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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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490529/
https://www.ncbi.nlm.nih.gov/pubmed/36066930
http://dx.doi.org/10.2196/37896
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author 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
author_facet 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
author_sort Xie, Fagen
collection PubMed
description 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(0/7)-34(6/7) gestational weeks. Second, clinical notes associated with triage and hospital encounters within 24(0/7)-34(6/7) 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(0/7) gestational weeks to 2.32% at 34(6/7) gestational weeks, and TVUS from 54.67% at 24(0/7) gestational weeks to 12.05% in 34(6/7) 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.
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spelling pubmed-94905292022-09-22 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 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 JMIR Med Inform Original Paper 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(0/7)-34(6/7) gestational weeks. Second, clinical notes associated with triage and hospital encounters within 24(0/7)-34(6/7) 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(0/7) gestational weeks to 2.32% at 34(6/7) gestational weeks, and TVUS from 54.67% at 24(0/7) gestational weeks to 12.05% in 34(6/7) 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. JMIR Publications 2022-09-06 /pmc/articles/PMC9490529/ /pubmed/36066930 http://dx.doi.org/10.2196/37896 Text en ©Fagen Xie, Nehaa Khadka, Michael J Fassett, Vicki Y Chiu, Chantal C Avila, Jiaxiao Shi, Meiyu Yeh, Aniket Kawatkar, Nana A Mensah, David A Sacks, Darios Getahun. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 06.09.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
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
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490529/
https://www.ncbi.nlm.nih.gov/pubmed/36066930
http://dx.doi.org/10.2196/37896
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