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12 Textbook of Diagnostic and Therapeutic Procedures in Allergy
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FEV, Forced Expiratory Volume.
J Allergy Clin Immunol Pract 2020;8:1616-24.
Waterfall plot depicting the heterogeneity of response in the subjects
Adapted from Denton E, Lee J, Tan T et al Systematic Assessment for Difficult and Severe Asthma
Improves Outcomes and Halves Oral Corticosteroid Burden Independent of Monoclonal Biologic Use
Figure 4. Waterfall Plot Depicting the Heterogeneity of the FEV1 Response in a Study on Asthma Control and Outcomes. Note that the majority had improvements in FEV1, a few had
no alteration in FEV1 and a minority did show a decline in FEV1. The magnitude of the changes and heterogeneity in outcomes is more revealing than numbers for mean and standard deviation.
Principles of Diagnostic Tests 13
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are depicted on the horizontal or X-axis, while vertical bars are drawn for each patient, either
above or below the baseline. In studies of asthma the vertical axis Y may be used to depict the
maximum % change from baseline for parameters, such as forced expiratory volume (FEV1),
absolute eosinophil count, or decrease in oral corticosteroid (OCS) use. For FEV1, vertical bars
above the baseline represent improvements in FEV1 with no change reflecting a null effect
of the intervention and vertical bars below the baseline a deterioration in lung function. The
latter suggests that the intervention did not modify the deterioration of lung function. Figure 4
is an example of changes in FEV1 plotted as an outcome of an intervention. The data are not
presented randomly or in order of when a patient was enrolled into a study but are organized
to provide a clear picture of the study subjects’ response; from best to worst based on the
parameter evaluated (Denton et al. 2020).
The individual bars, besides representing a single subject, can also be used to represent other
salient patient characteristics, such as the effect of smoking on FEV1 of asthmatics by using a
different color. Thus, a waterfall plot provides data about not only the primary outcome, such
as FEV1 but also adds relevant information about the other risk factors (such as smoking or
obesity) influencing the outcome.
b) META-ANALYSIS: Historical anecdotes, personal observations and hypothetical therapeutic
modalities have been replaced by carefully planned, scientifically designed randomized
controlled trials (RCT) in many fields and particularly in medicine ushering in an era of
evidence-based medicine. This has resulted in many publications addressing the same question
with each individual study reporting measurements that may have some error. A stringent
statistical analysis that combines the result of these scientific studies is called “meta-analysis.”
Data derived from meta-analyses can identify patterns or trends among study results, endorsing
some outcomes and identifying others that need further validation. This data is often presented
in what is called a “Forest Plot” or as a “Blobbogram.” In many of the clinical guidelines or
practice parameters data is presented as a Forest plot and recommendations are made based on
the quality of data used to derive the plot. Forest plots are commonly presented in two columns.
The left-hand column lists chronologically the names of the RCT. The right-hand column is
a plot of the measurement of effects (e.g., odds ratio) for each of the studies. The square is
proportional to the weights used and the confidence interval is represented by horizontal lines.
The summary measure also called pooled fixed effect is depicted by a diamond with the lateral
points of the diamond, indicating the confidence intervals for the measurement. A solid vertical
line representing no effect (null effect) for the outcome is also plotted. The net effect of the
outcome derived from meta-analysis should fall on either side of the null effect line. In the
example provided in Figure 5, it favors the combination of inhaled corticosteroids (ICS) plus
systemic corticosteroids (SCS) over SCS alone in acute asthma (Kearns et al. 2020).
Diagnostic tests can be very useful tools to support clinical decisions and patient management.
However, physicians should understand the limitations of diagnostic tests, their performance
characteristics and the influence of pre-test probability on the value of diagnostic tests. A better
understanding of integrating the clinical findings with judicious use of diagnostic tests greatly
improves the utility of diagnostic tests in the management of all patients in general and particularly
those with allergic and immunological diseases.
Conclusion
14 Textbook of Diagnostic and Therapeutic Procedures in Allergy
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SCS, Systemic Corticosteroid.
