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Identification of Preterm Labor Evaluation Visits and Extraction of Cervical Length Measures from Electronic Health
Fagen Xie1, Nehaa Khadka1, Michael J Fassett2,3
1Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Insights
A new algorithm accurately identifies preterm labor (PTL) evaluation visits and extracts cervical length (CL) data from electronic health records (EHRs). This tool aids in preterm birth (PTB) research and patient care reviews.
Area of Science:
- Medical Informatics
- Obstetrics and Gynecology
- Public Health
Background:
- Preterm birth (PTB) is a major global health concern.
- Accurate identification of preterm labor (PTL) evaluation visits is crucial for PTB research.
Purpose of the Study:
- Develop and validate a computerized algorithm to identify PTL evaluation visits.
- Extract cervical length (CL) measures from electronic health records (EHRs).
Main Methods:
- Utilized EHR data from Kaiser Permanente Southern California (2009-2020).
- Developed an algorithm to identify PTL evaluation visits based on fFN tests, TVUS, PTL medications, and diagnosis codes.
- Created a process to extract CL from clinical notes of identified PTL visits.
Main Results:
- The algorithm identified PTL evaluation visits in 23.35% of live birth pregnancies.
- Cervical length (CL) remained stable at a mean of 3.66 cm.
- The algorithm demonstrated high accuracy with positive predictive values ranging from 94.44% to 100% and sensitivity/specificity up to 100%.
Conclusions:
- The computerized algorithm effectively identifies PTL evaluation visits and extracts CL measures from EHRs.
- The algorithm achieves high accuracy and can be utilized for PTB-related research and patient care reviews.
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 240/7-346/7 gestational weeks. Second, clinical notes associated with triage and hospital encounters within 240/7-346/7 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 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 (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.
