Accurate Results in the Clinical Laboratory: A Guide to Error Detection and Correction [2nd Edition] 9780128137772

Table of contents :
COVER......Page 1
Accurate Results in the Clinical Laboratory......Page 2
List of contributors......Page 3
Copyright......Page 8
Foreword (from the first edition)......Page 5
Reference......Page 7
Preface......Page 9
Errors in clinical laboratory......Page 10
Quality improvement in clinical laboratory......Page 13
References......Page 16
Introduction......Page 17
Whole blood, plasma, and serum specimens for clinical laboratory analysis......Page 18
Whole blood......Page 19
Specimen composition......Page 21
Stoppers and stopper lubricants......Page 22
Serum separator gel tubes (SST)......Page 23
Anticoagulants......Page 24
Order of draw of various blood collection tubes......Page 25
Skin puncture......Page 27
Hemolysis......Page 28
Suprapubic aspiration......Page 29
References......Page 30
Transportation time......Page 33
Effects of temperature......Page 36
Effects of specimen handling and turbulence......Page 37
Special case: blood gases and ionized calcium......Page 38
Effect of centrifugation on test results......Page 39
Effect of storage conditions on laboratory results......Page 42
Specimen misidentification......Page 43
References......Page 46
Effect of age related changes on clinical laboratory test results......Page 50
Adulthood......Page 51
Elderly......Page 52
Gender related changes on clinical laboratory values......Page 53
Special diet-related changes on clinical laboratory values......Page 54
Fasting/starvation-related changes on clinical laboratory values......Page 55
Nutraceutical-related changes on clinical laboratory values......Page 56
Exercise related changes on clinical laboratory values......Page 57
Difference in laboratory test results among populations......Page 59
References......Page 60
Introduction......Page 62
Effect of hemolysis on laboratory tests......Page 63
In vivo hemolysis......Page 64
In vitro hemolysis......Page 65
Case report......Page 67
Lipemia......Page 68
Icterus......Page 69
References......Page 70
Immunoassay methods and assay principle......Page 73
Immunoassay reagents......Page 75
Specimen types for immunoassays......Page 76
References......Page 77
Heterophilic antibody interferences......Page 78
Mechanism of heterophilic antibody interference......Page 79
How problematic is heterophilic antibody interference?......Page 80
Interference from human anti-animal antibodies (HAAA)......Page 81
Detection and correction of heterophilic antibody interferences......Page 82
Removal of interfering substances......Page 83
References......Page 84
BIOTIN INTERFERENCE IN IMMUNOASSAYS......Page 86
The convergence of susceptible methods and supra-physiological biotin intake......Page 87
BIOTIN REQUIREMENT AND PHYSIOLOGICAL FUNCTIONS......Page 88
High dose biotin therapy beyond metabolic disease......Page 90
HISTORY OF BIOTIN INTERFERENCE......Page 91
Assessing the risk of adverse events......Page 92
Surveillance......Page 94
CONCLUSION......Page 95
References......Page 96
Introduction......Page 101
Creatinine analysis......Page 102
Limitations of the MDRD equation......Page 105
Jaffe-based methods......Page 106
Case reports......Page 107
Enzymatic creatinine assays......Page 108
Urea analysis......Page 109
Urea assay methods......Page 110
Pre-analytical factors......Page 111
Case report......Page 112
Glucose analysis......Page 113
Pre-analytical considerations for glucose measurement......Page 114
Hexokinase......Page 115
Analysis of electrolytes......Page 116
Physiologic pre-analytical issues......Page 119
Specimen issues......Page 122
Case report......Page 123
Adjustment for plasma water with indirect methods......Page 124
Phosphate Assays......Page 127
Pre-analytical issues......Page 128
Analytical issues......Page 129
Analytical issues......Page 131
Pre-analytical issues......Page 132
Analytical issues......Page 133
Fasting versus nonfasting lipid profiles......Page 134
Case report......Page 135
References......Page 136
Analytical issues and interferences......Page 141
Urinary albumin measurements to detect microalbuminuria......Page 142
Alanine and aspartate aminotransferases analysis......Page 143
Specimen processing......Page 145
γ-Glutamyl transferase and alkaline phosphatase analysis......Page 146
Amylase and lipase analysis......Page 147
Analytical issues......Page 148
Lactate dehydrogenase analysis......Page 149
Creatine kinase analysis......Page 150
Cardiac troponin analysis......Page 151
B-type natriuretic peptide analysis......Page 154
Iron studies......Page 156
Emerging markers in iron metabolism......Page 159
References......Page 160
Sample collection and processing......Page 164
High-dose hook effect......Page 165
Human anti-mouse antibodies, rheumatoid factor and heterophile antibodies......Page 166
Biotin ingestion-associated interference......Page 167
Growth hormone......Page 169
Adrenocorticotropic hormone......Page 170
Thyroid stimulating hormone......Page 171
Prolactin......Page 172
Challenges in measuring human chorionic gonadotropin......Page 173
Thyroglobulin......Page 174
Cortisol......Page 175
Aldosterone & renin......Page 176
Parathyroid hormone......Page 177
Gonadal and reproductive medicine......Page 178
Testing for insulin like growth factor-I......Page 179
Prenatal testing......Page 180
References......Page 181
Diagnosis of cancer......Page 189
Detecting relapses......Page 190
Elevated PSA in prostate cancer and other conditions......Page 191
Serum free and bound PSA......Page 192
False positive and unexpected PSA results......Page 193
Cancer antigen 125 (CA-125)......Page 194
Emerging biomarkers in diagnosis of ovarian cancer......Page 195
Alpha-fetoprotein (AFP)......Page 196
Serum CEA concentration and colorectal carcinoma......Page 197
False positive CEA......Page 198
Combined CEA and CA 19-9......Page 199
β2 microglobulin......Page 200
Causes and evaluation of persistent low levels of human chorionic gonadotropin......Page 201
False positive human chorionic gonadotropin......Page 202
Markers of breast cancer......Page 203
Hetrophilic antibody interference in tumor markers testing......Page 204
Less frequently monitored tumor markers......Page 205
References......Page 206
Sources of pre-analytical factors affecting drug levels......Page 210
Sources of analytical interferences in TDM......Page 212
Mechanisms of analytical interferences in TDM......Page 214
Chromatography and mass spectrometry......Page 215
Digoxin metabolites......Page 216
DLIF (Digoxin like immunoreactive factors)......Page 217
Herbal medicines......Page 218
Interferences in phenytoin measurement......Page 219
Interferences in measurement of immunosuppressants......Page 220
Drug metabolites......Page 221
Analytical variables......Page 222
Analytical variables......Page 223
References......Page 224
Introduction......Page 227
Amphetamine isomers/medications containing or metabolizing to amphetamines......Page 228
Opioids......Page 230
Opiates screening assays......Page 231
Oxycodone screening......Page 232
Benzodiazepines......Page 233
Cannabinoids......Page 234
References......Page 235
Purpose of drug testing......Page 237
Testing process for drug confirmation......Page 238
Confirmation of amphetamines......Page 240
Confirmation of opioids......Page 242
Codeine......Page 243
Interpretation of opioid results......Page 244
Confirmation of marijuana metabolite......Page 245
Confirmation of benzodiazepines......Page 246
Specimen validity testing......Page 247
References......Page 248
Abuse of NPS......Page 251
Rise of synthetic cannabinoids, cathinones and fentanyl analogues abuse......Page 252
Analytical challenges......Page 253
Reference standards for the appropriate analytical target......Page 255
Assay sensitivity......Page 256
Glucuronidation, hydrolysis, and metabolites......Page 257
Urine......Page 258
Blood......Page 259
Limitations of NPS immunoassays......Page 260
Confirmation of NPS......Page 261
Mass spectrometers and library searching......Page 262
References......Page 263
Introduction......Page 265
Pharmacodynamics of ethanol......Page 266
Pharmacokinetics of ethanol......Page 267
Alcohol measurement methods......Page 269
Testing methodologies: alcohol dehydrogenase (ADH)......Page 270
Performance evaluation of enzymatic alcohol assays......Page 271
Shortcomings of existing automated testing methods......Page 272
Shortcomings of existing automated testing methods: cross reactivity with other alcohols......Page 273
Shortcomings of existing automated testing methods: elevated lactate and LDH......Page 274
Eliminating interferences in alcohol assays......Page 275
Pre-analytical considerations......Page 276
Post-analytical considerations......Page 277
Markers of ethanol ingestion......Page 278
Markers of ethanol ingestion: osmole gap......Page 279
Markers of ethanol ingestion: ethyl glucuronide, ethyl sulfate, phosphatidylethanol (PEth) and fatty acid ethyl esters (FAEEs)......Page 280
Toxic alcohols......Page 281
References......Page 283
Introduction......Page 286
FDA warnings to toxic herbs......Page 287
Herbal supplements and abnormal liver function tests......Page 288
Kava......Page 289
Germander......Page 290
Other supplements associated with liver damage......Page 291
Herbal supplements associated with kidney damage......Page 292
Herbal supplements and hypoglycemia......Page 294
Adulteration of herbal supplements with oral hypoglycemic agents......Page 295
Kelp and abnormal thyroid function tests......Page 296
Interaction of St. John's wort with various drugs......Page 297
Interactions of warfarin with herbal supplements......Page 301
Other drug-herb interactions......Page 302
Herbs adulterated with Western drugs......Page 303
Grapefruit juice-drug interactions......Page 304
Conclusions......Page 305
References......Page 306
Challenges in hemoglobinopathy detection......Page 310
Hemoglobinopathy diagnosis errors......Page 311
Challenges in HIV testing......Page 313
Hepatitis testing......Page 314
Serology for hepatitis C......Page 315
References......Page 316
Detection of monoclonal proteins......Page 317
Free light chain (FLC) immunoassay......Page 318
Antinuclear antibodies......Page 319
References......Page 320
Specimen collection......Page 322
Specimen assessment......Page 323
Hybridization methods......Page 324
Amplification inhibitors......Page 325
Commonly encountered inhibitors and their sources......Page 326
Next-generation sequencing (NGS)......Page 327
Case report: false negative result of PCR testing for Neisseria meningitidis......Page 328
Quality control......Page 329
References......Page 330
Targeted single variant detection......Page 333
Multi-variant panels......Page 334
Sequencing......Page 335
KRAS......Page 337
BCR/ABL......Page 339
KIT......Page 340
HLA-B∗15:02......Page 341
CYP2D6......Page 342
Hemostasis (CYP2C19, CYP2C9, VKORC1)......Page 343
Precision medicine and pediatrics......Page 344
References......Page 345
Preanalytical challenges......Page 348
Method evaluation, quality control and quality assurance......Page 349
Amino acids disorders......Page 351
Organic acid disorders......Page 352
Fatty acid oxidation defects......Page 355
Lysosomal storage disorders......Page 356
References......Page 357
Test selection......Page 358
Specimen collection......Page 359
Case study......Page 360
Reporting......Page 361
Results archiving and specimen storage......Page 362
Quality improvement......Page 363
References......Page 364
Introduction......Page 366
Errors in WBC counts and WBC differential count......Page 367
Cold agglutinins......Page 368
Pseudothrombocytopenia......Page 369
Errors related to sample collection, transport and storage......Page 370
References......Page 371
Errors in thrombin time measurement......Page 373
Fibrinolysis products and rheumatoid factor......Page 374
Challenges in anticoagulants and lupus anticoagulant tests......Page 375
Conclusions......Page 376
References......Page 377
Cell viability......Page 378
Sample transit times......Page 381
Clotting, cell clumping and laminar flow......Page 383
Cell doublets......Page 384
Paraproteins and flow analysis......Page 385
Mab therapies......Page 387
Compensation......Page 390
Spillover beyond compensation......Page 391
Tandem dyes......Page 393
The strange case of calcium oxalate and CSF......Page 395
Case scenario 1......Page 396
References......Page 398
ABO TYPING......Page 400
Autocontrol and direct anti-human globulin test......Page 401
INTERFERENCES IN BASIC BLOOD BANK TESTING......Page 402
Unexpected RBC antigen-like reactivity......Page 403
Clinical summary and initial testing......Page 404
Clinical summary and initial testing......Page 405
Initial interpretation and further testing......Page 406
Final interpretation......Page 407
Clinical summary and initial testing......Page 408
Clinical summary and initial testing......Page 409
Initial interpretation and further testing......Page 410
References......Page 411
ERRORS IN TRANSFUSION......Page 413
Acute hemolytic transfusion reaction......Page 414
Febrile nonhemolytic transfusion reaction......Page 415
Septic transfusion reaction......Page 416
Transfusion-associated graft versus host disease......Page 417
Iron overload......Page 418
CONCLUSIONS......Page 419
References......Page 420
Design of POC devices......Page 422
Drugs of abuse......Page 423
Infectious disease (ID)......Page 425
Guidelines for using POCT devices......Page 426
References......Page 427
Alcohol analysis using breath analyzers: legal issues......Page 430
Technical aspect of breath alcohol measurement......Page 431
Issues with partition ratio......Page 433
Alcohol measurement in breath: cooperative versus noncooperative person......Page 434
Sources of errors in breath alcohol measurement......Page 435
Case report......Page 436
Interferences of volatiles in breath alcohol analysis......Page 437
Conclusions......Page 438
References......Page 439
A......Page 441
B......Page 443
C......Page 444
D......Page 446
E......Page 447
F......Page 448
H......Page 449
I......Page 451
L......Page 452
M......Page 453
N......Page 454
P......Page 455
S......Page 457
T......Page 458
W......Page 460
Z......Page 461

