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It’s not Black and White: Unraveling the puzzles of HematologyPart 2, Delta Checks

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Disclosures

u I am receiving an honorarium from Sysmex for preparing and delivering this presentation

u The views expressed in this presentation are those of the presenter and their healthcare facility and provided for illustration purposes only. Results of case

studies are not predictive of other cases and results may vary. Prior to using these devices, please review the manufacturer’s Instructions for Use.

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ObjectivesAt the end of this presentation, the attendee will be able to:

u List common preanalytical issues with hematology specimens

u Determine if a sample has a true abnormal value or a spurious result

u State the steps to resolving problematic samples, which include clumped platelets, cold agglutinins, critical low or high counts, and abnormal distribution/scattergrams

u Correlate cells seen on slides with numerical values of parameters

u Describe how to select, implement, and review delta check practices

u Investigate and interpret delta check alerts

u Make decisions as to when to report or cancel samples with delta failures

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u Comparison of a current patient result and the patient’s previous results

u Delta means change

u Function of LIS and middleware

u Deltas first described 19671

u LIS first used for delta checks in late 1970’s1

u Allows operator to detect large variations in patient results

u Results that exceed the standard will be flagged

u Choose Stable parameters

u Predefined limit

u Within set time frame

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Delta checks in

Hematology

1 Schifman, 2017

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Choosing Delta Check ParametersWBC count

HemoglobinHematocrit

MCV

MCHMCHC

RDW

Platelet count

u Stable parameters

u Low index of variabilityu Reduces false alerts

u Different rules for different populationsu NICU

u Oncology

u Outpatients

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Index of Individuality- fluctuation within an individualu High Index of individuality

u Individual values fluctuate anywhere within reference range

u Not a tight range for individual

u Large changes may fall outside reference range

u Thus, the reference range itself may alert us to change in results and patient status change

u Low Index of individuality

u Little fluctuation within an individual

u Variation exists between people

u Individual’s results expected to remain in a small area within range

u With large change, result will probably still be within reference range

u Reference range may not be helpful to indicate a change in patient status

u **Delta check is useful!**6

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Implementing Delta Checks

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Choose analytes with low biological variation

Choose individuals with results across

reference range

Run or Review serial specimens

Determine delta values between serial specimens

Determine intervals that

are valuable for monitoring

Establish laboratory

specific limits

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Laboratory specific limits

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ABSOLUTE PERCENT RATE OF CHANGE

MAY VARY BY ANALYTE

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Investigate Delta Checks

u Don’t just repeatu Tech should identify the reason before resulting

u Check current and previous samples and investigateu Detects testing problem with either former or current

sample

u Important component of autoverification proceduresu Improves laboratory efficiency

u Investigation required extra work for technologists

u May delay TAT

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Delta checks as Quality Control

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Patient based Quality Control

Detects testing problems

Specimen collectionAnalysis

Reporting problems

Safety net

Detect problems that may be normal so may otherwise go unnoticed

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Why do we use Delta Checks?

u Identify patient specific errors

u Early error detection leads to better patient care and safety

u Helps prevent errors such as incorrect drug dosing, blood transfusions, etc.

u By using delta checks, we can notify providers

u Changes in patient’s results may alter diagnosis or trigger additional testing

u Changes in patient’s results may indicate need for medical intervention

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Causes of Discrepant Results

Delta Checks Can detect

u Pre-Analytical variations

u Patient identification

u Specimen collection

u Analytical variation

u Instrument

u Methods

u Biological variations

u Physiological

u Changes with patient age

u Lifestyle changes

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Pre-Analytic Variation

If Multiple delta check limits fail, this indicate a likelihood of sample mislabeling or misidentification

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Pre-Analytic variation“Mislabeled” specimens

u 2 unique identifiers needed

u Label does not match order

u 2 contradictory labels on tube

u 0.1% of samples submitted to laboratory and Anatomic pathology in US1

Misidentified

u Wrong label

u Wrong blood in tube (WBIT)

u Sleeping, unconscious patients

u NICU, ER, elderly

u Language barriers

u Identical names

u Multiple births

u WBIT rate 0.05-8.8%2

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1 Ciesla, B 2018 2 Staseski,J 2012

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Identification Errors Statistics1

u Misidentified/WBIT were majority of ID errors in studies

u Statistics Patient ID errorsu General lab 1-7.4%u Stat lab 8.8%

u Transfusion medicine 0.05%u Smaller hospitals have greater rates

u Rates greater in Stat labs and general lab than in Blood Bank

u 85.5% of errors were caught by techs pre-verificationDelta checks work!

