Wallach's Interpretation of Diagnostic Tests: Pathways to Arriving at a Clinical Diagnosis (5 page)

Calculation of the area under the ROC curve allows comparison of different tests. A perfect test has an area under the curve (AUC) equal to 1. Therefore, the closer the AUC is to 1, the better the test. Similarly, if one wants to know the cutoff value for a test that minimizes both false positives and false negatives (and hence maximizes both sensitivity and specificity), one would select the point on the ROC curve closest to the far upper left corner (x = 0, y = 1).

However, finding the right balance between optimal sensitivity and specificity may not involve simultaneously minimizing false positives and false negatives in all situations. For example, when screening for a deadly disease that is curable, it may be desirable to accept more false positives (lower specificity) in return for fewer false negatives (higher sensitivity). ROC curves allow a more thorough evaluation of a test and potential cutoff values but are not the ultimate arbiters of how to set sensitivity and specificity.

POSTANALYTIC ERRORS

Approximately 70–80% of the patient chart or medical record is composed of laboratory test results. Postanalytic errors are dependent on the design and development of those processes and procedures that will ensure correct and timely notification of these test results to the patient’s medical record with right reference range and appropriate interpretation of the test result. Manual and telephone reporting should be discouraged as this reporting is subject to transcription errors at the receiver end. The introduction of a hospitalized computer order entry system has eliminated some errors, but it has not eliminated the risk of mismatching the patients.

   
REFERENCE INTERVALS

The term “reference values” has essentially replaced the obsolete term “normal values.” Laboratory tests are commonly compared to a reference interval before health care providers make physiologic assessments, medical diagnosis, or management decisions. These comparisons may be cross-sectional or longitudinal. A cross-sectional comparison is comparison of an analyte result for a single patient with the interval of results for that analyte obtained from a group of apparently healthy individuals. This is referred to as the “population-based” reference interval. Another example of a cross-sectional comparison is when a single patient result is compared with a fixed value or cutoff value. There are two types of population-based reference intervals. The most common type is derived from a reference sample of persons who are in good health (health associated). The other type of reference interval has been termed “decision based” and defines specific medical decision limits that clinicians use to diagnose or manage patients. Longitudinal comparisons are when a patient’s most recent value is compared with previous values for the same analyte. This may help detect a change in health status.

Comparison of patient results with a population-based reference interval or with the cutoff values is used for diagnostic or screening purposes. The reference value change over a period of time is used for monitoring patients. Both healthy reference limits and disease-associated reference limits are important for the clinical interpretation of the laboratory test results and vary from laboratory to laboratory. These variations may be caused by preanalytic processing procedures, populations of healthy individuals, inherent random biologic variations, analytic platforms, or analytic imprecision that was present when reference intervals were determined.

Decision limits for optimally classifying patients into “disease” versus “healthy” categories are difficult to define. Most diseases are not homogenous distributions but represent a continuum of mild and severe forms. Various statistical tools and models have been developed to formalize the medical decision process, but most of the models do not include the methodologic differences in laboratory test values. The major utility of healthy reference intervals for clinicians is to provide a rough assessment of the possibility that a test value on a specific patient is difficult for the values normally found in similar healthy subjects. The guidelines for medical decision making use a standard 95% reference interval. By defining the healthy reference interval to include central 95% of matched healthy subjects, there is less than a 1 in 20 chance for a value outside the reference interval to be found in a matched healthy subject. Conventionally, a common limit of acceptability is based on the mean of population data ±2 standard deviation (SD), because this encompassed roughly 95% of the observations expected to be “normal.” With this convention, it must be remembered that 5% (usually 2.5% on the low side and 2.5% on the high side) of results can be expected to fall outside the ±2 SD limit, even in a “normal” healthy population. This is best illustrated in the use of multitest chemistry profiles for screening of persons known to be free of disease. The probability of any given test being abnormal is about 2–5%, and the probability of disease if a screening test is abnormal is generally low (0–15%). The frequency of abnormal single tests is 1.5% (albumin) to 5.9% (glucose) and up to 16.6% for sodium. Based on statistical expectations, when a panel of eight tests is performed in a multiphasic health program, 25% of the patients have one or more abnormal results; when the panel includes 20 tests, 55% have one or more test abnormalities.

