e13004 Background: The aim of this study was to assess outcomes of a multidisciplinary model for combined prophylactic and/or therapeutic mastectomy with immediate reconstruction and gynecologic risk-reducing surgery in patients with hereditary breast cancer syndromes. Methods: Between 2012 and 2016, 12 patients with documented BRCA1 and BRCA2 mutations underwent combined surgery at our facility. Procedures included bilateral mastectomy, axillary lymph node staging, immediate expander based reconstruction and minimally invasive salpingo-oophorectomy with added hysterectomy when indicated. All procedures were performed in a single operating room setting by rotating subspecialty teams. Results: Patient characteristics included a mean (+SD) BMI of 32.1±6.7 (23-44) kg/m2 and ASA of 2.2±0.4 (2-3). Fifty-eight percent (7/12) were premenopausal. Patient’s average age was 45.8+10.8 (30-73). Therapeutic mastectomy for breast cancer was performed in 4/12 patients. Of the 4 affected patients 2 had neo-adjuvant chemotherapy for locally advanced cancer. The remaining 8/12 had prophylactic mastectomies. Risk-reducing salpingo-oophorectomy was performed in 12/12 patients. Seventy-five percent (9/12) underwent concurrent minimally invasive hysterectomy for suspected gynecologic malignancy, leiomyoma, complex endometrial hyperplasia, dysmenorrhea and menorrhagia. Two gynecologic specimens required mini-laparotomy for removal. Mean total operative time was 283.3±66.5 (206-447) minutes and estimated blood loss (EBL) was 209.2±139.2 (50-500) ml. Hospital length of stay (LOS) was 1.4±0.7 (1-3) days. There were no significant differences (p > 0.05) in operative time, EBL, or LOS in comparing therapeutic to prophylactic mastectomies. Follow-up revealed no postoperative wound infections. Conclusions: Combined mastectomy with immediate reconstruction and gynecologic risk reducing surgery had no untoward surgical complications with a zero postoperative wound infection rate. Although a small study population, results indicate this approach is a prudent and feasible multidisciplinary model that can be offered to BRCA mutation carriers.
No abstract
Background: Sarah Cannon has established a standardized nurse navigation program for breast, lung, and Gi cancer patients. Navigators play a significant role in addressing barriers that may adversely impact patient outcomes. Historically, nurse navigators were spending up to 65% of their time data mining to identify new patients for navigation. This lost time compromises a navigator’s ability to effectively support patients. Sarah Cannon implemented a technology solution to address this manual process. Methods: A patient identification software application (patient ID), utilizing natural language processing technology, was developed to identify positive pathology reports across the enterprise in real time. Patient ID instantly routes those reports to a tumor site-specific oncology nurse navigator. The impact of this technology was assessed in 3 Hospital Corporation of America (HCA) markets from December 2016–March 2017. Total patient recall, total volume of reports reviewed, navigated patient volumes, navigator time allocation, and time from diagnosis to first treatment were studied. Results: Patient ID reviewed 47,544 pathology reports during the 4-month pilot, identifying 7,224 potential cancer reports. 2,782 of those represented breast, lung, or Gi cancer patients and were routed to a nurse navigator. Patient ID performed with an overall total patient recall of 98%, respectively. Decreased time spent data mining was observed, and navigator caseload increased by 71%. Time from diagnosis to first treatment decreased by an average of 6 days. Time allocated to direct patient contact and physician interaction increased by 35%. Conclusions: Implementation of a technology solution to rapidly identify new cancer patients for navigation in a community health system is feasible and associated with multiple benefits. Increased navigator patient volumes and navigator productivity were observed. Navigator time spent with patients and physicians increased with a concurrent reduction in data mining time. Timeliness of care metrics improved, suggesting a favorable impact on quality. This technology is now being deployed across the HCA enterprise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.