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An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database.
Lonner, Jess H; Anderson, Mike B; Redfern, Roberta E; Van Andel, Dave; Ballard, James C; Parratte, Sébastien.
Afiliação
  • Lonner JH; Rothman Orthopaedic Institute, Philadelphia, PA, USA.
  • Anderson MB; Zimmer Biomet, Warsaw, IN, USA.
  • Redfern RE; Zimmer Biomet, Warsaw, IN, USA.
  • Van Andel D; Zimmer Biomet, Warsaw, IN, USA.
  • Ballard JC; Regnerative Orthopedic Center, Portland, OR, USA.
  • Parratte S; Department of Orthopaedic Surgery, International Knee and Joint Centre, Abu Dhabi, United Arab Emirates. sebastien.parratte@gmail.com.
Int Orthop ; 47(2): 485-494, 2023 02.
Article em En | MEDLINE | ID: mdl-36508053
PURPOSE: To evaluate the feasibility of using a smartphone-based care management platform (sbCMP) and robotic-assisted total knee arthroplasty (raTKA) to collect data throughout the episode-of-care and assess if intra-operative measures of soft tissue laxity in raTKA were associated with post-operative outcomes. METHODS: A secondary data analysis of 131 patients in a commercial database who underwent raTKA was performed. Pre-operative through six week post-operative step counts and KOOS JR scores were collected and cross-referenced with intra-operative laxity measures. A Kruskal-Wallis test or a Wilcoxon sign-rank was used to assess outcomes. RESULTS: There were higher step counts at six weeks post-operatively in knees with increased laxity in both the lateral compartment in extension and medial compartment in flexion (p < 0.05). Knees balanced in flexion within < 0.5 mm had higher KOOS JR scores at six weeks post-operative (p = 0.034) compared to knees balanced within 0.5-1.5 mm. CONCLUSION: A smartphone-based care management platform can be integrated with raTKA to passively collect data throughout the episode-of-care. Associations between intra-operative decisions regarding laxity and post-operative outcomes were identified. However, more robust analysis is needed to evaluate these associations and ensure clinical relevance to guide machine learning algorithms.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article