Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Key Laboratory for Reproductive Medicine of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Front Endocrinol (Lausanne). 2022 Jul 6;13:911225. doi: 10.3389/fendo.2022.911225. eCollection 2022.
This study aimed to develop multiphase big-data-based prediction models of ovarian hyperstimulation syndrome (OHSS) and a smartphone app for risk calculation and patients' self-monitoring.
Multiphase prediction models were developed from a retrospective cohort database of 21,566 women from January 2017 to December 2020 with controlled ovarian stimulation (COS). There were 17,445 women included in the final data analysis. Women were randomly assigned to either training cohort (n = 12,211) or validation cohort (n = 5,234). Their baseline clinical characteristics, COS-related characteristics, and embryo information were evaluated. The prediction models were divided into four phases: 1) prior to COS, 2) on the day of ovulation trigger, 3) after oocyte retrieval, and 4) prior to embryo transfer. The multiphase prediction models were built with stepwise regression and confirmed with LASSO regression. Internal validations were performed using the validation cohort and were assessed by discrimination and calibration, as well as clinical decision curves. A smartphone-based app "OHSS monitor" was constructed as part of the built-in app of the IVF-aid platform. The app had three modules, risk prediction module, symptom monitoring module, and treatment monitoring module.
The multiphase prediction models were developed with acceptable distinguishing ability to identify OHSS at-risk patients. The C-statistics of the first, second, third, and fourth phases in the training cohort were 0.628 (95% CI 0.598-0.658), 0.715 (95% CI 0.688-0.742), 0.792 (95% CI 0.770-0.815), and 0.814 (95% CI 0.793-0.834), respectively. The calibration plot showed the agreement of predictive and observed risks of OHSS, especially at the third- and fourth-phase prediction models in both training and validation cohorts. The net clinical benefits of the multiphase prediction models were also confirmed with a clinical decision curve. A smartphone-based app was constructed as a risk calculator based on the multiphase prediction models, and also as a self-monitoring tool for patients at risk.
We have built multiphase prediction models based on big data and constructed a user-friendly smartphone-based app for the personalized management of women at risk of moderate/severe OHSS. The multiphase prediction models and user-friendly app can be readily used in clinical practice for clinical decision-support and self-management of patients.
本研究旨在开发基于多阶段大数据的卵巢过度刺激综合征（OHSS）预测模型和一款用于风险计算和患者自我监测的智能手机应用程序。
从 2017 年 1 月至 2020 年 12 月接受控制性卵巢刺激（COS）的 21566 名女性的回顾性队列数据库中开发了多阶段预测模型。最终数据分析纳入了 17445 名女性。将女性随机分配至训练队列（n=12211）或验证队列（n=5234）。评估了她们的基线临床特征、COS 相关特征和胚胎信息。预测模型分为四个阶段：1）COS 前，2）排卵扳机日，3）取卵后，4）胚胎移植前。使用逐步回归和 LASSO 回归构建多阶段预测模型，并使用验证队列进行内部验证，通过区分度和校准度以及临床决策曲线进行评估。构建了基于智能手机的应用程序“OHSS 监测器”，作为 IVF-aid 平台内置应用程序的一部分。该应用程序有三个模块，风险预测模块、症状监测模块和治疗监测模块。
多阶段预测模型具有可接受的区分能力，可识别 OHSS 高危患者。训练队列中第一、二、三、四阶段的 C 统计量分别为 0.628（95%CI 0.598-0.658）、0.715（95%CI 0.688-0.742）、0.792（95%CI 0.770-0.815）和 0.814（95%CI 0.793-0.834）。校准图显示了 OHSS 预测风险与观察风险的一致性，特别是在训练和验证队列的第三和第四阶段预测模型中。多阶段预测模型的净临床获益也通过临床决策曲线得到了确认。基于多阶段预测模型构建了一款智能手机应用程序，作为风险计算器，也作为高危患者的自我监测工具。
我们基于大数据构建了多阶段预测模型，并构建了一个用户友好的基于智能手机的应用程序，用于中重度 OHSS 高危女性的个性化管理。多阶段预测模型和用户友好的应用程序可在临床实践中方便地用于临床决策支持和患者自我管理。
