| Literature DB >> 32225087 |
Max Mönckeberg1, Valentina Arias2, Rosario Fuenzalida2, Santiago Álvarez2, Victoria Toro2, Andrés Calvo2, Juan P Kusanovic3,4, Lara J Monteiro5, Manuel Schepeler1,6, Jyh K Nien1,6, Jaime Martinez6, Sebastián E Illanes1,5,6.
Abstract
Preeclampsia is a pregnancy-specific disorder defined by new onset of hypertension and proteinuria after 20 weeks of gestation. The early detection of patients at risk of developing preeclampsia is crucial, however, predictive models are still controversial. We aim to evaluate the diagnostic performance of a predictive algorithm in the first trimester of pregnancy, in order to identify patients that will subsequently develop preeclampsia, and to study the effect of aspirin on reducing the rate of this complication in patients classified as high risk by this algorithm. A retrospective cohort including 1132 patients attending prenatal care at Clínica Dávila in Santiago, Chile, was conceived. The risk of developing preeclampsia (early and late onset) was calculated using algorithms previously described by Plasencia et al. Patients classified as high risk, in the first trimester of pregnancy, by these algorithms, were candidates to receive 100 mg/daily aspirin as prophylaxis at the discretion of the attending physician. The overall incidence of preeclampsia in this cohort was 3.5% (40/1132), and the model for early onset preeclampsia prediction detected 33% of patients with early onset preeclampsia. Among the 105 patients considered at high risk of developing preeclampsia, 56 received aspirin and 49 patients did not. Among those who received aspirin, 12% (7/56) developed preeclampsia, which is equal to the rate of preeclampsia (12% (6/49)) of those who did not receive this medication. Therefore, the diagnostic performance of an algorithm combining uterine artery Doppler and maternal factors in the first trimester predicted only one third of patients that developed preeclampsia. Among those considered at high risk for developing the disease using this algorithm, aspirin did not change the incidence of preeclampsia, however, this could be due either to the small study sample size or the type of the study, a retrospective, non-interventional cohort study.Entities:
Keywords: aspirin; early prediction; gestational hypertension; predictive algorithm; preeclampsia; prenatal care; routine care; ultrasound; uterine artery Doppler
Year: 2020 PMID: 32225087 PMCID: PMC7235780 DOI: 10.3390/diagnostics10040182
Source DB: PubMed Journal: Diagnostics (Basel) ISSN: 2075-4418
Maternal baseline characteristics, medical and obstetric history, and pregnancy outcomes in unaffected and preeclampsia groups.
| Unaffected ( | Preeclampsia ( | ||
|---|---|---|---|
| Maternal age (years) | 30.6 (± 5.3) | 30.3 (± 4.8) | 0.776 |
| Nulliparity | 481 (44.1) | 27 (67.5) | 0.003 |
| Crown-rump length (mm) | 68.4 (± 9.0) | 65.7 (± 9.0) | 0.061 |
| BMI (kg/m2) | 26.4 (± 4.3) | 26.3 (± 4.7) | 0.905 |
| History of preeclampsia | 17 (1.6) | 2 (5.0) | 0.143 |
| Systolic blood pressure (mmHg) | 108 (± 10.2) | 113 (± 12.7) | 0.023 |
| Diastolic blood pressure (mmHg) | 65.4 (± 7.5) | 65.9 (± 7.8) | 0.680 |
| Chronic hypertension | 29 (2.7) | 4 (10.0) | 0.026 |
| Pregestational diabetes mellitus | 17 (1.6) | 2 (5.0) | 0.143 |
| Mean uterine artery pulpability index | 1.48 (± 0.5) | 1.72 (± 0.6) | 0.010 |
| Aspirin use | 96 (8.8) | 12 (30.0) | <0.001 |
| Gestational age at delivery (weeks) | 38.3 (± 2.2) | 37.0 (± 2.2) | 0.001 |
| Birthweight (grams) | 3.358 (± 484) | 3.039 (± 577) | <0.001 |
Values are mean (± standard deviation) or n (%). BMI: body mass index.
Diagnostic performance of different screening models of preeclampsia in the first trimester of pregnancy, using a fixed false positive rate of 5% (n = 1132).
| Preeclampsia ( | Early-onset Preeclampsia ( | Late-onset Preeclampsia ( | ||
|---|---|---|---|---|
| Early-onset preeclampsia model | Detection rate: | 8 (20) | 2 (33) | 6 (18) |
| PPV (IC 95%): | 14.04 (12.0116.06) | 3.51 (2.44–4.58) | 10.53 (8.74–12.31) | |
| NPV (IC 95%): | 97.02 (96.03–98.01) | 99.63 (99.27–99.98) | 97.40 (96.47–98.32) | |
| Late-onset preeclampsia model | Detection rate: | 7 (18) | 1 (17) | 6 (18) |
| PPV (IC 95%): | 12.28 (10.37–14.19) | 1.75 (0.99–2.52) | 10.53 (8.74–12.31) | |
| NPV (IC 95%): | 96.93 (95.93–97.94) | 99.53 (99.14–99.93) | 97.40 (96.47–98.32) | |
| Combined models | Detection rate: | 13 (33) | 2 (33) | 11 (32) |
| PPV (IC 95%): | 12.38 (10.46–14-30) | 1.90 (1.11–2.70) | 10.48 (8.69–12.26) | |
| NPV (IC 95%): | 97.37 (96.44–98.30) | 99.61 (99.25–99.97) | 97.76 (96.90–98.62) |
PPV: positive predictive value. NPV: negative predictive value.