The role of bone mineral density and cartilage volume to predict knee cartilage degeneration.

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Testing soft tissue radiodensity parameters interplay with age and self-reported physical activity.

Aging well is directly associated to a healthy lifestyle. The focus of this paper is to relate individual wellness with medical image features. Non-linear trimodal regression analysis (NTRA) is a novel method that models the radiodensitometric distributions of x-ray computed tomography (CT) cross-sections. It generates 11 patient-specific parameters that describe the quality and quantity of muscle, fat, and connective tissues. In this research, the relationship of these 11 NTRA parameters with age, physical activity, and lifestyle is investigated in the 3,157 elderly volunteers AGES-I dataset. First, univariate statistical analyses were performed, and subjects were grouped by age and self-reported past (youth-midlife) and present (within 12 months of the survey) physical activity to ascertain which parameters were the most influential. Then, machine learning (ML) analyses were conducted to classify patients using NTRA parameters as input features for three ML algorithms. ML is also used to classify a Lifestyle index using the age groups. This classification analysis yielded robust results with the lifestyle index underlying the relevant differences of the soft tissues between age groups, especially in fat and connective tissue. Univariate statistical models suggested that NTRA parameters may be susceptible to age and differences between past and present physical activity levels. Moreover, for both age and physical activity, lean muscle parameters expressed more significant variation than fat and connective tissues.

Health technology assessment through Six Sigma Methodology to assess cemented and uncemented protheses in total hip arthroplasty.

The purpose of this study is to use Health Technology Assessment (HTA) through the Six Sigma (SS) and DMAIC (Define, Measure, Analyse, Improve, Control) problem-solving strategies for comparing cemented and uncemented prostheses in terms of the costs incurred for Total hip arthroplasty (THA) and the length of hospital stay (LOS). Multinomial logistic regression analysis for modelling the data was also performed. Quantitative parameters extracted from gait analysis, electromyography and computed tomography images were used to compare the approaches, but the analysis did not show statistical significance. The variables regarding costs were studied with the Mann-Whitney and Kruskal-Wallis tests. No statistically significant difference between cemented and uncemented prosthesis for the total cost of LOS was found, but the cost of the surgeon had an influence on the overall expenses, affecting the cemented prosthetic approach. The material costs of surgery for the uncemented prosthesis and the cost of theatre of surgery for the cemented prosthesis were the most influential. Multinomial logistic regression identified the Vastus Lateralis variable as statistically significant. The overall accuracy of the model is 93.0%. The use of SS and DMAIC cycle as tools of HTA proved that the cemented and uncemented approaches for THA have similar costs and LOSy.

Factors Predicting Mobility and the Change in Activities of Daily Living After Hip Fracture: A 1-Year Prospective Cohort Study.

OBJECTIVES: To assess the change in ambulatory ability, need for walking aids, and activities of daily living (ADL) after femoral neck, intertrochanteric, or subtrochanteric fractures and to examine the determinants of these functional outcomes. DESIGN: A prospective observational cohort study. SETTING: A multicenter study involving 1 university hospital and 2 community hospitals. PATIENTS: A consecutive cohort of 552 patients (mean age, 78.3 years; range, 50-105) who underwent surgery for a hip fracture. MAIN OUTCOME MEASURES: Ambulatory ability, need for walking aids, and ADL index, 4 and 12 months after surgery. RESULTS: At both 4 months and 1-year follow-up time points, there was a significant decrease in ambulatory ability and the ADL index score and also an increase in the need for walking aids in comparison with the prefracture status. Ambulatory ability, but not ADL, significantly recovered between the 4-month and 1-year follow-up. One year after fracture, the prefracture functional status was regained by 57% of the patients, but approximately 13% of the formerly ambulating patients were unable to walk. The prefracture status was the most important determinant of ambulatory ability, need for walking aids, and ADL. Comorbidities, a poor cognitive status, and non-weight-bearing status after surgery were also negative predictors. Neither the fracture pattern nor its specific surgical treatment was predictive of any functional outcomes. CONCLUSIONS: Regardless of the type of fracture or surgical treatment used, 57% of the patients do not regain their prefracture ambulatory ability. Recovery of ambulatory ability can occur until 1 year postoperatively. The prefracture status and cognitive level are the most important determinant of all functional outcomes. LEVEL OF EVIDENCE: Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.