Hand-out associated with part I of the January Workload Webinar “Workload Issues for Computer-Aided Cytology Devices” ASCT Educational Webinar January.

Slides:



Advertisements
Similar presentations
FDA QS reg/CLIA Comparison: Overview
Advertisements

May F. Mo FDA/Industry Statistics Workshop
Quality Assurance Documentation Procedures and Records Stacy M. Howard, MT(ASCP)
The Impact of Gynecologic Pathology Diagnostic Errors on Patient Care Dana Marie Grzybicki MD, PhD Colleen M. Vrbin, BA Danielle Pirain, BS Stephen S.
Laboratory Accreditation Program Cytopathology Inspection College of American Pathologists Robert R. Rickert, MD, FCAP AudioConference March 21, 2001.
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter Seventeen Statistical Quality Control GOALS When.
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 13 Nonlinear and Multiple Regression.
Effect Size and Meta-Analysis
Batch Reworking and Reprocessing
Cap.org v. 1 Gynecologic Consensus Conference Topic Group 3: GYN Workload Limits June 4, 2011.
Organization & Supervision. Daily Work Schedules General Consideration: A work schedule must make the maximum use of the human recourses available and.
© 2010 Pearson Prentice Hall. All rights reserved Sampling Distributions and the Central Limit Theorem.
Copyright ©2010 Pearson Education, Inc. publishing as Prentice Hall 9- 1 Basic Marketing Research: Using Microsoft Excel Data Analysis, 3 rd edition Alvin.
On Comparing Classifiers: Pitfalls to Avoid and Recommended Approach Published by Steven L. Salzberg Presented by Prakash Tilwani MACS 598 April 25 th.
Unit 4: Monitoring Data Quality For HIV Case Surveillance Systems #6-0-1.
InterQC Delivering Simplicity in the Quality Control Management for your Laboratory Developed by Vitro.
X-bar and R Control Charts
1 Historical overview of FDA regulation of digital pathology imaging applications: the safety and effectiveness issues Tremel A. Faison, MS, RAC, SCT(ASCP)
Module 10: Understanding Laboratory Data *Image courtesy of: World Lung Foundation.
Slide 1 of 48 Measurements and Their Uncertainty
1 Dr. Jerrell T. Stracener EMIS 7370 STAT 5340 Probability and Statistics for Scientists and Engineers Department of Engineering Management, Information.
Session 4. Applied Regression -- Prof. Juran2 Outline for Session 4 Summary Measures for the Full Model –Top Section of the Output –Interval Estimation.
By the end of this chapter, you should:  Understand the properties of an engineering requirement and know how to develop well-formed requirements that.
Copyright © 2014, 2013, 2010 and 2007 Pearson Education, Inc. Chapter Sampling Distributions 8.
QUALITY ASSURANCE Reference Intervals Lecture 4. Normal range or Reference interval The term ‘normal range’ is commonly used when referring to the range.
1 Technical & Business Writing (ENG-315) Muhammad Bilal Bashir UIIT, Rawalpindi.
The Binomial Distribution © Christine Crisp “Teach A Level Maths” Statistics 1.
CCO Quality Pool Methodology February 7, 2014 Lori Coyner, Accountability and Quality Director 1.
Slide 1 of 48 Measurements and Their Uncertainty
Slide 1 of 48 Measurements and Their Uncertainty
© 2010 Pearson Prentice Hall. All rights reserved Chapter Sampling Distributions 8.
Statistics Measures Chapter 15 Sections
Chapter 6 Exponential and Logarithmic Functions and Applications Section 6.1.
Leukocyte-Reduced Blood Components Lore Fields MT(ASCP)SBB Consumer Safety Officer, DBA, OBRR, CBER September 16, 2009.
Overview, Latest Updates, Texas Specific Testing Trends GED Math Testing Tips Presenters: Denise Johnson LaShondia McNeal, Ed.D.
Rev.S08 MAC 1140 Module 9 System of Equations and Inequalities I.
Economics 173 Business Statistics Lecture 4 Fall, 2001 Professor J. Petry
1 Risk Assessment Tests Marina Kondratovich, Ph.D. OIVD/CDRH/FDA March 9, 2011 Molecular and Clinical Genetics Panel for Direct-to-Consumer (DTC) Genetic.
Economics 173 Business Statistics Lecture 3 Fall, 2001 Professor J. Petry
Confidence Interval Estimation For statistical inference in decision making:
Logic and Persuasion AGED 520V. Logic and Persuasion Why do scientists need to know logic and persuasion? Scientists are writers and must persuade their.
Statistical Process Control Chapter 4. Chapter Outline Foundations of quality control Product launch and quality control activities Quality measures and.
Case-Control Studies Abdualziz BinSaeed. Case-Control Studies Type of analytic study Unit of observation and analysis: Individual (not group)
Slide 1 of 48 Measurements and Their Uncertainty
Cap.org v. 1 Gynecologic Consensus Conference Working Group 2: Prospective and Retrospective Review June 4, 2011.
Jaro Vostal, MD, PhD Division of Hematology, OBRR, CBER, FDA
Mitosis and Meiosis Lab.
Allocation of Unidentified Gas Statement – Interim Report findings 17 th September 2012.
New Pap Technologies Pap smear screening under scrutiny since late’80s
HIDA 1 Guide to Your Specialized Technical Training III ~ Your Role after You Return Home ~
Research Design Quantitative. Quantitative Research Design Quantitative Research is the cornerstone of evidence-based practice It provides the knowledge.
Welcome to MM305 Unit 8 Seminar Prof. Dan Statistical Quality Control.
Updated Force Measurement Statistical Tool Jason Beardsley and Bob Fox.
Introduction to Quality Assurance. Quality assurance vs. Quality control.
A LOOK AT AMENDMENTS TO ISO/IEC (1999) Presented at NCSLI Conference Washington DC August 11, 2005 by Roxanne Robinson.
NSF International The Public Health and Safety Company
Department of Mathematics
Presented by Harry C. Elinsky, Jr. Filtech, Inc.
Tremel A. Faison, MS, RAC, SCT(ASCP) FDA/CDRH/OIVD/DIHD
Cost of Screening Outside of IPP Chlamydia Screening Guidelines
Chapter 12 Tests with Qualitative Data
Tremel A. Faison, MS, RAC, SCT(ASCP) FDA/CDRH/OIVD/DIHD
کنترل کيفی در سيتولوژی سرويکوواژينال
Quality Assurance Reference Intervals.
Descriptive Analysis and Presentation of Bivariate Data
Quality Assurance Documentation
Sampling Distributions
Chapter 7: Sampling Distributions
Research Design Quantitative.
PERCENTILE. PERCENTILE What is percentile? Percentile (or Centile) the value of a variable below which a certain percent of observations fall.
Presentation transcript:

Hand-out associated with part I of the January Workload Webinar “Workload Issues for Computer-Aided Cytology Devices” ASCT Educational Webinar January 19, 2011 Tremel Faison, MS, RAC, SCT(ASCP) Regulatory Scientist, FDA-OIVD

Background Joint project between CMS and FDA Role of Pap Smears in CLIA ‘88 Two issues: Counting slides-how do you weight? Setting workload limits

Slide Counting: The product labeling regarding workload counting was difficult to interpret: variability and lack of standardization Challenged with developing a counting approach that reflects clinical study performance AND is easy to use in real-life laboratory settings

Maximum workload limits Upper limit is NOT for everyday productivity or a performance target CLIA ’88 requires individual maximum workload limits to be established by the technical supervisor

As a result the FDA….. Required manufacturers to revise their product labeling and send customer bulletins Published laboratorian safety tip fety/AlertsandNotices/TipsandArticles onDeviceSafety/ucm htm

Computer-aided Semi-Automated Gynecologic Cytology Screening Devices presently on the market (FDA Approved) Hologic ThinPrep® Imaging System (TIS) BD FocalPoint™ GS (Guided Screening) Imaging System

Hologic ThinPrep® Imaging System Imaging technology identifies microscopic fields for cytotechnologist review Automated stage 22 Fields of View (FOV) If no abnormalities, FOV review only If abnormal, Full Manual Review performed (FMR) 200 slide upper limit

BD FocalPoint™ GS Imaging System Imaging technology identifies and ranks microscopic fields for cytotechnologist review Designates slides for QC 10 FOV If no abnormalities, FOV review only If abnormal, Full Manual Review performed (FMR) 170 slide upper limit

Pivotal Clinical Studies Basis for FDA Approval 2 Purposes Safety and Effectiveness Workload Study

Basic Clinical Study Design Four cytology laboratories in US Accuracy of manual screening was compared to accuracy of screening with computer aided device Because an increase in productivity was anticipated, the accuracy objective was equivalence (not superiority) Establish an upper limit for workload