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ACCURATE RESULTS IN THE CLINICAL LABORATORY A Guide to Error Detection and Correction

SECOND EDITION Edited by

AMITAVA DASGUPTA, PHD, DABCC Professor of Pathology and Laboratory Medicine University of Texas McGovern Medical School Houston, TX, United States

JORGE L. SEPULVEDA, MD, PHD Professor of Pathology and Cell Biology Columbia University Vagelos College of Physicians and Surgeons New York, NY, United States

https://t.me/MBS_MedicalBooksStore

List of contributors Susan J. Hsiao, MD, PhD Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, United States

Amid Abdullah, MD University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada Maria P. Alfaro, PhD Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States Chris Altomare, BS United States

Laura M. Jacobsen, MD Department of Pediatrics, Division of Endocrinology, University of Florida, College of Medicine, Gainesville, FL, United States

DRUGSCAN Inc., Horsham, PA,

Kamisha L. Johnson-Davis, PhD Department of Pathology, University of Utah School of Medicine, ARUP Laboratories, Salt Lake City, UT, United States

Leland Baskin, MD University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada

Steven C. Kazmierczak, PhD Department of Pathology, Oregon Health & Science University, Portland, OR, United States

Lindsay A.L. Bazydlo, PhD Department of Pathology, University of Virginia, Charlottesville, VA, United States Jessica M. Boyd, PhD Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Calgary Laboratory Services, Calgary, AB, Canada

Elaine Lyon, PhD Clinical Services Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States

Larry A. Broussard, PhD Department of Clinical Laboratory Sciences, Louisiana State University Health Sciences Center, New Orleans, LA, United States

Gwendolyn A. McMillin, PhD Department of Pathology, University of Utah School of Medicine, ARUP Laboratories, Salt Lake City, UT, United States

Violeta Cha´vez, PhD Department of Pathology and Laboratory Medicine, University of Texas Medical School at Houston, Houston, TX, United States

Christopher Naugler, MD University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada Elena G. Nedelcu, MD Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, United States

Alex Chin, PhD University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada

Andy Nguyen, MD Department of Pathology and Laboratory Medicine, University of Texas McGovern Medical School, Houston, TX, United States

