Machine Learning for Surgical Risk Assessment Decision Systems

Russell, Myles; Russell, Dylan; Corizzo, Roberto; Japkowicz, Nathalie

doi:10.1109/ijcnn55064.2022.9892752

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Utilizing objective clinical data that is readily accessible before or upon admission, ML algorithms can provide valuable insights to better characterize preoperative risk. [17][18][19][20][21] For instance, our analysis consistently identified preoperative hematocrit as a significant variable, which aligns with previous studies demonstrating the importance of addressing low hematocrit levels in patients undergoing reverse TSA and HA following a proximal humerus fracture to mitigate the risk of 30-day mortality. 15 This approach utilizes both subjective variables that are present in current preoperative risk classifications and objective variables such as comorbidities, functional status, and overall health, enhancing the accuracy and comprehensiveness of risk assessment.…”

Section: Auc Accuracy Sensitivity Specificity Negative Likelihood Rat...supporting

confidence: 88%

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

Dasari,

Rice,

Cabrera

et al. 2024

Int J Res Orthop

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Background: Artificial intelligence (AI) constructs and machine learning (ML) algorithms have demonstrated utility in predicting various clinical, surgical, and financial outcomes. In this study, we applied AI to shoulder hemiarthroplasty (HA) to predict various post-operative complications. Methods: The sample was queried from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) database for all shoulder HA cases from 2008-2018. Six ML algorithms-random forest classifier, gradient boosting classifier, decision tree classifier, SVM classifier-tuned model, Gaussian Naïve Bayes classifier, multi-layer perception-analyzed the sample dataset. Postoperative complications included extended length of stay, non-home discharge destination, transfusion, and any adverse event. Each ML model was compared to logistic regression (LR), and model strength was evaluated. Results: We identified a total of 1585 shoulder HA cases. Mean age, BMI, operative time, and length of stay were 66±12 years, 31±8 kg/m2, 114±61 minutes, and 2.93±6.61 days. Preop hematocrit, longer operative time, and older age were most predictive of extended length of stay. Preop hematocrit, operative time, and ASA class had the highest importance in any adverse events (AAE) prediction. ML models outperformed traditional comorbidity indices, LR, for predicting extended length of stay (79% vs. 66%), non-home discharge destination (79% vs. 65%), any adverse event (78% vs. 66%), and transfusion requirement (82% vs. 63%). Conclusions: ML algorithms predicted post-surgical outcomes of interest following shoulder HA at a higher rate to conventional LR and can assist orthopedic surgeons in decision making.

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