In terms of qualitative test reports (e.g., positive, negative), optimal decision limits (cutoff) can be determined with ROC curve analyses. If false-positive labeling leads to a more harmful outcome, the decision limits should be moved away from the ROC optimum in a direction to minimize false-positive diagnoses. Likewise, if false-negative labeling is more dangerous, the decision limits should be moved to minimize the false-negative diagnoses. Although decision limits are better tools than reference values for deriving diagnostic value from laboratory tests, they have some drawbacks. First, decision limits will not address the degree of deviation of a test result above or below the decision limit. A test result slightly above the limit will be regarded as positive the same as a result far above the decision limit, and a test result slightly below the cutoff limit will be reported as negative.

PERFORMING THE RIGHT TEST AT THE RIGHT TIME FOR THE RIGHT REASON

As with the absolute value of a result, a test result or change in sequential results must be interpreted in the context of the clinical situation, recent changes in patient management, and historical results. Excessive repetition of tests is wasteful, and the excess burden increases the possibility of laboratory errors. Appropriate intervals between tests should be dictated by the patient’s clinical condition. Negative laboratory values (or any other type of tests) do not necessarily rule out a clinical diagnosis. Tests should be performed only if they will alter the patient’s diagnosis, prognosis, treatment, or management. Incorrect test values or isolated individual variation in results may cause Ulysses syndrome and result in loss of time, money, and peace of mind.

SECTION
1

DISEASE STATES

Chapter
2

Autoimmune Diseases

M. Rabie Al-Turkmani

Organ-Specific Autoimmune Diseases

Systemic Autoimmune Diseases

Felty Syndrome

Mixed Connective Tissue Disease

Polymyalgia Rheumatica

Polymyositis, Dermatomyositis, and Inclusion Body Myositis

Psoriatic Arthritis

Reactive Arthritis

Retroperitoneal Fibrosis

Rheumatoid Arthritis

Sjögren Syndrome

Systemic Lupus Erythematosus

Systemic Sclerosis (Scleroderma)

Autoimmune Vasculitis

Eosinophilic Granulomatosis with Polyangiitis (Churg-Strauss Syndrome)

Giant Cell Arteritis

Granulomatosis with Polyangiitis (Wegener Granulomatosis)

Henoch-Schönlein Purpura

Hypersensitivity Vasculitis

Polyarteritis Nodosa

Takayasu Arteritis

This Chapter provides the latest information on the diagnosis of systemic autoimmune diseases. Each entry is organized with a brief definition of the disease, information regarding clinical presentation, and laboratory findings. The Chapter also provides a list of common organ-specific autoimmune diseases, with an indication to where these diseases are discussed elsewhere in this book.

Autoimmune disease is the pathologic result of autoimmunity, whereby the immune system attacks the person’s healthy body tissues. Autoimmunity is caused by the inappropriate activation of T cells or B cells, or both, in the absence of a definite cause. B lymphocytes can produce autoantibodies, which may interfere with a cellular function (e.g., Graves disease, myasthenia gravis) or cause tissue damage, either directly or by forming immune complexes that are deposited in tissues or blood vessels. T lymphocytes may aggregate in tissues (or a tissue) with resultant destruction.

There are more than 80 different autoimmune disorders, and more than one autoimmune disorder can be manifested by one patient. These disorders can be classified as systemic, affecting multiple organs or tissues (e.g., connective tissue autoimmune diseases such as systemic lupus erythematosus, Sjögren syndrome, or scleroderma), or organ specific, targeting one particular organ.

Multiple factors contribute to the development of autoimmune diseases:

   Genetic susceptibility, mostly due to linkage to particular HLA molecules

   Environmental triggers (e.g., drugs, chemicals)

   Infectious agents (e.g.,
Mycoplasma pneumoniae
, HIV)