Dreisbach Caitlin, Barcelona Veronica, Turchioe Meghan Reading, Bernstein Samantha, Erickson Elise
MCN Am J Matern Child Nurs. 2025;50(2):66-77. doi: 10.1097/NMC.0000000000001082. Epub 2025 Feb 25.
Predictive analytics has emerged as a promising approach for improving reproductive health care and patient outcomes. During pregnancy and birth, the ability to accurately predict risks and complications could enable earlier interventions and reduce adverse events. However, there are challenges and ethical considerations for implementing predictive models in perinatal care settings. We introduce major concepts in predictive analytics and describe application of predictive modeling to perinatal care topics such as fertility, preeclampsia, labor onset, vaginal birth after cesarean, uterine rupture, induction outcomes, postpartum hemorrhage, and postpartum mood disorders. Although some predictive models have achieved adequate accuracy (AUC 0.7-0.9), most require additional external validation across diverse populations and practice settings. Bias, particularly racial bias, remains a key limitation of current models. Nurses and advanced practice nurses, including nurse practitioners certified registered nurse anesthetists, and nurse-midwives, play a vital role in ensuring high-quality data collection and communicating predictive model outputs to clinicians and users of the health care system. Addressing the ethical challenges and limitations of predictive analytics is imperative to equitably translate these tools to support patient-centered perinatal care.
预测分析已成为改善生殖健康护理和患者治疗效果的一种有前景的方法。在怀孕和分娩期间，准确预测风险和并发症的能力可以实现更早的干预并减少不良事件。然而，在围产期护理环境中实施预测模型存在挑战和伦理考量。我们介绍预测分析中的主要概念，并描述预测模型在围产期护理主题中的应用，如生育力、先兆子痫、分娩发动、剖宫产术后阴道分娩、子宫破裂、引产结局、产后出血和产后情绪障碍。尽管一些预测模型已达到足够的准确性（AUC 0.7 - 0.9），但大多数模型需要在不同人群和实践环境中进行额外的外部验证。偏差，尤其是种族偏差，仍然是当前模型的关键限制。护士和高级实践护士，包括认证注册护士麻醉师和助产士等执业护士，在确保高质量数据收集以及将预测模型结果传达给医疗系统的临床医生和用户方面发挥着至关重要的作用。应对预测分析的伦理挑战和局限性对于公平地转化这些工具以支持以患者为中心的围产期护理至关重要。
