Day to Day Management of Quality Control Donna Walsh M.S. MT(ASCP) Deaconess Hospital Boston, MA

Day to Day Management of Quality Control

Donna Walsh M.S. MT(ASCP)

Deaconess HospitalBoston, MA

Why We Do Quality Control

Clients of laboratories services see the test results we produce as information and expect that information to be flawless, a very tall order. Statistical Quality Control practices is one of the tools of the trade utilized to approach reasonable levels of flawlessness.

Why We Do Quality Control

We are all familiar with classical approaches to quality control and the frustrations involved when these approaches fail and we need to troubleshoot methodology before releasing results.

Goal and Objective

This talk will review the basics of statistical quality control and some 'nuts and bolts' solutions to common QC problems.

Case studies will be used to illustrate effective strategies.

Goals of those Attending Today

Improving Day to Day QC Decisions & Documentation and Review of QC

Trying to get the days work reported out

Health Care Today

Emphasis today is on cost containment Laboratories have become cost centers as

opposed to revenue centers.» QC costs involve cost of QC material (special

controls can be very costly)» QC costs involve rework of out of control runs.

(labor, reagent and control cost)

How Did We Get Here?

Levy-Jennings Plots were adapted from industrial use and have been in use to this very day since the 1960’s

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Classic Quality Control

Principles of Statistical QC» Control Solutions are used to make Control

Measurements» Control Measurements validate Unknown

Measurements» Statistical Measurements Provide Guidelines for

Acceptable Control Measurements

Classic Quality Control

Example:

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trendshift

Youden Plots

Looking at two controls at once

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low control

high

con

trol

Ever Seen a QC Chart Like This?

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Dot chart of the

90’s

Multi-rule Quality Control

Based on the concept that improved error detection is provided by selecting multi-rules over single-rule control procedures.

Widely used multi-rule control procedure is the one recommended by Westgard

Westgard Multi-Rule Quality Control Scheme

control data

No

No No No No

No

Yes Yes Yes Yes Yes

IN-CONTROL ACCEPT RUN12s

13s22s R4s

41s 10X

OUT-OF-CONTROL REJECT RUN

Available Options

Classic QC» very visual

» tedious manual record keeping

» often available as an on-line QC package for many automated systems

Multi-Rule» better error detection

» more cumbersome for operator

» easily adaptable to computer analysis

Practical “Nuts & Bolts” Options

Include Analytical Performance in Control Parameter (mean & SD) Decisions» Use NCCLS EP-5 to evaluate precision.» Adapt Control Measurement Frequency to Testing Needs

Set Medically Useful Control Procedures» Controls Levels at Medical Decision Points

Consider Control Measurements Quality Checks» Keep track of lot numbers, calibrations & maintenance procedures » Use this procedural information to assign causes to failures of

quality checks

Case Studies .... practical QC

Incorporating NCCLS EP-5 data into QC decisions

NCCLS EP- 5 data QC

Methodwithin run

% CV% of total variance

daily % CV

% of total variance

total % CV

% of total variance

% CV set @

Carotene @ 97 12.8% 23.0% 14.7% 30.0% 18.4% 47.0% 15.0%Carotene @ 228 7.6% 24.0% 8.5% 29.0% 10.8% 47.0% 10.1%IgA @ 121 1.1% 6.0% 2.9% 45.0% 3.0% 45.0% 5.5%IgA @ 359 1.1% 7.0% 2.7% 44.0% 2.8% 48.0% 5.8%


Adapt Control Measurement Frequency to Testing Needs» NCCLS EP-5 calls for 2 replicates per run, 2 runs per day

for 20 day. Adapt to testing situation - Example: Stat Lab has three shifts and three controls for Oximetry

» Adopt CLIA definition of 24 hour = one run and assay 3 reps, for assay of new lot of control

» Implement different QC levels on each shift for periods of stable operation.

Practical ‘Nut & Bolts’ HINT

Many of today's testing procedures have much improved analytical performance. You can take advantage of this improved accuracy and precision in the design of your QC protocol.


Set Medically Useful Control Procedures» Example: Digoxin

YTD n=

1430

target mean

target sd

target% CV

YTD mean

YTD sd

YTD % CV

level 1 0.51 0.15 29.4% 0.70 0.20 28.6%level 2 1.51 0.20 13.2% 1.69 0.20 11.8%level 3 3.30 0.25 7.6% 3.33 0.27 8.1%


Set Medically Useful Control Procedures» Example: pH Reference Range is 7.37-7.44

(range = 0.07)

» Control Material A; standard deviation = 0.014; +/- 2 sd = 0.056

» Control Material B; standard deviation = 0.005; +/- 2 sd = 0.020

» Control Material B is more costly than Control material A

But it is costly to walk too fine a line...


Set the % CV tight or use a control protocol that has improved precision at Clinically Significant Decision Levels

Or put differently, make sure you have an appropriate target for the situation.


Consider Control Measurements Quality Checks» Keep track of lot numbers, calibrations

& maintenance procedures » Use this procedural information to assign

causes to failures of quality checks

» Best way to document assignable causes of mean & sd changes

lot numbers


Example: Preventative Maintenance Appropriate for Workload» Drugs by Fluorescent Polarization

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Increase PM frequency

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Example: new lot number of reagent

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Example: new lot number of reagent» shift in QC values correlated with

change in lot of reagent.» check to see if there is a shift in

unknowns (samples) or standards» matrix effect if shift is only in control

samples» adjust QC mean to account for shift and

avoid unnecessary repeat work.

Lot numbers5-1-95 xx02265-15-95 xx0228

66.0

69.0

72.0

75.0

78.0

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Long standing shifts may not be obvious until a lot change causes a shift out of control

Check Year to Date and

or lot to date QC for any shifts or

trends.lot change


Example: Glucose - shift in current month away from year to date when new electrode used.

YTD n= 398

target mean

target sd

YTD mean

YTD sd

monthmean

monthsd

level 1 72 3.0 74.5 2.20 76.3 2.2level 2 261 7.0 257.5 5.80 260.1 6.7

8.70

8.85

9.00

9.15

9.30

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Example: Change in QC post calibration of new lot of Calcium

calibration done

Lot related shift post cal

on 5-10. Recal on 5-20 due to bias in

pt. checks


Example of shift with no assignable cause» Lithium by ISE

0.68

0.71

0.74

0.77

0.80

0.83

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Trouble shooting- new control- recalibration- new electrode- fresh reagents

NOTHING WORKED!!!


Lithium Statistical QC information

YTD n = 274 mean sd % CV

target 0.78 0.03 3.8%YTD 0.77 0.02 2.6%monthly 0.74 0.06 8.1%


Running control samples is a check on quality; they do not control quality. You must do that yourself.

REPORT RESULTS!

There is NO such thing as a

QC Crystal Ball

Recommendations

View the test results we produce as information » Include Analytical Performance in Control Protocols» Set Medically Useful Control Procedures

Make use of on-line QC packages» facilitates statistical quality control calculations» provides visual dot charts for operators

Recommendations

Consider Control Measurements Quality Checks» Keep track of information that could validate a

change in the statistical QC parameters» Change QC parameters promptly when the situation

warrents» Use Patient Check Data to help in QC decisions

Benefits

Accurate and Reliable Reporting of Patient Test Data

Well Documented Quality Control Program Day to Day and Cumulative QC data will be more

useful.

Documents

Day to Day Management of Quality Control Donna Walsh M.S. MT(ASCP) Deaconess Hospital Boston, MA