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Bone & Joint Open
Vol. 4, Issue 6 | Pages 399 - 407
1 Jun 2023
Yeramosu T Ahmad W Satpathy J Farrar JM Golladay GJ Patel NK

Aims

To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA.

Methods

Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.


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.


The Journal of Bone & Joint Surgery British Volume
Vol. 91-B, Issue 12 | Pages 1575 - 1578
1 Dec 2009
Jaiswal PK Macmull S Bentley G Carrington RWJ Skinner JA Briggs TWR

Smoking is known to have an adverse effect on wound healing and musculoskeletal conditions. This case-controlled study looked at whether smoking has a deleterious effect in the outcome of autologous chondrocyte implantation for the treatment of full thickness chondral defects of the knee.

The mean Modified Cincinatti Knee score was statistically significantly lower in smokers (n = 48) than in non-smokers (n = 66) both before and after surgery (p < 0.05). Smokers experienced significantly less improvement in the knee score two years after surgery (p < 0.05). Graft failures were only seen in smokers (p = 0.016). There was a strong negative correlation between the number of cigarettes smoked and the outcome following surgery (Pearson’s correlation coefficient −0.65, p = 0.004).

These results suggest that patients who smoke have worse pre-operative function and obtain less benefit from this procedure than non-smokers. The counselling of patients undergoing autologous chondrocyte implantation should include smoking, not only as a general cardiopulmonary risk but also because poorer results can be expected in smokers following this procedure.