what’s new in statistical quality control guidance: clsi’s ...preparation for final draft...

1

What’s New in Statistical Quality Control Guidance: CLSI’s C24 Updates August 2, 2016

2

• Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions Curtis A. Parvin, PhD Nils B. Person, PhD, FACB Nikola Baumann, PhD Lili Duan, PhD A. Paul Durham Valerio M. Genta, MD Jeremie Gras, MD Greg Miller, PhD Megan E. Sawchuk, MT(ASCP)

CLSI C24 - 4th Edition

3

Document Status


Project Phase Activities Dates Proposed Draft Voting Delegates and Committee

members vote on Proposed Draft

Dec 1, 2015 – Feb 1, 2016

Comment Resolution Committee resolves comments and updates draft

February, 2016 – April, 2016

Preparation for Final Draft Voting

Editing, Final approval from chairholder

May, 2016 – August, 2016

Final Draft Vote Consensus Council Approves Publication

Aug 26 – Sep 15, 2016

Preparation for Publication

Editors prepare publication format

September, 2016

Publish September, 2016

4

What’s New?

• Notable changes and additions – Alignment with principles introduced in CLSI EP23:

Laboratory Quality Control Based on Risk Management – Additional performance measures related to patient risk – Expanded guidance on setting target values and SDs – Greater focus on QC frequency and QC schedules – More emphasis on recovering from an out-of-control

condition

5

Patient Risk

Sequence of Events Creating Risk of Harm for a Patient (Example)

Initiating cause

Testing process failure

Incorrect result

generated

Incorrect result

reported Misdiagnosis

Hazardous medical action

Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

6

Managing Patient Risk

Probability of Harm Negligible Minor Serious Critical Catastrophic

Frequent unacceptable unacceptable unacceptable unacceptable unacceptable

Probable acceptable unacceptable unacceptable unacceptable unacceptable

Occasional acceptable acceptable acceptable unacceptable unacceptable

Remote acceptable acceptable acceptable acceptable unacceptable

Improbable acceptable acceptable acceptable acceptable acceptable

Severity of Harm

CLSI EP23, Table 3. Risk Acceptability Matrix

7

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

8

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Laboratory QC

7

9

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Probability of detecting a testing process failure

7

10

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Probability (number) of incorrect patient results reported before the testing

process failure is detected

7

11

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Probability (number) of incorrect patient results identified and corrected before

misdiagnosis or hazardous action

7

12

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Focus on the measurement procedure

7

13

Patient Risk


Initiating cause


Incorrect result

generated

Incorrect result



Patient harmed

P1 P2 P3 P4 P5 P6

Hazardous Situation

CLSI EP23, Figure 6

Focus on risk of harm to patients

7

14

Patient Risk Based QC Performance Measures

• Number of erroneous patient results generated before detecting an out-of-control condition

• Depends on • Magnitude of out-of-control condition • QC Strategy

– # of QCs evaluated – QC rule(s) – Frequency of QC evaluations (QC schedule)

• Allowable total error for the analyte

8

15

Patient Risk Based QC Performance Measures

• Number of erroneous “final” patient results reported before detecting an out-of-control condition

• Depends on • When patient results are reported • What recovery strategy the laboratory employs for

identifying and correcting erroneous patient results

9

16

Main Chapters (Draft) 3. Purpose of Statistical Quality Control 4. Assessing Quality Control Performance 5. Planning a Statistical Quality Control Strategy 6. Recovering from an Out-of-Control Condition 7. Ongoing Assessment of Quality Control Programs 8. Worked Examples


10

17



10

18



Greg Miller: “Planning a Statistical Quality Control Strategy”

10

19



Niki Baumann: “Recovery From Out of Control Conditions”

10

20

Our Speakers

• Nikola Baumann, PhD, DABCC – PhD from University of Wisconsin in Madison – Postdoc in Clinical Chemistry at Washington University – Asst. Prof. of Lab Medicine and Pathology, Mayo Clinic – Co-Director Central Clinical Laboratory, Director of

Central Processing, Director of the Clinical Chemistry Fellowship Program

– Active in AACC at both the regional and national level – An influential contributor to the topic of preparing for

and recovering from large-scale testing errors

11

21

Our Speakers

• Greg Miller, PhD – PhD from University of Arizona in Tucson – Postdoc in Clinical Chemistry at Ohio State University – Professor of Pathology, Virginia Commonwealth – Director of Clinical Chemistry and Pathology

Information Systems – Past President of AACC (2012) and CLSI (2015) – Key contributor to committees and working groups for

several professional organizations involved with standardization, harmonization, quality control, and external quality assessment

12

what’s new in statistical quality control guidance: clsi’s ...preparation for final draft...

Documents