Establishment of a nomogram model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia

Abstract

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.

Cuidado de enfermería en gestantes con síndrome de HELLP en el servicio de ginecología

“…8 A menudo, el síndrome de HELLP tiene un inicio agudo, con un deterioro rápido e incluso repentino de las condiciones tanto de la madre como del feto y puede causar complicaciones graves en caso de realizar un reconocimiento temprano con una intervención clínica adecuada, lo cual se puede traducir en un mayor riesgo de muerte materna y mayores tasas de morbilidades maternas como insuficiencia renal aguda, coagulopatía intravascular diseminada (CID), desprendimiento de placenta, hemorragia o insuficiencia hepática y accidente cerebrovascular. 9 Además, la preeclampsia asociada al síndrome de HELLP puede ocasionar daños al hígado, los riñones y el sistema de coagulación de la sangre y en casos más graves, puede ocurrir eclampsia, edema pulmonar, accidente cerebrovascular en la madre, mientras que en los fetos, aumenta la probabilidad de muerte, nacimiento prematuro y muy bajo peso al nacer, unido a complicaciones a largo plazo, como trastornos del neurodesarrollo, resistencia a la insulina, diabetes, enfermedad coronaria e hipertensión. 10 Adicionalmente, aunque es poco común, otra complicación que puede presentarse en la ruptura espontánea del hígado durante el embarazo/puerperio (REHP) como resultado de un episodio hipertensivo asociado con HELLP, el cual es causa de altas tasas de mortalidad.…”

Analysis of the Nursing Effect of Anesthesia Care Integration Combined with Preventive Nursing on Older Patients with Lumbar Disc Herniation During the Perioperative Period

“… 18 Preventive nursing interventions contribute to the health of patients transform the status of nursing work from passive into active, stimulate the work motivation of nursing staff, and highlight the independent value of nursing work. 19 In addition, preventive nursing interventions improve the pertinence of nurses’ work and help reduce medical costs. Preventive nursing interventions change the concept of clinical nursing from an ideological perspective and promote high-quality service.…”

Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis

“…Nomogram is extensively used in the prognosis of cancers [ 47 – 49 ] as well as obstetric diseases [ 50 – 53 ] and achieves accurate predictions. However, predictive nomogram models for the risks of neuroblastoma are unclear.…”