Recovery from muscle weakness by exercise and FES: lessons from Masters, active or sedentary seniors and SCI patients.

Many factors contribute to the decline of skeletal muscle that occurs as we age. This is a reality that we may combat, but not prevent because it is written into our genome. The series of records from World Master Athletes reveals that skeletal muscle power begins to decline at the age of 30 years and continues, almost linearly, to zero at the age of 110 years. Here we discuss evidence that denervation contributes to the atrophy and slowness of aged muscle. We compared muscle from lifelong active seniors to that of sedentary elderly people and found that the sportsmen have more muscle bulk and slow fiber type groupings, providing evidence that physical activity maintains slow motoneurons which reinnervate muscle fibers. Further, accelerated muscle atrophy/degeneration occurs with irreversible Conus and Cauda Equina syndrome, a spinal cord injury in which the human leg muscles may be permanently disconnected from the nervous system with complete loss of muscle fibers within 5-8 years. We used histological morphometry and Muscle Color Computed Tomography to evaluate muscle from these peculiar persons and reveal that contraction produced by home-based Functional Electrical Stimulation (h-bFES) recovers muscle size and function which is reversed if h-bFES is discontinued. FES also reverses muscle atrophy in sedentary seniors and modulates mitochondria in horse muscles. All together these observations indicate that FES modifies muscle fibers by increasing contractions per day. Thus, FES should be considered in critical care units, rehabilitation centers and nursing facilities when patients are unable or reluctant to exercise.

Cardiorespiratory Fitness Attenuates the Deleterious Effects of Sleep Apnea on Cerebral Structure and Perfusion in the Wisconsin Sleep Cohort Study.

BACKGROUND: Emerging evidence suggests that age-related changes in cerebral health may be sensitive to vascular risk modifiers, such as physical activity and sleep. OBJECTIVE: We examine whether cardiorespiratory fitness modifies the association of obstructive sleep apnea (OSA) severity with MRI-assessed measures of cerebral structure and perfusion. METHODS: Using data from a cross-sectional sample of participants (n = 129, 51% female, age range 49.6-85.3 years) in the Wisconsin Sleep Cohort study, we estimated linear models of MRI-assessed total and regional gray matter (GM) and white matter (WM) volumes, WM hyperintensity (WMH:ICV ratio), total lesion volume, and arterial spin labeling (ASL) cerebral blood flow (CBF), using an estimated measure of cardiorespiratory fitness (CRF) and OSA severity as predictors. Participants' sleep was assessed using overnight in-laboratory polysomnography, and OSA severity was measured using the apnea-hypopnea index (AHI), or the mean number of recorded apnea and hypopnea events per hour of sleep. The mean±SD time difference between PSG data collection and MRI data collection was 1.7±1.5 years (range: [0, 4.9 years]). RESULTS: OSA severity was associated with reduced total GM volume (ß=-0.064; SE = 0.023; p = 0.007), greater total WM lesion volume (interaction p = 0.023), and greater WMHs (interaction p = 0.017) in less-fit subjects. Perfusion models revealed significant differences in the association of AHI and regional CBF between fitness groups (interaction ps < 0.05). CONCLUSION: This work provides new evidence for the protective role of cardiorespiratory fitness against the deleterious effects of OSA on brain aging in late-middle age to older adults.

Insulin Homeostasis Mediates the Relationship Between Cardiorespiratory Fitness and Cognitive Speed in Aging Adults.

BACKGROUND: Cardiorespiratory fitness (CRF) supports cognition, though it is unclear what mechanisms underly this relationship. Insulin resistance adversely affects cognition but can be reduced with habitual exercise. OBJECTIVE: We investigated whether insulin resistance statistically mediates the relationship between CRF and cognition. METHODS: In our observational study, we included n = 1,131 cognitively unimpaired, nondiabetic older adults from a cohort characterized by elevated Alzheimer's disease (AD) risk. We estimated CRF (eCRF) using a validated equation that takes age, sex, body mass index, resting heart rate, and habitual physical activity as inputs. The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) quantified insulin resistance. Standardized cognitive factor scores for cognitive speed/flexibility, working memory, verbal learning/memory, and immediate memory were calculated from a battery of neuropsychological tests. Linear regression models and bootstrapped estimates of indirect effects were used to determine whether HOMA-IR mediated significant relationships between eCRF and cognition. RESULTS: eCRF was positively associated with cognitive speed/flexibility (p = 0.034). When controlling for HOMA-IR, eCRF was no longer associated with cognitive speed/flexibility (p = 0.383). HOMA-IR had a significant indirect effect on the eCRF-cognition relationship (B = 0.025, CI = [0.003,0.051]). eCRF was not associated with working memory (p = 0.236), immediate memory (p = 0.345), or verbal learning/memory (p = 0.650). CONCLUSION: Among older adults at risk for AD, peripheral insulin resistance mediates the relationship between CRF and cognitive speed.

