摘要:目的: 分析兇險型前置胎盤的臨床特點, 預防產后出血和子宮切除的發生。 方法 :對11例兇險型前置胎盤與75例普通型前置胎盤的病例進行回顧性分析。 結果 :兇險型組與普通型組發生產前出血的量差異無統計學意義(Pgt;0.05);在發生胎盤植入、產后出血的量差異有統計學意義(Plt;0.05);子宮切除的發生率差異有統計學意義(Plt;0.05)。 結論 :兇險型前置胎盤對孕產婦有極大的威脅,應努力做好兇險型前置胎盤產后出血的搶救,減少子宮切除的發生。Abstract: Objective: To assess the clinical feature of dangerous placenta praevia in order to prevent postpartum hemorrhage and intrapartal hysterectomy. Methods : Retrospective analysis was done between the 11 cases of dangerous placenta praevia and ordinary placenta praevia . Results : There were no significant difference in blood volume antepartum (Pgt;0.05); There was significant difference in placenta increta and postpartum hemorrhage (Plt;0.05). Conclusion : Dangerous placenta praevia have great threat to gravid and puerperant, we should try our best to rescue postpartum hemorrhage about dangerous placenta praevia and reduce the incidence of intrapartal hysterectomy.
ObjectiveTo explore the utilization of longitudinal data in constructing non-time-varying outcome prediction models and to compare the impact of different modeling approaches on prediction performance. MethodsClinical predictors were selected using univariate analysis and Lasso regression. Non-time-varying outcome prediction models were developed based on latent class trajectory analysis, the two-stage model, and logistic regression. Internal validation was performed using Bootstrapping resampling, and model performance was evaluated using ROC curves, PR curves, sensitivity, specificity and other relevant metrics. ResultsA total of 49 629 pregnant women were included in the study, with mean age of 31.42±4.13 years and pre-pregnancy BMI of 20.91±2.62kg/m2. Fourteen predictors were incorporated into the final model. Prediction models utilizing longitudinal data demonstrated high accuracy, with AUROC values exceeding 0.90 and PR-AUC values greater than 0.47. The two-stage model based on late-pregnancy hemoglobin data showed the best performance, achieving AUROC of 0.93 (95%CI 0.92 to 0.94) and PR-AUC of 0.60 (95%CI 0.56 to 0.64). Internal validation confirmed robust model performance, and calibration curves indicated a good agreement between predicted and observed outcomes. ConclusionFor the longitudinal data, the two-stage model can well capture the dynamic change trajectory of the longitudinal data. For different clinical outcomes, the predictive value of repeated measurement data is different.
The use of repeated measurement data from patients to improve the classification ability of prediction models is a key methodological issue in the current development of clinical prediction models. This study aims to investigate the statistical modeling approach of the two-stage model in developing prediction models for non-time-varying outcomes using repeated measurement data. Using the prediction of the risk of severe postpartum hemorrhage as a case study, this study presents the implementation process of the two-stage model from various perspectives, including data structure, basic principles, software utilization, and model evaluation, to provide methodological support for clinical investigators.
