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Preeclampsia Risk Prediction Model for Chinese Pregnant Patients With Systemic Lupus Erythematosus
Author(s) -
Jiang Meng,
Wang You,
Fu Qiong,
Lin Sihan,
Wu Jiayue,
Di Wen
Publication year - 2020
Publication title -
arthritis care and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.032
H-Index - 163
eISSN - 2151-4658
pISSN - 2151-464X
DOI - 10.1002/acr.24265
Subject(s) - medicine , preeclampsia , confidence interval , receiver operating characteristic , odds ratio , area under the curve , obstetrics , pregnancy , genetics , biology
Objective To screen for a high risk of preeclampsia in women with systemic lupus erythematosus (SLE). Methods A total of 513 antenatal care records of pregnant patients with SLE were obtained, and the data were randomly assigned to either a development set (n = 342) or a validation set (n = 171). Preeclampsia predictors were identified with stepwise regression, and a coefficient B of each variable was used to establish a prediction model and risk scoring system. Goodness‐of‐fit was assessed by the Hosmer‐Lemeshow and Omnibus tests, and the area under the receiver operating characteristic curve (area under the curve) was used to assess discrimination. Validation was performed using the validation set. Results The preeclampsia incidence was 14.4% in the pregnant patients with SLE. A mean arterial pressure (MAP) ≥96.5 mm Hg (odds ratio [OR] 213.15 [95% confidence interval (95% CI) 24.39–999.99]), prepregnancy hypertension (OR 18.19 [95% CI 2.67–125.01]), a hematologic disorder (OR 4.13 [95% CI 1.03–16.67]), positive IgM anticardiolipin antibodies (aCLs) (OR 19.85 [95% CI 1.11–333.33]), serum albumin <31.5 grams/liter (OR 9.88 [95% CI 2.07–47.62]), serum uric acid ≥303 μmoles/liter (OR 5.58 [95% CI 1.40–22.22]), and 24‐hour urinary protein ≥0.286 grams (OR 14.39 [95% CI 2.43–83.33]) were selected for the preeclampsia prediction model. The area under the curve was 0.975. Preeclampsia prediction model scores >4 indicated a high risk of preeclampsia. For the validation set, the preeclampsia prediction accuracy was 93.6% (sensitivity 88.5%, specificity 94.5%). Conclusion A model for predicting the risk of preeclampsia in pregnant patients with SLE was established on the basis of MAP, prepregnancy hypertension, hematologic disorders, IgM aCLs, albumin, uric acid, and 24‐hour urinary protein. The model had good predictive efficiency and can help clinicians improve pregnancy outcomes in high‐risk women with early interventions.