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The Bone & Joint Journal
Vol. 103-B, Issue 8 | Pages 1358 - 1366
2 Aug 2021
Wei C Quan T Wang KY Gu A Fassihi SC Kahlenberg CA Malahias M Liu J Thakkar S Gonzalez Della Valle A Sculco PK

Aims. This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods. Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. Results. The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. Conclusion. Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358–1366


The Bone & Joint Journal
Vol. 103-B, Issue 6 Supple A | Pages 45 - 50
1 Jun 2021
Kerbel YE Johnson MA Barchick SR Cohen JS Stevenson KL Israelite CL Nelson CL

Aims

It has been shown that the preoperative modification of risk factors associated with obesity may reduce complications after total knee arthroplasty (TKA). However, the optimal method of doing so remains unclear. The aim of this study was to investigate whether a preoperative Risk Stratification Tool (RST) devised in our institution could reduce unexpected intensive care unit (ICU) transfers and 90-day emergency department (ED) visits, readmissions, and reoperations after TKA in obese patients.

Methods

We retrospectively reviewed 1,614 consecutive patients undergoing primary unilateral TKA. Their mean age was 65.1 years (17.9 to 87.7) and the mean BMI was 34.2 kg/m2 (SD 7.7). All patients underwent perioperative optimization and monitoring using the RST, which is a validated calculation tool that provides a recommendation for postoperative ICU care or increased nursing support. Patients were divided into three groups: non-obese (BMI < 30 kg/m2, n = 512); obese (BMI 30 kg/m2 to 39.9 kg/m2, n = 748); and morbidly obese (BMI > 40 kg/m2, n = 354). Logistic regression analysis was used to evaluate the outcomes among the groups adjusted for age, sex, smoking, and diabetes.