Predicting subjective failure of ACL reconstruction: a machine learning analysis of the Norwegian Knee Ligament Register and patient reported outcomes

Martin, R. Kyle; Wastvedt, Solvejg; Pareek, Ayoosh; Persson, Andreas; Visnes, Håvard; Fenstad, Anne Marie; Moatshe, Gilbert; Wolfson, Julian; Engebretsen, Lars

doi:10.1016/j.jisako.2021.12.005

Cited by 18 publications

(13 citation statements)

References 34 publications

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“…The GAM model achieved the highest AUC (.68; 95%, CI: .64-.71) and was used to create an in-clinic, patient specific calculator to predict subjective failure of ACL reconstruction. 22 The product of this study was an ML model that adequately predicted PROs after ACL reconstruction to better inform patients about individual expectations prior to surgery.…”

Section: Applications In Surgerymentioning

confidence: 99%

“…21 While models have been developed to predict PROs based on objective data, ML algorithms have also been developed to predict outcomes based on PRO data. Martin et al 22 utilized ML to predict subjective failure of anterior cruciate ligament (ACL) reconstruction. The model was developed to identify risk factors based on PROs that are associated with poor outcomes after ACL reconstruction.…”

Section: Orthopedic Surgerymentioning

confidence: 99%

“…The model was developed to identify risk factors based on PROs that are associated with poor outcomes after ACL reconstruction. 22 The ML model was trained on the Norwegian Knee Ligament Register (NKLR) to help develop a clinically useful tool for identifying patients at risk of subjective failure of ACL reconstruction. The 2-year follow-up questionnaire was completed by 11 630 patients, of which 22% reported subjective failure.…”

Section: Orthopedic Surgerymentioning

confidence: 99%

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Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Hassan

Biaggi-Ondina

Rajesh

et al. 2022

The American Surgeon

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Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.

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Section: Applications In Surgerymentioning

confidence: 99%

Section: Orthopedic Surgerymentioning

confidence: 99%

Section: Orthopedic Surgerymentioning

confidence: 99%

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Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Hassan

Biaggi-Ondina

Rajesh

et al. 2022

The American Surgeon

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“…6 Another interesting observation was that preoperative PRO scores were significant predictors of postoperative subjective outcome, consistent with our recent ML evaluation of subjective failure after anterior cruciate ligament reconstruction. 7 This highlights the importance of collecting preoperative PRO from our patients for more than purely academic purposes. It may also suggest there is something about how people answer the surveys that influences their subjective outcome rather than the PRO itself being a true marker of success/failure, a confounding variable.…”

mentioning

confidence: 97%

Editorial Commentary: Machine Learning in Medicine Requires Clinician Input, Faces Barriers, and High-Quality Evidence Is Required to Demonstrate Improved Patient Outcomes

Pareek

Martin

2022

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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“…Clinical tools based on machine learning analysis now exist for outcome prediction after anterior cruciate ligament reconstruction (ACLR) including revision surgery 30 and inferior patientreported outcomes. 31 These models were developed from analyses of the Norwegian Knee Ligament Register (NKLR), and the revision prediction model has also been externally validated using the Danish Knee Ligament Reconstruction Registry (DKRR). 32 The accurate prediction of outcomes after ACLR holds value for both the patient and surgeon.…”

mentioning

confidence: 99%

Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction

et al. 2023

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Background: Clinical tools based on machine learning analysis now exist for outcome prediction after primary anterior cruciate ligament reconstruction (ACLR). Relying partly on data volume, the general principle is that more data may lead to improved model accuracy. Purpose/Hypothesis: The purpose was to apply machine learning to a combined data set from the Norwegian and Danish knee ligament registers (NKLR and DKRR, respectively), with the aim of producing an algorithm that can predict revision surgery with improved accuracy relative to a previously published model developed using only the NKLR. The hypothesis was that the additional patient data would result in an algorithm that is more accurate. Study Design: Cohort study; Level of evidence, 3. Methods: Machine learning analysis was performed on combined data from the NKLR and DKRR. The primary outcome was the probability of revision ACLR within 1, 2, and 5 years. Data were split randomly into training sets (75%) and test sets (25%). There were 4 machine learning models examined: Cox lasso, random survival forest, gradient boosting, and super learner. Concordance and calibration were calculated for all 4 models. Results: The data set included 62,955 patients in which 5% underwent a revision surgical procedure with a mean follow-up of 7.6 ± 4.5 years. The 3 nonparametric models (random survival forest, gradient boosting, and super learner) performed best, demonstrating moderate concordance (0.67 [95% CI, 0.64-0.70]), and were well calibrated at 1 and 2 years. Model performance was similar to that of the previously published model (NKLR-only model: concordance, 0.67-0.69; well calibrated). Conclusion: Machine learning analysis of the combined NKLR and DKRR enabled prediction of the revision ACLR risk with moderate accuracy. However, the resulting algorithms were less user-friendly and did not demonstrate superior accuracy in comparison with the previously developed model based on patients from the NKLR alone, despite the analysis of nearly 63,000 patients. This ceiling effect suggests that simply adding more patients to current national knee ligament registers is unlikely to improve predictive capability and may prompt future changes to increase variable inclusion.

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Predicting subjective failure of ACL reconstruction: a machine learning analysis of the Norwegian Knee Ligament Register and patient reported outcomes

Cited by 18 publications

References 34 publications

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Editorial Commentary: Machine Learning in Medicine Requires Clinician Input, Faces Barriers, and High-Quality Evidence Is Required to Demonstrate Improved Patient Outcomes

Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction

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