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.

Soft tissue radiodensity parameters mediate the relationship between self-reported physical activity and lower extremity function in AGES-Reykjavík participants.

Although previous studies have highlighted the association between physical activity and lower extremity function (LEF) in elderly individuals, the mechanisms underlying this relationship remain debated. Our recent work has recognized the utility of nonlinear trimodal regression analysis (NTRA) parameters in characterizing changes in soft tissue radiodensity as a quantitative construct for sarcopenia in the longitudinal, population-based cohort of the AGES-Reykjavík study. For the present work, we assembled a series of prospective multivariate regression models to interrogate whether NTRA parameters mediate the 5-year longitudinal relationship between physical activity and LEF in AGES-Reykjavík participants. Healthy elderly volunteers from the AGES-Reykjavík cohort underwent mid-thigh X-ray CT scans along with a four-part battery of LEF tasks: normal gait speed, fastest-comfortable gait speed, isometric leg strength, and timed up-and-go. These data were recorded at two study timepoints which were separated by approximately 5 years: AGES-I (n = 3157) and AGES-II (n = 3098). Participants in AGES-I were likewise administered a survey to approximate their weekly frequency of engaging in moderate-to-vigorous physical activity (PAAGES-I). Using a multivariate mediation analysis framework, linear regression models were assembled to test whether NTRA parameters mediated the longitudinal relationship between PAAGES-I and LEFAGES-II; all models were covariate-adjusted for age, sex, BMI, and baseline LEF, and results were corrected for multiple statistical comparisons. Our first series of models confirmed that all four LEF tasks were significantly related to PAAGES-I; next, modelling the relationship between PAAGES-I and NTRAAGES-II identified muscle amplitude (Nm) and location (µm) as potential mediators of LEF to test. Finally, adding these two parameters into our PAAGES-I → LEFAGES-II models attenuated the prior effect of PAAGES-I; bootstrapping confirmed Nm and µm as significant partial mediators of the PAAGES-I → LEFAGES-II relationship, with the strongest effect found in isometric leg strength. This work describes a novel approach toward clarifying the mechanisms that underly the relationship between physical activity and LEF in aging individuals. Identifying Nm and µm as significant partial mediators of this relationship provides strong evidence that physical activity protects aging mobility through the preservation of both lean tissue quantity and quality.

Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension.

The strong age dependency of many deleterious health outcomes likely reflects the cumulative effects from a variety of risk and protective factors that occur over one's life course. This notion has become increasingly explored in the etiology of chronic disease and associated comorbidities in aging. Our recent work has shown the robust classification of individuals at risk for cardiovascular pathophysiology using CT-based soft tissue radiodensity parameters obtained from nonlinear trimodal regression analysis (NTRA). Past and present lifestyle influences the incidence of comorbidities like hypertension (HTN), diabetes (DM) and cardiac diseases. 2,943 elderly subjects from the AGES-Reykjavik study were sorted into a three-level binary-tree structure defined by: 1) lifestyle factors (smoking and self-reported physical activity level), 2) comorbid HTN or DM, and 3) cardiac pathophysiology. NTRA parameters were extracted from mid-thigh CT cross-sections to quantify radiodensitometric changes in three tissue types: lean muscle, fat, and loose-connective tissue. Between-group differences were assessed at each binary-tree level, which were then used in tree-based machine learning (ML) models to classify subjects with DM or HTN. Classification scores for detecting HTN or DM based on lifestyle factors were excellent (AUCROC: 0.978 and 0.990, respectively). Finally, tissue importance analysis underlined the comparatively-high significance of connective tissue parameters in ML classification, while predictive models of DM onset from five-year longitudinal data gave a classification accuracy of 94.9%. Altogether, this work serves as an important milestone toward the construction of predictive tools for assessing the impact of lifestyle factors and healthy aging based on a single image.

Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass index and isometric leg strength using tree-based regression algorithms. Results obtained from these models demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parameters and comorbidities.

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions.

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.

Postural Control Adaptation and Habituation During Vibratory Proprioceptive Stimulation: An HD-EEG Investigation of Cortical Recruitment and Kinematics.

