Clinical Laboratory Tests: - PowerPoint Presentation

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Clinical Laboratory Tests:Which, Why, and What Do The Results Mean?

2018 Texas Association for Clinical Laboratory ScienceThursday, 22 Mar 2018Wyndham, El Paso Airport

Frank H. Wians, Jr., PhD, MT(ASCP), MASCP, DABCC, FACB

Professor of Pathology

Texas

Tech University of the Health Sciences Center El Paso

and the

Paul L. Foster School of Medicine

Technical Director, Clinical Chemistry, University Medical Center (UMC) El Paso

Medical Director, UMC Far East and West Clinic Laboratories

Lt Col (

Ret

), USAF, Biomedical Sciences Corps

Editor-in-Chief

,

Lab

Medicine

, 2004-2011

frank.wians@ttuhsc.edu

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Disclosures and ReferencesDisclosures

NoneSelected ReferencesWians FH Jr. Clinical laboratory tests: which, why, and what do the results mean? Lab Med. 2009;40:105-113.Wians FH Jr, Gill GW. Clinical and anatomic pathology test volume by specialty and subspecialty among high-complexity CLIA-certified laboratories in 2011.

Lab Med

. 2013:44:163-167.

Carraro P, Plebani M. Errors in stat laboratory: types and frequencies 10 years later. Clin Chem. 2007;53:1338-1342.Fillippo A, et al. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11:47-58.Obuchowski NA, Lieber ML, Wians FH Jr. ROC curves in Clinical Chemistry: Uses, Misuses, and Possible Solutions. Clin Chem. 2004;50:1118-1125.Ziadie M, Wians FH Jr. A guide to the interpretation of CSF indices. Lab Med. 2005;36:558-562

2009-present

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OutlineThe Laboratory Testing Cycle

Diagnostic Decision MakingMedical NecessityQuestion to Ask Before Ordering a Laboratory TestReasons for Ordering a Laboratory Test

Approaches to Establishing a Diagnosis Based on Laboratory Test Results

Clinical Performance Characteristics of Laboratory Tests

Receiver-operator characteristic (ROC) curvesReference Interval for Interpreting Laboratory Test ResultsCritical Difference Between Consecutive Laboratory Test ResultsArtificial Neural Networks (ANNs)Summary

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Distribution of Test Volume (~6 Billion) by Clinical or Anatomic Pathology Specialty in CLIA-Certified High Complexity Laboratories (n = 172,000 in 2011)*

*Wians FH Jr, Gill GW.

Lab Medicine

. 2013;44:163-67.

Summary (2011)

~6 billion laboratory tests performed annually in ~172,000

high complexity

labs (~72%) out of the 239,000 labs in the U.S.

AP tests represent ~10% of all tests;

CP (laboratory medicine) tests represent the remaining

90

%!

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The Laboratory Testing CycleLaboratory Testing Cycle

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% of Stat Test Errors by Category:Preanalytical (PreA), Analytical (A), and

Postanalytical (PostA)Adapted from: Carraro P, Plebani

M. Errors in stat

laboratory

: types and frequencies 10 years later. Clin Chem. 2007;53:1338-1342.5 PreA types of errors accounted for 44.4% of all PreA errors!If the clinical laboratory is provided with the:Right specimenAt the right

time

On the correct patientWith a complete and accurate test request

The

laboratory

will

provide

a quality result in

a

timely manner.

What did the data look like in another 10 years (2017)?

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Recent Source of Pre-analytical Error:Biotin Interference in Biotin-Streptavidin Immunoassays

Ortho

Vitros

Tests

Affected by Biotin Interference Performed in the UMC Clinical Lab(Quantitative, numerical value; Qualitative, Pos/Neg

)

