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 aim of this study was to compare the operating time, length of stay (LOS), adverse events and rate of re-admission for elderly patients with a fracture of the hip treated using either general or spinal anaesthesia. Patients aged ≥ 70 years who underwent surgery for a fracture of the hip between 2010 and 2012 were identified from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Of the 9842 patients who met the inclusion criteria, 7253 (73.7%) were treated with general anaesthesia and 2589 (26.3%) with spinal anaesthesia. On propensity-adjusted multivariate analysis, general anaesthesia was associated with slightly increased operating time (+5 minutes, 95% confidence interval (CI) +4 to +6, p < 0.001) and post-operative time in the operating room (+5 minutes, 95% CI +2 to +8, p < 0.001) compared with spinal anaesthesia. General anaesthesia was associated with a shorter LOS (hazard ratio (HR) 1.28, 95% CI 1.22 to 1.34, p < 0.001). Any adverse event (odds ratio (OR) 1.21, 95% CI 1.10 to 1.32, p < 0.001), thromboembolic events (OR 1.90, 95% CI 1.24 to 2.89, p = 0.003), any minor adverse event (OR 1.19, 95% CI 1.09 to 1.32, p < 0.001), and blood transfusion (OR 1.34, 95% CI 1.22 to 1.49, p < 0.001) were associated with general anaesthesia. General anaesthesia was associated with decreased rates of urinary tract infection (OR 0.73, 95% CI 0.62 to 0.87, p < 0.001). There was no clear overall advantage of one type of anaesthesia over the other, and surgeons should be aware of the specific risks and benefits associated with each type.

Cite this article: Bone Joint J 2015; 97-B:689–95.

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

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.