Leveraging the Tracking Operations and Outcomes for Plastic Surgeons Database for Plastic Surgery Research: A "How-To" Guide.
Journal
Plastic and reconstructive surgery
ISSN: 1529-4242
Titre abrégé: Plast Reconstr Surg
Pays: United States
ID NLM: 1306050
Informations de publication
Date de publication:
01 Nov 2021
01 Nov 2021
Historique:
pubmed:
17
9
2021
medline:
19
1
2022
entrez:
16
9
2021
Statut:
ppublish
Résumé
The Plastic Surgeries Registry Network supported by the American Society of Plastic Surgeons (ASPS) and the Plastic Surgery Foundation offers a variety of options for procedural data and outcomes assessment and research. The Tracking Operations and Outcomes for Plastic Surgeons (TOPS) database is a registry created for and used by active members of ASPS to monitor all types of procedural outcomes. It functions as a way for individual or group practices to follow surgical outcomes and constitutes a huge research registry available to ASPS members to access for registry-based projects. The TOPS registry was launched in 2002 and has undergone several iterations and improvements over the years and now includes more than 1 million procedure records. Although ASPS member surgeons have proven valuable assets in contributing their data to the TOPS registry, fewer have leveraged the database for registry-based research. This article overviews the authors' experience using the TOPS registry for a database research project to demonstrate the process, usefulness, and accessibility of TOPS data for ASPS member surgeons to conduct registry-based research. This article pairs with the report of the authors' TOPS registry investigation related to 30-day adverse events associated with incision location for augmentation mammaplasty.
Identifiants
pubmed: 34529595
doi: 10.1097/PRS.0000000000008483
pii: 00006534-202111000-00015
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
735e-741eInformations de copyright
Copyright © 2021 by the American Society of Plastic Surgeons.
Déclaration de conflit d'intérêts
Disclosure:The authors have no commercial associations or financial disclosures that might pose or create a conflict of interest with information presented in this article.
Références
Bottle A, Cohen C, Lucas A, et al. How an electronic health record became a real-world research resource: Comparison between London’s Whole Systems Integrated Care database and the Clinical Practice Research Datalink. BMC Med Inform Decis Mak. 2020;20:71.
Colquhoun DA, Shanks AM, Kapeles SR, et al. Considerations for integration of perioperative electronic health records across institutions for research and quality improvement: The approach taken by the Multicenter Perioperative Outcomes Group. Anesth Analg. 2020;130:1133–1146.
Shi W, Kelsey T, Sullivan F. Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research register (SHARE). BMC Med Inform Decis Mak. 2020;20:70.
Wang L, Chang-rui WU. Database model and applications for electronic medical records (EMRs) system. DEStech Transact Comput Sci Eng. 2019;1:85–88.
Lunet N. Epidemiology: Current Perspectives on Research and Practice. 2012.London: IntechOpen Limited;
Hu Z, Melton GB, Arsoniadis EG, Wang Y, Kwaan MR, Simon GJ. Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. J Biomed Inform. 2017;68:112–120.
Papageorgiou G, Grant SW, Takkenberg JJM, Mokhles MM. Statistical primer: How to deal with missing data in scientific research? Interact Cardiovasc Thorac Surg. 2018;27:153–158.
Madhu G, Bharadwaj BL, Nagachandrika G, Vardhan KS. A novel algorithm for missing data imputation on machine learning. Paper presented at: 2019 International Conference on Smart Systems Inventive Technology (ICSSIT); November 27–29, 2019; Tirunelveli, India.
Vieira BL, Lanier ST, Mlodinow AS, et al. A multi-institutional analysis of insurance status as a predictor of morbidity following breast reconstruction. Plast Reconstr Surg Glob Open2014;2:e255.
Khavanin N, Jordan SW, Vieira BL, et al. Combining abdominal and cosmetic breast surgery does not increase short-term complication rates: A comparison of each individual procedure and pretreatment risk stratification tool. Aesthet Surg J. 2015;35:999–1006.
Kim JY, Mlodinow AS, Khavanin N, et al. Individualized risk of surgical complications: An application of the breast reconstruction risk assessment score. Plast Reconstr Surg Glob Open2015;3:e405.
Chow I, Alghoul MS, Khavanin N, et al. Is there a safe lipoaspirate volume? A risk assessment model of liposuction volume as a function of body mass index. Plast Reconstr Surg. 2015;136:474–483.
Drury KE, Lanier ST, Khavanin N, et al. Impact of postoperative antibiotic prophylaxis duration on surgical site infections in autologous breast reconstruction. Ann Plast Surg. 2016;76:174–179.
Vieira BL, Chow I, Sinno S, Dorfman RG, Hanwright P, Gutowski KA. Is there a limit? A risk assessment model of liposuction and lipoaspirate volume on complications in abdominoplasty. Plast Reconstr Surg. 2018;141:892–901.
Pannucci CJ, Antony AK, Wilkins EG. The impact of acellular dermal matrix on tissue expander/implant loss in breast reconstruction: An analysis of the tracking outcomes and operations in plastic surgery database. Plast Reconstr Surg. 2013;132:1–10.
Alderman AK, Collins ED, Streu R, et al. Benchmarking outcomes in plastic surgery: National complication rates for abdominoplasty and breast augmentation. Plast Reconstr Surg. 2009;124:2127–2133.
Manahan MA, Wooden WA, Becker SM, et al. Evidence-based performance measures: Quality metrics for the care of patients undergoing breast reconstruction. Plast Reconstr Surg. 2017;140:775e–781e.