Capsular contracture (CC) is the most common complication of breast augmentation and reconstruction with implants. [1][2][3][4] The original classification scheme, developed by Spear and Baker, 5 is the most broadly adopted and provides a straightforward metric for evaluating CC in countless studies that have shaped our understanding of outcomes in breast implant surgery. The Baker classification (Table 1) considers physician and patient perceptions of implant palpability, visibility, breast firmness, and pain to generate a score ranging from I to IV. 5 By strict definition, though, CC refers specifically to morphologic and physiomechanical changes to the fibrous capsule that forms around breast implants. 4 Although the palpability, visibility, firmness, and pain that develop around a breast implant can result exclusively from contracture of the periprosthetic capsule, there are myriad factors that may influence this. Despite a thoughtful modification of the Baker grade to evaluate CC following Background: Breast implants are the most commonly used medical devices in plastic surgery, and capsular contracture (CC) is one of the most common complications. However, our assessment of CC is based largely on Baker grade, which is problematically subjective and affords only four possible values. Methods: The authors performed a systematic review concluding in September of 2021 in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. It identified 19 articles that propose approaches to measuring CC. Results: In addition to Baker grade, the authors identified several modalities reported to measure CC. These included magnetic resonance imaging, ultrasonography, sonoelastography, mammacompliance measuring devices, applanation tonometry, histologic evaluation, and serology. Capsule thickness and other measures of CC inconsistently correlated with Baker grade, whereas the presence of synovial metaplasia was consistently associated with Baker grade I and II, but not III and IV capsules. Conclusions: There remains no particular method to reliably and specifically measure the contracture of capsules that form around breast implants. As such, we would recommend that research investigators use more than one modality to measure CC. Other variables that can impact breast implant stiffness and associated discomfort beyond CC need to be considered when evaluating patient outcomes. Given the value placed on CC outcomes in assessing breast implant safety, and the prevalence of breast implants overall, the need for a more reliable approach to measuring this outcome persists.
Patient decision aids can support shared decision making and improve decision quality. However, decision aids are not widely used in clinical practice due to multiple barriers. Integrating patient decision aids into the electronic health record (EHR) can increase their use by making them more clinically relevant, personalized, and actionable. In this article, we describe the procedures and considerations for integrating a patient decision aid into the EHR, based on the example of BREASTChoice, a decision aid for breast reconstruction after mastectomy. BREASTChoice’s unique features include 1) personalized risk prediction using clinical data from the EHR, 2) clinician- and patient-facing components, and 3) an interactive format. Integrating a decision aid with patient- and clinician-facing components plus interactive sections presents unique deployment issues. Based on this experience, we outline 5 key implementation recommendations: 1) engage all relevant stakeholders, including patients, clinicians, and informatics experts; 2) explicitly and continually map all persons and processes; 3) actively seek out pertinent institutional policies and procedures; 4) plan for integration to take longer than development of a stand-alone decision aid or one with static components; and 5) transfer knowledge about the software programming from one institution to another but expect local and context-specific changes. Integration of patient decision aids into the EHR is feasible and scalable but requires preparation for specific challenges and a flexible mindset focused on implementation. Highlights Integrating an interactive decision aid with patient- and clinician-facing components into the electronic health record could advance shared decision making but presents unique implementation challenges. We successfully integrated a decision aid for breast reconstruction after mastectomy called BREASTChoice into the electronic health record. Based on this experience, we offer these implementation recommendations: 1) engage relevant stakeholders, 2) explicitly and continually map persons and processes, 3) seek out institutional policies and procedures, 4) plan for it to take longer than for a stand-alone decision aid, and 5) transfer software programming from one site to another but expect local changes.
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