Anthony G. Costantino, PhD DRUGSCAN Inc., Horsham, PA, United States Amitava Dasgupta, PhD, DABCC Department of Pathology and Laboratory Medicine, University of Texas McGovern Medical School, Houston, TX, United States

Octavia M. Peck Palmer, PhD Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Clinical and Translational Science, University of Pittsburgh School, Pittsburgh, PA, United States

Pradip Datta, PhD Siemens Healthineers, Newark, DE, United States Robert A. DeSimone, MD Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, NY, United States

Amy L. Pyle-Eilola, PhD Pathology and Laboratory Medicine, Nationwide Children’s Hospital, Columbus, OH, United States

Uttam Garg, PhD Department of Pathology and Laboratory Medicine, Children’s Mercy Hospitals and Clinics, The University of Missouri School of Medicine, Kansas City, MO, United States

S.M. Hossein Sadrzadeh, PhD Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Calgary Laboratory Services, Calgary, AB, Canada

Neil S. Harris, MD Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, Gainesville, FL, United States

Jorge L. Sepulveda, MD, PhD Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States

Joshua Hayden, PhD Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York, NY, United States

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LIST OF CONTRIBUTORS

Brian Rudolph Shy, MD, PhD Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, United States

George Vlad, PhD Department of Pathology & Cell Biology, Columbia University College of Physicians and Surgeons, New York, NY, United States

Aaron Stella, PhD University of Massachusetts Lowell, Lowell, MA, United States

Amer Wahed, MD Department of Pathology and Laboratory Medicine, University of Texas McGovern Medical School, Houston, TX, United States

Yvette C. Tanhehco, PhD Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York-Presbyterian Hospital, New York, NY, United States Ashok Tholpady, MD Department of Pathology and Laboratory Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States

William E. Winter, MD Department of Pediatrics, Division of Endocrinology, University of Florida, College of Medicine, Gainesville, FL, United States; Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, Gainesville, FL, United States

Christina Trambas, MD, PhD Chemical Pathologist, Chemical Pathology Department, Melbourne Pathology, Collingwood, VIC, Australia

Alison Woodworth, PhD Pathology and Laboratory Medicine, University of Kentucky Medical Center, Lexington, KY, United States

Foreword (from the first edition) Clinicians must make decisions from information presented to them, both by the patient and ancillary resources available to the physician. Laboratory data generally provide quantitative information, which may be more helpful to physicians than the subjective information from a patient’s history or physical examination. Indeed, with the prevalent pressure for physicians to see more patients in a limited timeframe, laboratory testing has become a more essential component of a patient’s diagnostic work-up, partly as a timesaving measure but also because it does provide information against which prior or subsequent test results, and hence patients’ health, may be compared. Tests should be ordered if they could be expected to provide additional information beyond that obtained from a physician’s first encounter with a patient and if the results could be expected to influence a patient’s care. Typically, clinicians use clinical laboratory testing as an adjunct to their history taking and physical examination to help confirm a preliminary diagnosis, although some testing may establish a diagnosis, for example molecular tests for inborn errors of metabolism. Microbiological cultures of body fluids may not only establish the identity of an infecting organism, but also establish the treatment of the associated medical condition. In outpatient practice clinicians primarily order tests to assist them in their diagnostic practice, whereas for hospitalized patients, in whom a diagnosis has typically been established, laboratory tests are primarily used to monitor a patient’s status and response to treatment. Tests of organ function are used to look for drug toxicity and the measurement of the circulating concentrations of drugs with narrow therapeutic windows is done to ensure that optimal drug dosing is achieved and maintained. The importance of laboratory testing is evident when some physicians rely more on laboratory data than a patient’s own assessment as to how he or she feels, opening them to the criticism of treating the laboratory data rather than the patient. In the modern, tightly regulated, clinical laboratory in a developed country few errors are likely to be made, with the majority labeled as laboratory errors occurring outside the laboratory itself. One study from 1995

showed that when errors were made 75% still produced results that fell within the reference interval (when perhaps they should not) [1]. Half of the other errors were associated with results that were so absurd that they were discounted clinically. Such results clearly should not have been released to a physician by the laboratory and could largely be avoided by a simple review by human or computer before being verified. However, the remaining 12.5% of errors produced results that could have impacted patient management. The prevalence of errors may be less now than previously, since the quality of analytical testing has improved, but the ramifications of each error are not likely to be less. The consequences of an error vary depending on the analyte or analytes affected and whether the patient involved is an inpatient or outpatient. If the patient is an inpatient a physician, if suspicious about the result, will likely have the opportunity to verify the result by repeating the test or other tests addressing the same physiological functions, before taking action. However, if the error occurs with a specimen from an outpatient causing an abnormal result to appear normal, that patient may be lost to follow-up and present later with advanced disease. Despite the great preponderance of accurate results clinicians should always be wary of any result that does not seem to fit with the patient’s clinical picture. It is, of course, equally important for physicians not to dismiss any result that they do not like as a “laboratory error”. The unexpected result should always prompt an appropriate follow-up. The laboratory has a responsibility to ensure that physicians have confidence in its test results while still retaining a healthy skepticism about unexpected results. Normal laboratory data may provide some assurance to worried patients who believe that they might have a medical problem, an issue seemingly more prevalent now with the ready accessibility of medical information available through computer search engines. Yet both patients and physicians tend to become overreliant on laboratory information, either not knowing or ignoring the weakness of laboratory tests, in general. A culture has arisen of physicians and patients believing that the published upper and lower limits of

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FOREWORD (FROM THE FIRST EDITION)

the reference range (or interval) of a test define normality. They do not realize that such a range has probably been derived from 95% of a group of presumed healthy individuals, not necessarily selected with respect to all demographic factors or habits that were an appropriate comparative reference for a particular patient. Even if appropriate, 1 in 20 individuals would be expected to have an abnormal result for a single test. In the usual situation in which many tests are ordered together the probability of abnormal results in a healthy individual increases in proportion to the number of tests ordered. Studies have hypothesized that the likelihood of all of 20 tests ordered at the same time falling within their respective reference intervals is only 36%. The studies performed to derive the reference limits are usually conducted under optimized conditions such as the time since the volunteer last ate, his or her posture during blood collection and, often the time of day. Such idealized conditions are rarely likely to be attained in an office or hospital practice. Factors affecting the usefulness of laboratory data may arise in any of the preanalytical, analytical or postanalytical phase of the testing cycle. Failures to consider these factors do constitute errors. If these errors occur prior to collection of blood or after results have been produced, while still likely to be labeled as laboratory errors because they involve laboratory tests, the laboratory staffs are typically not liable for them. Yet the staff does have the responsibility to educate those individuals who may have caused them to ensure that such errors do not recur. If practicing clinicians were able to use the knowledge that experienced laboratorians have about the strengths and weaknesses of tests it is likely that much more clinically useful information could be extracted from existing tests. Outside the laboratory, physicians rarely are knowledgeable about the intra- and interindividual variation observed when serial studies are performed on the same individuals. For some tests a significant change for an individual may occur when his/her test values shift from toward one end of the reference interval toward the other. Thus a test value does not necessarily have to exceed the reference limits for it to be abnormal for a given patient. If the preanalytical steps are not standardized when repeated testing is done on the same person, it is more likely that trends in laboratory data may be missed. There is an onus on everyone involved in test ordering and test performance to standardize the processes to facilitate the maximal extraction of information from the laboratory data. The combined goal