Manual screening arm  100% manual screening (“Manual”)  At least 10% QC rescreening Computer-aided review arm  Review of FOVs (“FOV only”)  If FOVs have abnormal findings, manual review of full slide (“Manual with FOV”)  At least 10% QC rescreening

In the Clinical Studies….. CT reviews only FOV (NOT allowed to do even a quick check outside of FOVs); If FOV does not have abnormal findings, CT is NOT allowed to do a manual review. OTHERWISE estimation of computer-aided device accuracy will be BIASED (overestimated) – it will be easy to demonstrate an equivalence of computer- aided device and manual screening

Workload Study Design Each day number of slides and number of hours were recorded Data for days with number of hours <4 were deleted from calculations If CT showed a decrease in accuracy the data was deleted from the calculation of the workload data

Workload Study con’t Using the adjusted data:  Average rate per hour was calculated (among all days)  Low rate per hour; high rate per hour  85-90% percentile was taken  These rates were multiplied by 8 hours to obtain “extrapolated” rate per day (theoretical rate per day, breaks during the day were not considered)

Workload Study con’t o“Extrapolation” (8 hours) is OK for the determination of upper limit of workload oNOT for determination of everyday productivity

For TIS, the computer-aided review arm had 22% of slides in average reviewed manually after FOV review By gold standard: Prevalence of ASC-US+ =7.3% Prevalence of LSIL+ =2.4% Prevalence of HSIL+=1.5%

For BD FocalPoint GS, the computer-aided review arm had 31% of slides in average reviewed manually after FOV review* By gold standard: Prevalence of ASC-US+ =14.8% * Study included seeded samples

Workload Limit per 8 hours An upper limit; NOT a productivity level Breaks were not considered 200 slides for TIS and 170 slides for BD FocalPoint GS Is an upper limit dependent on the number slides that were manually reviewed in the clinical study In each laboratory, the number of slides manually reviewed varies and therefore, the workload limit could vary

Why the Product Inserts were not clear Count any slide screened on imager once; whether FOV review only or screened manually after FOV review This method is correct ONLY if the percent of manual review slides with FOV is less than the rate seen in the clinical studies

Suppose the percent of manual review among all slides is 50% (100/200). X=100 slides (manual review with FOV) Y=100 slides (FOV review only) Since you can only screen 100 manual slides per CLIA ’88 you will exceed your maximum if you screen 100 additional FOVs Example

We know…. The upper limit for 8 hours according to CLIA ’88 is 100 slides, therefore….. It takes approximately 4.8 minutes to manually screen one slide Using the 200 slide limit determined in the TIS study and 22% manual review rate, we can calculate that screening:  FOV takes ~ 1.35 minutes  FOV + manual review takes ~ 6.15 minutes

In the TIS Clinical Study… If we let X be a number of slides with manual review with FOV and Y be a number of slides with FOV review only, then for 8 hours : 6.15*X *Y = 480 minutes or 1.28*X *Y = 100 slides Upper limit for the total number of slides is X+Y

Example: X=60 (42.3%) number of slides with manual review with FOV; Then using the formula, Y=82 – number of slides with FOV review only. Total number of slides 142 (60+82) Upper limit of the total number of slides = 142 (not 200) 1.28* *Y = 100 slides

Relationship of the total number of slides (X+Y) vs number of slides with manual review with FOV (X) for 8 hours (480 minutes)

170 upper workload limit with slides with full manual review = 31%  Same formula 6.15*X+1.35*Y=480 for two independent clinical studies (TIS and BD)!  Provides some additional validity for these calculations Same Calculations for BD-FPGS:

Challenge Formula for calculating upper limit from clinical study is ~ 1.3*X+0.3*Y=100 slides These weights are not easy to use in real-life laboratory settings Prevalence varies lab to lab How can we develop a counting method that reflects the clinical study performance AND is realistic for use?

Simpler and Safer Approach 1.5*X + 0.5*Y = 100 slides

Relationships of the total number of slides vs percent of slides with manual review with FOV for 8 hours Percent of slides which require manual review with FOV in average Upper limit for total number of slides Safer Alternative Upper limit for total number of slides Clinical Study Upper limit for total number of slides Product Insert 20% % % % % % % % % %

Laboratory Safety Tip FMR = 1 slide FOV = 0.5 slide FMR + FOV = 1.5 slides Upper Limit = 100 slides

Thank you! MDR: Problem/FormsandInstructions/default.htmhttp:// Problem/FormsandInstructions/default.htm