Review and Meta analysis. J Allergy Clin Immunol Pract 2020;8:605-17
Kearns N, Ingrid M, Harper J, Beasley R and Weatherall M. Inhaled Corticosteroids in Acute Asthma: A Systemic
combination as a more effective regimen. A conclusion derived from meta-analysis is often used to develop treatment guidelines or practice parameters. ICS, Inhaled Corticosteroid;
Figure 5. A Forest Data Plot of the Meta-Analyses. Outcome data derived from 7 studies addressing the value of adding ICS to SCS versus SCS alone in acute asthma favors the
Glossary of Abbreviations
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ACTH – Adrenocorticotropic Hormone
ALT – Alanine Transaminase
ANA – Antinuclear Antibody
AUC – Area Under the Curve
BMI – Body Mass Index
BNP – Brain Natriuretic Peptide
CDSCO – Central Drugs Standard Control Organization
CLSI – Clinical Laboratory Standards Institute
CRP – C-Reactive Protein
FDA – Food and Drug Administration
FEV – Forced Expiratory Volume
FN – False Negative
FP – False Positive
FSH – Follicle Stimulating Hormone
HDL – High-Density Lipids
INR – International Normalized Ratio
kUA – Kilounit Allergen
LH – Luteinizing Hormone
LR – Likelihood Ratio
NPV – Negative Predictive Value
PPV – Positive Predictive Value
PTH – Parathyroid Hormone
RCT – Randomized Controlled Trials
ROC – Receiver Operator Characteristics
sIgE – Specific Immunoglobulin E
SPT – Skin Prick Test
TN – True Negative
TP – True Positive
TSH – Thyroid Stimulating Hormone
Principles of Diagnostic Tests 15
Beyer, K., Grabenhenrich, L., Hartl, M., Beder, A., Kalb, B., Ziegert, M. et al. 2015. Predictive values of component-
specific IGE for the outcome of peanut and hazelnut food challenges in children. Allergy 70(1): 90–98.
Biggs, C. M., Haddad, E., Issekutz, T. B., Roifman, C. M. and Turvey, S. E. 2017. Newborn screening for severe
combined immunodeficiency: A primer for clinicians. CMAJ 189(50): E1551–E1557.
Denton, E., Lee, J., Tay, T., Radhakrishna, N., Hore-Lacy, F., Mackay, A. et al. 2020. Systematic assessment for
difficult and severe asthma improves outcomes and halves oral corticosteroid burden independent of monoclonal
biologic use. J. Allergy Clin. Immunol. Pract. 8(5): 1616–1624.
Eng j. ROC analysis: Web-based calculator for ROC curves. Baltimore: Johns Hopkins University [updated 2014
March 19; cited 12.9.2021]. Available from: http://www.jrocfit.org.
Fagan, T. J. 1975. Letter: Nomogram for Bayes theorem. N Engl. J. Med. 293(5): 257.
Farhat, M., Greenaway, C., Pai, M. and Menzies, D. 2006. False-positive tuberculin skin tests: What is the absolute
effect of BCG and non-tuberculous mycobacteria? Int. J. Tuberc. Lung Dis. 10(11): 1192–1204.
Fierz, W. and Bossuyt, X. 2020. Likelihood ratios as value proposition for diagnostic laboratory tests. J. Appl. Lab
Med. 5(5): 1061–1069.
Fleischer, D. M., Bock, S. A., Spears, G. C., Wilson, C. G., Miyazawa, N. K., Gleason, M. C. et al. 2011. Oral food
challenges in children with a diagnosis of food allergy. J. Pediatr. 158(4): 578–583 e571.
Foong, R. X., Dantzer, J. A., Wood, R. A. and Santos, A. F. 2021. Improving diagnostic accuracy in food allergy.
J. Allergy Clin. Immunol. Pract. 9(1): 71–80.
Foong, R. X. and Santos, A. F. 2021. Biomarkers of diagnosis and resolution of food allergy. Pediatr Allergy Immunol.
32(2): 223–233.
References
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Greenhawt, M., Shaker, M., Wang, J., Oppenheimer, J. J., Sicherer, S., Keet, C. et al. 2020. Peanut allergy
diagnosis: A 2020 practice parameter update, systematic review, and grade analysis. J. Allergy Clin. Immunol.
146(6): 1302–1334.
Hadithi, M., von Blomberg, B. M., Crusius, J. B., Bloemena, E., Kostense, P. J., Meijer, J. W. et al. 2007. Accuracy of
serologic tests and HLA-DQ typing for diagnosing celiac disease. Ann. Intern. Med. 147(5): 294–302.
Ho, M. H., Heine, R. G., Wong, W. and Hill, D. J. 2006. Diagnostic accuracy of skin prick testing in children with tree
nut allergy. J. Allergy Clin. Immunol. 117(6): 1506–1508.
Hurt, C. B., Nelson, J. A. E., Hightow-Weidman, L. B. and Miller, W. C. 2017. Selecting an HIV test: A narrative
review for clinicians and researchers. Sex Transm. Dis. 44(12): 739–746.
Johnson, K. M. 2017. Using Bayes’ rule in diagnostic testing: A graphical explanation. Diagnosis (Berl)
4(3): 159–167.
Kearns, N., Maijers, I., Harper, J., Beasley, R. and Weatherall, M. 2020. Inhaled corticosteroids in acute asthma: A
systemic review and meta-analysis. J. Allergy Clin. Immunol. Pract. 8(2): 605–617 e606.
Lin, J. S., Piper, M. A., Perdue, L. A., Rutter, C. M., Webber, E. M., O’Connor, E. et al. 2016. Screening for colorectal
cancer: Updated evidence report and systematic review for the US preventive services task force. JAMA
315(23): 2576–2594.
Maglione, M. A., Okunogbe, A., Ewing, B. et al. 2016. Diagnosis of Celiac Disease [Internet]. Rockville (MD):
Agency for Healthcare Research and Quality (US); 2016 Jan. (Comparative Effectiveness Reviews, No.
162.) Available from: https://www.ncbi.nlm.nih.gov/books/NBK344454/.
Mandrekar, J. N. 2010. Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol.
5(9): 1315–1316.
Narain, S., Richards, H. B., Satoh, M., Sarmiento, M., Davidson, R., Shuster, J. et al. 2004. Diagnostic
accuracy for lupus and other systemic autoimmune diseases in the community setting. Arch. Intern. Med.
164(22): 2435–2441.
Oeffinger, K. C., Fontham, E. T., Etzioni, R., Herzig, A., Michaelson, J. S., Shih, Y. C. et al. 2015. Breast cancer
screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA
314(15): 1599–1614.
Safari, S., Baratloo, A., Elfil, M. and Negida, A. 2016. Evidence based emergency medicine; part 4: Pre-test and
post-test probabilities and Fagan’s nomogram. Emerg (Tehran) 4(1): 48–51.
Shreffler, J. and Huecker, M. R. 2021. Diagnostic testing accuracy: sensitivity, specificity, predictive values and
likelihood ratios. [Updated 2021 Mar 3]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing;
2022 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557491/.
Simel, D. L., Samsa, G. P. and Matchar, D. B. 1991. Likelihood ratios with confidence: Sample size estimation for
diagnostic test studies. J. Clin. Epidemiol. 44(8): 763–770.
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latent tuberculosis infection: Recommendations from the National Tuberculosis Controllers Association and
CDC, 2020. MMWR Recomm. Rep. 69(1): 1–11.
Soares-Weiser, K., Takwoingi, Y., Panesar, S. S., Muraro, A., Werfel, T., Hoffmann-Sommergruber, K. et al. 2014. The
diagnosis of food allergy: A systematic review and meta-analysis. Allergy 69(1): 76–86.
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criteria and classification of mast cell disorders: A consensus proposal. Hemasphere 5(11): e646.
Wei, Q., Jiang, Y., Xie, J., Lv, Q., Xie, Y., Tu, L. et al. 2020. Analysis of antinuclear antibody titers and patterns by
using HEP-2 and primate liver tissue substrate indirect immunofluorescence assay in patients with systemic
autoimmune rheumatic diseases. J. Clin. Lab Anal. 34(12): e23546.
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Chapter 2
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Routine Laboratory Tests
Vijaya Knight1 and Mandakolathur R. Murali2,*
Introduction
Workup for atopy or allergic diseases typically includes evaluation of peripheral blood cells, serum
levels of immunoglobulin E (IgE) and allergen-specific IgE (sIgE). In addition, measurement
of serum tryptase may be of value when evaluating an anaphylactic reaction or when mast cell
disorders are on the differential diagnosis. Analysis of markers of acute inflammation, including
C-reactive protein and erythrocyte sedimentation rate, can be helpful when considering autoimmune
or infectious etiologies in the differential diagnosis. This chapter describes the clinical utility of
these routine diagnostic tests.