should be of pursuit of information rather than just data. Laboratory information systems provide the potential to integrate all laboratory data that can then be integrated with clinical and other diagnostic information by hospital information systems. Laboratory actions to highlight values outside the reference interval on their comprehensive reports of test results to physicians with codes such as “H” or “L” for high and low values exceeding the reference interval have tended to obscure the actual numerical result and to cement the concept that the upper and lower reference limits define normality and that the presence of one of these symbols necessitates further testing. The use of the reference limits as published decision limits for national programs for renal function, lipid or glucose screening has again placed a greater burden on the values than they deserve. Every measurement is subject to analytical error, such that repeated determinations will not always yield the same result, even under optimal testing conditions. Would it then be more appropriate to make multiple measurements and use an average to establish the number to be acted upon by a clinician? Much of the opportunity to reduce errors (in the broadest sense) rests with the physicians who use test results. Over-ordering leads to the possibility of more errors. Inappropriate ordering, for example repetitive ordering of tests whose previous results have been normal, or ordering the wrong test or wrong sequence of tests to elucidate a problem should be minimized by careful supervision by attending physicians of their trainees involved in the direct management of their patients. Laboratorians need to be more involved in teaching medical students so that when they become residents their test ordering practices are not learned from senior residents who had learned their habits from the previous generation of residents. Blanket application of clinical guidelines or test order-sets has probably led to much misuse of clinical laboratory tests. Many clinicians and laboratorians have attempted to reduce inappropriate test ordering, but the overall conclusion seems to be that education is the most effective means. Unfortunately, the education needs to be continuously reinforced to have a lasting effect. The education needs to address the clinical sensitivity of diagnostic tests, the context in which they are ordered and their half-lives. Above all education needs to address issues of biological variation and preanalytical factors that may affect test values, possibly masking trends or making the abnormal result appear normal and vice versa.

FOREWORD (FROM THE FIRST EDITION)

This book provides a comprehensive review of the factors leading to errors in all the areas of clinical laboratory testing. As such it will be of great value to all laboratory directors and trainees in laboratory medicine and the technical staff who perform the tests in daily practice. By clearly identifying problem areas, the book lays out the opportunities for improvement. This book

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should be of equal value to clinicians, as to laboratorians, as they seek the optimal outcome from their care of their patients.

Reference [1] Goldschmidt HMJ, Lent RW. Gross errors and workflow analysis in the clinical laboratory. Klin Biochem Metab 1995;3:131e49.

Donald S. Young MD, Ph.D Professor of Pathology and Laboratory Medicine University of Pennsylvania Perelman College of Medicine, Philadelphia, PA

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/ permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/ or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-813776-5 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Stacy Masucci Acquisition Editor: Tari Broderick Editorial Project Manager: Megan Ashdown Production Project Manager: Punithavathy Govindaradjane Cover Designer: Mark Rogers Typeset by TNQ Technologies

Preface Clinical laboratory tests have significant impact on patient safety and patient management because more than 70% of all medical diagnosis are based on laboratory test results. Physicians rely on hospital laboratories for obtaining accurate results and a falsely elevated or falsely lower value due to interference or pre-analytical errors may have significant influence on diagnosis and management of patients. Usually, a clinician questions the validity of a test result if the result does not match with clinical evaluation of the patient and calls laboratory professionals for interpretation. However, clinically significant inaccuracies in laboratory results may go unnoticed and mislead the clinicians into inappropriate diagnostic and therapeutic approaches, sometimes with very adverse outcomes. The first edition of “Accurate Results in the Clinical Laboratory: A Guide to Error Detection and Correction” was published by Elsevier in 2013 and was intended as a guide to increase awareness of both clinicians and laboratory professionals about the various sources of errors in clinical laboratory tests and what can be done to minimize or eliminate such errors. The first edition of the book had 22 chapters and was well received by readers. Due to success of the first edition, Elsevier requested a second edition of the book. In this edition, we not only updated all chapters of the first edition, but also added 9 new chapters so that the second book could be a concise but comprehensive guide for both clinicians and laboratory professionals to detect errors and sources of misinterpretation in the clinical laboratory and to prevent or correct such results. Recently, biotin interferences in immunoassays that utilize biotinylated antibodies have been described which may lead to wrong diagnosis of Grave’s disease due to falsely low TSH (sandwich assay that shows negative interference due to biotin) but falsely elevated T3, T4 and FT4 (competitive immunoassays showing positive biotin interferences). The Food and Drug Administration reported a fatal outcome due to a falsely low troponin value as a result of negative interference

of biotin in the troponin assay. Because people take megadoses of biotin, this is a serious public health concern. Therefore, we added a new chapter (Chapter 8). Another new chapter (Chapter 16) is also added to discuss issues of false negative results in toxicology due to the difficulty in detecting certain drugs such as synthetic cathinone (bath salts) and synthetic cannabinoids (spices). Chapter 27 is also added to discuss sources of errors in flow cytometry. Moreover, Chapters 29e31 are also newly added chapters in the second edition. The objective of this second edition book is to provide a comprehensive guide for laboratory professionals and clinicians regarding sources of errors and misinterpretation in the clinical laboratory and how to resolve such errors and identify discordant specimens. Accurate laboratory result interpretation is essential for patient safety. This book is intended as a practical guide to laboratory professionals and clinicians who deal with erroneous results on a regular basis. We hope this book will help them to be aware of such sources of errors and empower them to eliminate such errors when feasible or to account for known sources of variability when interpreting changes in laboratory results. We would like to thank all contributors for taking time from their busy professional demands to write chapters. Without their dedicated contributions this project would never materialize. We also thank our families for putting up with us for the last year when we spent many hours during weekends and evenings writing chapters and editing this book. Finally our readers will be the judges of the success of this project. If our readers find this book useful, all the hard work of contributors and editors will be rewarded. Respectfully Submitted Amitava Dasgupta Houston, TX

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Jorge L. Sepulveda New York, NY

C H A P T E R

1 Variation, errors, and quality in the clinical laboratory Jorge L. Sepulveda Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States

INTRODUCTION

5. The analytical assay measured the concentration of the analyte corresponding to its “true” level (compared to a “gold standard” measurement) within a clinically acceptable margin of error (the total acceptable analytical error (TAAE)). 6. The report reaching the clinician contained the right result, together with interpretative information, such as a reference range and other comments, aiding clinicians in the decision-making process.

Recent studies demonstrated that in vitro diagnostic tests are performed in up to 96% of patients and that up to 80% of clinical decisions involve consideration of laboratory results [1]. In addition, approximately 40e94% of all objective health record data are laboratory results [2e4]. Diagnostic errors accounted for 26e78% of identified medical errors [5] and nearly 60% of malpractice claims [6], and were involved in 17% of adverse effects due to medical errors in one large study [7]. Undoubtedly, appropriate ordering and interpretation of accurate test results are essential for major clinical decisions involving disease identification, classification, treatment, and monitoring. Factors that constitute an accurate laboratory result involve more than analytical accuracy and can be summarized as follows:

Failure at any of these steps can result in an erroneous or misleading laboratory result, sometimes with adverse outcomes. For example, interferences with point-of-care glucose testing due to treatment with maltose containing fluids have led to failure to recognize significant hypoglycemia and to mortality or severe morbidity [11].

1. The right test, with the right costs and right method, was ordered for the right patient, at the right time, for the right reason [8]: the importance of appropriate test selection cannot be minimized as studies have shown that at least 20% of all test orders are inappropriate [9], up to 68% of tests ordered do not contribute to improve patient management [10] and conversely tests were not ordered when needed in nearly 50% of patients [9]. 2. The right sample was collected on the right patient, at the correct time, with appropriate patient preparation. 3. The right technique was used collecting the sample to avoid contamination with intravenous fluids, tissue damage, prolonged venous stasis, or hemolysis. 4. The sample was properly transported to the laboratory, stored at the right temperature, processed for analysis, and analyzed in a manner that avoids artifactual changes in the measured analyte levels. Accurate Results in the Clinical Laboratory, Second Edition https://doi.org/10.1016/B978-0-12-813776-5.00001-7

ERRORS IN CLINICAL LABORATORY Errors can occur in all the steps in the laboratory testing process, and such errors can be classified as follows (see Table 1.1): 1. Pre-analytical steps, encompassing the decision to test, transmission of the order to the laboratory for analysis, patient preparation and identification, sample collection, and specimen processing. 2. Analytical assay, which produces a laboratory result. 3. Post-analytical steps, involving the transmission of the laboratory data to the clinical provider, who uses the information for decision making. Although minimization of analytical errors has been the main focus of developments in laboratory medicine, the other steps are more frequent sources of erroneous

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Copyright © 2019 Elsevier Inc. All rights reserved.

