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基于 ICD-10 的算法验证以识别 Sentinel 系统中的死胎。

Validation of an ICD-10-based algorithm to identify stillbirth in the Sentinel System.

机构信息

The Meyers Primary Care Institute, a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health, University of Massachusetts Medical School, Worcester, Massachusetts, USA.

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA.

出版信息

Pharmacoepidemiol Drug Saf. 2021 Sep;30(9):1175-1183. doi: 10.1002/pds.5300. Epub 2021 Jun 11.

Abstract

PURPOSE

To develop and validate an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify cases of stillbirth using electronic healthcare data.

METHODS

We conducted a retrospective study using claims data from three Data Partners (healthcare systems and insurers) in the Sentinel Distributed Database. Algorithms were developed using ICD-10-CM diagnosis codes to identify potential stillbirths among females aged 12-55 years between July 2016 and June 2018. A random sample of medical charts (N = 169) was identified for chart abstraction and adjudication. Two physician adjudicators reviewed potential cases to determine whether a stillbirth event was definite/probable, the date of the event, and the gestational age at delivery. Positive predictive values (PPVs) were calculated for the algorithms. Among confirmed cases, agreement between the claims data and medical charts was determined for the outcome date and gestational age at stillbirth.

RESULTS

Of the 110 potential cases identified, adjudicators determined that 54 were stillbirth events. Criteria for the algorithm with the highest PPV (82.5%; 95% CI, 70.9%-91.0%) included the presence of a diagnosis code indicating gestational age ≥20 weeks and occurrence of either >1 stillbirth-related code or no other pregnancy outcome code (i.e., livebirth, spontaneous abortion, induced abortion) recorded on the index date. We found ≥90% agreement within 7 days between the claims data and medical charts for both the outcome date and gestational age at stillbirth.

CONCLUSIONS

Our results suggest that electronic healthcare data may be useful for signal detection of medical product exposures potentially associated with stillbirth.

摘要

目的

开发并验证一种基于国际疾病分类第十版临床修订版(ICD-10-CM)的算法,以利用电子医疗保健数据识别死胎病例。

方法

我们使用 Sentinel 分布式数据库中的三个数据合作伙伴(医疗保健系统和保险公司)的索赔数据进行了回顾性研究。该算法使用 ICD-10-CM 诊断代码来识别 2016 年 7 月至 2018 年 6 月期间年龄在 12-55 岁的女性中的潜在死胎病例。随机抽取了 169 份病历进行病历摘录和裁定。两名医师裁决者审查了潜在病例,以确定是否存在明确/可能的死胎事件、事件日期以及分娩时的胎龄。计算了算法的阳性预测值(PPV)。在确认病例中,根据索赔数据和病历确定了结局日期和死胎时的胎龄之间的一致性。

结果

在确定的 110 例潜在病例中,裁决者确定了 54 例死胎事件。具有最高 PPV(82.5%;95%CI,70.9%-91.0%)的算法标准包括存在指示胎龄≥20 周的诊断代码,以及在索引日期记录了>1 个与死胎相关的代码或没有其他妊娠结局代码(即活产、自然流产、人工流产)。我们发现索赔数据和病历在结局日期和死胎时的胎龄方面的一致性≥90%,在 7 天内。

结论

我们的结果表明,电子医疗保健数据可能有助于检测与死胎潜在相关的医疗产品暴露的信号。

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