ABSTRACT
AIMS: Standard outcome sets enable the value-based evaluation of health care delivery. Whereas the attainment of expert opinion has been structured using methods such as the modified-Delphi process, standardized guidelines for extraction of candidate outcomes from literature are lacking. As such, we aimed to describe an approach to obtain a comprehensive list of candidate outcomes for potential inclusion in standard outcome sets. METHODS: This study describes an iterative saturation approach, using randomly selected batches from a systematic literature search to develop a long list of candidate outcomes to evaluate healthcare. This approach can be preceded with an optional benchmark review of relevant registries and Clinical Practice Guidelines and data visualization techniques (e.g. as a WordCloud) to potentially decrease the number of iterations. The development of the International Consortium of Health Outcome Measures Heart valve disease set is used to illustrate the approach. Batch cutoff choices of the iterative saturation approach were validated using data of 1000 simulated cases. RESULTS: Simulation showed that on average 98% (range 92-100%) saturation is reached using a 100-article batch initially, with 25 articles in the subsequent batches. On average 4.7 repeating rounds (range 1-9) of 25 new articles were necessary to achieve saturation if no outcomes are first identified from a benchmark review or a data visualization. CONCLUSION: In this paper a standardized approach is proposed to identify relevant candidate outcomes for a standard outcome set. This approach creates a balance between comprehensiveness and feasibility in conducting literature reviews for the identification of candidate outcomes.
Subject(s)
Delivery of Health Care , Humans , Delphi Technique , Registries , Review Literature as TopicABSTRACT
Background: In the treatment of gender dysphoria, appropriate nipple-areola complex (NAC) positioning is essential for achieving a natural appearing male chest after subcutaneous mastectomy. An accurate predictive model for the ideal personalized position of the NAC is still lacking. The aim of this study is to determine the anthropometry of the male chest to create individualized guidelines for appropriate NAC positioning in the preoperative setting. Materials and methods: Cisgender male participants were recruited. Multiple chest measurements were manually recorded. Best subset regression using linear models was used to select predictors for the horizontal coordinate (nipple-nipple distance; NN) and vertical coordinate (sternal notch-nipple distance; SNN) of the NAC. Internal validation was assessed using bootstrapping. Furthermore, a cohort of transgender men who had received a mastectomy with replantation of nipples according to current practice was identified. Comparison testing between the algorithm and standard practice was performed to test the limitations of standard practice. Results: One hundred and fifty cis male participants were included (median age: 26, IQR: 22-34 years). Four predictors were found to predict NN (age, weight, chest circumference (CC), anterior-axillar fold to anterior-axillar fold (AUX-AUX)) and reads as follows: NN = 4.11 + 0.035*age + 0.041*weight + 0.093*CC + 0.140*AUX-AUX Two predictors were found to predict SNN (NN and weight), and reads as follows: SNN = 7.248 + 0.303*NN + 0.072*weight. Both models performed well (Bootstrapped R2: 0.63 (NN), 0.50 (SNN)) and outperformed previous models predicting NAC position. Ninety-six transgender men were eligible for evaluation of current practice and showed an average placement error of -0.9 cm for NN and +2.2 cm for SNN. Conclusion: The non-standardized approach of NAC repositioning results in a significant error of nipple placement. We suggest that the two predictive models for NN and SNN can be used to optimize NAC positioning on the masculinized chest wall.Supplemental data for this article is available online at https://doi.org/10.1080/26895269.2021.1884926.