Complete Blood Count
The Complete Blood Count (CBC) is perhaps the most widely used laboratory test and has been
refined and automated following decades of pioneering work (Verso 1964). The earliest known
method for the analysis of blood cells was developed by Karl von Vierordt, a professor of theoretical
medicine at the University of Tubingen in the mid-1800s. In addition to developing techniques
to study blood flow and a prototype instrument for the measurement of blood pressure, Karl von
Vierordt developed the first slide-based method for counting blood cells. In 1874, Louis-Charles
Malassez, a French anatomist and histologist developed the first hemocytometer; Paul Erlich later
developed methods to stain and differentiate the various peripheral blood leucocytes (Kay 2016)
and Maxwell Wintrobe developed the Wintrobe hematocrit method in the early 1900s (Fred 2007).
Automated blood counts became mainstream with the development of the Coulter counter that used
electrical impedance to analyze and measure peripheral blood cells (Robinson 2013). The Coulter
principle continues to form the basis of analytical measurements in modern hematology analyzers.
A CBC provides information on the relative percentages and absolute numbers (generally
expressed per microliter (mcL) or liter (L) of blood) of red blood cells (RBCs), white blood cells
(WBCs) and platelets, and includes hemoglobin concentration and various characteristics of RBCs
(George-Gay and Parker 2003). When combined with a WBC differential, the percentages and
1
Associate Professor, University of Colorado School of Medicine, Department of Pediatrics, Section of Allergy, and
Immunology, Children’s Hospital, Colorado, Translational and Diagnostic Immunology Laboratory, 13123 East
16th Avenue, Aurora, Colorado 80045.
2
Director of Clinical Immunology laboratory, Departments of Medicine and Pathology, Massachusetts General Hospital,
Assistant Professor of Medicine, Harvard University, Boston, Massachusetts 02114.
Email: vijaya.knight@childrenscolorado.org
* Corresponding author: murali50@aol.com
Table 1. Components of CBC with differential.
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Parameter Units Description Clinical Signicance
WBC Cells/L Number of WBC per liter of blood Low WBC counts: Bone marrow suppression due to infection, inherited bone marrow defects,
autoimmune destruction of WBC and drug-induced bone marrow suppression.
Elevated WBC counts: Response to infection or inammation, immune dysregulation and
malignancy
RBC Cells/L Number of RBC per liter of blood Low RBC counts: Result in anemia and may be a consequence of blood loss, defective RBC
production (e.g., iron deciency, vitamin B12 or folic acid deciency, PRCA, bone marrow
failure and infections, such as Parvovirus B19), or increased destruction (autoimmune causes such
as PNH, cold agglutinin disease; congenital hemolytic anemias, DIC, drug-induced hemolytic
anemia, transfusion reactions, infections, such as malaria, mechanical trauma due to prosthetic
heart valves).
Elevated RBC counts (polycythemia): hypoxic conditions such as smoking or heart failure;
malignancy such as PV.
Hemoglobin (Hb) g/dL Amount of hemoglobin expressed in grams
per deciliter
Hematocrit (Hct) % Also called “packed cell volume” or PCV. Hct
is the ratio of the volume of RBCs to the total
volume of blood
Mean Corpuscular
Volume (MCV)
MCH (Mean
Corpuscular
Hemoglobin)
Femtoliters
() or cubic
microns
pg Amount of hemoglobin per red blood cell Low MCH: iron deciency; thalassemias
Denes the size of red blood cells Low MCV (< 80 fL): microcytic anemic commonly seen in iron deciency, thalassemias and
Anemia: Hb is below the age-matched reference interval.
