Evidence-Based Machine Learning Algorithm to Predict Failure Following Cartilage Preservation Procedures in the Knee...Specialty Day 2023, March 11, 2023, Venetian Convention & Expo Center in Las Vegas, Nevada.
Objectives: To develop machine learning algorithms to predict failure of surgical procedures that address cartilage defects of the knee and detect the most valuable variables associated with failure. Methods: A single institution prospectively collected database of cartilage procedures was queried f...
| Published in: | Orthopaedic Journal of Sports Medicine Vol. 11; pp. 20 - 25 |
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| Main Authors: | , , , , , , , , , |
| Format: | abstract proceedings research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2023 Supplement
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164537727&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164537727 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23259671 FUGT jtl: Orthopaedic Journal of Sports Medicine issn: 23259671 maglogo: Y pubinfo: dt: 2023 Supplement vid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 164537727 164537727 164537727 10.1177/2325967123S00019 164537727 ppf: 20 ppct: 5 formats: tig: atl: Evidence-Based Machine Learning Algorithm to Predict Failure Following Cartilage Preservation Procedures in the Knee...Specialty Day 2023, March 11, 2023, Venetian Convention & Expo Center in Las Vegas, Nevada. aug: au: Gilat, Ron Gilat, Ben Patel, Sumit Wagner, Kyle Haunschild, Eric Tauro, Tracy Kaiser, Joshua Chahla, Jorge Yanke, Adam Cole, Brian affil: Midwest Orthopaedics at Rush sug: subj: Machine Learning Algorithms Cartilage Diseases Surgery Knee Surgery Tissue Preservation Prediction Models Treatment Failure Congresses and Conferences Nevada Nevada ab: Objectives: To develop machine learning algorithms to predict failure of surgical procedures that address cartilage defects of the knee and detect the most valuable variables associated with failure. Methods: A single institution prospectively collected database of cartilage procedures was queried for procedures performed between 2000 and 2018. Failure was defined as revision cartilage surgery and/or knee arthroplasty. One hundred and one preoperative and intraoperative features were evaluated as potential predictors. The dataset was randomly divided into training (70%) and independent testing (30%) sets. Four machine learning algorithms were trained and internally validated. Algorithm performance was assessed using area under curve (AUC) and the Brier score. Local Interpretable Model-agnostic Explanations (LIME) was utilized to assess the optimized algorithm fidelity. Results: A total of 1091 patients who underwent surgical procedures addressing cartilage defects in the knee with a minimum of 2-years of follow-up were included. The mean follow-up was 3.5 ± 2.8 years. The mean age was 40.5 ± 15 years. There were 205 (18.8%) patients who failed at final follow-up. The Random Forest algorithm was found to be the best performing algorithm, with an AUC of 0.765 and a Brier score of 0.135. The 10 most important features for predicting failure following surgical procedures addressing cartilage defects of the knee were: symptom duration, age, body mass index (BMI), lesion grade, total lesion area (sum of all lesion areas), number of previous surgeries, number of lesions in the knee, gender, athletic level, and traumatic etiology. LIME analysis allowed for assessment of the optimized algorithm fidelity, as well as provided a patient-specific comparison for the risk of failure of an individual patient being assigned various types of cartilage procedures. Conclusions: Machine learning algorithms were accurate in predicting the risk of failure following cartilage procedures of the knee, with the most important features being symptom duration, age, BMI, lesion grade, and total lesion area. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. Integrated human and machine learning decision-making may improve patient selection and bring about the new era of patient-tailored evidence-based clinical care. pubtype: Academic Journal doctype: abstract proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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