Journal of shoulder and elbow surgery · 2026
Prediction of acromial and scapular spine fractures after reverse total shoulder arthroplasty using machine learning: a retrospective cohort study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Schneller Tim, Cina Andrea, Maggini Emanuele, Klimov Artem, Braun Mirjam, Pfender Aaron, Lazaridou Asimina, Scheibel Markus
Аннотация
BACKGROUND: Acromial and scapular spine fractures (ASSF) are an uncommon but significant complication following reverse total shoulder arthroplasty (rTSA). The study aims were to (1) determine the incidence and type of ASSF captured within a local shoulder arthroplasty registry, (2) develop a novel, interpretable machine learning model to predict individual risk of fracture after rTSA, and (3) assess model performance variation based on implant design.
METHODS: We conducted a retrospective cohort study including 2,256 registry-documented patients who underwent primary rTSA between 2006 and 2025. Ten preoperative features, including implant design (medialized-distalized (MD), lateralized, and lateralized-distalized) were used as input to a logistic regression machine learning model aimed at predicting ASSF risk. Model performance was evaluated in the overall cohort and different implant design subgroups based on sensitivity, specificity, and area under the receiver operating characteristic curve.
RESULTS: Overall ASSF incidence was 4.1%, whereby 15% were Levy type 1, 51.6% type 2 and 33.4% type 3 fractures. A greater incidence of Levy type 2 fractures was observed for lateralized implant designs, whereas a higher proportion of Levy type 3 fractures occurred with MD designs. Design MD, cuff tear arthropathy, higher age and osteoporosis emerged as the strongest predictors. In the test set, the final model had a sensitivity of 0.71, specificity of 0.61, and area under the receiver operating characteristic curve of 0.71. All implant design subgroups showed fair predictive performance.
CONCLUSION: Our model demonstrated promising capability for preoperative identification of patients at risk of ASSF following rTSA. Implant design MD, cuff tear arthropathy, higher age, and osteoporosis were the strongest predictors of ASSF. Overall, these findings support the integration of personalized risk stratification to guide implant selection and postoperative surveillance.
METHODS: We conducted a retrospective cohort study including 2,256 registry-documented patients who underwent primary rTSA between 2006 and 2025. Ten preoperative features, including implant design (medialized-distalized (MD), lateralized, and lateralized-distalized) were used as input to a logistic regression machine learning model aimed at predicting ASSF risk. Model performance was evaluated in the overall cohort and different implant design subgroups based on sensitivity, specificity, and area under the receiver operating characteristic curve.
RESULTS: Overall ASSF incidence was 4.1%, whereby 15% were Levy type 1, 51.6% type 2 and 33.4% type 3 fractures. A greater incidence of Levy type 2 fractures was observed for lateralized implant designs, whereas a higher proportion of Levy type 3 fractures occurred with MD designs. Design MD, cuff tear arthropathy, higher age and osteoporosis emerged as the strongest predictors. In the test set, the final model had a sensitivity of 0.71, specificity of 0.61, and area under the receiver operating characteristic curve of 0.71. All implant design subgroups showed fair predictive performance.
CONCLUSION: Our model demonstrated promising capability for preoperative identification of patients at risk of ASSF following rTSA. Implant design MD, cuff tear arthropathy, higher age, and osteoporosis were the strongest predictors of ASSF. Overall, these findings support the integration of personalized risk stratification to guide implant selection and postoperative surveillance.