4 TABLE 1.1

1. VARIATION, ERRORS, AND QUALITY IN THE CLINICAL LABORATORY

Types of error in the clinical laboratory.

TABLE 1.1 Types of error in the clinical laboratory.dcont’d

PRE-ANALYTICAL

ANALYTICAL

Test ordering

• High analytical turnaround time • Instrument caused random error • Instrument malfunction • QC failure • QC not completed

• Duplicate Order • Ordering provider not identified • Ordered test not performed (include add-ons)

• Order misinterpreted (test ordered intended test) • Inappropriate/outmoded test ordered • Order not pulled by specimen collector

Sample collection • Unsuccessful phlebotomy • Traumatic phlebotomy • Patient complaint about phlebotomy

• Check-in not performed (in the LIS) • Wrong patient preparation (e.g., non-fasting) • Therapeutic drug monitoring test timing error

Specimen transport • Inappropriate sample transport conditions • Specimen leaked in transit

• Specimen damaged during transport • Specimen damaged during centrifugation/analysis

Specimen identification • Specimen unlabeled • Specimen mislabeled: No Name or ID on tube • Specimen mislabeled: No Name on tube • Specimen mislabeled: Incomplete ID on tube • Wrong specimen label • Wrong name on tube • Wrong ID on tube • Wrong blood type

• • • • • • •

Date/time missing Collector’s initials missing Label illegible Two contradictory labels Overlapping labels Mismatch requisition/label Specimen information misread by automated reader

High pre-analytical turnaround time • Delay in receiving specimen in lab • Delay in performing test

• STAT not processed urgently

Specimen quality • Specimen contaminated with infusion fluid • Specimen contaminated with microbes • Specimen too old for analysis

• Hemolyzed • Clotted or platelet clumps

Specimen containers • No specimens received/ Missing tube • Specimen lost in laboratory • Wrong specimen type • Inappropriate container/tube type • Wrong tube collection instructions

• Wrong preservative/ anticoagulant • Insufficient specimen quantity for analysis • Tube filling error (too much anticoagulant) • Tube filing error (too little anticoagulant) • Empty tube

• Test perform by unauthorized personnel • Results discrepant with other clinical or laboratory data • Testing not completed • Wrong test performed (different from test ordered)

POST-ANALYTICAL • • • •

Report not completed Delay in reporting results Critical results not called Delay in calling critical results • Results reported incorrectly • Results reported incorrectly from outside laboratory • Results reported to wrong provider

• Reported questionable results, detected by laboratory • Reported questionable results, detected by clinician • Failure to append proper comment • Read back not done • Results misinterpreted • Failure to act on results of tests

OTHER • • • •

Proficiency test failure Product wastage Product not delivered timely Product recall

• • • •

Employee injury Safety failure Environmental failure Damage to equipment

results. An analysis indicated that pre-analytical errors accounted for 62% of all errors, with post-analytical representing 23% and analytical 15% of all laboratory errors [12]. The most common pre-analytical errors included incorrect order transmission (at a frequency of approximately 3% of all orders) and hemolysis (approximately 0.3% of all samples) [13]. Other frequent causes of preanalytical errors include the following: • Patient identification error • Tube filling error, empty tubes, missing tubes, or wrong sample container • Sample contamination or collected from infusion route • Inadequate sample temperature Particular attention should be paid to patient identification because errors in this critical step can have severe consequences, including fatal outcomes, for example, due to transfusion reactions or misguided therapeutic decisions. To minimize identification errors, health care systems are using point-of-care identification systems, which typically involve the following: 1. Handheld devices connected to the laboratory information systems (LIS) that can objectively identify the patient by scanning a patient-attached bar code, typically a wrist band.

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

ERRORS IN CLINICAL LABORATORY

2. Current laboratory orders can be retrieved from the LIS. 3. Ideally, collection information, such as correct tube types, is displayed in the device. 4. Bar-coded labels are printed at the patient’s side, minimizing the possibility of misplacing the labels on the wrong patient samples. 5. After attaching to containers with the patient samples, bar-coded labels should be scanned to confirm that they were applied to the right patient, especially if any significant delay has occurred between label printing and sample collection. In this case, rescanning of patient-attached identifiers should be done in close temporal proximity to sample scanning. Analytical errors are mostly due to interference or other unrecognized causes of inaccuracy, whereas instrument random errors accounted for only 2% of all laboratory errors in one study [12]. According to that study, most common post-analytical errors were due to communication breakdown between the laboratory and the clinicians, whereas only 1% were due to miscommunication within the laboratory, and 1% of the results had excessive turnaround time for reporting [12]. Post-analytical errors due to incorrect transcription of laboratory data have been greatly reduced because of the availability of automated analyzers and bidirectional interfaces with the LIS [12]. However, transcription errors and calculation errors remain a major area of concern in those testing areas without automated interfaces between the instrument and the LIS. Further developments to reduce reporting errors and minimize the testing turnaround time include auto-validation of test results falling within pre-established rule-based parameters and systems for automatic paging of critical results to providers. When classifying sources of error, it is important to distinguish between cognitive errors, or mistakes, which are due to poor knowledge or judgment, and noncognitive errors, commonly known as slips and lapses, due to interruptions in a process that is routine or relatively automatic. Whereas the first type can be prevented by increased training, competency evaluation, and process aids such as checklists or “cheat sheets” summarizing important steps in a procedure, noncognitive errors are best addressed by process improvement and environment re-engineering to minimize distractions and fatigue. Furthermore, it is useful to classify adverse occurrences as activedthat is, the immediate result of an action by the person performing a taskdor as latent or system errors, which are system deficiencies due to poor design or implementation that enable or amplify active errors. In one study, only approximately 11% of the errors were cognitive, all in the pre-analytical phase,

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and approximately 33% of the errors were latent [12]. Therefore, the vast majority of errors are noncognitive slips and lapses performed by the personnel directly involved in the process. Importantly, 92% of the preanalytical, 88% of analytical, and 14% of post-analytical errors were preventable. Undoubtedly, human factors, engineering, and ergonomicsdoptimization of systems and process redesigning to include increased automation and user-friendly, simple, and rule-based functions, alerts, barriers, and visual feedbackdare more effective than education and personnel-specific solutions to consistently increase laboratory quality and minimize errors. Immediate reporting of errors to a database accessible to all the personnel in the health care system, followed by automatic alerts to quality management personnel, is important for accurate tracking and timely correction of latent errors. In our experience, reporting is improved by using an online form that includes checkboxes for the most common types of errors together with free-text for additional information (Fig. 1.1). Reviewers can subsequently classify errors as cognitive/noncognitive, latent/active, and internal to laboratory/internal to institution/external to institution; determine and classify root causes as involving human factors (e.g., communication and training or judgment), software, or physical factors (environment, instrument, hardware, etc.); and perform outcome analysis. Outcomes of errors can be classified as follows: 1. Target of error (patient, staff, visitors, or equipment). 2. Actual outcome on a severity scale (from unnoticed to fatal). 3. Worst outcome likelihood if error was not intercepted on the same severity scale, since many errors are corrected before they cause injury. Errors with significant outcomes or likelihoods of adverse outcomes should be discussed by quality management staff and laboratory directors to determine appropriate corrective actions and process improvement initiatives. Clearly, efforts to improve accuracy of laboratory results should encompass all of the steps of the testing cycle, a concept expressed as “total testing process” or “brain-to-brain testing loop” [14]. Approaches to achieve error minimization derived from industrial processes include total quality management (TQM) [15]; lean dynamics and Toyota production systems [16]; root cause analysis (RCA) [17]; health care failure modes and effects analysis (HFMEA) [18,19]; failure review analysis and corrective action system (FRACAS) [20]; and Six Sigma [21,22], which aims at minimizing the variability of products such that the statistical frequency of errors is below 3.4 per million. A detailed description

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

6

1. VARIATION, ERRORS, AND QUALITY IN THE CLINICAL LABORATORY

FIG. 1.1 Example of an error reporting form for the clinical laboratory.

of these approaches is beyond the scope of this book, but laboratorians and quality management specialists should be familiar with these principles for error prevention, error detection, and error management to achieve efficient, high-quality laboratory operation and patient care [15].

