Seattle Reproductive Medicine, Seattle, Washington.
Quick Step Analytics LLC, Seattle, Washington.
Fertil Steril. 2020 Nov;114(5):1026-1031. doi: 10.1016/j.fertnstert.2020.06.006. Epub 2020 Oct 1.
To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm's accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by the clinical team.
Descriptive and comparative study of new technology.
Private fertility practice.
INTERVENTION(S): None.
PATIENT(S): Data were derived from monitoring during ovarian stimulation from IVF cycles. The database consisted of 2,603 cycles (1,853 autologous and 750 donor cycles) incorporating 7,376 visits for training. An additional 556 unique cycles were used for challenge and to calculate accuracy. There were 59,706 data points. Input variables included estradiol concentrations in picograms per milliliter; ultrasound measurements of follicle diameters in two dimensions in millimeters; cycle day during stimulation and dose of recombinant follicle-stimulating hormone during ovarian stimulation for IVF.
MAIN OUTCOME MEASURE(S): Accuracy of the algorithm to predict four critical clinical decisions during ovarian stimulation for IVF: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel. If the decision was to return, then the next key decisions were [3] number of days to follow-up and [4] whether any dosage adjustment was needed.
RESULT(S): Algorithm accuracies for these four decisions are as follows: continue or stop treatment: 0.92; trigger and schedule oocyte retrieval or cancel cycle: 0.96; dose of medication adjustment: 0.82; and number of days to follow-up: 0.87. These accuracies are for first iteration of the algorithm.
CONCLUSION(S): We describe a first iteration of a predictive analytic algorithm that is highly accurate and in agreement with evidence-based decisions by expert teams during ovarian stimulation during IVF. These tools offer a potential platform to optimize clinical decision making during IVF.
描述一个专为体外受精（IVF）管理而设计的计算机算法，并评估该算法在与临床团队基于证据的决策相比，在 IVF 卵巢刺激期间日常决策中的准确性。
新技术的描述性和对比研究。
私人生育诊所。
无。
数据来自 IVF 周期卵巢刺激监测。该数据库包含 2603 个周期（1853 个自体周期和 750 个供体周期），包含 7376 次培训就诊。另外 556 个独特周期用于挑战并计算准确性。共有 59706 个数据点。输入变量包括每毫升皮克的雌二醇浓度；毫米二维超声测量卵泡直径；刺激期间的周期天数和 IVF 卵巢刺激期间重组卵泡刺激素的剂量。
该算法在预测 IVF 卵巢刺激期间四项关键临床决策中的准确性：[1]停止刺激或继续刺激。如果决定停止，则下一个自动决策是[2]触发或取消。如果决定返回，则下一个关键决策是[3]随访天数和[4]是否需要任何剂量调整。
这四项决策的算法准确性如下：继续或停止治疗：0.92；触发和安排取卵或取消周期：0.96；药物剂量调整：0.82；和随访天数：0.87。这些准确性是算法的第一次迭代。
我们描述了一个预测分析算法的首次迭代，该算法具有高度准确性，并且与专家团队在 IVF 卵巢刺激期间基于证据的决策一致。这些工具为优化 IVF 期间的临床决策提供了潜在的平台。
