TY - JOUR
T1 - A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma
AU - Bulstra, Anne Eva J.
AU - Machine Learning Consortium
AU - Buijze, Geert A.
AU - Cohen, Abigail
AU - Colaris, Joost W.
AU - Court-Brown, Charles M.
AU - Doornberg, Job N.
AU - Duckworth, Andrew D.
AU - Goslings, J. Carel
AU - Gray, Alasdair
AU - Hendrickx, Laurent A. M.
AU - Jaarsma, Ruurd L.
AU - Mallee, Wouter H.
AU - Mulders, Marjolein A. M.
AU - McQueen, Margaret M.
AU - Moran, Matthew
AU - Obdeijn, Miryam C.
AU - Kerkhoffs, Gino M. M. J.
AU - Ring, David
AU - Schep, Niels W. L.
AU - Walenkamp, Monique M. J.
PY - 2022/8
Y1 - 2022/8
N2 - Purpose: To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate advanced imaging in high-risk patients. Methods: Two prospective cohorts including 422 patients with radial wrist pain following wrist trauma were combined. There were 117 scaphoid fractures (28%) confirmed on computed tomography, magnetic resonance imaging, or radiographs. Eighteen fractures (15%) were occult. Predictors of a scaphoid fracture were identified among demographics, mechanism of injury and examination maneuvers. Five ML-algorithms were trained in calculating scaphoid fracture probability. ML-algorithms were assessed on ability to discriminate between patients with and without a fracture (area under the receiver operating characteristic curve), agreement between observed and predicted probabilities (calibration), and overall performance (Brier score). The best performing ML-algorithm was incorporated into a probability calculator. A decision rule was proposed to initiate advanced imaging among patients with negative radiographs. Results: Pain over the scaphoid on ulnar deviation, sex, age, and mechanism of injury were most strongly associated with a true scaphoid fracture. The best performing ML-algorithm yielded an area under the receiver operating characteristic curve, calibration slope, intercept, and Brier score of 0.77, 0.84, −0.01 and 0.159, respectively. The ML-derived decision rule proposes to initiate advanced imaging in patients with radial-sided wrist pain, negative radiographs, and a fracture probability of ≥10%. When applied to our cohort, this would yield 100% sensitivity, 38% specificity, and would have reduced the number of patients undergoing advanced imaging by 36% without missing a fracture. Conclusions: The ML-algorithm accurately calculated scaphoid fracture probability based on scaphoid pain on ulnar deviation, sex, age, and mechanism of injury. The ML-decision rule may reduce the number of patients undergoing advanced imaging by a third with a small risk of missing a fracture. External validation is required before implementation. Type of study/level of evidence: Diagnostic II.
AB - Purpose: To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate advanced imaging in high-risk patients. Methods: Two prospective cohorts including 422 patients with radial wrist pain following wrist trauma were combined. There were 117 scaphoid fractures (28%) confirmed on computed tomography, magnetic resonance imaging, or radiographs. Eighteen fractures (15%) were occult. Predictors of a scaphoid fracture were identified among demographics, mechanism of injury and examination maneuvers. Five ML-algorithms were trained in calculating scaphoid fracture probability. ML-algorithms were assessed on ability to discriminate between patients with and without a fracture (area under the receiver operating characteristic curve), agreement between observed and predicted probabilities (calibration), and overall performance (Brier score). The best performing ML-algorithm was incorporated into a probability calculator. A decision rule was proposed to initiate advanced imaging among patients with negative radiographs. Results: Pain over the scaphoid on ulnar deviation, sex, age, and mechanism of injury were most strongly associated with a true scaphoid fracture. The best performing ML-algorithm yielded an area under the receiver operating characteristic curve, calibration slope, intercept, and Brier score of 0.77, 0.84, −0.01 and 0.159, respectively. The ML-derived decision rule proposes to initiate advanced imaging in patients with radial-sided wrist pain, negative radiographs, and a fracture probability of ≥10%. When applied to our cohort, this would yield 100% sensitivity, 38% specificity, and would have reduced the number of patients undergoing advanced imaging by 36% without missing a fracture. Conclusions: The ML-algorithm accurately calculated scaphoid fracture probability based on scaphoid pain on ulnar deviation, sex, age, and mechanism of injury. The ML-decision rule may reduce the number of patients undergoing advanced imaging by a third with a small risk of missing a fracture. External validation is required before implementation. Type of study/level of evidence: Diagnostic II.
KW - Algorithm
KW - decision rule
KW - fracture
KW - machine learning
KW - scaphoid
UR - http://www.scopus.com/inward/record.url?scp=85135596209&partnerID=8YFLogxK
U2 - 10.1016/j.jhsa.2022.02.023
DO - 10.1016/j.jhsa.2022.02.023
M3 - Article
C2 - 35667955
AN - SCOPUS:85135596209
SN - 0363-5023
VL - 47
SP - 709
EP - 718
JO - Journal of Hand Surgery
JF - Journal of Hand Surgery
IS - 8
ER -