Cargando…

Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.

OBJECTIVES: Desirable outcomes following surgery for anterior shoulder instability (ASI) include a multitude of functional and clinical functions, yet it remains uncertain if all of these outcomes are simultaneously achievable (i.e. can patients have full motion and no instability; does excellent st...

Descripción completa

Detalles Bibliográficos
Autores principales: Till, Sara, Lu, Yining, Reinholz, Anna, Krych, Aaron, Okoroha, Kelechi, Camp, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392275/
http://dx.doi.org/10.1177/2325967123S00069
_version_ 1785082919518208000
author Till, Sara
Lu, Yining
Reinholz, Anna
Krych, Aaron
Okoroha, Kelechi
Camp, Christopher
author_facet Till, Sara
Lu, Yining
Reinholz, Anna
Krych, Aaron
Okoroha, Kelechi
Camp, Christopher
author_sort Till, Sara
collection PubMed
description OBJECTIVES: Desirable outcomes following surgery for anterior shoulder instability (ASI) include a multitude of functional and clinical functions, yet it remains uncertain if all of these outcomes are simultaneously achievable (i.e. can patients have full motion and no instability; does excellent stability coincide with stiffness?). The purpose of this study was to employ unsupervised machine learning techniques to define the actual "optimal observed outcome” for patients undergoing surgical treatment for ASI and to identify predictors of obtaining this outcome. METHODS: Patients <40 years with an initial diagnosis of ASI from 1994 -2016 were included. Four unsupervised machine learning clustering algorithms were evaluated to partition subjects into “optimal observed outcome” or “suboptimal outcome” based on combinations of actually observed outcomes. Demographic, clinical, and treatment variables were compared between groups using descriptive statistics and Kaplan-Meier survival curves; multivariate stepwise logistic regression evaluated variable prognostic value. RESULTS: 200 patients with a mean follow-up of 11 years were included. 146 (64%) obtained the “optimal observed outcome”, characterized by significantly (P <0.001) lower rates of: recurrent postoperative pain (23% vs 52%), recurrent instability (12% vs 41%), revision surgery (10% vs 24%), progression to osteoarthritis (OA) (5% vs. 19%), and mildly restricted motion (161° vs. 168°). Stepwise multivariate logistic regression identified time from initial instability to presentation (OR: 0.96, 95% CI: 0.92-0.98) and habitual instability (OR: 0.17, 95%CI: 0.04-0.77) as negative predictors of “observed optimal outcome”. Increased rate of subluxation over frank dislocation pre-operatively (OR: 1.30, 95% CI: 1.02-1.65) was a positive predictor. Type of surgery performed was not a significant predictor. CONCLUSIONS: Following surgical treatment for ASI, an appropriate “optimal observed outcome” can be defined as: minimal postoperative pain, absence of recurrent instability, low rates of revision surgery, absence of OA, and increased ROM. This “optimal observed outcome” was achieved in over 2/3rds of the cohort and this work demonstrated the synergistic relationship of these. The most significant predictors included shorter time to presentation and history of subluxations over frank dislocations pre-operatively. This definition of the “optimal observed outcome” may be more appropriate for surgical decision making and setting appropriate expectations.
format Online
Article
Text
id pubmed-10392275
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-103922752023-08-02 Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up. Till, Sara Lu, Yining Reinholz, Anna Krych, Aaron Okoroha, Kelechi Camp, Christopher Orthop J Sports Med Article OBJECTIVES: Desirable outcomes following surgery for anterior shoulder instability (ASI) include a multitude of functional and clinical functions, yet it remains uncertain if all of these outcomes are simultaneously achievable (i.e. can patients have full motion and no instability; does excellent stability coincide with stiffness?). The purpose of this study was to employ unsupervised machine learning techniques to define the actual "optimal observed outcome” for patients undergoing surgical treatment for ASI and to identify predictors of obtaining this outcome. METHODS: Patients <40 years with an initial diagnosis of ASI from 1994 -2016 were included. Four unsupervised machine learning clustering algorithms were evaluated to partition subjects into “optimal observed outcome” or “suboptimal outcome” based on combinations of actually observed outcomes. Demographic, clinical, and treatment variables were compared between groups using descriptive statistics and Kaplan-Meier survival curves; multivariate stepwise logistic regression evaluated variable prognostic value. RESULTS: 200 patients with a mean follow-up of 11 years were included. 146 (64%) obtained the “optimal observed outcome”, characterized by significantly (P <0.001) lower rates of: recurrent postoperative pain (23% vs 52%), recurrent instability (12% vs 41%), revision surgery (10% vs 24%), progression to osteoarthritis (OA) (5% vs. 19%), and mildly restricted motion (161° vs. 168°). Stepwise multivariate logistic regression identified time from initial instability to presentation (OR: 0.96, 95% CI: 0.92-0.98) and habitual instability (OR: 0.17, 95%CI: 0.04-0.77) as negative predictors of “observed optimal outcome”. Increased rate of subluxation over frank dislocation pre-operatively (OR: 1.30, 95% CI: 1.02-1.65) was a positive predictor. Type of surgery performed was not a significant predictor. CONCLUSIONS: Following surgical treatment for ASI, an appropriate “optimal observed outcome” can be defined as: minimal postoperative pain, absence of recurrent instability, low rates of revision surgery, absence of OA, and increased ROM. This “optimal observed outcome” was achieved in over 2/3rds of the cohort and this work demonstrated the synergistic relationship of these. The most significant predictors included shorter time to presentation and history of subluxations over frank dislocations pre-operatively. This definition of the “optimal observed outcome” may be more appropriate for surgical decision making and setting appropriate expectations. SAGE Publications 2023-07-31 /pmc/articles/PMC10392275/ http://dx.doi.org/10.1177/2325967123S00069 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This open-access article is published and distributed under the Creative Commons Attribution - NonCommercial - No Derivatives License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits the noncommercial use, distribution, and reproduction of the article in any medium, provided the original author and source are credited. You may not alter, transform, or build upon this article without the permission of the Author(s). For article reuse guidelines, please visit SAGE’s website at http://www.sagepub.com/journals-permissions.
spellingShingle Article
Till, Sara
Lu, Yining
Reinholz, Anna
Krych, Aaron
Okoroha, Kelechi
Camp, Christopher
Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title_full Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title_fullStr Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title_full_unstemmed Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title_short Paper 43: Defining and Predicting the “Optimal Observed Outcome” Following Surgical Treatment of Anterior Shoulder Instability: A Machine Learning Clustering Analysis of 200 patients with 11-year mean follow up.
title_sort paper 43: defining and predicting the “optimal observed outcome” following surgical treatment of anterior shoulder instability: a machine learning clustering analysis of 200 patients with 11-year mean follow up.
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392275/
http://dx.doi.org/10.1177/2325967123S00069
work_keys_str_mv AT tillsara paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup
AT luyining paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup
AT reinholzanna paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup
AT krychaaron paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup
AT okorohakelechi paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup
AT campchristopher paper43definingandpredictingtheoptimalobservedoutcomefollowingsurgicaltreatmentofanteriorshoulderinstabilityamachinelearningclusteringanalysisof200patientswith11yearmeanfollowup