QUALITY IMPROVEMENT IN CLINICAL LABORATORY Quality is defined as all the features of a product that meet the requirements of the customers and the health care system. Many approaches are used to improve and ensure the quality of laboratory operations. The concept of TQM involves a philosophy of excellence concerned with all aspects of laboratory operations that impact on the quality of the results. Specifically,

TQM approaches apply a system of statistical process control tools to monitor quality and productivity (quality assurance) and encourage efforts to continuously improve the quality of the products, a concept known as continuous quality improvement. A major component of a quality assurance program is quality control (QC), which involves the use of periodic measurements of product quality, thresholds for acceptable performance, and rejection of products that do not meet acceptability criteria. Most notably, QC is applied to all clinical laboratory testing processes and equipment, including testing reagents, analytical instruments, centrifuges, and refrigerators. Typically, for each clinical test, external QC materials with known performance, also known as controls, are run two or three times daily in parallel with patient specimens. Controls usually have preassigned analyte concentrations covering important medical decision levels, often at low, medium, and

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

QUALITY IMPROVEMENT IN CLINICAL LABORATORY

high concentrations. Good laboratory QC practice involves establishment of a laboratory- and instrumentspecific mean and standard deviation for each lot of each control and also a set of rules intended to maximize error detection while minimizing false rejections, such as Westgard rules [23]. Another important component of quality assurance for clinical laboratories is participation in proficiency testing (or external quality assessment programs such as proficiency surveys sent by the College of American Pathologists), which involves the sharing of samples with a large number of other laboratories and comparison of the results from each laboratory with its peers, usually with reporting of the mean and standard deviation (SD) of all the laboratories running the same analyzer/reagent combination. Criteria for QC rules and proficiency testing acceptability should take into consideration the concept of total acceptable analytical error because deviations smaller than the total analytical errors are unlikely to be clinically significant and therefore do not need to be detected. Total analytical error (TAE) is usually considered to combine the following (Fig. 1.2): (1) systematic error (SE), or bias, as defined by deviation between the average values obtained from a large series of test results and an accepted reference or gold standard value, and (2) random error (RE), or imprecision, represented by the coefficient of variation of multiple independent test results obtained under stipulated conditions (CVa). Assuming a normal distribution of repeated test results, at the 95% confidence level, the RE is equal to 1.65 times the CVa for the method; consequently.

FIG. 1.2 Total analytical error (TE) components: random error (RE), or imprecision and systematic error (SE), or bias, which cause the difference between the true value and the measured value. Random error can increase or decrease the difference from the true value. Because in a normal distribution, 95% of the observations are contained within the mean  1.65 standard deviations (SDs), the total error will not exceed bias þ 1.65  SD in 95% of the observations.

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TAE ¼ 1:65  CVa þ bias Clinical laboratories frequently evaluate imprecision by performing repeated measurements on control materials, preferably using runs performed on different days (between-day precision), whereas bias (or trueness) is assessed by comparison with standard reference materials with assigned values and also by peer comparison, where either the peer mean or median are considered the reference values. One important concept that some clinicians disregard is that no laboratory measurement is exempt of error; that is, it is impossible to produce a laboratory result with 0% bias and 0% imprecision. The role of technologic developments, good manufacturing practices, proficiency testing, and QC is to identify and minimize the magnitude of the TAE. A practical approach is to consider the clinically acceptable total analytical error or TAAE for each test. Clinical acceptability has been defined by legislation (e.g., the Clinical Laboratory Improvement Act (CLIA)), by clinical expert opinion, and by scientific and statistical principles that take into consideration expected sources of variation. For example, Callum Fraser proposed that clinically acceptable imprecision, or random error, should be less than half of the intraindividual biologic variation for the analyte and less than 25% of the total analytical error [24]. The systematic error, or bias, should be less than 25% of the combined intraindividual (CVw) and interindividual biological (CVg) variation: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi TAAE95% < 1:65  0:5  CVw þ 0:25  CV2w þ CV2g Tables of intra- and interindividual biological variation, with corresponding allowable errors, are available and frequently updated [25]. See Table 1.2 for examples. Importantly, the allowable errors may be different at specific medical decision levels because analytical imprecision tends to vary with the analyte concentration, with higher imprecision at lower levels. Also, biological variation may be different in the various clinical conditions, and available databases are starting to incorporate studies of biologic variation in different diseases [25]. A related concept is the reference change value (RCV), also called significant change value (SCV)dthat is, the variability around a measurement that is a consequence of analytical imprecision, within-subject biologic variability, and the number of repeated tests performed [24,26,27]. Assuming a normal distribution, at the 95% confidence level, RCV can be calculated as follows: pffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RCV95% ¼ 1:96  2  CV2a þ CV2w Because multiple repeats decrease imprecision errors, if the change is determined from the mean of repeated

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

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1. VARIATION, ERRORS, AND QUALITY IN THE CLINICAL LABORATORY

tests, the formula can be modified to take into consideration the number of repeats in each measurement (n1 and n2) [27]: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 RCV95% ¼ 1:96   CVa2 þ CVw2 n1  n2 For example, for a serum creatinine measurement with an analytical imprecision (CVa) of 7.6% and within-subject biologic variation of 5.95%, the RCV at 95% confidence is 26.8% with one measurement for each sample. With two measurements for each sample, the RCV is 18.9%. Therefore, a change between two results that does not exceed the RCV has a greater than

TABLE 1.2

95% probability that it is due to the combined analytical and intraindividual biological variation; in other words, the difference between the two creatinine results (measured without repeats) should exceed 26.8% to be 95% confident that the change is due to a pathological condition. Conversely, for any change in laboratory values, the RCV formula can be used to calculate the probability that it is due to analytical and biological variation [24,26,27]. See Table 1.2 for examples of RCV at the 95% confidence limit, using published intraindividual variation and typical laboratory imprecision for each test. Ideally, future LIS should integrate available knowledge and patient-specific information and automatically provide estimates of expected variation based

Allowable errors and reference change values for selected tests.

Test

CVa

CVw

CVg

CLIA TAAE

Bio TAAE

Allowable imprecision

Allowable bias

RCV95

Amylase

5.3

8.7

28.3

30

14.6

4.4

7.4

28.2

Alanine aminotransferase

2.8

19.4

41.6

20

27.48

9.7

11.48

54.3

Albumin

2.6

3.2

4.75

10

4.07

1.6

1.43

11.4

Alkaline phosphatase

4.2

6.45

26.1

30

12.04

3.23

6.72

21.3

Aspartate aminotransferase

2.2

12.3

23.1

20

16.69

6.15

6.54

34.6

Bilirubin total

10.0

21.8

28.4

20

26.94

10.9

8.95

66.5

Chloride

2.4

1.2

1.5

5

1.5

0.6

0.5

7.4

Cholesterol

2.7

5.95

15.3

10

9.01

2.98

4.1

18.1

Cortisol

5.3

21.7

46.2

25

30.66

10.85

12.76

61.9

Creatine kinase

3.6

22.8

40

30

30.3

11.4

11.5

64.0

Creatinine

7.6

5.95

14.7

15

8.87

2.98

3.96

26.8

Glucose

3.4

4.5

5.8

10

5.5

2.3

1.8

15.6

HDL cholesterol

3.3

7.3

21.2

30

11.63

3.65

5.61

22.2

Iron

2.5

26.5

23.2

20

30.7

13.3

8.8

73.8

Lactate dehydrogenase (LDH)