Test Abbreviation

Type of Result Reported

Effect of Interference

AFP

Quantitative

Falsely

low

CA 125

Quantitative

Falsely

low

CA 15-3

Quantitative

Falsely

low

CEA

Quantitative

Falsely

low

CK-MB

Quantitative

Falsely

low

Ferritin

Quantitative

Falsely

low

FSH

Quantitative

Falsely

low

iPTH

Quantitative

Falsely

low

LH

Quantitative

Falsely

low

Myoglobin

Quantitative

Falsely

low

NT-

proBNP

Quantitative

Falsely

low

Prolactin

Quantitative

Falsely

low

PSA

Quantitative

Falsely

low

Total beta-hCG

Quantitative

Falsely

low

Troponin I

Quantitative

Falsely

low

TSH

Quantitative

Falsely

low

B12

Quantitative

Falsely

high

Cortisol

Quantitative

Falsely

high

Folate

Quantitative

Falsely

high

aHAV IgM

Qualitative

Falsely

Negative

aHBc IgM

Qualitative

Falsely

Negative

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Diagnostic Decision MakingThe medical specialty that nearly every practicing physician relies on every day, for which training in many medical schools is limited to no more than a scattered few lectures throughout the entire curriculum, is “laboratory medicine

.” Dr. Michael Laposata

Number and complexity of laboratory tests is large and growing as new tests become available in emerging subspecialties of laboratory medicine (e.g.,

pharmacogenomics

).Over a 90 day period, Mayo Medical Labs added 56 new tests to their Test Directory of ~3,200 total tests related to over 2,400 diseases, syndromes, conditions, and infectious agents.~90% of all laboratory tests (AP + CP) ordered are laboratory medicine (CP) testsThese tests are associated with 60-70% of all critical decision-making such as:Patient admittanceDischargeDrug therapyWithout sufficient knowledge of laboratory tests, health care providers are more prone to:Inappropriate test orderingMistakes in interpreting test results

(i.e., what do the results mean?)Poor case management

Increased costs per patientAdverse outcomes (Jennifer

Rufer

case

)

“America’s first clinical biochemist,” and among the fathers of the subspecialty of laboratory medicine known today as “clinical chemistry.”

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How important are clinical laboratory test resultsin patient care outcomes?

Is there any other medical specialty with such a scope of influence on medical care in the U.S.?

From: Lewin Group: Laboratory Medicine: A National Status Report

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Medical NecessityOrdering the minimum number of appropriate laboratory tests necessary to provide optimal patient care.

Reduce, if not eliminate, the ordering of “unnecessary” laboratory tests.What constitutes an “unnecessary” laboratory test? Any test for which, a priori, the results are not likely to be useful in the appropriate management of a patient’s medical condition.

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Questions Clinicians Should Ask Before Ordering a Laboratory Test

Why is the test being ordered?What are the consequences of not ordering the test?How good is the test in discriminating between health vs disease?How are the test results interpreted?How will the test results influence patient management and outcome?

Source:

Rudolf RW et al.

Am J Clin Pathol. 2017;148:128-135.

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“Legitimate” Reasons for Ordering a Laboratory Test

Diagnosis (to rule in or rule out a diagnosis)Monitoring (e.g., the effect of drug therapy)

Screening

(e.g., for congenital hypothyroidism)Research (understand pathophysiology of Dx)

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Approaches to Establishing a Diagnosis Based on Laboratory Test Results

Hypothesis-deductionPattern recognition – a key component of Artificial Neural Networks (ANNs)Medical algorithmsRifle versus shotgun approach (to ordering laboratory tests)

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Approaches to Establishing a Diagnosis Based on Laboratory Test Results

Hypothesis-Deduction (HD)PatternRecognition (PR)

Medical

Algorithms (MA)

Rifle vsShotgun (RS)Based on patient-specific non-laboratory findings, hypothesize what conditions (differential diagnoses) could cause these findings, followed by deducing the most likely cause based on the results of appropriate laboratory, and other types of, tests.Matching patients’ results for selected lab tests to results observed typically in a variety of disorders associated with the patient’s principal lab test finding (e.g., thrombocytopenia in a pregnant woman)Diagnosis of systemic infection (sepsis) and 28-day all-cause mortality risk based on procalcito-nin test results.Discriminately ordering only 7 lab tests known to have adequate diagnostic accuracy and predictive value in identifying a particular disease vs indiscriminate ordering of 20 lab tests, some or none of which may have clinical utility in identifying the disease.

