Casikar Ishwari, Lu Chuan, Reid Shannon, Condous George
Acute Gynaecology, Early Pregnancy and Advanced Endosurgery Unit, Nepean Centre for Perinatal Care, Nepean Clinical School, Nepean Hospital, University of Sydney, Sydney, New South Wales, Australia.
Aust N Z J Obstet Gynaecol. 2013 Feb;53(1):58-63. doi: 10.1111/ajo.12053.
To generate and evaluate a new logistic regression model for the prediction of successful expectant management of first trimester miscarriage.
Data were collected prospectively from women diagnosed with 1st trimester miscarriage. Clinical and ultrasonographic variables were recorded for multivariate analysis. Clinically stable women who were managed expectantly were followed up for two weeks until the outcome was established: success or failure. A multinomial logistic regression (MLR) model was developed on 186 training cases for the prediction of successful expectant management and tested prospectively on a further 126 cases. The performance of the model was evaluated using receiver operating characteristic (ROC) curve as well as in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Two thousand and forty eight consecutive first trimester women underwent TVS. Complete data from 312 (15.2%) women with miscarriage managed expectantly were included in the final analysis. The most important independent prognostic variables for the MLR model were as follows: type of miscarriage at primary scan, vaginal bleeding and maternal age. When developed retrospectively on a training data set, MLR model gave an area under the ROC curve (AUC) of 0.796. Prospective validation of MLR model on a new test data set resulted in an AUC of 0.803.
We have developed and validated a new mathematical model to predict successful management of first trimester miscarriage.
生成并评估一种新的逻辑回归模型，用于预测孕早期流产期待治疗的成功与否。
前瞻性收集被诊断为孕早期流产的女性的数据。记录临床和超声变量以进行多变量分析。对接受期待治疗且临床稳定的女性进行为期两周的随访，直至确定结局：成功或失败。在186例训练病例上建立多项逻辑回归（MLR）模型，用于预测期待治疗的成功，并在前瞻性地对另外126例病例进行测试。使用受试者工作特征（ROC）曲线以及敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）来评估模型的性能。
连续2048例孕早期女性接受了经阴道超声检查。最终分析纳入了312例（15.2%）接受期待治疗的流产女性的完整数据。MLR模型最重要的独立预后变量如下：初次扫描时的流产类型、阴道出血和产妇年龄。当在训练数据集上进行回顾性开发时，MLR模型的ROC曲线下面积（AUC）为0.796。在新的测试数据集上对MLR模型进行前瞻性验证，结果AUC为0.803。
我们已经开发并验证了一种新的数学模型，用于预测孕早期流产的成功治疗。
