Sema4, Stamford, Connecticut, USA.
Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
J Am Med Inform Assoc. 2022 Jan 12;29(2):321-328. doi: 10.1093/jamia/ocab181.
We aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased and ignore complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contributes to ongoing challenges in tracking PPH outcomes, developing more accurate risk assessments, and identifying novel interventions.
We constructed a cohort of 71 944 deliveries from the Mount Sinai Health System. Estimates of postpartum blood loss, shifts in hematocrit, administration of uterotonics, surgical interventions, and diagnostic codes were combined to identify PPH, retrospectively. Clinical features were extracted from EMRs and mapped to common data models for maximum interoperability across hospitals. Blinded chart review was done by a physician on a subset of PPH and non-PPH patients and performance was compared to alternate PPH phenotypes. PPH was defined as clinical diagnosis of postpartum hemorrhage documented in the patient's chart upon chart review.
We identified 6639 PPH deliveries (9% prevalence) using our phenotype-more than 3 times as many as using blood loss alone (N = 1,747), supporting the need to incorporate other diagnostic and intervention data. Chart review revealed our phenotype had 89% accuracy and an F1-score of 0.92. Alternate phenotypes were less accurate, including a common blood loss-based definition (67%) and a previously published digital phenotype (74%).
We have developed a scalable, accurate, and valid digital phenotype that may be of significant use for tracking outcomes and ongoing clinical research to deliver better preventative interventions for PPH.
我们旨在建立产后出血（PPH）的综合数字表型。当前的指南主要依赖于失血量的估计，这可能不准确且存在偏差，并且忽略了电子病历（EMR）中易于获得的补充信息。不准确和不完整的表型会导致在跟踪 PPH 结果、开发更准确的风险评估和确定新的干预措施方面持续存在挑战。
我们从西奈山卫生系统构建了一个包含 71944 次分娩的队列。估计产后失血量、血细胞比容的变化、子宫收缩剂的使用、手术干预和诊断代码相结合，以回顾性地识别 PPH。从 EMR 中提取临床特征，并映射到通用数据模型，以实现医院之间的最大互操作性。在 PPH 和非 PPH 患者的亚组中，由医生进行盲法图表审查，并将表现与替代 PPH 表型进行比较。PPH 是指在图表审查时在患者图表中记录的产后出血的临床诊断。
我们使用我们的表型识别了 6639 例 PPH 分娩（9%的患病率）-比仅使用失血量多 3 倍以上，这支持了需要纳入其他诊断和干预数据的需求。图表审查显示，我们的表型具有 89%的准确性和 0.92 的 F1 分数。替代表型的准确性较低，包括常见的基于失血量的定义（67%）和以前发表的数字表型（74%）。
我们已经开发了一种可扩展、准确和有效的数字表型，对于跟踪结果和正在进行的临床研究可能具有重要意义，以便为 PPH 提供更好的预防干预措施。
