Keywordsmultivariable prediction models, proteomics, metabolomics, cell-free fetal DNA, podocyturia, angiogenic factors, uterine artery Doppler velocimetry
Editors’ comment: As noted by the authors in their Introduction, prediction tests had hardly been studied when Chesley published his single-authored edition. However, this concept was never far from his mind, and he was amongst the pioneers in the field suggesting urate clearance tests to predict the disease. As noted in the current chapter, this approach proved imprecise. One problem with the prediction literature is its reliance primarily on clinical criteria alone to establish a diagnosis of preeclampsia. As discussed further in Chapter 6 and elsewhere, diagnosis by purely clinical criteria can be erroneous in a considerable number of patients, especially in multiparous women, when histopathological confirmation is sought (e.g,. renal biopsy). Suggestions are made elsewhere in the text that specific biomarkers be sought and utilized in conjunction with classical clinical signs and symptoms; these are likely to aid in the accurate diagnosis of preeclampsia and should enhance the precision of predictive tests .
The prediction of preeclampsia is a worthwhile goal because identification of patients at risk could result in earlier diagnosis of the disease, monitoring the mother and fetus at risk, and testing/implementing preventive strategies. In addition, longitudinal studies of patients at risk are a means to gain insight into the pathogenesis of the disease and the development of mechanistic-based strategies for prevention and treatment.
In the single-authored first edition of this textbook, Chesley foresaw the importance of predictive tests; yet, the available tests were few. Chesley highlighted three possible methods: indices of exchangeable sodium, the flicker fusion test (the point at which a rapidly flickering light is perceived as steady), and a variety of pressor responses ranging from angiotensin infusion to the roll-over test. Much work has been conducted since the publication of the first edition of the textbook, and this chapter will review tests which have been proposed for the prediction of the syndrome of preeclampsia.
Assessing the Quality of Tests to Predict Disease
“Prediction” refers to the administration of a test to asymptomatic individuals with the goal of assessing the likelihood of developing a particular disease. The evaluation of predictive tests in preeclampsia is important and necessary for several reasons. Preeclampsia is associated with increased maternal and perinatal morbidity and mortality. Interventions to prevent preeclampsia (e.g., low-dose aspirin and calcium) can have small/moderate benefits that caregivers may wish to offer women at higher risk of developing the disease. Intensive maternal and fetal surveillance could be offered, and earlier interventions may decrease the severity of maternal morbidity.
The properties of a predictive test are traditionally assessed using sensitivity, specificity, and predictive values. Sensitivity is the probability of a test result being positive in a patient who will develop the disease, and specificity is the likelihood of a test result being negative in a patient who will not develop the disease. The performance of a test can also be measured in terms of positive and negative predictive values. Sensitivity and specificity cannot be used to estimate the probability of disease for an individual patient.
Predictive values (positive and negative) allow estimation of the probability of subsequently developing the disorder or not developing the disorder; yet they depend on the prevalence of the disorder in the population. Likelihood ratios are alternative indices for summarizing the performance of a test, and are independent of the prevalence of the disease. Likelihood ratios depend on the inherent value of the test to distinguish between patients who will and will not develop the disease, and are calculated by combining sensitivity and specificity.
The likelihood ratio of a positive test is the ratio of the probability of a positive predictive test result in women who subsequently develop preeclampsia to the probability of a positive predictive test result in women who do not develop the disease (sensitivity/[1−specificity]). The likelihood ratio of a negative test is the ratio of the probability of a negative predictive test result in women who subsequently develop preeclampsia to the probability of the negative predictive test result in women who do not develop the disease ([1−sensitivity]/specificity). The greater the positive likelihood ratio, the larger is the increase in the post-test probability of developing disease, while the smaller the negative likelihood ratio, the larger is the decrease in the probability of developing disease.
Tests with a high positive and a low negative likelihood ratio are considered to be clinically useful. Tests with positive likelihood ratios of 10 or greater and negative likelihood ratios of 0.1 or lower are most likely to be useful in clinical practice. Moderate prediction can be obtained with tests with a likelihood ratio value of 5–10 and 0.1–0.2, respectively, whereas those below 5 and above 0.2, respectively, yield only minimal prediction.
