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Acta Orthopaedica Volume 92, 2021 - Issue 5
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Research Article

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review

Paul T Oginka Department of Orthopedic Surgery, University Medical Center Utrecht – Utrecht University, Utrecht, The NetherlandsCorrespondence[email protected]
,
Olivier Q Grootb Department of Orthopedic Surgery, Orthopedic Oncology Service, Massachusetts General Hospital – Harvard Medical School, Boston, USA
,
Aditya V Karhadeb Department of Orthopedic Surgery, Orthopedic Oncology Service, Massachusetts General Hospital – Harvard Medical School, Boston, USA
,
Michiel E R Bongersb Department of Orthopedic Surgery, Orthopedic Oncology Service, Massachusetts General Hospital – Harvard Medical School, Boston, USA
,
F Cumhur Onera Department of Orthopedic Surgery, University Medical Center Utrecht – Utrecht University, Utrecht, The Netherlands
,
Jorrit-Jan Verlaana Department of Orthopedic Surgery, University Medical Center Utrecht – Utrecht University, Utrecht, The Netherlands
&
Joseph H Schwabb Department of Orthopedic Surgery, Orthopedic Oncology Service, Massachusetts General Hospital – Harvard Medical School, Boston, USA
 show all
Pages 526-531 | Received 15 Nov 2020, Accepted 14 Apr 2021, Published online: 10 Jun 2021

  • AAOS Department of Clinical Quality and Value. Orthopaedic Practice in the US 2018. 2019 (January): 1–68.
  • Bini S A. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty 2018; 33(8): 2358–61.
  • Boulesteix A L, Stierle V, Hapfelmeier A. Publication bias in methodological computational research. Cancer Inform 2015; 14(Suppl. 5): 11–19.
  • Bouwmeester W, Zuithoff N P A, Mallett S, Geerlings M I, Vergouwe Y, Steyerberg E W, Altman D G, Moons K G M. Reporting and methods in clinical prediction research: a systematic review. PLoS Med 2012; 9(5): e1001221.
  • Bryan K. Policies for reducing delayed discharge from hospital. Br Med Bull 2010; 95(1): 33–46.
  • Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol 2018; 6: 75.
  • Collins G S, Reitsma J B, Altman D G, Moons K G M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Eur Urol 2015; 67(6): 1142–51.
  • Heus P, Damen J A A G, Pajouheshnia R, Scholten R J P M, Reitsma J B, Collins G S, Altman D G, Moons K G M, Hooft L. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 2018; 16(1): 120.
  • Hopfield J J. Artificial neural networks. IEEE Circuits Devices 1988; 4(5): 3–10.
  • Janssen S J, van der Heijden A S, van Dijke M, Ready J E, Raskin K A, Ferrone M L, Hornicek F J, Schwab J H. 2015 Marshall Urist Young Investigator Award: Prognostication in patients with long bone metastases: does a boosting algorithm improve survival estimates? Clin Orthop Relat Res 2015; 473(10): 3112–21.
  • Janssen D M C, van Kuijk S M J, D’Aumerie B B, Willems P C. External validation of a prediction model for surgical site infection after thoracolumbar spine surgery in a Western European cohort. J Orthop Surg Res 2018; 13(1): 114.
  • Jayakumar P, Moore M L G, Bozic K J. Value-based healthcare: can artificial intelligence provide value in orthopaedic surgery? Clin Orthop Relat Res 2019; 477(8): 1777–80.
  • Johnson W C, Seifi A. Trends of the neurosurgical economy in the United States. J Clin Neurosci 2018; 53(2018): 20–6.
  • Langerhuizen D W G, Janssen S J, Mallee W H, Van Den Bekerom M P J, Ring D, Kerkhoffs G M M J, Jaarsma R L, Doornberg J N. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clin Orthop Relat Res 2019; 477(11): 2482–91.
  • OECD 2019. Health at a Glance 2019. Available at: https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2019_4dd50c09-en.
  • Ou L, Chen J, Young L, Santiano N, Baramy L-S, Hillman K. Effective discharge planning: timely assignment of an estimated date of discharge. Aust Heal Rev 2011; 35(3): 357.
  • Papanicolas I, Woskie L R, Jha A K. Health care spending in the United States and other high-income countries. JAMA 2018; 319(10): 1024–39.
  • Pereira N R P, Janssen S J, Van Dijk E, Harris M B, Hornicek F J, Ferrone M L, Schwab J H. Development of a prognostic survival algorithm for patients with metastatic spine disease. J Bone Joint Surg Am 2016; 98(21): 1767–76.
  • Quinn R H, Randall R L, Benevenia J, Berven S H, Raskin K A. Contemporary management of metastatic bone disease: tips and tools of the trade for general practitioners. Instr Course Lect 2014; 63: 431–41.
  • Rolston J D, Han S J, Chang E F. Systemic inaccuracies in the National Surgical Quality Improvement Program database: implications for accuracy and validity for neurosurgery outcomes research. J Clin Neurosci 2017; 37(2017): 44–7.
  • Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks 2015: 85–117.
  • Schwierz C. Cost-containment in the European Union 2016. https://ec.europa.eu/info/publications/economy-finance/cost-containment-policies-hospital-expenditure-european-union_en
  • Senders J T, Staples P C, Karhade A V, Zaki M M, Gormley W B, Broekman M L D, Smith T R, Arnaout O. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg 2018; 109: 476–486.e1.
  • Shah A A, Ogink P T, Nelson S B, Harris M B, Schwab J H. Nonoperative management of spinal epidural abscess: development of a predictive algorithm for failure. J Bone Joint Surg Am 2018; 100(7): 546–55.
  • Shrank W H, Rogstad T L, Parekh N. Waste in the US health care system: estimated costs and potential for savings. JAMA 2019; 322(15): 1501–9.
  • Slim K, Nini E, Forestier D, Kwiatkowski F, Panis Y, Chipponi J. Methodological index for non-randomized studies (Minors): development and validation of a new instrument. ANZ J Surg 2003; 73(9): 712–16.
  • Staartjes V E, Stumpo V, Kernbach J M, Klukowska A M, Gadjradj P S, Schröder M L, Veeravagu A, Stienen M N, van Niftrik C H B, Serra C, Regli L. Machine learning in neurosurgery: a global survey. Acta Neurochir (Wien) 2020; 162(12): 3081–91.
  • Van Calster B, Vickers A J. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Mak 2015; 35(2): 162–9.
  • Van Calster B, McLernon D J, Van Smeden M, Wynants L, Steyerberg E W, Bossuyt P, Collins G S, MacAskill P, McLernon D J, Moons K G M, Steyerberg E W, Van Calster B, Van Smeden M, Vickers A J. Calibration: the Achilles heel of predictive analytics. BMC Med 2019; 17(1): 1–7.
  • Wan H, Zhang L, Witz S, Musselman K J, Yi F, Mullen C J, Benneyan J C, Zayas-Castro J L, Rico F, Cure L N, Martinez D A. A literature review of preventable hospital readmissions: [receding the Readmissions Reduction Act. IIE Trans Healthc Syst Eng 2016; 6(4): 193–211.

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