<p>Identification and validation of uterine perforation, intrauterine device expulsion, and breastfeeding in four health care systems with electronic health records</p>

Abstract

Objective To validate algorithms identifying uterine perforations and intrauterine device (IUD) expulsions and to ascertain availability of breastfeeding status at the time of IUD insertion. Study design and setting Four health care systems with electronic health records (EHRs) participated: Kaiser Permanente Northern California (KPNC), Kaiser Permanente Southern California (KPSC), Kaiser Permanente Washington (KPWA), and Regenstrief Institute (RI). The study included women ≤50 years of age with an IUD insertion. Site-specific algorithms using structured and unstructured data were developed and a sample validated by EHR review. Positive predictive values (PPVs) of the algorithms were calculated. Breastfeeding status was assessed in a random sample of 125 women at each research site with IUD placement within 52 weeks postpartum. Results The study population included 282,028 women with 325,582 IUD insertions. The PPVs for uterine perforation were KPNC 77%, KPSC 81%, KPWA 82%, and RI 47%; PPVs for IUD expulsion were KPNC 77%, KPSC 87%, KPWA 68%, and RI 37%. Across all research sites, breastfeeding status at the time of IUD insertion was determined for 94% of those sampled. Conclusions Algorithms with a high PPV for uterine perforation and IUD expulsion were developed at 3 of the 4 research sites. Breastfeeding status at the time of IUD insertion could be determined at all research sites. Our findings suggest that a study to evaluate the associations of breastfeeding and postpartum IUD insertions with risk of uterine perforation and IUD expulsion can be successfully conducted retrospectively; however, automated application of algorithms must be supplemented with chart review for some outcomes at one research site due to low PPV.

Association of the Timing of Postpartum Intrauterine Device Insertion and Breastfeeding With Risks of Intrauterine Device Expulsion

“…Study methods, including study size calculations, development of propensity scores to control for bias from measured confounding, and validation of outcomes and exposures, have been described in detail previously. 9,10 All research sites received either institutional review board approval or an JAMA Network Open | Obstetrics and Gynecology exemption for the conduct of this study, which qualified for a waiver of informed consent requirements because of the use of deidentified data and/or minimal risk to participants. KPSC also received approval from California state agencies for use of vital statistics data.…”

Design of the Association of Uterine Perforation and Expulsion of Intrauterine Device study: a multisite retrospective cohort study

“…Algorithms were developed to identify potential risk factors and outcomes using operational definitions, natural language processing (NLP), and medical record review at all sites. 5 These algorithms were developed collaboratively to capture the same concepts but implemented separately at each site and differed where appropriate; for instance, some ICD codes performed better at some sites than others owing to the variation in local coding practices. Algorithms for the outcome variables, uterine perforation and IUD expulsion, were validated by obstetrician-gynecologist clinicians from each site via chart review while ICD-9-CM codes were in use, prior to use of ICD-10-CM coding.…”

“…Power calculations for uterine perforation using the expected number of IUD insertions and risk factor allocation based on the validation study 5 and EURAS-IUD 4 were performed using PASS 14 software (NCSS Statistical Software, Kaysville, UT) for a 2-sided test of the hazard ratio (HR). 8 Because IUD expulsion rates are higher than uterine perforation rates, the power for IUD expulsion comparisons is greater than for perforation.…”

“…Although the FDA initially recommended a prospective observational study, the research team instead suggested that a retrospective study drawing data from electronic health records (EHRs) would provide greater efficiency and more timely results while reflecting current US clinical practice. Following a validation study demonstrating that algorithms could be used to identify uterine perforation and IUD expulsion in proposed data sources and that breastfeeding could be identified in EHRs of women who had given birth, 5 the Association of Uterine Perforation and Expulsion of IUD (APEX-IUD) study was planned in coordination with Bayer and the FDA.…”

Identifying Ectopic Pregnancy in a Large Integrated Health Care Delivery System: Algorithm Validation

“…They are also easily transferable to other EHR systems and more cost-efficient than paper-based data sources [13][14][15]. Over the last decade, there have been a number of studies that evaluated the accuracy of health data (hospital discharge data, outpatient encounter data, and claims data) extracted from the EHRs of various regions of the Kaiser Permanente health care system [16][17][18][19] and other health care systems [20,21]. Published validation studies investigated demographic characteristics [17], body weight and height data [22], perinatal outcomes [18,23], phenotype for genomic study [21], and phenotype of HIV infection [20].…”