Your browser doesn't support javascript.
loading
Establishment of a nomogram model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia.
Lv, Bohan; Zhang, Yan; Yuan, Guanghui; Gu, Ruting; Wang, Jingyuan; Zou, Yujiao; Wei, Lili.
Afiliación
  • Lv B; School of Nursing, Qingdao University, Qingdao, China.
  • Zhang Y; Department of Nursing, The Affiliated Hospital of Qingdao University, #16 Jiangsu Road, Qingdao, 266003, Shandong Province, China.
  • Yuan G; Medical College of Qingdao University, Qingdao, China.
  • Gu R; Department of Nursing, The Affiliated Hospital of Qingdao University, #16 Jiangsu Road, Qingdao, 266003, Shandong Province, China.
  • Wang J; Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zou Y; School of Nursing, Qingdao University, Qingdao, China.
  • Wei L; Department of Nursing, The Affiliated Hospital of Qingdao University, #16 Jiangsu Road, Qingdao, 266003, Shandong Province, China. weilili@qduhospital.cn.
BMC Pregnancy Childbirth ; 22(1): 221, 2022 Mar 19.
Article en En | MEDLINE | ID: mdl-35305610
AIM: To establish a model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia in China. METHODS: We retrospectively collected the medical records of 896 pregnant women with preterm preeclampsia who were older than 35 years and delivered at the Affiliated Hospital of Qingdao University from June 2018 to December 2020. The pregnant women were divided into an adverse outcome group and a non-adverse outcome group according to the occurrence of adverse outcomes. The data were divided into a training set and a verification set at a ratio of 8:2. A nomogram model was developed according to a binary logistic regression model created to predict the adverse outcomes in advanced-age pregnant women with preterm preeclampsia. ROC curves and their AUCs were used to evaluate the predictive ability of the model. The model was internally verified by using 1000 bootstrap samples, and a calibration diagram was drawn. RESULTS: Binary logistic regression analysis showed that platelet count (PLT), uric acid (UA), blood urea nitrogen (BUN), prothrombin time (PT), and lactate dehydrogenase (LDH) were the factors that independently influenced adverse outcomes (P < 0.05). The AUCs of the internal and external verification of the model were 0.788 (95% CI: 0.737 ~ 0.764) and 0.742 (95% CI: 0.565 ~ 0.847), respectively. The calibration curve was close to the diagonal. CONCLUSIONS: The model we constructed can accurately predict the risk of adverse outcomes of pregnant women of advanced age with preterm preeclampsia, providing corresponding guidance and serving as a basis for preventing adverse outcomes and improving clinical treatment and maternal and infant prognosis.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Preeclampsia / Complicaciones del Embarazo / Resultado del Embarazo / Edad Materna / Nomogramas Tipo de estudio: Diagnostic_studies / Etiology_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Pregnancy País/Región como asunto: Asia Idioma: En Revista: BMC Pregnancy Childbirth Asunto de la revista: OBSTETRICIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Preeclampsia / Complicaciones del Embarazo / Resultado del Embarazo / Edad Materna / Nomogramas Tipo de estudio: Diagnostic_studies / Etiology_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Pregnancy País/Región como asunto: Asia Idioma: En Revista: BMC Pregnancy Childbirth Asunto de la revista: OBSTETRICIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido