Purpose
To (1) develop and validate a machine learning algorithm to predict clinically significant
functional improvements after hip arthroscopy for femoroacetabular impingement syndrome
and to (2) develop a digital application capable of providing patients with individual
risk profiles to determine their propensity to gain clinically significant improvements
in function.
Methods
A retrospective review of consecutive hip arthroscopy patients who underwent cam/pincer
correction, labral preservation, and capsular closure between January 2012 and 2017
from 1 large academic and 3 community hospitals operated on by a single high-volume
hip arthroscopist was performed. The primary outcome was the minimal clinically important
difference (MCID) for the Hip Outcome Score (HOS)–Activities of Daily Living (ADL)
at 2 years postoperatively, which was calculated using a distribution-based method.
A total of 21 demographic, radiographic, and patient-reported outcome measures were
considered as potential covariates. An 80:20 random split was used to create training
and testing sets from the patient cohort. Five supervised machine learning algorithms
were developed using 3 iterations of 10-fold cross-validation on the training set
and assessed by discrimination, calibration, Brier score, and decision curve analysis
on an independent testing set of patients.
Results
A total of 818 patients with a median (interquartile range) age of 32.0 (22.0-42.0)
and 69.2% female were included, of whom 74.3% achieved the MCID for the HOS-ADL. The
best-performing algorithm was the stochastic gradient boosting model (c-statistic =
0.84, calibration intercept = 0.20, calibration slope = 0.83, and Brier score = 0.13).
Of the initial 21 candidate variables, the 8 most important features for predicting
the MCID for the HOS-ADL included in model training were body mass index, age, preoperative
HOS-ADL score, preoperative pain level, sex, Tönnis grade, symptom duration, and drug
allergies. The algorithm was subsequently transformed into a digital application using
local explanations to provide customized risk assessment: https://orthoapps.shinyapps.io/HPRG_ADL/.
Conclusions
The stochastic boosting gradient model conferred excellent predictive ability for
propensity to gain clinically significant improvements in function after hip arthroscopy.
An open-access digital application was created, which may augment shared decision-making
and allow for preoperative risk stratification. External validation of this model
is warranted to confirm the performance of these algorithms, as the generalizability
is currently unknown.
Level of Evidence
IV, Case series.
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Article Info
Publication History
Published online: January 15, 2021
Accepted:
January 3,
2021
Received:
June 3,
2020
Footnotes
See commentary on page 1498
The authors report the following potential conflicts of interest or sources of funding: B.U.N. reports other from Smith & Nephew, and personal fees from Stryker , Wright Medical, and Zimmer Biomet , outside the submitted work. S.J.N. reports other from Allosource, Arthrex , Athletico, DJ Orthopaedics, Linvtec, Miomed, Ossur, Smith & Nephew, Stryker , and Stringer, outside the submitted work. J.C. reports other from Arthrex and Smith & Nephew, outside the submitted work and is a paid consultant for Arthrex , Smith & Nephew, and ConMed Linvatec. Full ICMJE author disclosure forms are available for this article online, as supplementary material.
Identification
Copyright
© 2021 by the Arthroscopy Association of North America
