Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty


  • Prathyusha Dasari University of California Riverside School of Medicine, Riverside, CA, USA
  • Richard C. Rice Department of Orthopaedic Surgery, Loma Linda University School of Medicine, Loma Linda, CA, USA
  • Andrew Cabrera Loma Linda University School of Medicine, Loma Linda, CA, USA
  • Mikayla Kricfalusi California University of Science and Medicine, Colton, CA, USA
  • Trevor Case California University of Science and Medicine, Colton, CA, USA
  • Matthew T. Gulbrandsen Department of Orthopaedic Surgery, Loma Linda University School of Medicine, Loma Linda, CA, USA
  • Jeremy Brown Department of Orthopaedic Surgery, Loma Linda University School of Medicine, Loma Linda, CA, USA
  • Wesley P. Phipatanakul Department of Orthopaedic Surgery, Loma Linda University School of Medicine, Loma Linda, CA, USA



ML, Shoulder HA, Postoperative complications, Outcomes, AI


Background: Artificial intelligence (AI) constructs and machine learning (ML) algorithms have demonstrated utility in predicting various clinical, surgical, and financial outcomes. In this study, we applied AI to shoulder hemiarthroplasty (HA) to predict various post-operative complications.

Methods: The sample was queried from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) database for all shoulder HA cases from 2008-2018. Six ML algorithms-random forest classifier, gradient boosting classifier, decision tree classifier, SVM classifier-tuned model, Gaussian Naïve Bayes classifier, multi-layer perception-analyzed the sample dataset. Postoperative complications included extended length of stay, non-home discharge destination, transfusion, and any adverse event. Each ML model was compared to logistic regression (LR), and model strength was evaluated.

Results: We identified a total of 1585 shoulder HA cases. Mean age, BMI, operative time, and length of stay were 66±12 years, 31±8 kg/m2, 114±61 minutes, and 2.93±6.61 days. Preop hematocrit, longer operative time, and older age were most predictive of extended length of stay. Preop hematocrit, operative time, and ASA class had the highest importance in any adverse events (AAE) prediction. ML models outperformed traditional comorbidity indices, LR, for predicting extended length of stay (79% vs. 66%), non-home discharge destination (79% vs. 65%), any adverse event (78% vs. 66%), and transfusion requirement (82% vs. 63%). 

Conclusions: ML algorithms predicted post-surgical outcomes of interest following shoulder HA at a higher rate to conventional LR and can assist orthopedic surgeons in decision making.



Politzer CS, Bala A, Seyler TM, Bolognesi MP, Garrigues GE. Use and Cost of Reverse Shoulder Arthroplasty Versus Hemiarthroplasty for Acute Proximal Humerus Fractures. Orthopedics. 2020;43(2):119–25.

London DA, Cagle PJ, Parsons BO, Galatz LM, Anthony SG, Zubizarreta N et al. Impact of Increasing Comorbidity Burden on Resource Utilization in Patients with Proximal Humerus Fractures. J Am Academy Orthop Surg. 2020;28(21):e954.

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. RadioGraphics. 2017;37(2):505-15.

Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discovery Today. 2017;22(11):1680-5.

Gowd AK, Agarwalla A, Amin NH, Romeo AA, Nicholson GP, Verma NN et al. Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. 2019;28(12):e410-21.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al. Scikit-learn: Machine Learning in Python. J Machine Learning Res. 2011;12:2825-30.

Rossum FLJDGV. The Python Language Reference Manual.

Gholamy A, Kreinovich V, Kosheleva O. Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. Scholarworks.utep. 2018.

Testa EJ, Haglin JM, Li NY, Moore ML, Gil JA, Daniels AH et al. Temporal and Geographic Trends in Medicare Reimbursement of Primary and Revision Shoulder Arthroplasty: 2000 to 2020. J Am Academy Orthop Surgeons. 2021;29(24):e1396.

Jaeschke R, Guyatt GH, Sackett DL, Guyatt G, Bass E, Brill-Edwards P et al. Users’ Guides to the Medical Literature: III. How to Use an Article About a Diagnostic Test B. What Are the Results and Will They Help Me in Caring for My Patients? JAMA. 1994;271(9):703-7.

Hosmer DW, Lemeshow S. Applied Logistic Regression: Hosmer/Applied Logistic Regression. Hoboken, NJ, USA: John Wiley and Sons, Inc. 2000.

Hunter JD. Matplotlib: A 2D Graphics Environment. Computing Sci Engineering. 2007;9(3):90-5.

Kaneko H. Cross‐validated permutation feature importance considering correlation between features. Analytical Sci Adv. 2022;3(9-10):278-87.

Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics. 2009;10(1):213.

Khazzam M, Ahn J, Sager B, Gates S, Sorich M, Boes N. 30-Day Postoperative Complications After Surgical Treatment of Proximal Humerus Fractures: Reverse Total Shoulder Arthroplasty Versus Hemiarthroplasty. JAAOS Global Res Rev. 2023;7(3):e22.00174.

Koh J, Galvin JW, Sing DC, Curry EJ, Li X. Thirty-day Complications and Readmission Rates in Elderly Patients After Shoulder Arthroplasty. J Am Acad Orthop Surg Glob Res Rev. 2018;2(11):e068.

Hill BL, Brown R, Gabel E, Rakocz N, Lee C, Cannesson M et al. An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Brit J Anaesthesia. 2019;123(6):877-86.

Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A et al. Deep-learning model for predicting 30-day postoperative mortality. Brit J Anaesthesia. 2019;123(5):688-95.

Hofer IS, Kupina M, Laddaran L, Halperin E. Integration of feature vectors from raw laboratory, medication and procedure names improves the precision and recall of models to predict postoperative mortality and acute kidney injury. Sci Rep. 2022;12(1):10254.

Kowadlo G, Mittelberg Y, Ghomlaghi M, Stiglitz D, Kishore K, Guha R et al. Development and Validation of ‘Patient Optimizer’ (POP) Algorithms for Predicting Surgical Risk with Machine Learnin. 2022. Available at: 10.1101/2022.10.03.22280539v2. Accessed on 12 January, 2024.

Russell M, Russell D, Corizzo R, Japkowicz N. Machine Learning for Surgical Risk Assessment Decision Systems. In: 2022 International Joint Conference on Neural Networks (IJCNN). 2022;1-8.

Kim K, Iorio R. The 5 Clinical Pillars of Value for Total Joint Arthroplasty in a Bundled Payment Paradigm. J Arthroplasty. 2017;32(6):1712-6.

Mayfield CK, Haglin JM, Levine B, Della Valle C, Lieberman JR, Heckmann N. Medicare Reimbursement for Hip and Knee Arthroplasty From 2000 to 2019: An Unsustainable Trend. J Arthroplasty. 2020;35(5):1174-8.

Wilensky GR. Will MACRA Improve Physician Reimbursement? N Engl J Med. 2018;378(14):1269-71.

Karnuta JM, Navarro SM, Haeberle HS, Billow DG, Krebs VE, Ramkumar PN. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model. J Orthopaed Trauma. 2019;33(7):324.

Thio QCBS, Karhade AV, Ogink PT, Raskin KA, De Amorim Bernstein K, Lozano Calderon SA et al. Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients with Chondrosarcoma? Clin Orthop Relat Res. 2018;476(10):2040-8.






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