Erythrocytosis: Elevated Hb count is an indirect reection of increased RBC production, generally
in response to low oxygen levels (e.g., smoking, living at a high altitude, COPD, lung or cardiac
abnormalities, heart failure)
Low hematocrit: Associated with anemia (decreased RBC), nutritional deciencies, and blood loss
Elevated hematocrit: Associated with dehydration, conditions in which RBCs are increased, such
as PV, lung or cardiac disorders leading to a hypoxic state
sideroblastic anemia
Increased MCV (> 100 fL): macrocytic anemia, classied as megaloblastic anemia that occurs due
to impaired DNA synthesis (e.g., folic acid and/or vitamin B12 deciency, orotic aciduria) and
non-megaloblastic anemia (e.g., chronic alcoholism, hypothyroidism, liver disease and primary
bone marrow disease)
High MCH: vitamin B12 and/or folate deciency
18 Textbook of Diagnostic and Therapeutic Procedures in Allergy
MCHC (Mean
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Corpuscular
Hemoglobin
Concentration)
RDW (Red Cell
Distribution Width)
Platelets Cells/L Number of platelets per liter of blood Thromobocytopenia (low platelet count): drug-induced, particularly following chemotherapy;
Plateletcrit (PCT) % The volume occupied by platelets in blood Genetics, age, race, alcohol consumption, physical activity can all modify PCT and MPV
MPV (Mean Platelet
Volume)
% Segmented
Neutrophils
Segmented
Neutrophils (Absolute
count) *
Immature
Neutrophils*
Immature Neutrophils
(Absolute count)
% Lymphocytes % Percentage of lymphocytes Lymphocytosis: infections such as CMV, infectious mononucleosis; drug hypersensitivity
Lymphocytes
(Absolute count)
% Monocytes % Percentage of monocytes Monocytosis: malignancies, response to infections (e.g., infectious mononucleosis)
Monocytes (Absolute
count)
g/dL Amount of hemoglobin per unit volume Low MCHC (hypochromic): e.g., iron deciency, poor absorption of iron and chronic blood loss
High MCHC (hyperchromic): e.g., hereditary spherocytosis, hemolytic anemias and
hypersplenism
% Coecient of RBC volume or RBC size
variation
Femtoliters
()
% Percentage of neutrophils with normal,
Cells/mcL Calculated as (WBC count × % segmented
% Percentage of immature forms (band
Cells/mcL Calculated as (WBC count × % immature
Cells/mcL Calculated as (WBC count ×
Cells/mcL Calculated as (WBC count × %
Calculated as the plateletcrit (PCT) by the
total number of platelets
segmented nucleus (typically 3–5 segments)
neutrophils)/100
forms, metamyelocytes, myelocytes or
promyelocytes)
granulocytes)/100
%lymphocytes)/100
monocytes)/100
High RDW (variable sizes of RBCs): associated with nutritional deciencies (iron, folate and
vitamin B12), following blood loss
Low RDW: not associated with any specic hematological disorder
hematological malignancies, autoimmunity and genetic causes
Thrombocytosis (high platelet count): primary or essential thrombocytosis, secondary to infection,
inammation or malignancies
Neutropenia: drug-induced, following viral, bacterial, or parasitic infections, nutritional
deciencies, autoimmunity, bone marrow suppression, inherited defects in genes responsible for
neutrophil development
Neutrophilia: normal physiological variation, response to acute infection, inammatory processes,
such as rheumatoid arthritis, ulcerative colitis, chronic hepatitis, medications, neoplastic processes,
genetic disorders (e.g., leukocyte adhesion defect 1 and LAD1) and corticosteroid therapy
An increase in immature granulocytes may occur due to physiological stresses, such as pregnancy,
infection, or inammation; or following administration of G-CSF; or due to hematological
malignancies
reactions, stress, LGL leukemia, CLL, early HIV-1 infection
Lymphopenia: inherited defects in genes responsible for lymphocyte development,
immunosuppressants, such as glucocorticoids, chemotherapy, malignancy and AIDS
Routine Laboratory Tests 19
Table 1 contd. ...
...Table 1 contd.
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Parameter Units Description Clinical Signicance
% Eosinophils % Percentage of eosinophils Eosinophilia: allergic diseases, parasitic infections, drug reactions, neoplastic diseases such as
Eosinophils (Absolute
count)
% Basophils % Percentage of basophils Allergic reactions, neoplastic diseases, such as CML and acute myeloid leukemia
Basophils (Absolute
count)
* The absolute neutrophil count (ANC) includes both mature and immature neutrophils.
