Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning.

ABSTRACT:

Background

Identification of prognostic variables for poor outcomes following open reduction internal fixation (ORIF) of displaced proximal humerus fractures have been limited to singular, linear factors and subjective clinical intuition. Machine learning (ML) has the capability to objectively segregate patients based on various outcome metrics and reports the connectivity of variables resulting in the optimal outcome. Therefore, the purpose of this study was to (1) use unsupervised ML to stratify patients to high-risk and low-risk clusters based on postoperative events, (2) compare the ML clusters to the American Society of Anesthesiologists (ASA) classification for assessment of risk, and (3) determine the variables that were associated with high-risk patients after proximal hume

SUBMITTER: Agarwalla A 

PROVIDER: S-EPMC11401551 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature