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BACKGROUND: Previous studies have shown the correlation between lumbosacral sagittal plane parameters and natural absorption of lumbar disc herniation. However, the lumbosacral sagittal plane parameters included lumbar lordosis angle, lumbosacral joint angle, sacral inclination angle and many other parameters. The effects of each parameter on the natural absorption of the herniated disc were different. In addition, there are few studies on the reabsorption of a specific segment of intervertebral disc herniation at present, and most of the measured data are obtained from digital radiography or CT, while the correlation between lumbosacral sagittal plane parameters measured from MRI and reabsorption after L5/S1 intervertebral disc herniation is rarely reported. OBJECTIVE: To study the corresponding changes of lumbar sagittal plane parameters after L5/S1 intervertebral disc herniation reabsorption and to screen out the lumbosacral sagittal plane parameters with the most significant changes during intervertebral disc reabsorption. METHODS: Totally 57 patients with lumbar disc herniation who had complete MRI image data were selected and met the diagnostic criteria for lumbar disc herniation and only received non-surgical treatment for reabsorption of L5/S1 protrusion segments. MRI measured the protrusion area of the maximum protrusion plane in the coronal plane, lumbosacral sagittal plane parameters [lumbar curvature index, lumbar lordosis (α), L5/S1 disc angle (β), intervertebral height measurement, lumbosacral joint angle, sacral platform angle, sacral inclination angle, and lower lumbar lordosis angle]. Besides, lumbosacral sagittal plane parameters were ranked in the importance of variables by random forest model in R software, and then significant variables were fitted with multiple linear regression. The changes between parameters before and after treatment were analyzed and compared by paired sample t-test. RESULTS AND CONCLUSION: (1) A total of 57 patients with L5/S1 lumbar disc herniation were included in this study, and the symptoms and imaging features of the patients were significantly relieved to a large extent. (2) Before treatment, there were 4 cases of grade 1, 29 cases of grade 2 and 24 cases of grade 3 according to the Classification of Michigan State University. After treatment, there were 48 cases of grade 1 and 9 cases of grade 2. (3) The random forest model suggested that intervertebral height, lumbar curve index, sacral inclination angle, and lower lumbar lordosis angle changed significantly in L5/S1 disc herniation reabsorption, and the order of their change significance was lumbar curve index > intervertebral space height > sacral inclination angle > lower lumbar lordosis angle. (4) Lumbar curve index, lumbar lordosis and sacral platform angle increased, with statistical significance (P < 0.05). There were no significant differences in disc angle, intervertebral height, lower lumbar lordosis angle, sacral inclination angle or lumbosacral joint angle (P > 0.05). (5) Lumbar curvature index was the most significant parameter of the lumbosacral sagittal plane in herniated disc reabsorption. In addition, lumbar curve index, sacral inclination angle, and lower lumbar lordosis angle are commonly used clinically to describe the change of lumbar curvature, suggesting that L5/S1 disc herniation reabsorption is correlated with the change of lumbar curvature. It is indicated that in the treatment of lumbar disc herniation, a clinical cure can be achieved by improving or restoring the disordered lumbar curvature. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence of hip fracture patients is increasing day by day. Because of their poor physical condition, these people often need to stay in hospital for a long time. However, the prolonged length of stay leads to a decrease in bed circulation rate and an increase in economic burden. At present, there are few prediction models for delayed discharge of hip fractures. This study aims to find the best model for delayed discharge of hip fractures and guide clinical decision-making. OBJECTIVE: To explore the risk factors of prolonged length of stay in patients with hip fractures, establish two different risk prediction models, obtain the best risk prediction tools, and provide a reference for clinical intervention and management METHODS: Data from 683 elderly patients with hip fractures in Tianjin Hospital from January 2019 to December 2021 were retrospectively analyzed. All patients were randomly divided into a modeling group (479 cases) and a verification group (204 cases) according to the ratio of 7:3. The 75th percentile of length of stay was taken as the cut-off point (> 28 days), and they were divided into extended hospitalization group and normal group. Single-factor and multifactor Logistic regression analysis and variable importance ranking were used to screen the best prediction model; the nomogram and random forest models were constructed. The prediction efficiency of the two models was evaluated by the receiver operating characteristic curve area, accuracy, sensitivity, specificity, positive prediction value and negative prediction value. RESULTS AND CONCLUSION: (1) Logistic regression analysis showed that bone traction, pneumonia, refolding, multiple trauma, venous thrombosis, lung infection and age-adjusted Charlson Comorbidity Index were the risk factors of prolonged length of stay of hip fracture patients. (2) The random forest model showed that age, bone traction, surgical type, age-adjusted Charlson Comorbidity Index and pneumonia were the first five indexes according to the average reduction of the Gini index, which had an important influence on the prediction of delayed discharge. (3) The area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive prediction value and negative prediction value of the Logistic regression model and random forest prediction model were 0.774(95%CI: 0.696-0.853) and 0.708(95%CI: 0.627-0.789), 60.78% and 90.85%, 80.39% and 23.53%, 50.82% and 78.09%, 86.01% and 46.15%, respectively. The results exhibited that the Logistic model had good prediction efficiency. (4) Above findings confirm that the Logistics regression model and random forest model have high predictive value for a prolonged length of stay in patients with hip fractures, which is of great significance for clinical medical staff to identify high-risk patients in time and take effective intervention measures to reduce the length of stay of patients with hip fractures. [ABSTRACT FROM AUTHOR]

