Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 10, 2022
Date Accepted: Aug 12, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Identifying Preterm Labor Evaluation Visits and Extraction of Cervical Length Measures from Electronic Health Records Within a large Integrated Healthcare System
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 (EHR) within a large integrated healthcare system.
Methods:
We utilized data extracted from Kaiser Permanente Southern California (KPSC) EHRs between 2009 and 2020. First, we identified triage and hospital encounters with any fetal fibronectin (fFN) tests, transvaginal ultrasounds (TVUS), PTL medications, or PTL diagnosis codes within 240/7-346/7 gestational weeks. Second, clinical notes associated with triage and hospital encounters within 240/7-346/7 gestational weeks were then extracted from EHRs. A computerized algorithm and automated process were developed and refined by multiple iterations of chart review and adjudication to search the following PTL indicators: fFN tests, TVUS, abdominal pain, uterine contractions, PTL medications, and descriptions of PTL evaluations. An additional process was constructed to extract CL 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 PTL visits, 19,439 (18.85%) and 44,423 (43.97%) had an fFN test and TVUS, respectively. The percentage of first PTL visits with an fFN test decreased from 18.06% at 240/7 gestational weeks to 2.32% at 346/7 gestational weeks, and TVUS from 54.67% at 240/7 gestational weeks to 12.05% in 346/7 gestational weeks. The mean and standard deviation of CL was 3.66 (0.99) with a mean range of 3.61-3.69 centimeters 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 a positive predictive value of 97.00%, 94.44%, 100.00%, and 96.43% for PTL evaluation visits, fFN test, TVUS, and CL, respectively, and a sensitivity of 100.00%, 90.00%, 90.00%, and a specificity of 98.80%, 100.00%, 98.60% for 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 utilized for conducting PTL- or PTB-related pharmacoepidemiologic studies and patient care reviews.
Citation