2.5

8.6

14.7

20

11.4

4.3

4.3

24.8

Magnesium

2.8

5.6

11.3

25

7.8

2.8

3.2

17.4

pCO2

1.5

4.8

5.3

8

5.7

2.4

1.8

13.9

Protein, total

2.6

2.75

4.7

10

3.63

1.38

1.36

10.5

Thyroxine (T4)

4.8

4.9

10.9

20

7

2.5

3

19.0

Triglyceride

3.9

19.9

32.7

25

25.99

9.95

9.57

56.2

Urate

2.9

8.6

17.5

17

11.97

4.3

4.87

25.2

Urea nitrogen

6.2

12.1

18.7

9

15.55

6.05

5.57

37.7

All values are percentages. Bio TAAE, total allowable analytical error based on interindividual and intraindividual variation; CLIATAAE, total allowable analytical error based on Clinical Laboratory Improvement Act (CLIA); CVa, analytical variability in a typical clinical laboratory; CVg, interindividual variability; CVw, intraindividual qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi variability. Allowable imprecision ¼ 50% of CVw. Allowable bias ¼ 0:25  CV2w  CV2g . RCV95, reference change value at 95% confidence based on CVw and CVa. Based on Westgard J. Desirable specifications for total error, imprecision, and bias, derived from intra- and inter-individual biologic variation. 2014. Available from: http://www. westgard.com/biodatabase1.htm.

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

REFERENCES

on the previous formulas to facilitate interpretation of changes in laboratory values and guide laboratory staff regarding the meaning of deviations from expected results. In summary, the use of TAAE and RCV brings objectivity to error evaluation, QC and proficiency testing practices, and clinical decision making based on changes in laboratory values.

CONCLUSIONS As in other areas of medicine, errors are unavoidable in the whole diagnostic process involving laboratory testing. A good understanding of the sources of error, frequently involving pre-analytical factors, together with a quantitative evaluation of the clinical significance of the magnitude of analytical errors, aided by the establishment of limits of acceptability based on statistical principles of analytical and intraindividual biological variation, are critical to design a quality program to minimize the clinical impact of errors in the clinical laboratory.

References [1] Rohr UP, Binder C, Dieterle T, Giusti F, Messina CG, Toerien E, et al. The value of in vitro diagnostic testing in medical practice: a status report. PLoS One 2016;11(3):e0149856. [2] Forsman RW. The value of the laboratory professional in the continuum of care. Clin Leadersh Manag Rev 2002;16(6):370e3. [3] Forsman RW. Why is the laboratory an afterthought for managed care organizations? Clin Chem 1996;42(5):813e6. [4] Hallworth MJ. The ‘70% claim’: what is the evidence base? Ann Clin Biochem 2011;48(Pt 6):487e8. [5] Sandars J, Esmail A. The frequency and nature of medical error in primary care: understanding the diversity across studies. Fam Pract 2003;20(3):231e6. [6] Gandhi TK, Kachalia A, Thomas EJ, Puopolo AL, Yoon C, Brennan TA, et al. Missed and delayed diagnoses in the ambulatory setting: a study of closed malpractice claims. Ann Intern Med 2006;145(7):488e96. [7] Leape LL, Brennan TA, Laird N, Lawthers AG, Localio AR, Barnes BA, et al. The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N Engl J Med 1991;324(6):377e84. [8] Lippi G, Bovo C, Ciaccio M. Inappropriateness in laboratory medicine: an elephant in the room? Ann Transl Med 2017;5(4):82. [9] Zhi M, Ding EL, Theisen-Toupal J, Whelan J, Arnaout R. The landscape of inappropriate laboratory testing: a 15-year meta-analysis. PLoS One 2013;8(11):e78962.

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[10] Miyakis S, Karamanof G, Liontos M, Mountokalakis TD. Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy. Postgrad Med J 2006;82(974):823e9. [11] Gaines AR, Pierce LR, Bernhardt PA. Fatal iatrogenic hypoglycemia: falsely elevated blood glucose readings with a point-of-care meter due to a maltose-containing intravenous immune globulin product. 2009 [Updated 06/18/2009]. Available from: http:// www.fda.gov/BiologicsBloodVaccines/SafetyAvailability/ucm15 5099.htm. [12] Carraro P, Plebani M. Errors in a stat laboratory: types and frequencies 10 years later. Clin Chem 2007;53(7):1338e42. [13] Carraro P, Zago T, Plebani M. Exploring the initial steps of the testing process: frequency and nature of pre-preanalytic errors. Clin Chem 2012;58(3):638e42. [14] Plebani M, Lippi G. Closing the brain-to-brain loop in laboratory testing. Clin Chem Lab Med 2011;49(7):1131e3. [15] Valenstein P, editor. Quality management in clinical laboratories. Northfield (IL): College of American Pathologists; 2005. [16] Rutledge J, Xu M, Simpson J. Application of the Toyota production system improves core laboratory operations. Am J Clin Pathol 2010;133(1):24e31. [17] Dunn EJ, Moga PJ. Patient misidentification in laboratory medicine: a qualitative analysis of 227 root cause analysis reports in the Veterans Health Administration. Arch Pathol Lab Med 2010; 134(2):244e55. [18] Chiozza ML, Ponzetti C. FMEA: a model for reducing medical errors. Clin Chim Acta 2009;404(1):75e8. [19] Southard PB, Kumar S, Southard CA. A modified Delphi methodology to conduct a failure modes effects analysis: a patient-centric effort in a clinical medical laboratory. Qual Manag Health Care 2011;20(2):131e51. [20] Krouwer J. Using a learning curve approach to reduce laboratory errors. Accred Qual Assur 2002;7(11):461e7. [21] Llopis MA, Trujillo G, Llovet MI, Tarres E, Ibarz M, Biosca C, et al. Quality indicators and specifications for key analyticalextranalytical processes in the clinical laboratory. Five years’ experience using the Six Sigma concept. Clin Chem Lab Med 2011; 49(3):463e70. [22] Gras JM, Philippe M. Application of the Six Sigma concept in clinical laboratories: a review. Clin Chem Lab Med 2007;45(6):789e96. [23] Westgard JO, Darcy T. The truth about quality: medical usefulness and analytical reliability of laboratory tests. Clin Chim Acta 2004; 346(1):3e11. [24] Fraser CG. Biological variation: from principles to practice. Washington (DC): AACC Press; 2001. [25] Westgard J. Desirable specifications for total error, imprecision, and bias, derived from intra- and inter-individual biologic variation. 2014. Available from: http://www.westgard.com/biodata base1.htm. [26] Kroll MH. Multiple patient samples of an analyte improve detection of changes in clinical status. Arch Pathol Lab Med 2010;134(1):81e9. [27] Fraser CG. Improved monitoring of differences in serial laboratory results. Clin Chem 2011;57(12):1635e7.

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

C H A P T E R

2 Errors in patient preparation, specimen collection, anticoagulant and preservative use: how to avoid such pre-analytical errors Leland Baskin, Alex Chin, Amid Abdullah, Christopher Naugler University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada

INTRODUCTION

coagulation. Anticoagulants for plasma and/or whole blood collection include ethylenediaminetetraacetic acid (EDTA), heparin, hirudin, oxalate, and citrate, which are available in solid or liquid form. Optimal anticoagulant-to-blood ratios are crucial to prevent clot formation while avoiding interference with analyte measurement, including dilution effects associated with liquid anticoagulants. Given the availability of multiple anticoagulants and additives, blood collection tubes should be filled according to a specified order to minimize contamination and carryover. Other factors to consider regarding blood collection tubes include differences between plastic and glass surfaces, surfactants, tube stopper lubricants, and gel separators, which all affect analyte measurement. The second most popular clinical specimen is urine, which is essentially an ultrafiltrate of blood before elimination from the body and is the preferred specimen to detect metabolic activity as well as urinary tract infections. Proper timing must be ensured for urine collections depending on the need for routine tests, patient convenience, clinical sensitivity, or quantitation. Furthermore, proper technique is required for clean catch samples for subsequent microbiological examination. Certain urine specimens require additives to preserve cellular integrity for cytological analysis and to prevent bacterial overgrowth. It is important to recognize the pre-analytical variables that affect analyte measurement in patient specimens so that properly informed decisions can be made regarding assay selection and development as well as troubleshooting unexpected outcomes from laboratory analysis.