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1Straesky, 2012

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Pre-analytic Variation- CollectionDelta checks designed to identify:

u Contaminated specimens

u Difficulty in phlebotomyu Fibrinu Clotsu Hemolysis

u Overfilled or underfilled tubes

u Sample transportu Refrigerated specimens- Platelet clumpingu Pneumatic tubes can cause RBC damage

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Analytical Variation

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Analyzer problemsClogs

Variations in reagent volumes, delivery

Reagent, lot changesCalibration

Operator errorsDilution errors

Improper mixing

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Biologic variation

u The human body always tries to maintain homeostasis

u RBC indicies, hemoglobin tend to be very stable

u Ideal parameters for delta checks

u Delta check limits may vary by age

u Nutrition status may cause variationu Iron deficiency anemiau Dehydration/fastingu Lipemic specimens

u Disease states and treatment may cause variationu Anemiau Chemotherapy

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Goals of Delta

checks

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Identify changes in patient conditions

Identify sample quality issues/patient misidentification

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Deltas- is it real?Spurious results- sample quality

Patient identification errors

Clotted samples

Short samples

Contaminated specimen

Changes in patient condition-Biologic variationu Newborn ages

u Trauma

u True disease statesu Canceru Anemiau ITP

u Chemotherapy, surgery, dialysis

u Blood transfusion

u Nutrition

u Dehydration/rehydration 20

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Delta Case Study- Platelet decrease

u Drop in platelet count, delta flag, not below 100

u No plt clump flag

u SOP and OP alert do not say ‘check for clot’

u You’ve repeated it, but need to investigate why count dropped, review smear

u Low plt with previous normal result, suspect spurious result

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Delta Case Study- Platelet increase75-year-old oncology patient, CBC before infusionu WBC =5.6, Hgb =11.9, Hct =35, Plt =164

u PLT delta, no other flags

u Rerun added, slide made

u Repeat PLT =166, no flags. PLT count held for slide review

u Infusion services requests platelet count

u Patient history T-7, PLT= 42. prior PLT =167,155

u outlier was the 42

u Check previous history, not just most recent!

u Saves time, no slide review needed

u Improves TAT22

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Platelet Delta Check failuresSpurious samples

u Clots

u Difficult draws, failure to mix properly

u EDTA induced Thrombocytopenia

True patient change in status

u Platelets go up and down in oncology patients/chemotherapy

u Thrombocytopenia, ITP, TTP

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Tips to confirm discrepant Results

u Do values match previous results?

u Look at result history

u Look at past results to confirm trend

u Were the previous results questionable?

u If so, Was it investigated?u Look at patient history

u Reason for these results

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MCV Delta Case Study

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MCV Deltas on patients, trending up

Several patients rerun, 2nd run matched first run, reported

Change of shift, no communication

3 more new delta flags in a row

Problem recognized

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Starting the investigation

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Investigateu Repeat

u Confirm correct label, patient ID on tube

u Investigate for sample collection issuesu Sufficient sampleu No clotsu hemolysis, lipemia, icterus

u Investigate analytical issuesu QCu Reagentsu Isolated event, or others?

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MCV Delta Case Study-Some Things we noticed

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ALL DELTAS OCCURRED ON THE

SAME SIDE

SAMPLES RERUN ON ALTERNATE

INSTRUMENT, ON RERUN MCV MATCHED

HISTORY

SAMPLES RERUN ON SAME SIDE REPEATED WITH A DELTA FLAG

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Questions to ask yourself

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WHAT MIGHT BE THE PROBLEM?

STEPS TO RESOLVE?

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sample PreviousMCV

Original MCV

RerunMCV

Reported MCV

Corrected? comments

1 none 90.3 None 90.3 Yes Autoverified, no previous, no flags

2 79.7 92.9 82.1 82.1 No Reran on alternate XN

3 96.0 105.9 95.1 95.1 No Reran on alternate XN

4 80.8 87.7 88.1 88.1 Yes Reran on same XN

5 96.2 106.5 None 106.5 Yes Autoverified, prev from 12/19 exceeded 28 day delta

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Delta Case Study- Investigationu All delta flags were on samples run on XN-L

u MCV’s erroneously high on XN-L

u If repeated on XN-R, rerun results matched history

u QC failed on XN-L, passed on XN-R

u Reran patients back until we could see when problem started

u Reviewed all results back to last acceptable QC against previous in EPIC

u Clog in RBC aperture!

u 1st sample- short draw, clot went throughu Aperture clog removal, Aperture cleanedu QC repeated and passed on XN-Lu 6 reports needed correction for MCV and associated parameters

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Analytical Variation

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This case is an example of instrument specific issues

RBC aperture clog

MCVs great predictor, few false positive rates

Transfusion

To solve, evaluateQCImprecision

Bias

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Case Study: High MCHCs

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u MCHC >37.5 flags, delta on several patients in a row on same analyzer

u Repeated on same instrument with same results

u Run on alternate analyzer

u Normal MCHCs

u QC

u QC failed on 1st instrument

u OK on alternate instrument

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MCHC Case Study

u Suspected spurious results due to deltas on several patients

u When delta flag is present, check all parameters

u Several analytes affected. MCH, MCHC, Hgb

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XN-L

WBC 5.0RBC 3.88Hgb 14.0Hct 36.2 MCHC 38.7MCV 93.3MCH 25.8

XN-R

WBC 5.1RBC 3.88Hgb 12.1Hct 36.3MCHC 33.3MCV 93.6MCH 31.2

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MCHC Case StudyHemoglobin related problem

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XN-L

WBC 5.0RBC 3.88Hgb 14.0Hct 36.2 MCHC 38.7MCV 93.3MCH 25.8

XN-R

WBC 5.1RBC 3.88Hgb 12.1Hct 36.3MCHC 33.3MCV 93.6MCH 31.2

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What Happened?

