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在大型综合医疗保健系统中从电子健康记录中识别早产评估就诊情况并提取宫颈长度测量值:算法开发与验证

Identification of Preterm Labor Evaluation Visits and Extraction of Cervical Length Measures from Electronic Health Records Within a Large Integrated Health Care System: Algorithm Development and Validation.

作者信息

Xie Fagen, Khadka Nehaa, Fassett Michael J, Chiu Vicki Y, Avila Chantal C, Shi Jiaxiao, Yeh Meiyu, Kawatkar Aniket, Mensah Nana A, Sacks David A, Getahun Darios

机构信息

Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.

Department of Obstetrics & Gynecology, Kaiser Permanente West Los Angeles Medical Center, Los Angeles, CA, United States.

出版信息

JMIR Med Inform. 2022 Sep 6;10(9):e37896. doi: 10.2196/37896.

DOI:10.2196/37896
PMID:36066930
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9490529/
Abstract

BACKGROUND

Preterm birth (PTB) represents a significant public health problem in the United States and throughout the world. Accurate identification of preterm labor (PTL) evaluation visits is the first step in conducting PTB-related research.

OBJECTIVE

We aimed to develop a validated computerized algorithm to identify PTL evaluation visits and extract cervical length (CL) measures from electronic health records (EHRs) within a large integrated health care system.

METHODS

We used data extracted from the EHRs at Kaiser Permanente Southern California between 2009 and 2020. First, we identified triage and hospital encounters with fetal fibronectin (fFN) tests, transvaginal ultrasound (TVUS) procedures, PTL medications, or PTL diagnosis codes within 24-34 gestational weeks. Second, clinical notes associated with triage and hospital encounters within 24-34 gestational weeks were extracted from EHRs. A computerized algorithm and an automated process were developed and refined by multiple iterations of chart review and adjudication to search the following PTL indicators: fFN tests, TVUS procedures, abdominal pain, uterine contractions, PTL medications, and descriptions of PTL evaluations. An additional process was constructed to extract the CLs from the corresponding clinical notes of these identified PTL evaluation visits.

RESULTS

A total of 441,673 live birth pregnancies were identified between 2009 and 2020. Of these, 103,139 pregnancies (23.35%) had documented PTL evaluation visits identified by the computerized algorithm. The trend of pregnancies with PTL evaluation visits slightly decreased from 24.41% (2009) to 17.42% (2020). Of the first 103,139 PTL visits, 19,439 (18.85%) and 44,423 (43.97%) had an fFN test and a TVUS, respectively. The percentage of first PTL visits with an fFN test decreased from 18.06% at 24 gestational weeks to 2.32% at 34 gestational weeks, and TVUS from 54.67% at 24 gestational weeks to 12.05% in 34 gestational weeks. The mean (SD) of the CL was 3.66 (0.99) cm with a mean range of 3.61-3.69 cm that remained stable across the study period. Of the pregnancies with PTL evaluation visits, the rate of PTB remained stable over time (20,399, 19.78%). Validation of the computerized algorithms against 100 randomly selected records from these potential PTL visits showed positive predictive values of 97%, 94.44%, 100%, and 96.43% for the PTL evaluation visits, fFN tests, TVUS, and CL, respectively, along with sensitivity values of 100%, 90%, and 90%, and specificity values of 98.8%, 100%, and 98.6% for the fFN test, TVUS, and CL, respectively.

CONCLUSIONS

The developed computerized algorithm effectively identified PTL evaluation visits and extracted the corresponding CL measures from the EHRs. Validation against this algorithm achieved a high level of accuracy. This computerized algorithm can be used for conducting PTL- or PTB-related pharmacoepidemiologic studies and patient care reviews.

摘要

背景

早产在美国乃至全球都是一个重大的公共卫生问题。准确识别早产临产(PTL)评估就诊是开展与早产相关研究的第一步。

目的

我们旨在开发一种经过验证的计算机算法,以识别PTL评估就诊,并从大型综合医疗保健系统的电子健康记录(EHR)中提取宫颈长度(CL)测量值。

方法

我们使用了2009年至2020年期间从南加州凯撒医疗集团的EHR中提取的数据。首先,我们识别出在孕24 - 34周内进行了胎儿纤连蛋白(fFN)检测、经阴道超声(TVUS)检查、PTL药物治疗或有PTL诊断代码的分诊和住院诊疗记录。其次,从EHR中提取与孕24 - 34周内分诊和住院诊疗记录相关的临床笔记。通过多次图表审查和判定迭代,开发并完善了一种计算机算法和自动化流程,以搜索以下PTL指标:fFN检测、TVUS检查、腹痛、子宫收缩、PTL药物治疗以及PTL评估描述。构建了一个额外的流程,从这些已识别的PTL评估就诊的相应临床笔记中提取CL值。

结果

2009年至2020年期间共识别出441,673例活产妊娠。其中,103,139例妊娠(23.35%)有经计算机算法识别出的记录在案的PTL评估就诊。有PTL评估就诊的妊娠比例从2009年的24.41%略有下降至2020年的17.42%。在前103,139次PTL就诊中,分别有19,439次(18.85%)进行了fFN检测,44,423次(43.97%)进行了TVUS检查。首次PTL就诊时进行fFN检测的比例从孕24周时的18.06%降至孕34周时的2.32%,TVUS检查的比例从孕24周时的54.67%降至孕34周时的12.05%。CL的平均值(标准差)为3.66(0.99)cm,平均范围为3.61 - 3.69 cm,在整个研究期间保持稳定。在有PTL评估就诊的妊娠中,早产率随时间保持稳定(20,399例,19.78%)。针对这些潜在PTL就诊中随机选择的100份记录对计算机算法进行验证,结果显示PTL评估就诊、fFN检测、TVUS检查和CL的阳性预测值分别为97%、94.44%、100%和96.43%,fFN检测、TVUS检查和CL的敏感度值分别为100%、90%和90%,特异度值分别为98.8%、100%和98.6%。

结论

所开发的计算机算法有效地识别了PTL评估就诊,并从EHR中提取了相应的CL测量值。针对该算法的验证达到了较高的准确性水平。这种计算机算法可用于开展与PTL或早产相关的药物流行病学研究和患者护理评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4aa0/9490529/cd6305f84a13/medinform_v10i9e37896_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4aa0/9490529/cd6305f84a13/medinform_v10i9e37896_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4aa0/9490529/cd6305f84a13/medinform_v10i9e37896_fig1.jpg

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