Cohort profile: pathways to care among people with disorders of sex development (DSD).

PURPOSE: The 'DSD Pathways' study was initiated to assess health status and patterns of care among people enrolled in large integrated healthcare systems and diagnosed with conditions comprising the broad category of disorders (differences) of sex development (DSD). The objectives of this communication are to describe methods of cohort ascertainment for two specific DSD conditions-classic congenital adrenal hyperplasia with 46,XX karyotype (46,XX CAH) and complete androgen insensitivity syndrome (CAIS). PARTICIPANTS: Using electronic health records we developed an algorithm that combined diagnostic codes, clinical notes, laboratory data and pharmacy records to assign each cohort candidate a 'strength-of-evidence' score supporting the diagnosis of interest. A sample of cohort candidates underwent a review of the full medical record to determine the score cutoffs for final cohort validation. FINDINGS TO DATE: Among 5404 classic 46,XX CAH cohort candidates the strength-of-evidence scores ranged between 0 and 10. Based on sample validation, the eligibility cut-off for full review was set at the strength-of-evidence score of ≥7 among children under the age of 8 years and ≥8 among older cohort candidates. The final validation of all cohort candidates who met the cut-off criteria identified 115 persons with classic 46,XX CAH. The strength-of-evidence scores among 648 CAIS cohort candidates ranged from 2 to 10. There were no confirmed CAIS cases among cohort candidates with scores <6. The in-depth medical record review for candidates with scores ≥6 identified 61 confirmed cases of CAIS. FUTURE PLANS: As the first cohort of this type, the DSD Pathways study is well-positioned to fill existing knowledge gaps related to management and outcomes in this heterogeneous population. Analyses will examine diagnostic and referral patterns, adherence to care recommendations and physical and mental health morbidities examined through comparisons of DSD and reference populations and analyses of health status across DSD categories.

An automated algorithm using free-text clinical notes to improve identification of transgender people.

Accurate identification of transgender persons is a critical first step in conducting transgender health studies. To develop an automated algorithm for identifying transgender individuals from electronic medical records (EMR) using free-text clinical notes. The development and validation of the algorithm was based on data from an integrated healthcare system that served as a participating site in the multicenter Study of Transition Outcomes and Gender. The training and test datasets each contained a total of 300 individuals identified between 2006 and 2014. Both datasets underwent a full medical record review by experienced research abstractors. The validated algorithm was then implemented to identify transgender individuals in the EMR using all clinical notes of patients that received care between January 1, 2015 and June 30, 2018. Validation of the algorithm against the full chart review demonstrated a high degree of accuracy with 97% sensitivity, 95% specificity, 94% positive predictive value, and 97% negative predictive value. The algorithm classified 7,409 individuals (3.5%) as "Definitely transgender" and 679 individuals (0.3%) as "Probably transgender" out of 212,138 candidates with a total of 378,641 clinical notes. The computerized NLP algorithm can support essential efforts to improve the health of transgender people.