genexpert omni implementation trials assessing impact … · 2016-10-31 · genexpert omni...
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GeneXpert Omni Implementation Trials – Assessing Impact and Minimizing Bias
Adithya Cattamanchi, MD, MAS
GeneXpert Omni
• Single-cartridge, point-of-care platform
• Low power consumption (solid-state)
• Integrated battery (4 hours) + supplemental battery (12 hours)
• Automatic connectivity
Rapid, onsite molecular testing at primary care clinics in high-burden countries
Trials of GeneXpert impact
• Five primary care clinics in S. Africa, Tanzania, Zambia, and Zimbabwe • Compared POC Xpert testing vs. sputum smear microscopy
• 20 clusters (lab + 2 primary care clinics) in S. Africa • Compared Xpert testing vs. sputum smear microscopy
GeneXpert trials – summary of key findings
• Bacteriologically-confirmed cases increased
• Time to treatment of confirmed TB decreased
• Patient outcomes largely unchanged – TB NEAT
• No difference in morbidity score in TB culture-positive patients who had begun treatment
• No difference in all-cause mortality (8% in both groups) • Decreased pre-treatment loss to follow-up (8% vs. 15%)
– XTEND • No difference in all-cause mortality (3.9% vs. 5%) • No difference in pre-treatment loss to follow-up (17% vs. 14.9%)
Why no impact?
• High rates of empiric treatment – TB NEAT: 22% (17% in Xpert arm; 26% in microscopy arm)
– XTEND: about 10% in both arms
• Offsets gains in sensitivity and impact associated with Xpert
• Trial design: How to account for empiric treatment?
Menzies NA et al. Lancet ID 2014
Design choices – bias towards the null?
• TB NEAT and XTEND are among the best examples of diagnostic intervention trials
• Designing any trial involves a series of tradeoffs
• Key questions for next round of trials
– Could specific design choices have biased results toward the null?
– Can we give greater weight to avoiding bias towards the null when making design choices?
Design choices: bias towards null? TB NEAT
Design choice Potential impact
Individual patient randomized trial •Higher pre-test probability of TB lower threshold for empiric treatment
Patients screened, consented and enrolled by study staff
•Reduce loss to follow-up
Offered VCT for HIV and CXR while awaiting test results
• Increase likelihood of treatment at initial visit • Increase in empiric treatment rates
TB culture performed on all patients • Increase in bacteriologic confirmation in smear arm > Xpert arm •Minimize delay in treatment initiation in
smear arm > Xpert arm
Active follow-up at 2 months and 6 months
•Minimize loss to follow-up • Increase opportunity for empiric treatment
Primary outcome (morbidity) assessed among culture-positive patients starting treatment
•Decrease morbidity in smear arm > Xpert arm
Design choices: bias towards null? XTEND
Design choice Potential impact
Patients screened, consented and enrolled by study staff
•Reduce loss to follow-up
CXR, sputum culture for HIV-positives if index test negative
• Increase in empiric treatment rates • Increase in bacteriologic confirmation in
smear arm > Xpert arm •Minimize delay in treatment initiation in
smear arm > Xpert arm
Phone follow-up at 1 week and 1, 2 and 4 months; air-time vouchers/home visits
•Minimize loss to follow-up
Clusters randomized within geographic strata only
•Other co-variates unbalanced lower power
Other issues to consider - 1
• Power for assessing mortality as the primary outcome
– Mortality is uncommon in primary care settings
– Prohibitively large sample size required to detect small but meaningful effect sizes
Possible solutions: Combined endpoints, meta-analysis
TB NEAT
XTEND
Cox et al
I-V Overall (I-squared = 0.0%, p = 0.941)
study
TBNEAT(1)
Xtend(1)
D+L Overall
Cox et al.
0.88 (0.68, 1.14)
ES (95% CI)
0.94 (0.59, 1.50)
0.85 (0.56, 1.27)
0.88 (0.68, 1.14)
0.87 (0.54, 1.40)
100.00
%
(I-V)
30.66
40.18
29.16
Weight
0.88 (0.68, 1.14)
ES (95% CI)
0.94 (0.59, 1.50)
0.85 (0.56, 1.27)
0.88 (0.68, 1.14)
0.87 (0.54, 1.40)
100.00
%
(I-V)
30.66
40.18
29.16
Weight
Xpert reduces risk of mortality Xpert increases risk of mortality
1.5 2
I-V Overall (I-squared = 0.0%, p = 0.941)
study
TBNEAT(1)
Xtend(1)
D+L Overall
Cox et al.
0.88 (0.68, 1.14)
ES (95% CI)
0.94 (0.59, 1.50)
0.85 (0.56, 1.27)
0.88 (0.68, 1.14)
0.87 (0.54, 1.40)
100.00
%
(I-V)
30.66
40.18
29.16
Weight
0.88 (0.68, 1.14)
ES (95% CI)
0.94 (0.59, 1.50)
0.85 (0.56, 1.27)
0.88 (0.68, 1.14)
0.87 (0.54, 1.40)
100.00
%
(I-V)
30.66
40.18
29.16
Weight
Xpert reduces risk of mortality Xpert increases risk of mortality
1.5 2
0.94 (0.59, 1.50)
0.85 (0.56, 1.27)
0.87 (0.54, 1.40)
0.88 (0.68, 1.14) Overall
Favors Xpert Favors no Xpert
Peter JG et al. Effect on mortality of point-of-care, urine-based lipoarabinomannan testing to
guide tuberculosis treatment initiation in HIV-positive hospital inpatients: a pragmatic, parallel-
group, multicountry, open-label, randomised controlled trial. Lancet 2016.