Procalcitonin

Medical Algorithm

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Another Example of Pattern Recognition: CSF Indices

The

number of possible permutations

of “A” and “N” for 4 indices is

24 or 16.

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Clinical Performance Characteristics of Laboratory Tests

How do clinicians improve prevalence when ordering lab tests?

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Receiver-Operator Characteristic (ROC) CurvesAUC values range from 0.5 to 1.0 and provide a quantitative representation of overall test accuracy:

0.5 to 0.7 (low accuracy) 0.7 to 0.9 (possibly useful) >0.9 (high accuracy)The ROC

curves below indicate that PSA (AUC = 0.66) has significantly higher diagnostic accuracy than PAP but only modest power in discriminating

BPH from organ-confined prostate

cancer:The sensitivity and specificity of a test is of limited value because these parameters represent the answer to the question:What is the probability of a patient having a positive test result if this patient has disease X?The more challenging question facing clinicians, however, is:What is the probability of this patient having disease X if the test result is positive (or negative)? Note: requires calculation of likelihood ratios.Diagnostic accuracy refers to the sensitivity AND specificity of a laboratory test.Area-under-the-(ROC) curve (AUC) values are a powerful tool in comparing diagnostic accuracy between 2 or more laboratory tests.

Diagonal

represents a test with no diagnostic value (AUC = 0.50)

PSA, prostate-specific antigen

PAP = prostatic acid

phoshpatase

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Reference Interval for Interpreting Laboratory Test ResultsReference interval

(RI), more precisely, population-based, as opposed to individual-specific RI, refers to the two values representing the 2.5 and 97.5 percentile values for all values obtained on a sufficient number of “apparently healthy” individuals and these values are normally (aka Gaussian) distributed.

Stratified RI (e.g., based on sex and/or age)

Decision level

refers to a single cutoff value for a test that provides the highest discriminatory power in identifying samples from individuals with disease from those without the disease.

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Critical Difference Between Consecutive Laboratory Test ResultsSources of variation affecting laboratory test results within and between individuals, and dates of testing include:

Analytical errorRandom error (RE)Systematic error (SE)Constant error (CE)

Proportional error (PE)

Biological (intra-individual) variation (CV

I) – an often forgotten source of variation in laboratory test results

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Sources of Biological Variation (BV)

Chronological ageAlkaline phosphatase (ALP) values increase during pubertyGenderTestosterone values are higher in healthy males than in healthy femalesEstradiol values are higher in healthy, premenopausal females than in healthy males

Pulsatile and circadian biorhythms

Pulses of luteinizing hormone (LH) can be detected about every 90 min

ACTH and cortisol concentration begin to rise early in the morning, peak at ~8 am, and gradually fall over the course of the day, reaching a nadir at midnight Seasonal variationALT levels are ~12% higher in the Spring than in the FallGeographic variationCarboxyhemoglobin levels are higher in individuals living in areas with heavier automobile traffic than in individuals living in rural areas

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What are the effects of BV on test results for individuals?Serially measured test results may be different at different times of the:

DayMonthSeasonGeographic locationSerially measured test results for some analytes

may remain relatively constant regardless of the day/month/season/geographic location

Sodium and potassium values vary very little in healthy individuals

Population-based reference intervals are not applicable to all individuals and all analytes regardless of the timing of the sample for testing

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The Conundrum of Serial Testing

Physicians frequently order the same test at multiple time points (i.e., serial testing) during the course of the patients’ management.Laboratory test results for any analyte change over time within and between individuals (see graph below

):

Mean

Values and Ranges of Serum Creatinine Values in Four Samples at Different Time Points From 10 Healthy Men (dotted lines indicate reference interval: 0.72-1.36 mg/dL)The magnitude of the change is influenced by both analytical and biological sources of variation [i.e., analytical imprecision (CVA) and biological variability (CVI)]

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The Conundrum of Serial TestingThe critical question facing clinicians are:

Is the magnitude of the change in serially measured laboratory test values on my patient statistically significant?If the probability that the change is statistically significant is high (e.g. > 80%), does this mean that some patient medical intervention is necessary, desirable, or strongly encouraged?

