a new sampling algorithm for use with atp...
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A new sampling algorithm for use with ATP testing
Greg Whiteley Managing Director – Whiteley Corporation
Declaration of conflict of interest
• None to declare: with respect to this presentation
• I have no association with any brand of ATP device, nor does my company, nor do any of my affiliates
• I have no financial links to ATP testing, other than to use these devices and conduct validation research through WSU
• All data referenced in regards to any ATP testing device has been peer reviewed and internationally published except where noted that writing for submission, or current peer review is still underway
Project Design features
Cleaning processes
Lets change just one item and use a control group
Outcome: Measurable cleanliness
Should be a snack!
We looked for an ICU
The ICU manager wasn’t happy with the micro results after the hygiene audit
Background on ATP testing
Advantages:
Easy to use; real time results; broad indicator of cellular contamination
Problems:
Variability & imprecision; relative scaling; lack of brand to brand interoperability; sampling error
Early Validation work on ATP testing devices
• Each device uses a unique
scale – all named ‘RLU’
[Relative Light Units]
• The Lower Limit of
Quantitation is different
from the lower level of
detection for some brands
• Several brands do not
read down to zero, so a
practical zero is required
leading to user confusion
• Imprecision is virtually
undetectable in use Whiteley et al., Healthcare Infection:2012:17:91-97
y = 6E-05x - 0.0242R² = 1
0
2
4
6
8
10
12
0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000
Co
nce
ntr
atio
n (
pp
m)
Mean Peak Area
HPLC Calibration curve using pure ATP
Calibration testing of ATP testing devices compared to a
laboratory standard analytical tool - HPLC
Variance measurements Cv = σ /
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30 40 50 60
Co
V
Number of dilutions tested for each unit
Coeffecient of Variance CoV for three portable ATP bioluminometers
Cleantrace 3M [n=57 (246 swabs)]
Kikkoman [n=49 (222 swabs)]
Hygiena [n=47(199 swabs)]
LCMS [n=22 (72 runs)]
n = number of separatedilutions tested per brand
Whiteley et al., Infect Control Hosp Epidemiol:2013:34:538-540
Whiteley et al., ICHE 2015
Summary findings on variability
Whiteley et al., Infect Control Hosp Epidemiol:2015:36:658
Key Findings
1. The variability with bacteria is the same as the variability with the pure source ATP
2. With a Cv of 0.4, any reading has a 20% chance to be wrong by a factor of two: i.e. the error potential on a reading of 100 RLU is from 50 RLU to 200 RLU.
3. Readings using more than a single point are required for statistical validity.
Finding the bad bugs in a busy ICU
Ref: Whiteley et al., Am J Infect Control:2015:43:1270-5
So where are the bad bugs?
A cross sectional ICU pilot survey
Data: pre-publication Knight, Whiteley, Jensen, Gosbel and others., 2016
SO WE DEVELOPED A NEW SAMPLING ALGORITHM
We needed to improve certainty over the ATP readings
Key features of the new sampling algorithm
• Starts with duplicate sampling on proximal surfaces of an HTO or RMD
• Uses a standardised sampling area – normally 2cm x 5cm = 10cm2
• Compares results to a predetermined initial cleanliness threshold
• Uses a cleaning intervention step for internal validation of cleanability & cleanliness
A new ATP testing Sampling Algorithm
2 samples both
RLU <100 RLU*
Cleaning Intervention
Step
Resample aiming for cleanliness at
< 50 RLU
2 samples both
RLU > 100 RLU*
Cleaning Intervention
Step
Resample aiming for cleanliness at <50RLU
Cleanliness intervention step to repeat and sample
repeat until cleanliness is < 50 RLU
2+ samples: one < 100 RLU*
& one > 100 RLU*
Continue sampling for up to four samples to indicate outlier effect
Cleaning Intervention
Step
Cleanliness intervention step to repeat and sample
repeat until cleanliness is <50RLU
* 100 RLU measure is specific only to Hygiena
Food Premises Cleanliness Study*
• Conducted in regional location in NSW
• 8 Food Premises & 72 items examined
• Statistics using Mann Whitney and Wilcoxon
• Cleaning Intervention Step using disposable detergent (anionic) wipe
• Cleaning Principle: “one wipe, used on one surface, wiping in only one direction”**[**Sattar & Maillard, 2013. Am J Infect Control:41:S97-S104]
*Paper: Whiteley Nolan & Fahey: J Env Health (USA) under peer review, 2016
Results: Spread of duplicate samples with
Line of best fit between duplicate samples
Paper: Whiteley Nolan & Fahey: J Env Health (USA) under peer review, 2016
Before and after Cleaning Intervention Step results
Paper: Whiteley Nolan & Fahey: J Env Health (USA) under peer review, 2016
Key findings from the Food Premises survey
• Before and after findings – all statistically significant (P>.001) for all categories except <25 RLU (P=0.136)
Classification Before After Significance
Clean 2x<100 RLU
19 6 P = 0.001
Unclean2x > 100RLU
922 10 P = 0.001
Mixed± 100RLU
192 5 P = 0.001
Very CleanAv < 25 RLU
8 5 P = 0.136
Paper: Whiteley Nolan & Fahey: J Env Health (USA) under peer review, 2016
The algorithm mapped – IDH 2016
Paper: Whiteley Glasbey Fahey, IDH 2016: pending DOI
Key comparative charts
Cleanliness Threshold
Hygiena Cleantrace Kikkoman
Initial Cleanliness threshold TC1 100 RLU 500 RLU 460 RLU
Secondary Cleanliness threshold TC2 50 RLU 250 RLU 230 RLU
Tertiary Cleanliness threshold TC3 25 RLU 125 RLU 115 RLU
Lower Limit of Quantitation LLQ 0 RLU 100 RLU 90 RLU
Identified Cleanliness Threshold
Defining the Cleanliness Thresholds relationships
TC1 TC1 = TC2 x 2 = TC3 x 4
TC2 TC2 = TC1 ÷ 2 = TC3 x 2
TC3 TC3 = LLQ + 25 RLU (may be higher for some devices)
Table 1 from the paper
Table 2 from the paper
Paper: Whiteley Glasbey Fahey, IDH 2016: pending DOI
Medical Device Study
• Survey is currently underway and on-going
• Tested 258 individual surfaces and items so far
• Over 1000 swabs so far
• Cleaning intervention step using two different wipes – testing difference in cleaning outcomes between wipes
• Statistical analysis – standard methods
Conclusions
1. New sampling algorithm improves certainty when using ATP testing devices;
2. Algorithm is simple to use in field hygiene assessments with slightly increased costs;
3. New sampling algorithm requires further study in well designed trials to test validity and precision improvement of ATP testing…
Acknowledgements
• Dr Trevor Glasbey, Regulatory and Research Manager, Whiteley Corporation
• Paul Fahey, Statistician, Western Sydney University
• Mark Nolan, Environmental Health Officer, National Parks and Wildlife Service of NSW
• ASUM
• ASUM Staff: Lyndal Macpherson, Associate Professor Sue Westaway, Dr Jocelyne Basseal, Ann-Marie Gibbons
• Jessica Knight, Professor Iain Gosbell, and Associate Professor Slade Jensen – all based at Western Sydney University and the Ingham Research Institute
• Thank youThank you
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