Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records

Background: Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids. Objective: Our objective was to develop an electronic health record–based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record–based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record–based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management. Study Design: We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data–mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm. Results: Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39–56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50–79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71–81%). Conclusion: An electronic health record–based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment

Optimising the delivery of food allergy information. An assessment of food allergic consumer preferences for different information delivery formats

In this study, the preferences of food allergic consumers for different prototype information delivery tools was examined, with the aim of improving informed product choices. Sixty-two self-reported food allergic participants from the Netherlands and Germany were included in the study. Each tested three prototype information delivery tools (a food label, a handheld electronic scanner, and an information booklet) to access allergy information. Participants rated each tool in terms of perceived convenience, usefulness and confidence. Principal Component Analysis indicated that convenience and usefulness loaded on one construct, namely functionality. The impact of information delivery tool and country on functionality and confidence was analysed with two repeated measures generalised linear models. The highest perceived functionality was found for the label. The electronic scanner was rated as the next most functional method to deliver information, followed by the booklet. Food allergic consumers were equally confident about using all three information delivery tools. The results have implications for developing new policies and legislation concerning information provision to food allergic consumer

Preferred Information Strategies for Food Allergic Consumers. A Study in Germany, Greece, and The Netherlands

Information provided on food packaging is currently the most important method enabling food allergic consumers to eliminate allergens from their diet. This study aimed to identify the preferences of food allergic consumers regarding different information provision scenarios. Respondents (N = 287) filled out a web-based questionnaire on their preferences regarding a food label, an in-store booklet, and an ICT-solution. ICT methods will not replace effective food labelling, but may be used to supplement information provided by labels. Recommendations for information delivery to food allergic patients in the form of labels and booklets, as well as personalised (novel ICT) approaches, are provided