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u Delta checks alerted us to problem quickly

u Affected results on only 1 analyzer

u Therefore, not patient related

u Instrument related

u Affected multiple analytes

u All related to Hgb

u No sulfolyser

u Sensor not working, No alarm

u Sulfolyser is used for Hemoglobin determination

u Sulfolyser replaced

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Shortcomings of Delta Checksu Need to balance error detection with false positives

u Choose stable analytes to monitor

u Investigations cause delay in patient results

u Most delta failures are due to actual patient change in status

u Consider the patient population

u Inpatient vs outpatient

u Oncology patients have lots of fluctuation in results

u Neonatesu Delta check limits are often determined in healthy populations

u Special rules can be written for various patient populations

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“

”

If you're gonna play the game, boy, you gotta learn to play it right. You've got to know when to hold ‘em, know when to fold ‘em

Kenny Rogers

When to cancel, when to report?38

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Delta flags- Hgb/Hctu Hgb/ Hct increased

u Recently Transfused?u Patient dehydrated?

u Hgb, Hct decreased –u Surgery?u GI bleed?u Trauma?u Maternity, L&D?u History of anemia?u Receiving fluids?u Oncology?

u True patient change of status

u Review patient chart

u Call nursing floor if questions, to confirm

u Document in notes

u Comment if appropriateu Report results

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Delta flags -WBC

u Fever

u Infectionu Labor

u Trauma

u True patient change of status

u Check patient chartu Call nursing floor if

questionsu Document in notes

u Comment if appropriate

u Report results

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Delta Flags- Indicies

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MCV very stable

Best predictor, fewest false positives

You should not seeHgb> Hct

High MCV with low MCHC

Morphology that does not correlate with indicies

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MCV delta check failures

Since few false positives, investigate!

u Transfused?

u Review WBC, platelet, RDW if contamination with IV fluid is suspected

u Age related- Neonates

u Oncology patients not as stable due to meds

u Check chemistry results – electrolyte balances

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Spurious resultsInadequate samples- Cancel!

u Exceeds analysis time

u Clotted

u Short draw

u QNS

u Can alter indicies

u Hemolyzed sample

u Confirm by spinning aliquot

u Check chemistry

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Other investigationsu Call provider to question if results are expected

u If results are suspicious but care giver requests results

u Report and comment per SOP

u Ex: “questionable results; advise repeat”

u Several deltas on same sample?

u Suspect mislabeled or misidentified

u If patient has ABO type in LIS, type sample

u If blood types differ, can determine WBITu If types match, cannot rule out WBIT

u If WBIT suspected, request redraw

u Cancel specimen

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To Cancel or Report?Follow SOPsResult cancellation implications

u Difficult draws

u Adult

u NICU

u Loss of blood volume

u Delayed results

u Delay in care

Implications of reporting incorrect results

u Inappropriate medical care

u Expense

u Lengthened hospital stays

u Psychological and social issues

u Beyond hematology

u Transfusions, Infectious diseases, genetic testing

u Harm may not be realized for hours, days or longer

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Conclusionsu Autoverification gives us more time to

investigate problem specimens

u Delta checks are important to have in place to use autoverification

u Most handling errors are pre-analytical

u Delta checksu used as a laboratory specific quality

indicator

u vital to ensure precise results and patient safety

u We can’t be good detectives without looking at all the clues!

u Questions?46

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References

u Ciesla, Betty. Hematology in Practice, 3rd ed. FA Davis, Philadelphia. 2018.

u Lyashchenko, A. et al. Trust but Verify: Repeat CBC Sample Measurements Help Identify Automated Hematology Analyzer Errors. American Jourm=nal of Clinical pathology. Vol 149, 11 Jan 2018.

u Schifman, R, et al. Delta Check Practices and Outcomes. A Q Probe Study Involving 49 Health Care facilities and 6541 Delta Check Alerts. Arch Pathol Lab Med. Vol 141, June 2017.

u Straseski, J. Delta Checks in Action: An essential Quality Improvement tool. AACC webinar. 2012.

u Zandcki, M. et al. Spurious counts and spurious results on haematology analysers: a review. Part I: platelets. International Journal of Laboratory Hematology. 09January 2007.

u Zandcki, M. et al. Spurious counts and spurious results on haematology analysers: a review. Part II:

white blood cells, red blood cells, haemoglobin, red cell indicies and treticulocytes. International Journal of Laboratory Hematology. 09January 2007.

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