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Nonoperative Treatment for Displaced Proximal Humeral Fractures in Elderly Patients: Correlation Between Deviations and Clinical Outcomes.

Mauro Emilio Conforto GracitelliGustavo Jum YamamotoEduardo Angeli MalavoltaFernando Brandão DE Andrade E SilvaKodi Edson KojimaArnaldo Amado Ferreira Neto
Published in: Revista brasileira de ortopedia (2020)
Objectives  To describe the functional result of the conservative treatment of displaced proximal humerus fractures (PHF) using the American Shoulder and Elbow Surgeons (ASES) score after 12 months and assess whether the different initial classifications and radiographic measurements are related to clinical results. Methods  Forty patients > 60 years old, with displaced PHUs submitted to conservative treatment were evaluated at standardized times (3, 6, and 12 months). The American Society of Shoulder and Elbow Surgeons (ASES), Constant-Murley and Single Assessment Numeric Evaluation (SANE) scales were used as clinical outcomes. Radiographic variables included the Neer and Resch classifications, the presence and displacement of tuberosity fracture, metaphyseal comminution, medial periosteal lesion, and angular and translational deviations of the head in the coronal and sagittal plane. Results  The result of the ASES score was 77.7 ± 23.2 for the whole sample, the mean absolute values of the Constant-Murley score were 68.7 ± 16 and 82.6% for the scale relative to the contralateral side. The SANE scale at 12 months was 84.8 ± 19. We observed that the severity of the Neer classification and the coronal plane angular deviation (measured by the head-shaft angle) and the presence of fractures in both tuberosities negatively influenced the ASES score after 12 months of treatment. Conclusion  Nonoperative treatment of displaced proximal humerus fractures in elderly patients results in good clinical results. Clinical results are negatively influenced by the angular deviation of the humeral head and the presence of fractures of the greater and lesser tubercles, as well as by the Neer classification.
Keyphrases
  • machine learning
  • prognostic factors
  • ejection fraction