1-Dec-11 2-Dec-11 4-Dec-11 TBAg lot discontinued 5-Dec-11 6-Dec-11 7-Dec-11 8-Dec-11 9-Dec Dec Dec Dec Dec 'Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec-11 7

8 Within-subject Comparison of QFT-GIT Results 31% vs 5% n=463 FDA informed via CDC. Investigation Outcomes Cellestis conducted an internal investigation and could not reproduce our findings. We could not culture viable organisms. Slater et al JCM 2012 Sources of IGRA Variability Pre-analytical Analytical Manufacturing Immunological van Zyl-Smit et al AJRCCM 2009 Amnestic Response to PPD Role of PAMPs in Modulating IGRA IGRA Boosting by PPD - PPD contains RD1 antigens - In TST+ subjects - Observed >3 days post TST TB Response Lymphoid Tissue PAMPs IL-12 + APC Th 0 Th 1 _ IFN-γ Th 2 CD 4 IFN-γ + APC ESAT6 CFP-10 TB7.7 RD1 antigens present in PPD van Zyl-Smit et al PLoS ONE 2009 Ritz et al Ritz Int J Tuberc Lung Dis 2011 Sauzullo et al Tuberculosis 2011 Site of Infection Th 1 IFN-γ + TNF-α Mφ Mogensen Clin Mic Rev

9 Recognition of Different PAMPs by PRRs TLR Agonists Activate Adaptive Immune Responses LPS Naïve T-cell activation Antigen presentation Corss-presentation Cell recruitment DC maturation: Costimulatory molecules, MHC Mogensen Clin Mic Rev 2009 Role of PAMPs in Modulating IGRA PAMPs APC Lymphoid Tissue IL-12 + Th 0 Th 1 _ IFN-γ Th 2 Site of Infection PAMPs Th 1 IFN-γ + TNF-α Mφ Mogensen Clin Mic Rev 2009 Effect of Microbiota on IGRA Response CD 4 PAMPs IFN-γ + APC ESAT6 CFP-10 TB7.7 Iwasaki & Medzhitov Nat Immun

10 PAMPs Increase TB Response in QFT-IT Assay Healthy Controls Subjects with LTBI Poly(I:C) & LPS Induce Inflammatory Cytokines & IFN-α in Whole Blood in QFT Nil Tube N=10 per grup Gaur et al PLoS One 2012 N=8 LPS Induce Maturation of Monocytes in Whole Blood in QFT Nil Tube PAMPS Triger Earlier and Greater IFN-γ Release PAMPs Enhance Response in High Risk Subjects Role of PAMPs in Modulating IGRA PAMPs APC Lymphoid Tissue IL-12 + Th 0 Th 1 _ IFN-γ Th 2 Site of Infection PAMPs Th 1 IFN-γ + TNF-α Mφ Mogensen Clin Mic Rev

11 SHC Blood Culture Monthly Contamination Rate Clarke et al Nat Med 2010 Ichinohe et al PNAS 2011 Skin Flora and Antisepsis Stanford Medical Center Blood Culture Contamination Rate Hair Follicle Bacteria Effect of Microbiota on IGRA Response CD 4 Skin Biota IFN-γ + APC ESAT6 CFP-10 TB7.7 Conclusions and sugges+ons Exis+ng guidelines for HCW screening needs to be revised We need to ask if so many low risk HCWs need repeated screening We should avoid tes+ng low risk people If we do test them, we will need to deal with false- posi+ve results Should the $$ be invested in controlling TB in high burden countries instead? Companies, health ins+tu+ons and labs must do everything they can to standardize tes+ng protocols, to minimize varia+on IGRA manufacturers should consider +ghtening the ranges in the current product insert (e.g. +me to incuba+on) 11

12 Conclusions and sugges+ons We need to rethink the cut- offs for serial tes+ng and for diagnosis Simple neg to pos cut- offs are no longer acceptable We must start interpre+ng con+nuous IGRA results (like TST) We need a borderline zone or some other strategy (e.g. re- tes+ng) to handle conversions and reversions We may need to go back and rethink the diagnos+c cut- off To derive beher cut- offs, we need to es+mate all the sources of varia+on, and compute the overall expected random varia+on Aier doing this, we need to figure out the sens/spec of the new cut- offs Stanford University Victor Herrera Rajiv Gaur David Doberne Mady Slater Julie Parsonnet Acknowledgements: Banaei UCSF/Hanoi Payam Nahid Adithya Cattamanchi Minh-Chi Tran Financial Support Stanford Pathology Stanford SPARK/ Global Health McGill University Madhukar Pai 12