Balikuddembe Michael S, Wakholi Peter K, Tumwesigye Nazarius M, Tylleskar Thorkild
Center for International Health, University of Bergen, Bergen, Norway.
Division of Maternal and Foetal Medicine, Mulago Specialised Women and Newborn Hospital, Mulago Hospital, Kampala, Uganda.
JMIR Med Inform. 2021 May 27;9(5):e17056. doi: 10.2196/17056.
After determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts' suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts.
The objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making.
The LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts' selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm.
The mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91.
The LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.
在从专家处确定关键分娩监测项目后，我们设计了一种算法（LaD）来体现专家的建议并对其进行验证。在本文中，我们描述了一种用于分娩管理的简化算法，并使用理论案例将其性能与人类分娩专家进行比较。
本研究的目的是描述LaD算法、其开发过程及其验证情况。此外，在验证阶段，我们想评估该算法在分娩决策过程中推荐必要临床行动时是否不如、等同于或优于人类专家。
LaD算法涵盖了使用世界卫生组织（WHO）产程图监测的12个分娩参数中的6个。它对参数超出正常范围时如何管理患者有相关建议。我们通过有目的地选择专家，针对分层抽样的患者病例场景选择行动，对该算法进行了验证。比较专家的选择结果，以获得他们与算法之间的成对灵敏度和假阳性率（FPR）。
专家之间的平均加权成对灵敏度为68.2%（标准差6.95；95%置信区间59.6 - 76.8），而专家与LaD算法之间的为69.4%（标准差17.95；95%置信区间47.1 - 91.7）。专家之间的成对FPR范围为12%至33%，平均为23.9%（标准差9.14；95%置信区间12.6 - 35.2），而专家与算法之间的范围为18%至43%（平均26.3%；标准差10.4；95%置信区间13.3 - 39.3）。不同患者病例中专家对所选行动之间存在相关性（平均0.67[标准差0.06]），可靠性系数（α）为0.91。
LaD算法更敏感，但假阳性率高于分娩专家，尽管差异无统计学意义。一种使用较少WHO推荐参数的分娩监测电子工具在分娩临床决策支持方面可能并不逊色于人类专家。
