Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables

Wei, Chapman; Wang, Kevin Y.; Gu, Alex; Fassihi, Safa C.; Kahlenberg, Cynthia A.; Malahias, Michael‐Alexander; Liu, Jiabin; Thakkar, Savyasachi C.; Valle, Alejandro González Della; Sculco, Peter K.

doi:10.1302/0301-620x.103b8.bjj-2020-1013.r2

Cited by 20 publications

(27 citation statements)

References 59 publications

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“…Recently, more ML techniques have been used in retrospective database studies to develop possible tools for preoperative patient selection for outpatient TKA and THA [28,29,30] Wei et al. developed an ANN model to predict outpatient vs inpatient status after TKA, which demonstrated good predictive and discriminative ability (AUC >0.80) [ 32 ]. Zhong et al.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty

et al. 2022

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Background: Patient selection for outpatient total joint arthroplasty (TJA) is important for optimizing patient outcomes. This study develops machine learning models that may aid in patient selection for outpatient TJA based on medical comorbidities and demographic factors. Methods: This study queried elective total knee arthroplasty (TKA) and total hip arthroplasty (THA) cases during 2010-2018 in the American College of Surgeons National Surgical Quality Improvement Program. Artificial neural network models predicted same-day discharge and length of stay (LOS) fewer than 2 days (short LOS). Multiple linear and logistic regression analyses were used to identify variables significantly associated with predicted outcomes. Results: A total of 284,731 TKA cases and 153,053 THA cases met inclusion criteria. For TKA, prediction of short LOS had an area under the receiver operating characteristic curve (AUC) of 0.767 and accuracy of 84.1%; prediction of same-day discharge had an AUC of 0.802 and accuracy of 89.2%. For THA, prediction of short LOS had an AUC of 0.757 and accuracy of 70.6%; prediction of same-day discharge had an AUC of 0.814 and accuracy of 78.8%. Conclusion: This study developed machine learning models for aiding patient selection for outpatient TJA, through accurately predicting short LOS or outpatient vs inpatient cases. As outpatient TJA expands, it will be important to optimize preoperative patient selection and effectively screen surgical candidates from a broader patient population. Incorporating models such as these into electronic medical records could aid in decision-making and resource planning in real time.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty

et al. 2022

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“…In hopes of improving performance in the future input features can be added or substituted, or one could even change the 2-day LOS boundary. However, the use of LOS > 2 days as primary outcome may limit prediction based only on preoperative characteristics, as LOS has been found to correlate strongly with peri- and postoperative factors such as operating time and adverse events, which naturally cannot be included in this preoperative machine-learning algorithm ( 14 , 15 ). Also, other preoperative patient-related factors such as patient expectations and cognitive appraisal, which have been shown to influence 3-month outcome scores, could potentially affect LOS ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model

Johannesdottir

Kehlet

Petersen

et al. 2022

ActaO

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Background and purpose — Prediction of postoperative outcomes and length of hospital stay (LOS) of patients is vital for allocation of healthcare resources. We investigated the performance of prediction models based on machinelearning algorithms compared with a previous risk stratification model using traditional multiple logistic regression, for predicting the risk of a LOS of > 2 days after fast-track total hip and knee replacement. Patients and methods — 3 different machine learning classifiers were trained on data from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database (LCDB) collected from 9,512 patients between 2016 and 2017. The chosen classifiers were a random forest classifier (RF), a support vector machine classifier with a polynomial kernel (SVM), and a multinomial Naïve-Bayes classifier (NB). Results — Comparing performance measures of the classifiers with the traditional model revealed that all the models had a similar performance in terms of F1 score, accuracy, sensitivity, specificity, area under the receiver operating curve (AUC), and area under the precision-recall curve (AUPRC). A feature importance analysis of the RF classifier found hospital, age, use of walking aid, living alone, and joint operated on to be the most relevant input features. None of the classifiers reached a clinically relevant performance with the input data from the LCDB. Interpretation — Despite the promising prospects of machine-learning practices for disease and risk prediction, none of the machine learning models tested outperformed the traditional multiple regression model in predicting which patients in this cohort had a LOS > 2 days.

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“…Artificial neural network prediction of same-day discharge following primary TKA based on preoperative and intraoperative variables In 2021, Wei et al used an ANN model to determine the preoperative and perioperative variables that could predict same-day discharge for TKA patients (21). Data for their study were collected from the National Surgery Quality Improvement Program database from 2018.…”

Section: Validity In Making Predictionsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) * Detection of perioperative factors that can predict discharge on the same day of surgery: preoperative sodium level, INR, BMI, age, type of anesthesia, operative time, dyspnea status, functional status, race, anaemia status, and COPD (21). Convolutional neural networks (CNNs)…”

Section: System Used Current Uses Of Ai (Reference)mentioning

confidence: 99%

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The current role of the virtual elements of artificial intelligence in total knee arthroplasty

Rodríguez-Merchán

2022

EFORT Open Reviews

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The current applications of the virtual elements of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in total knee arthroplasty (TKA) are diverse. ML can predict the length of stay (LOS) and costs before primary TKA, the risk of transfusion after primary TKA, postoperative dissatisfaction after TKA, the size of TKA components, and poorest outcomes. The prediction of distinct results with ML models applying specific data is already possible; nevertheless, the prediction of more complex results is still imprecise. Remote patient monitoring systems offer the ability to more completely assess the individuals experiencing TKA in terms of mobility and rehabilitation compliance. DL can accurately identify the presence of TKA, distinguish between specific arthroplasty designs, and identify and classify knee osteoarthritis as accurately as an orthopedic surgeon. DL allows for the detection of prosthetic loosening from radiographs. Regarding the architectures associated with DL, artificial neural networks (ANNs) and convolutional neural networks (CNNs), ANNs can predict LOS, inpatient charges, and discharge disposition prior to primary TKA and CNNs allow for differentiation between different implant types with near-perfect accuracy.

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Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables

Cited by 20 publications

References 59 publications

Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty

Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty

Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model

The current role of the virtual elements of artificial intelligence in total knee arthroplasty

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