Khaki A et al. The effect of automated nucleic acid amplification assays on mortality in routine
care settings: meta-analysis of individual participant data. Union World Lung Health
Conference, 2015.
IPD meta-analysis of Xpert RCTs RR 0.88 (0.68, 1.14) [3 RCTs, 8140 participants, 399 deaths]
LAM RCT RR 0.82 (0.70, 0.96) [1 RCT, 2528 participants, 578 deaths]
Other issues to consider - 2
• How pragmatic should a pragmatic trial be?
– Omni has potential to improve health center processes contributing to losses to follow-up
– Differences between arms minimized if study-related processes impact routine processes
StaRI Draft Checklist:
(StaRI: Standards for Reporting of Implementation studies)
• Patients should be recruited to the service and not to the study
• Outcomes should be assessed using routinely collected data
Pinnock H et al. Imp Sci 2015
XPEL TB Trial Design Xpert Omni Performance Evaluation to facilitate Linkage to care
Adithya Cattamanchi Margaret Handley
Sara Ackerman
Achilles Katamba Moses Joloba
David Moore Katherine Fielding
David Dowdy Luke Davis
Steering Committee: Gerald Friedland (Yale), Andrew Ramsay (WHO/TDR), Madhukar Pai (McGill), Noah Kiwanuka (MakCHS), Grant Theron (Stellenbosch) Funding: NIH/NHLBI R01 HL130192; FIND/Cepheid RFA for devices/cartridges
Frank Mugabe
Specific Aims
• Aim 1: To compare patient outcomes at health centers randomized to GeneXpert Omni vs. standard-of-care TB diagnostic evaluation strategies.
• Aim 2: To identify processes and contextual factors that influence the effectiveness of GeneXpert Omni.
• Aim 3: To compare the costs and epidemiological impact of GeneXpert Omni vs. standard-of-care TB diagnostic evaluation strategies
Comparison of intervention and control arms
VIS
IT 1
Standard-of-Care
(CONTROL ARM)
All Patients Patients with HIV
Onsite Omni
(INTERVENTION ARM)
All Patients
Treat if Xpert positive*
Perform Xpert testing
Collect sputum
Treat if Xpert positive*
Collect sputum
Refer for Xpert testing
Collect sputum
Prepare/examine smear
Collect sputum
Prepare/examine smear
Smear-positive: Treat
Treat if Xpert positive*
Smear-negative: Collect sputum and
refer for Xpert testing
VIS
IT 2
V
ISIT
3
VIS
IT 4
* Empiric treatment or additional testing at clinician’s discretion
Design Overview
• Cluster-randomized trial
• Sites (N=20-24): Primary care clinics Inclusion Criteria – Perform sputum smear microscopy – Participate in NTP-sponsored EQA – Send samples to a central site for Xpert testing Exclusion Criteria – Do not agree to be randomized – Perform smear examination on <150 patients per year – Diagnose <15 smear-positive TB cases per year
• Participants (N≈8000): All adults initiating TB evaluation over 2-year period Exclusion Criteria – Have sputum collected as part of active, community-based case finding – Extra-pulmonary TB only
Outcomes
• Primary – Proportion treated for microbiologically-confirmed TB
within 2 weeks
• Secondary – Number tested for TB – Number and proportion with confirmed TB – Number and proportion treated for TB (overall and if
confirmed) – Mortality + pre-treatment LTFU at 6 months – Incremental cost-effectiveness ratio – 10-year TB incidence
Procedures
• Patient-level data using routine data sources (Aim 1) – TB registers, ART register and Xpert referral forms
• Photos uploaded to secure server monthly
– GxAlert server download
• Patient vital status (Aim 1) – Phone call or home visit 6 months after initial health center visit – Intensive tracing of participants unable to be contacted
• Focused data collection during quarterly site visits (Aim 2) – Patient surveys to assess costs and satisfaction with care (N=20-40/site) – Provider surveys to assess attitudes, self-efficacy
• Health system costing studies (Aim 3)
-6 0 24 30 Pre-trial Trial Complete
follow-up
Randomization
• Stratification – reduce between cluster variation – Health centers randomized within strata defined by
primary outcome
• Restriction – balance between arms of site- and patient-level covariates – HIV prevalence among patients evaluated for TB
– Health center size (volume of presumed TB patients)
– Health center region (four quadrants of Uganda)
– Proportion treated empirically
Summary
• Lack of impact of GeneXpert to date is in part due to the technology
• Rapid, onsite molecular testing could improve factors that contribute to missed and delayed diagnosis/treatment
• Omni implementation trials should be as pragmatic as possible
Questions/Comments?
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