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a Chapter 24 Doppler Velocimetry for Fetal Surveillance: Adverse Perinatal Outcome and Fetal Hypoxia 365<br />

Diagnostic Efficacy of Doppler Velocimetry<br />

Among High-Risk Pregnancies<br />

The efficacy of fetal Doppler investigations for predicting<br />

adverse perinatal outcome in complicated<br />

pregnancies has been widely investigated. A selection<br />

of these studies is summarized in Table 24.1, some of<br />

which are also discussed later.<br />

Most studies indicated that the Doppler results<br />

were efficacious for identifying the fetus at risk in<br />

complicated pregnancies. There are, however, wide<br />

variations in the performance of the technique, with<br />

the sensitivity ranging from 21% to more than 90%<br />

and the specificity from 63% to 97%. This range may<br />

not be surprising, as the studies were heterogeneous<br />

in several ways, including the population selection<br />

criteria, the method of using Doppler surveillance<br />

(e.g., the frequency of examination), the diagnostic<br />

threshold value of the test result, and the outcome<br />

parameters. As is evident, the investigators were not<br />

uniform in their selection criteria of an adverse perinatal<br />

outcome, which included fetal smallness for gestational<br />

age (SGA), operative delivery for fetal distress,<br />

Apgar score, the need for admission to the neonatal<br />

intensive care unit (NICU), and various other<br />

conditions. These variations are not surprising as the<br />

criteria for morbid perinatal outcome remain controversial.<br />

Many of the traditional measures of morbidity<br />

are now known to be of little significance for<br />

long-term prognosis of the infant. Despite these limitations,<br />

traditional measures of perinatal morbidity<br />

have not yet been replaced by any more insightful alternatives.<br />

In addition, some of these studies were deficient in<br />

their experimental approach. For example, many investigators<br />

did not employ a blind technique, which might<br />

have compromised the studies' validity as the clinicians'<br />

preconceived notion about the efficacy of the test<br />

would inevitably introduce bias. If the physician was already<br />

favorably disposed toward the test, the results<br />

could be erroneously affirmative; on the other hand,<br />

the chances of obtaining false-negative results would<br />

increase if the physician had no confidence in the test.<br />

Thus blind evaluations significantly enhance the validity<br />

of the study results. In one of the first studies reported<br />

on the diagnostic efficacy of Doppler velocimetry,<br />

the investigators [2] did not state whether the clinicians<br />

were blind to the Doppler results. Moreover, in<br />

one of the largest studies on Doppler efficacy, which<br />

also included a nonstress test (NST) and biophysical<br />

Fig. 24.2. Principle of receiver operating characteristic<br />

(ROC) curve analysis for assessing the efficacy of a diagnostic<br />

test. D (±) disease-free, D (+) diseased, T (±) test negative,<br />

T(+) test positive, TP true positive, FP true negative. Top:<br />

Distribution pattern of the diseased and disease-free populations<br />

along the test value represented by the horizontal<br />

line in the middle of each panel. Bottom: ROC curves. Note<br />

that with a useless test there is an equal chance of being<br />

diseased or disease-free at any given value of the test (A1);<br />

the corresponding curve (A2) is the diagonal from the lower<br />

left to the upper right corner. In contrast, a perfect test<br />

completely discriminates between the two populations<br />

(B1). The arrow points to the most discriminatory value of<br />

the test. The corresponding ROC curve (B2) is represented<br />

by the left and upper margins of the graph with no false<br />

positives. The upper left corner corresponds to only truepositive<br />

and no false-positive results and therefore represents<br />

the absolute discriminatory value of the test. A useful,<br />

but less than perfect, test predominantly separates the<br />

two populations with some degree of overlap that contributes<br />

to the false-positive and false-negative results (C1).<br />

The arrow, as in the previous case, points to the most discriminatory<br />

value of the test. The ROC curve covers an area<br />

(black shaded) that is a measure of the test's overall efficacy<br />

(C2). The test value nearest to the upper left corner is<br />

the most efficacious

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