Cells/mcL Calculated as (WBC count × %
eosinophils)/100
Cells/mcL Calculated as (WBC count × % basophils)/100
acute eosinophilic leukemia, CML, systemic mastocytosis and hypereosinophilic syndromes
20 Textbook of Diagnostic and Therapeutic Procedures in Allergy
Routine Laboratory Tests 21
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absolute values of neutrophils, lymphocytes, monocytes, eosinophils and basophils are reported.
CBC parameters including WBC differential are detailed in Table 1.
CBCs are performed using manual methods or automated instruments (hematology analyzers
or hematology systems) (Chhabra 2018). While manual methods have the advantage of low cost and
require little infrastructure, they are labor-intensive and have relatively low throughput. Automated
methods offer the advantage of high throughput analysis but require significantly more investment.
Most clinical laboratories now use automated analyzers for CBCs; however, unusual findings on an
automated CBC require a manual review of a blood smear by a pathologist to identify and quantify
unusual or abnormal cell types. The International Consensus Group for Hematology Review (Barnes
et al. 2005) which included 17 laboratories from six countries published suggested consensus criteria
and recommended actions for follow-up of abnormal findings following automated CBC analyses.
These criteria include delta checks and delta limits (recognition of previous abnormal CBC findings
and the extent to which results differ from each other), results exceeding or falling below instrument
linearity, limits for the various CBC parameters and abnormal findings on the WBC differential
(including immature or atypical granulocytes or lymphocytes, presence of blasts and other immature
forms of neutrophils). The presence of atypical or immature cells typically triggers a manual slide
review of the sample. Individual laboratories establish triggers for manual slide reviews according
to their patient populations.
CBCs are routinely performed using ethylenediaminetetraacetic acid (EDTA) anticoagulated
blood, which is collected via venipuncture. Blood must be thoroughly mixed with EDTA prior to
analysis to avoid spurious results due to inadequate anti-coagulation, and preferably analyzed within
6–8 hours of collection for optimal results. However, individual laboratories perform validation
studies to determine acceptable sample stability following venipuncture and may analyze samples
up to 24–48 hours following collection.
CBCs can provide important information to support clinical findings; however, as with
any laboratory tests, the results must be interpreted within the context of clinical findings
(see Chapter 1, pre- and post-test probability). Peripheral blood parameters are influenced by
circadian rhythms, age, sex and race, as well as by extrinsic environmental factors (Feriel et al.
2021). For instance, hemoglobin levels vary between males and females after puberty owing to
increased testosterone production in males that supports increased RBC production, and blood loss
due to menstruation in females that results in relatively lower hemoglobin levels. WBC counts
vary with age as do the absolute counts of individual WBC populations; lymphocyte counts are
highest in newborns and infants, and slowly decline and eventually stabilize in early adulthood.
This contrasts with absolute neutrophil counts that are relatively low compared with lymphocytes
in infancy and increase with age. CBC parameters must therefore be reported with appropriate
age-matched reference intervals to be interpreted accurately. Race and ethnicity also influence CBC
parameters; Enujung Lim et al. highlighted the challenges of developing race or ethnicity-specific
reference intervals for a variety of laboratory tests, including the CBC (Lim et al. 2015). While the
development of race-specific CBC reference intervals is much more challenging, the majority of
clinical laboratories report CBCs with age-specific reference intervals.
Eosinophils make up approximately 0–6% of WBC. Normal peripheral blood contains approximately
0.05–0.5 × 103 eosinophils/mcL.
Eosinophils develop from CD34+IL5Rα+ progenitors, that in turn arise from common myeloid
progenitor cells within the bone marrow (Blanchard and Rothenberg 2009). Mature eosinophils
contain a variety of pre-formed granules including major basic protein-1 and -2 (MBP-1 and
MBP-2), eosinophil cationic protein (ECP), eosinophil-derived neurotoxin (EDN) and eosinophil
peroxidase (EPX) (Acharya and Ackerman 2014). A variety of pre-formed growth factors
(GM-CSF), chemokines [Eotaxin, C-C Motif Chemokine Ligand 5 (CCL5)], and cytokines (IL-2,
Absolute Eosinophil Count (AEC)
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