The answer to both of these questions requires an understanding of how to calculate the

critical difference

(CD), preferred term is now: reference change value (RCV), which is a presentation for another day, especially, given the current circumstance that the vast majority of clinicians are either not aware or are not enamored of the value of the RCV in their medical decision making.

Slide24

Artificial Neural Networks (ANNs)

ANN*

A computer-derived system that uses multiple data inputs for pattern recognition of a clinical event (e.g., the likelihood of developing prostate cancer

).

*See Demirci F et al. Artificial neural network approach in laboratory reporting. Am J Clin Pathol. 2016;146:227-337. A superb article on how ANNs are created using multiple criteria for rejecting laboratory test results.

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Final ThoughtsIn the final analysis, it is important for clinicians and laboratorians to recognize that laboratory data, although potentially extremely useful in diagnostic decision making, should be used as an aid and adjunct to the constellation of findings (

eg, history, physical exam, etc.) relevant to the patient.Data are never a substitute for a good physical exam and patient history (clinicians should treat the patient, not the laboratory results).

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SummaryRather than a traditional Summary, the “Jennifer Rufer Case” illustrates with a real-world example the critical role medical laboratory individuals play daily in the practice of medicine and the treatment of patients, no less in accordance with the oath, “Do no harm,” taken and practiced by physicians.

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The Jennifer Rufer Case

22 y.o., recently married, using contraceptives, presented with irregular bleeding between menstrual periodsSerum hCG concentration was elevatedUltrasound showed no fetal sacWorked up for ectopic

pregnancy

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History of Case ContinuedLaparoscopy was negative for ectopic pregnancy

Dilatation and curettage (D&C) performed with histopathology indicating the presence of only normal menstrual tissueJuly 1998: [hCG] still elevated - trophoblastic choriocarcinoma suspected

Chemotherapy administered

with no effect on [

hCG] (i.e., remained elevated)Hysterectomy performed with no pathological abnormalities notedRare, resistant (to ChemoRx) form of choriocarcinoma suspected with possible metastasis to extrauterine locationAttending physician became suspicious about persistently elevated [hCG]Patient’s serum sample sent to 2 referral labs for hCG testingBoth referral labs used the same hCG immunoassay as the initial lab! hCG results elevated.

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History of Case ContinuedMore chemotherapy administered

using 5 chemotherapeutic agentsCT scan performed which demonstrated a suspicious lung noduleLung lobectomy performed

Finally, the validity of the

hCG

results was questioned after a total of >45 hCG determinations had been performed on the patient’s serum during the course of her treatmentPatient’s persistently elevated hCG levels attributed to false-positive (FP) test results due to HAMA interference

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Prevention, Detection, and Treatment to Reduce or Eliminate HAMA InterferencePrevention of HAMA production

Blocking HAMARemoving HAMAQuantifying HAMADiluting HAMA

Re-assay of patient’s sample with an immunoassay that uses different antibodies (e.g., goat antibodies) from the initial immunoassay

Redesign of immunoassays by manufacturers to eliminate HAMA interference

Treat patients with immunosuppressant Rx before, during, and after the administration of mouse antibody agentsCsACyclophosphamideAzathioprineDeoxyspergualin

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History of Case Continued

SummaryPatient underwent:D&CHysterectomy

A round of initial

ChemRx

A second, more aggressive, round of ChemoRxLung lobectomyDue, in part, to FP hCG results and unrecognized human anti-mouse antibody (HAMA) interference

Presumably, there was no consultation with clinical pathology staff?

Why

?

A survey of 1,768 primary care physicians indicated*:

In 14.7% of patient encounters requiring laboratory testing there was uncertainty about which test(s) to order.

In 8.3% of these encounters there was uncertainty in interpreting the lab test results.

When physicians do not know

which lab test to order

or

what the results mean

, they consult a “lab professional” only 6% of the time, despite being satisfied with the outcome >50% of the time.

*

Hickner

J,

et al.

J

Am Board Fam Med

. 2014;27:268–274.

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Thank You For Your Attention!

Behind every doctor is a great clinical laboratory scientist

!