Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

Chong, Yi Sheng; Chan, Ping Keung; Chan, Vincent; Cheung, Ankie Tan; Luk, Michelle Hilda; Cheung, Man Hong; Fu, Henry; Chiu, Kwong Yuen

doi:10.1186/s42836-023-00195-2

Cited by 4 publications

(1 citation statement)

References 58 publications

(105 reference statements)

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“…Machine learning and artificial intelligence technologies are gradually finding their way into diverse areas. A systematic review with regard to clinical applications of machine learning to PJI prevention revealed its value with respect to preoperative health optimization, surgical planning, early infection diagnosis, early antibiotic use, and prediction of clinical outcomes 61 . Mehta et al 62 compared 14 pathologist-scored histology features and computer vision-quantified cell density (147 patients with osteoarthritis and 60 patients with rheumatoid arthritis) in hematoxylin and eosin-stained images of synovial tissue samples from TKA explants.…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

What’s New in Adult Reconstructive Knee Surgery

Villa,

Hosseinzadeh,

Higuera-Rueda

2023

Journal of Bone and Joint Surgery

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In the current Guest Editorial, we evaluate and summarize the most relevant findings from recent, meaningful, peer-reviewed publications in different research areas. Articles at various Levels of Evidence were included, but increased attention was paid to Level-I and award-winning reports. EconomicsConsiderable debate continues with regard to the economics of reconstructive knee surgery. An updated Medicare projection revealed that, by 2040, the annual number of revision total knee arthroplasties (TKAs) performed would be 115,147, whereas, by 2060, it would be 286,740. These values are important for understanding the future health-care resource utilization involving revision TKA, which is only expected to increase 1 . A secondary analysis of a randomized clinical trial (RCT) evaluating health-care resource utilization up to 12 months after primary TKA, among dissatisfied and satisfied patients, showed that patients dissatisfied with their return to function incurred a mean cost of $19,007.70, compared with $13,523.83 for satisfied patients (p = 0.07). Although the cost difference was not significant, its magnitude deserves attention 2 . Data from the New Zealand Joint Registry indicated that patients undergoing TKA have a mortality rate 8% higher than that of the general population 3 . Assessing mortality after primary TKA is relevant for future economic analyses.

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Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

What’s New in Adult Reconstructive Knee Surgery

Villa,

Hosseinzadeh,

Higuera-Rueda

2023

Journal of Bone and Joint Surgery

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Preoperative factors predict prolonged length of stay, serious adverse complications, and readmission following operative intervention of proximal humerus fractures: a machine learning analysis of a national database

Hornung,

Rudisill,

McCormick

et al. 2024

JSES International

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Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis

Yang,

Shin,

Shin

et al. 2024

BMC Musculoskelet Disord

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Background Although high tibial osteotomy (HTO) is an established treatment option for medial compartment osteoarthritis, predictive factors for HTO treatment success remain unclear. This study aimed to identify informative variables associated with HTO treatment success and to develop and internally validate machine learning algorithms to predict which patients will achieve HTO treatment success for medial compartmental osteoarthritis. Methods This study retrospectively reviewed patients who underwent medial opening-wedge HTO (MOWHTO) at our center between March 2010 and December 2015. The primary outcomes were a lack of conversion to total knee arthroplasty (TKA) and achievement of a minimal clinically important difference of improvement in the Knee Injury and Osteoarthritis Outcome Score (KOOS) at a minimum of five years postoperatively. Recursive feature selection was used to identify the combination of variables from an initial pool of 25 features that optimized model performance. Five machine learning algorithms (XGBoost, multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using five-fold cross-validation three times and applied to an independent test set of patients. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC). Results A total of 231 patients were included, and 200 patients (86.6%) achieved treatment success at the mean of 9 years of follow-up. A combination of seven variables optimized algorithm performance, and the following specific cutoffs increased the likelihood of MOWHTO treatment success: body mass index (BMI) ≤ 26.8 kg/m2, preoperative KOOS for pain ≤ 46.0, preoperative KOOS for quality of life ≤ 33.0, preoperative International Knee Documentation Committee score ≤ 42.0, preoperative Short-Form 36 questionnaire (SF-36) score > 42.25, three-month postoperative hip-knee-ankle angle > 1.0°, and three-month postoperative medial proximal tibial angle (MPTA) > 91.5° and ≤ 94.7°. The random forest model demonstrated the best performance (F1 score: 0.93; AUC: 0.81) and was transformed into an online application as an educational tool to demonstrate the capabilities of machine learning. Conclusions The random forest machine learning algorithm best predicted MOWHTO treatment success. Patients with a lower BMI, poor clinical status, slight valgus overcorrection, and postoperative MPTA < 94.7 more frequently achieved a greater likelihood of treatment success. Level of evidence Level III, retrospective cohort study.

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Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

Cited by 4 publications

References 58 publications

What’s New in Adult Reconstructive Knee Surgery

What’s New in Adult Reconstructive Knee Surgery

Preoperative factors predict prolonged length of stay, serious adverse complications, and readmission following operative intervention of proximal humerus fractures: a machine learning analysis of a national database

Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis

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