Matheny, Michael

Objectives To evaluate the feasibility for use of electronic health record (EHR) data in conducting adverse event surveillance among women who received mid-urethral slings (MUS) to treat stress urinary incontinence (SUI) in five health systems. Design Retrospective observational study using EHR data from 2010 through 2021. Women with a history of MUS were identified using common data models; a common analytic code was executed at each site. A manual chart review was conducted in a per-site random patient subset to establish a reference standard. Automated text processing (Text Processed Integrated (TPI)) was developed and evaluated at each site to determine the surgical approach and synthetic mesh implantation. Patients were characterized and surgical outcomes were ascertained over 730 subsequent days. Setting Five large tertiary care academic medical centers. Participants Across five health systems, 9,906 eligible patients (mean age 57–60 per site) were identified. Main outcome measures Determination of surgical approach, synthetic mesh implantation, and assessment of the duration of surveillance for mortality and reoperation rates following MUS implantation. Results In the TPI cohort analysis, 3,331 patients were identified. Surgical approach per site was retropubic (42% to 77%), transobturator (6% to 44%), single incision (0% to 24%), and adjustable sling (0% to <4%). Concordance rates for TPI using chart review were 71%–90% at each site for the surgical approach and 28%–85% for synthetic mesh implantation. Patient follow-up observation rates for mortality and reoperation ranged from 22% to 36% at 90 days, 15% to 30% at 365 days, and 8% to 19% at 730 days. Conclusion Using EHR data alone, identification of medical devices and surgical approaches was feasible among women with MUS surgery for SUI, but long-term follow-up ascertainment rates were low. Medical device surveillance using EHR data should be evaluated in the context of the clinical use case, as applicability may vary.

Key Points A computable phenotype combines routinely collected data elements from the EHR with logic elements to identify a condition of interest. This validated computable phenotype has strong classification characteristics to identify individuals with primary nephrotic syndrome. This computable phenotype for primary nephrotic syndrome can facilitate future research of these rare diseases. Background Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet CDM at three institutions from January 1, 2009 to January 1, 2018, where a random selection of 50 cases and 50 noncases (individuals not meeting case criteria seen within the same calendar year and within 5 years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 noncases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results The algorithm identified a total of 2708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009 to 2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI, 97% to 99%), 79% (95% CI, 74% to 85%), and 0.9 (0.84 to 0.97), respectively. The most common causes of false positive classification were secondary FSGS (nine out of 39) and lupus nephritis (nine out of 39). Conclusion This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.

AbstractLarge observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.