Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Data Science and Research Unit, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
PLoS One. 2021 Feb 3;16(2):e0246119. doi: 10.1371/journal.pone.0246119. eCollection 2021.
Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes.
We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014-2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods.
Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences.
Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement.
产后出血仍然是全球孕产妇发病率和死亡率的主要原因。因此，为了提高产科安全性，通常会在产科医院或国家内部监测产后出血和严重产后出血的累计发生率，并进行比较。这些指标通常基于医院出院数据，但很少对其准确性进行评估。我们旨在分别使用电子健康记录和医院出院数据来测量产后出血和严重产后出血，并比较这些方法与手动图表审查的检测准确性，并检查这些潜在可避免不良结局的累计发生率的时间趋势。
我们对一家大型瑞士大学医院的 7904 例单胎分娩的常规收集数据进行了分析，时间为三年（2014-2016 年）。我们通过对出院记录和手术报告进行文本挖掘并计算实验室检查血红蛋白下降来识别电子健康记录中的产后出血和严重产后出血。诊断和手术代码用于在医院出院数据中识别病例。对 334 份病历进行了手动审查，以提供参考标准并评估其他检测方法的准确性。
基于电子健康记录和医院出院数据的检测算法的敏感性分别为 95.2%（95%置信区间：92.6%至 97.8%）和 38.2%（33.3%至 43.0%），用于产后出血，87.5%（85.2%至 89.8%）和 36.2%（26.3%至 46.1%）用于严重产后出血。基于电子健康记录的产后出血累计发生率从 2014 年初到 2016 年底从 15.6%（13.1%至 18.2%）下降到 8.5%（6.7%至 10.5%），平均为 12.5%（11.8%至 13.3%）。严重产后出血的累计发生率仍约为 4%（3.5%至 4.4%）。基于医院出院数据的算法提供的发生率明显低估。
医院出院数据不足以评估医院或国家一级的产后出血发生率。相反，应考虑基于电子健康记录中的结构化和文本数据的自动算法，因为它们可以为监测和改善产科安全性提供准确和及时的估计。此外，它们有可能更好地对产后出血进行编码，从而提高医院的报销。