Patient preparation and the specimen type are important pre-analytical factors to consider for laboratory assessment. Although the clinical laboratory has limited capabilities in controlling for the physiological state of the patient, such as biological rhythms and nutritional status, these variables as well as the effect of patient posture, tourniquets, and serum/plasma indices (hemolysis, icterus, lipemia) on measurement of analytes must be understood by both the clinical team and laboratory personnel. The most accessible specimen types include blood, urine, and oral fluid. The numerous functions associated with blood make it an ideal specimen to measure biomarkers corresponding to various physiological and pathophysiological processes. Blood can be collected by skin puncture (capillary), which is preferred when blood conservation and minimal invasiveness is stressed, such as in the pediatric population. Other modes of collection include venipuncture and arterial puncture, where issues to consider include the physical state of the site of collection and patient safety. Blood can also be taken from catheters and other intravascular lines, but care must be taken to eliminate contamination and dilution effects associated with heparin and other drugs. Clinical laboratory specimens derived from blood include whole blood, plasma, and serum. However, noticeable differences between these specimen types need to be considered when choosing the optimal specimen type for laboratory analysis. Such important factors include the presence of anticoagulants in plasma and in whole blood, hematocrit variability, and the differences in serum characteristics associated with blood

Accurate Results in the Clinical Laboratory, Second Edition https://doi.org/10.1016/B978-0-12-813776-5.00002-9

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2. PATIENT PREPARATION AND OTHER ISSUES AFFECTING LAB TESTS

BIOLOGICAL RHYTHMS AND LABORATORY TEST RESULTS Predictable patterns in the temporal variation of certain analytes, reflecting patterns in human needs, constitute biological rhythms. Different analytes have different rhythms, ranging from a few hours to monthly changes. Awareness of such changes can be relevant to proper interpretation of laboratory results. These changes can be divided into circadian, ultradian, and infradian rhythms according to the time interval of their completion. During a 24-h period of human metabolic activity, programming of metabolic needs may cause certain laboratory tests to fluctuate between a maximum and a minimum value. The amplitude of change of these circadian rhythms is defined as one-half of the difference between the maximum and the minimum values. Although, in general, these variations occur consistently, alteration in these natural circadian rhythms may be induced by artificial changes in sleep/wake cycles such as those induced by different work shifts. Therefore, in someone working an overnight (“graveyard”) shift, an elevated blood iron level taken at midnight would be normal for that individual; however, the norm is for high iron levels to be seen only in early morning. Patterns of biological variation occurring on cycles less than 24 h are known as ultradian rhythms. Analytes that are secreted in a pulsatile manner throughout the day show this pattern. Testosterone, which usually peaks between 10:00 a.m. and 5 p.m., is an example of an analyte showing this pattern. The final pattern of biological variation is infradian. This involves cycles greater than 24 h. The example most commonly cited is the monthly menstrual cycle, which takes approximately 28e32 days to complete. Constituents such as pituitary gonadotropin, ovarian hormones, and prostaglandins are significantly affected by this cycle.

PATIENT PREPARATION There are certain important issues regarding patient preparation for obtaining meaningful clinical laboratory test results. For example, glucose testing must be done after the patient has fasted overnight. These issues are discussed in this section.

Fasting The effects of meals on blood test results have been known for some time. Increases in serum glucose, triglycerides, bilirubin, and aspartate aminotransferase

are commonly observed after meal consumption. On the other hand, fasting will increase fat metabolism and increase the formation of acetone, b-hydroxybutyric acid, and acetoacetate both in serum and in urine. Longer periods of fasting (more than 48 h) may result in up to a 30-fold increase in these ketone bodies. Glucose is primarily affected by fasting because insulin keeps the serum concentration in a tight range (70e110 mg/dL). Diabetes mellitus, which results from either a deficiency of insulin or an increase in tissue resistance to its effects, manifests as an increase in blood glucose levels. In normal individuals, after an average of 2 h of fasting, the blood glucose level should be below 7.0 mmol/L (126 mg/dL). However, in diabetic individuals, fasting serum levels are elevated and thus constitute one criterion for making the diagnosis of diabetes. Other well-known examples of analytes showing variation with fasting interval include serum bilirubin, lipids, and serum iron.

Body position Physiologically, blood distribution differs significantly in relation to body posture. Gravity pulls the blood into various parts of the body when recumbent, and the blood moves back into the circulation, away from tissues, when standing or ambulatory. These shifts directly affect certain analytes due to dilution effects. This process is differential, meaning that only constituents of the blood that are non-diffusible will rise because there is a reduction in plasma volume upon standing from a supine position. This includes, but is not limited to, cells, proteins, enzymes, and protein-bound analytes (e.g., thyroid-stimulating hormone, cholesterol, T4, and medications such as warfarin). The reverse will take place when shifting from erect to supine because there will be a hemodilution effect involving the same previously mentioned analytes. Postural changes affect some groups of analytes in a much more profound waydat times up to a twofold increase or decrease depending on whether the sample was obtained from a supine or an erect patient. Most affected are factors directly influencing homeostasis, including renin, aldosterone, and catecholamines. It is vital for laboratory requisitions to specify the need for supine samples when these analytes are requested.

WHOLE BLOOD, PLASMA, AND SERUM SPECIMENS FOR CLINICAL LABORATORY ANALYSIS Approximately 8% of total human body weight is represented by blood, with an average volume in females and males of 5 and 5.5 L, respectively [1]. Whole

I. SOURCES OF ERRORS IN CLINICAL LABORATORIES: AN OVERVIEW

WHOLE BLOOD, PLASMA, AND SERUM SPECIMENS FOR CLINICAL LABORATORY ANALYSIS

blood consists of a cellular fraction (w45%) composed of erythrocytes (red blood cells), leukocytes (white blood cells), and thrombocytes (platelets), and a liquid fraction (plasma) (w55%) that transports these elements throughout the body. Blood vessels interconnect all the organ systems in the body and play a vital role in communication and transportation between tissue compartments. Blood serves numerous functions, including delivery of nutrients to tissues; gas exchange; transport of waste products such as metabolic by-products for disposal; communication to target tissues through hormones, proteins and other mediators; and cellular protection against invading organisms and foreign material. Given these myriad roles, blood is an ideal specimen for measuring biomarkers associated with various physiological conditions, whether it is direct measurement of cellular material and surface markers or measurement of soluble factors associated with certain physiological conditions. Plasma consists of approximately 93% water, with the remaining 7% composed of electrolytes, small organic molecules, and proteins. Various constituents of plasma are summarized in Table 2.1. These analytes are in transit between cells in the body and are present in varying concentrations depending on the physiological state of the various organs. Therefore, accurate analysis of the plasma is crucial for obtaining information regarding diagnosis and treatment of diseases. In clinical TABLE 2.1 Principal components of plasma. Component

Reference range

Units

Sodium

136e145

mmol/L

Potassium

3.5e5.1

mmol/L

Bicarbonate

17e25

mmol/L

Chloride

98e107

mmol/L

Hydrogen ions

40

mmol/L

Calcium

8.6e10.2

mg/dL

Magnesium

1.6e2.6

mg/dL

Inorganic phosphate

2.5e4.5

mg/dL

Glucose

70e99

mg/dL

Cholesterol