Background: Robotic-assisted total knee arthroplasty (RA-TKA) is becoming more and more popular as a treatment option for advanced knee diseases due to its potential to reduce operator-induced errors. However, the development of accurate prediction models for postoperative outcomes is challenging. This study aimed to develop a nomogram model to predict the likelihood of achieving a beneficial functional outcome. The beneficial outcome is defined as a postoperative improvement of the functional Knee Society Score (fKSS) of more than 10 points, 3 months after RA-TKA by early collection and analysis of possible predictors. Methods: This is a retrospective study on 171 patients who underwent unilateral RA-TKA at our hospital. The collected data included demographic information, preoperative imaging data, surgical data, and preoperative and postoperative scale scores. Participants were randomly divided into a training set (N=120) and a test set (N=51). Univariate and multivariate logistic regression analyses were employed to screen for relevant factors. Variance inflation factor was used to investigate for variable collinearity. The accuracy and stability of the models were evaluated using calibration curves with the Hosmer–Lemeshow goodness-of-fit test, consistency index and receiver operating characteristic curves. Results: Predictors of the nomogram included preoperative hip-knee-ankle angle deviation, preoperative 10-cm Visual Analogue Scale score, preoperative fKSS score and preoperative range of motion. Collinearity analysis with demonstrated no collinearity among the variables. The consistency index values for the training and test sets were 0.908 and 0.902, respectively. Finally, the area under the receiver operating characteristic curve was 0.908 (95% CI 0.846–0.971) in the training set and 0.902 (95% CI 0.806–0.998) in the test set. Conclusion: A nomogram model was designed hereby aiming to predict the functional outcome 3 months after RA-TKA in patients. Rigorous validation showed that the model is robust and reliable. The identified key predictors include preoperative hip-knee-ankle angle deviation, preoperative visual analogue scale score, preoperative fKSS score, and preoperative range of motion. These findings have major implications for improving therapeutic interventions and informing clinical decision-making in patients undergoing RA-TKA.

Objective The purpose of the present study was to determine the learning curve for a novel seven-axis robot-assisted (RA) total knee arthroplasty (TKA) system and to explore whether it could provide superior short-term clinical and radiological outcomes compared with conventional surgery. Methods In the present retrospective study, 90 patients who underwent RA-TKA were included in robot-assisted system (RAS) group and 90 patients who underwent conventional TKA were included in the conventional group. The duration of surgery and robot-related complications were recorded to evaluate the learning curve through cumulative sum and risk-adjusted cumulative sum methods. The demographic data, preoperative clinical data, preoperative imaging data, duration of surgery, alignment of the prosthesis, lower limb force line alignment, Knee Society score, 10-cm visual analog scale pain score and range of motion were compared between the RAS and conventional groups. In addition, the proficiency group was compared with the conventional group using propensity score matching. Results RA-TKA was associated with a learning curve of 20 cases for the duration of surgery. There was no significant difference in indicators representing the accuracy of the prosthetic installation between the learning and proficiency phases in RA-TKA group patients. A total of 49 patients in the proficiency group were matched with 49 patients from the conventional group. The number of postoperative hip–knee–ankle (HKA) angle, component femoral coronal angle (CFCA), component tibial coronal angle (CTCA), and sagittal tibial component angle (STCA) outliers in the proficiency phase was lower than that in the conventional group, while deviations of the HKA angle, CFCA, CTCA, and STCA in the proficiency phase were significantly lower than those in the conventional group (P < 0.05). Conclusion In summary, from the learning curve data, 20 cases are required for a surgeon using a novel seven-axis RA-TKA system to enter the proficiency phase. In the proficiency group, compared with the conventional group using propensity score matching, the RAS was found to be superior to the conventional group in prosthesis and lower limb alignment.

Robot-assisted total knee arthroplasty (RA-TKA) requires a lot of training from surgeons to master it. The purpose of the present study was to determine the learning curve for a novel seven-axis RA-TKA system, and to explore whether it could provide superior short-term clinical and radiological outcomes compared with conventional surgery. In the present retrospective study, 180 patients from our hospital with primary unilateral TKA were included, from January 2021 to June 2022. Of these, 90 patients underwent RA-TKA and were included in robot-assisted system (RAS) group, while the remaining 90 patients underwent conventional TKA and were included in the conventional group. The learning curve for the RA-TKA system was evaluated by cumulative sum (CUSUM) and risk-adjusted cumulative sum (RA-CUSUM) methods. Depending on the learning curve data, the RAS group patients were categorized as either in a learning or proficiency group. In addition, the proficiency group was compared with the conventional group using propensity score matching. There was no significant difference in postoperative Hip–Knee–Ankle (HKA) angle, or deviations in the postoperative HKA angle, component tibial coronal angle (CTCA), component femoral coronal angle (CFCA), sagittal tibial component angle (STCA), or sagittal femoral component angle (SFCA) between the learning and proficiency phases in RA-TKA group patients. A total of 49 patients in the proficiency group were matched with 49 patients from the conventional group. The indicators representing the accuracy of the prosthetic installation differ between the proficiency group and conventional group. (P < 0.05). From the learning curve data, 20 cases are required for a surgeon using a novel seven-axis RA-TKA system to enter the proficiency phase. In the proficiency group, compared with the conventional group using propensity score matching, the RAS was found to be superior to the conventional group in prosthesis and lower limb alignment.

Background Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease that is characterized by the interruption of blood supply to the femoral head. This leads to ischemia of the internal tissues, subchondral bone fractures, necrosis, and ultimately, the collapse of the weight-bearing portion of the femoral head, resulting in severe functional impairment, pain, and even disability of the hip joint. Currently, available animal models of ONFH are limited in their ability to accurately replicate the natural progression of the disease. Therefore, there is a need for the development of a new animal model that can better simulate the localized pressure on the human femoral head to facilitate research related to ONFH.Method In this study, we have developed a novel method for modeling ONFH that incorporates stress factors into the modeling process using 3D printing technology and principles of biomechanics. 36 animals were randomly assigned to six groups and received either a novel modeling technique or traditional hormone induction. Following an 8-week treatment period, Micro CT scans and histological evaluations were conducted to assess tissue outcomes.Results The new model effectively replicates the pathological features of ONFH, including femoral head collapse, with a large number of empty bone lacunae observed, cartilage defects, and subchondral bone fractures in the subchondral bone region. Furthermore, the new model shows the ability to simulate the progression of the disease, making it a valuable tool for research in this field.Conclusion In conclusion, our study provides evidence that the new ONFH model is a useful tool for simulating the disease and can contribute to the development of better treatment strategies for this debilitating condition. It holds great promise for advancing our understanding of the pathogenesis of ONFH and the potential therapeutic interventions for this challenging clinical problem.