2023 AOSSM Annual Meeting Recordings with CME-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.

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.

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Video Transcription

Video Summary

In this video, the speaker discusses the use of machine learning and cluster analysis in shoulder instability. They raise the question of whether it is realistic to expect all patients to achieve perfect outcomes, as some goals may conflict with each other. To address this, they utilize cluster analysis, an unsupervised machine learning tool, to identify patterns in data and determine optimal and suboptimal outcome clusters. The study involves 228 patients who underwent anterior shoulder instability surgery, with a median follow-up of 11 years. The results show that 64% of patients were in the optimal outcome cluster, while 36% were in the suboptimal outcome cluster. Additionally, 41% of patients achieved a perfect outcome. Factors such as younger age, shorter time of presentation, and earlier surgery were associated with higher chances of achieving optimal outcomes. The findings can help guide patient counseling and expectations. No credits were mentioned.

Asset Caption

Christopher Camp, MD

Keywords

machine learning

cluster analysis

shoulder instability

optimal outcome

suboptimal outcome