BACKGROUND: Selecting key technical indicators to guide special training or competition is the key to improving competition performance. There are obvious differences in the techniques of male and female athletes and the key technical indicators of athletes of different sexes have not yet been known. OBJECTIVE: To select key technical indicators that affect the competition performance of elite speed climbing athletes, thereby providing direction and theoretical support for the scientific training of speed climbing. METHODS: The videos of speed climbing finals in the 2017-2021 World Championships, World Cup and China Climbing League were selected to obtain the competition samples of the top four male and female athletes (109 men and 117 women). Reaction time, contact time and segmental speed were collected and then random forest and XGBoost models of male and female speed climbing athletes were established using two-dimensional kinematic analysis. Accordingly, the influence of each technical indicator on the competition performance was calculated. RESULTS AND CONCLUSION: For male athletes, the root-mean-square error values of random forest and XGBoost models were 0.224 and 0.265, respectively, and r2 was 0.765 and 0.686, respectively. The key technical indicators were total contact time of the left hand, motion frequency of the right hand, total contact time of the right hand, speed of holds 3-6, motion frequency of the left foot, total contact time of the right foot, speed of holds 11-13, speed of holds 13- 18, speed of holds 8-10, total contact time of the left foot, number of total holds of the left hand and motion frequency of the right foot. For female athletes, the root-mean-square error indicator of random forest and XGBoost models were 0.066 and 0.055, respectively, and r2 were 0.846 and 0.887, respectively. The key technical indicators were total contact time of the left hand, total contact time of the right hand, motion frequency of the right hand, motion frequency of the left foot, total contact time of the left foot, speed of holds 8-10, total contact time of the right foot, and speed of holds 10-11 and 13-18. To conclude, the technical indicators of the upper limbs (contact time sum and action frequency) on speed rock climbing competition are more influential than those of the lower limbs. The number of holds has no high influence on the competition results. The key segments for winning in male and female elite athletes are holds 3-6 and 8-11 respectively. Athletes should focus on optimizing the upper limb contact time of the key segments for winning and then optimize the lower limb contact time, so as to improve the competition performance. [ABSTRACT FROM AUTHOR]

BACKGROUND: Radiomics can be used to quantify image heterogeneity. Whether radiomics can be used to screen out features such as fingerprint from MRI images of osteoporotic vertebral bodies to predict the occurrence of new fracture is worth studying. OBJECTIVE: To explore the feasibility of constructing a machine learning prediction model for thoracolumbar refracture after vertebral augmentation through combining MRI radiomics features and clinical information. METHODS: This study retrospectively collected the data of patients who were diagnosed with osteoporotic vertebral compression fracture by MRI and treated with percutaneous vertebral augmentation in Chengdu First People’s Hospital from May 2014 to April 2019. PyRadiomics was used to extract the imaging features of T1 sequences of vertebral MRI at the T11-L2 segments before percutaneous vertebral augmentation. All models were constructed in the training set, and prediction performance evaluation was performed in the validation set. Feature dimension reduction was conducted by applying least absolute shrinkage and selection operator regression. The corresponding refracture prediction models were constructed by multivariate logistic regression, random forest and adaptive lifting algorithm analysis using clinical parameters, selected features or the integrating of both. The diagnostic efficacy of the model was evaluated using the receiver operating characteristic curve. The decision analysis curve was used to compare the clinical value of each model. RESULTS AND CONCLUSION: (1) A total of 336 vertebrae were included in 135 patients, of which 67 vertebrae had refractures. 1 746 features were extracted from each vertebra, and 13 important features were obtained through dimension reduction. (2) Among the three models, area under curve of the combined model in the training set and validation set was significantly higher than that of the clinical model (P < 0.05), and the decision analysis curve also showed that the net benefit of the combined model in predicting thoracolumbar refracture was higher than that of the clinical model in most threshold intervals. (3) The results indicated that it was feasible to construct a refracture prediction model based on MRI T1 sequence imaging and clinical information, which could help to identify the vertebrae with high risk of refracture at early stage. [ABSTRACT FROM AUTHOR]

BACKGROUND: To maintain the hemodynamic stability of patients with total knee replacement, blood transfusion is necessary, but this is often accompanied by adverse reactions. Studying the risk factors of blood transfusion after total knee replacement can help determine which patients need blood transfusion, which is conducive to preoperative evaluation and clinical decision-making. OBJECTIVE: To establish a prediction model based on a machine learning algorithm and explore its predictive value in predicting the possibility of blood transfusion after total knee replacement. METHODS: The clinical data after total knee arthroplasty in the Panyu Hospital of Chinese Medicine from January 2012 to December 2019 were retrospectively analyzed, and divided the patients into a non-transfusion group and a blood transfusion group according to whether blood transfusion was performed after the operation. The data of sex, age, body mass index, preoperative hemoglobin, ASA anesthesia score, anesthesia mode, operation duration, operation type, smoking history, past medical history, and the use of insulin were compared between the two groups. The abovementioned potential influencing factors were incorporated into logistic regression, support vector machine, random forest and XGBoost algorithm to establish four kinds of prediction model, obtain the importance of predictive variables and draw receiver working curve, and test the predictive value of the model. RESULTS AND CONCLUSION: (1) We included a total of 634 samples, including 527 untransfused total knee arthroplasty patients and 107 total knee arthroplasty patients requiring transfusion. (2) Combining the four models, the top five prediction importance scores were hemoglobin, age, operation length, body mass index and operation type were the top five variables with the highest correlation. (3) The areas under the curve of logistic regression, support vector machine, random forest and XGBoost algorithm were 0.816, 0.864, 0.773 and 0.888, respectively. By comparison, the XGBoost algorithm performed best. (4) It is concluded that the machine learning model based on XGBoost algorithm can accurately predict the risk of blood transfusion in patients after total knee arthroplasty, which is conducive to preoperative evaluation and clinical decision-making. Hemoglobin, age, length of surgery, body mass index, and type of surgery may be important predictors of the risk of transfusion after total knee arthroplasty. [ABSTRACT FROM AUTHOR]