The Bayes theorem permits the use of likelihood ratios in conjunction with pre-test probability of preeclampsia to estimate the post-test probability that an individual will develop this disorder once the result of a test is known. The use of odds rather than risk makes the calculation slightly complex; however, a nomogram can be used to avoid having to make conversions between odds and probabilities. The Fagan nomogram is a useful and convenient graphical tool that permits estimation of the post-test probability of disease based on the pre-test or anterior probability of disease and the likelihood ratio.
Likelihood ratios indicate by how much a given test result will increase or decrease the probability of developing preeclampsia. To use the Fagan nomogram (as depicted in Fig. 11.1 ), a line is drawn from the estimated pre-test probability (left vertical line) through the likelihood ratio of the test result (center vertical line) and the intersection of the line with the right vertical line provides the post-test probability. For example, if a test has a likelihood ratio of a positive result of 15 and a negative likelihood ratio of 0.1, and if a pregnant woman has a pre-test probability of 5% and the test is positive, the post-test probability of disease would be 44% (red line). On the other hand, if the test is negative, the post-test probability would be approximately 0.6% (blue line).
When assessing a test during pregnancy, its accuracy is a function of the prevalence of the disease in the population under study. Since incidence rates of preeclampsia for all women in developing (1.3 to 6.7%) and developed countries (0.4 to 2.8%) are relatively low, any predictive test for this disorder would require a very high positive likelihood ratio (>15) to increase the probability that preeclampsia will occur, and a very low negative likelihood ratio (<0.1) to confidently exclude the probability that the woman will develop the disorder. Criteria for the ideal predictive test for preeclampsia are shown in Table 11.1 .
Table 11.2 depicts tests proposed for the prediction of preeclampsia. Such methods have been chosen on the basis of proposed mechanisms of disease implicated in the pathophysiology of preeclampsia.
|1. Placental perfusion/vascular resistance dysfunction-related tests|
|Isometric handgrip exercise test|
|Cold pressor test|
|Flicker-fusion threshold test|
|Pressor response to aerobic exercise|
|Intravenous infusion of angiotensin II|
|Mean arterial blood pressure|
|Assessment of arterial stiffness|
|Platelet angiotensin II binding|
|Platelet calcium response to arginine vasopressin|
|24-hour ambulatory blood pressure monitoring|
|Transcranial Doppler velocimetry|
|Ophthalmic artery Doppler velocimetry|
|Ultrasonographic placental volume, location, and vascularization|
|3D power Doppler of the uteroplacental circulation space|
|Uterine artery Doppler velocimetry|
|2. Fetal and placental unit endocrinology dysfunction-related tests|
|Human chorionic gonadotropin|
|Pregnancy-associated plasma protein A (PAPP-A)|
|Placental protein 13 (PP-13)|
|A disintegrin and metalloprotease 12 (ADAM-12)|
|Pregnancy-specific β 1 -glycoprotein (SP1)|
|3. Renal dysfunction-related tests|
|Serum uric acid|
|Urinary calcium excretion|
|4. Endothelial/oxidant stress dysfunction-related tests|
|Platelet count and volume|
|Matrix metalloproteinase-9 (MMP-9)|
|Inositol phosphoglycan P-type|
|Plasminogen activator inhibitor|
|Endothelial cell adhesion molecules (P- and E-selectin, vascular cell adhesion molecule-1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1))|
|Angiogenic factors (placental growth factor; vascular endothelial growth factor; fms-like tyrosine kinase receptor-1 [sFlt-1]; endoglin; angiopoietin)|
|Atrial natriuretic peptide|
|β 2 -Microglobulin|
|High temperature requirement A3 (HtrA3) protease|
|Thyroid function-related tests|
|Liver function-related tests|
|Lymphocyte micronucleus (maternal chromosomal damage)|
|Hydroxysteroid (17-β) dehydrogenase 1|
|Cell-free fetal DNA|
|Insulin-like growth factors/insulin-like growth factor binding protein-1|
|Proteomic, metabolomic and transcriptomic markers|
|Combination of tests and maternal characteristics|
We will summarize the accuracy of predictive tests for preeclampsia based on the best available evidence (published and updated systematic reviews and meta-analyses on the topic). In accordance with recent recommendations, the terms “early preeclampsia” or “early-onset preeclampsia” will refer to preeclampsia that resulted in delivery before 34 weeks of gestation, whereas the terms “late preeclampsia” or “late-onset preeclampsia” will refer to preeclampsia requiring delivery at or after 34 weeks of gestation.