A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide "Malleolus First" Fixation.

Autor:	Hendrickx LAM; Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.; Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands., Sobol GL; Department of Orthopaedic Surgery, Rutgers New Jersey Medical School, Newark, NJ., Langerhuizen DWG; Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.; Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands., Bulstra AEJ; Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.; Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands., Hreha J; Department of Orthopaedic Surgery, Rutgers New Jersey Medical School, Newark, NJ., Sprague S; Department of Surgery, McMaster University, Hamilton, ON, Canada; and., Sirkin MS; Department of Orthopaedic Surgery, Rutgers New Jersey Medical School, Newark, NJ., Ring D; Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, TX., Kerkhoffs GMMJ; Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands., Jaarsma RL; Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia., Doornberg JN; Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.; Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Journal of orthopaedic trauma [J Orthop Trauma] 2020 Mar; Vol. 34 (3), pp. 131-138.
DOI:	10.1097/BOT.0000000000001663
Abstrakt:	Objectives: To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF. Methods: Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs: (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool. Results: Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.02, -0.06, and 0.106, respectively. This prediction model was deployed as an open-access online prediction tool. Conclusion: A ML-based prediction model accurately predicted the probability of a (occult) PMF in patients with a TSF based on patient- and fracture-specific characteristics. This prediction model can guide surgeons in their diagnostic workup and preoperative planning. Further research is required to externally validate the model before implementation in clinical practice. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Databáze:	MEDLINE
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