Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada; Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; Division of Maternal and Fetal Medicine, Department of Obstetrics & Gynaecology, Mount Sinai Hospital, University of Toronto, Toronto, Canada.
Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada.
Best Pract Res Clin Obstet Gynaecol. 2022 Mar;79:42-54. doi: 10.1016/j.bpobgyn.2021.12.005. Epub 2021 Dec 25.
The purpose of this study was to systematically identify and critically appraise models developed in the past few years, using universally accessible clinical and demographic factors, that have been derived and validated to predict the success of labour induction. Our search identified 26 studies describing 24 prediction models derived between 1966 and 2021. Models varied with regard to methodology, scope and performance. Before any prediction model can be recommended for use in clinical practice, there is a need to determine thresholds of risk at which IoL should not be offered, subgroups that are most likely to benefit from the use of prediction models, and the clinical impact of prediction models on shared decision-making, parental satisfaction, caesarean rates, clinical outcomes and costs. A list of recommendations for improving model performance and utilization, as well as measures for encouraging appropriate use of prediction models and directions for future research, is provided.
本研究旨在系统地识别和批判性评估过去几年开发的模型,这些模型利用普遍可获得的临床和人口统计学因素,旨在预测分娩诱导的成功率。我们的搜索确定了 26 项研究,描述了 24 个预测模型,这些模型是在 1966 年至 2021 年之间得出和验证的。这些模型在方法、范围和性能方面存在差异。在任何预测模型被推荐用于临床实践之前,需要确定不应提供宫内人工授精的风险阈值、最有可能从预测模型使用中受益的亚组,以及预测模型对共同决策、父母满意度、剖宫产率、临床结果和成本的临床影响。提供了一系列提高模型性能和利用率的建议,以及鼓励适当使用预测模型的措施和未来研究的方向。