The objective of the present work is to measure postural kinematics and power spectral variation from HD-EEG to assess changes in cortical activity during adaptation and habituation to postural perturbation. To evoke proprioceptive postural perturbation, vibratory stimulation at 85 Hz was applied to the calf muscles of 33 subjects over four 75-second stimulation periods. Stimulation was performed according to a pseudorandom binary sequence. Vibratory impulses were synchronized to high-density electroencephalography (HD-EEG, 256 channels). Changes in absolute spectral power (ASP) were analyzed over four frequency bands ( ∆ : 0.5-3.5 Hz; θ : 3.5-7.5 Hz; α : 7.5-12.5 Hz; ß : 12.5-30 Hz). A force platform recorded torque actuated by the feet, and normalized sway path length (SPL) was computed as a construct for postural performance during each period. SPL values indicated improvement in postural performance over the trial periods. Significant variation in absolute power values (ASP) was found in assessing postural adaptation: an increase in θ band ASP in the frontal-central region for closed-eyes trials, an increase in θ and ß band ASP in the parietal region for open-eyes trials. In habituation, no significant variations in ASP were observed during closed-eyes trials, whereas an increase in θ , α , and ß band ASP was observed with open eyes. Furthermore, open-eyed trials generally yielded a greater number of significant ASP differences across all bands during both adaptation and habituation, suggesting that following cortical activity during postural perturbation may be up-regulated with the availability of visual feedback. These results altogether provide deeper insight into pathological postural control failure by exploring the dynamic changes in both cortical activity and postural kinematics during adaptation and habituation to proprioceptive postural perturbation.

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard.

Patient-specific mobility assessment to monitor recovery after total hip arthroplasty.

Total hip arthroplasty is a ubiquitously successful orthopedic surgical procedure, whose prevalence is rising worldwide. While many investigations focus on characterizing periprosthetic pathophysiology, the objective of our research is to develop and describe multi-metric assemblies as a first step toward creating a patient-specific mobility index that rehabilitators and orthopedic surgeons can utilize for prescribing their respective procedures. In total, 48 total hip arthroplasty patients (both cemented and uncemented) undergoing unilateral, primary surgery went through computed tomographic scans and gait analysis measurements both before and 1 year following their surgery. Altogether, the reported quantitative metrics include 11 spatial and temporal gait parameters, muscle density, and electromyography signals from the rectus femoris, vastus lateralis, and vastus medialis, and bone mineral density values from bioimage analysis around the implant stem. We found that measured parameters from a subgroup were sensitive to changes observed during patient recovery, implicating the predictive sensitivity of these patient conditions. Most post-operative gait parameters changed significantly, while electromyography data indicated few significant differences. Moreover, results from bioimage analyses indicate a general reduction of periprosthetic bone mineral density after 1 year, in association with increasing density of the quadriceps muscles. Furthermore, this work identifies which quantitative metrics undergo the greatest variation after total hip arthroplasty and demonstrates the clinical feasibility of a multimodal approach to mobility assessment that may ultimately support decision-making for post-surgical rehabilitation protocols.

New Directions in 3D Medical Modeling: 3D-Printing Anatomy and Functions in Neurosurgical Planning

This paper illustrates the feasibility and utility of combining cranial anatomy and brain function on the same 3D-printed model, as evidenced by a neurosurgical planning case study of a 29-year-old female patient with a low-grade frontal-lobe glioma. We herein report the rapid prototyping methodology utilized in conjunction with surgical navigation to prepare and plan a complex neurosurgery. The method introduced here combines CT and MRI images with DTI tractography, while using various image segmentation protocols to 3D model the skull base, tumor, and five eloquent fiber tracts. This 3D model is rapid-prototyped and coregistered with patient images and a reported surgical navigation system, establishing a clear link between the printed model and surgical navigation. This methodology highlights the potential for advanced neurosurgical preparation, which can begin before the patient enters the operation theatre. Moreover, the work presented here demonstrates the workflow developed at the National University Hospital of Iceland, Landspitali, focusing on the processes of anatomy segmentation, fiber tract extrapolation, MRI/CT registration, and 3D printing. Furthermore, we present a qualitative and quantitative assessment for fiber tract generation in a case study where these processes are applied in the preparation of brain tumor resection surgery.

New Directions in 3D Medical Modeling: 3D-Printing Anatomy and Functions in Neurosurgical Planning.

This paper illustrates the feasibility and utility of combining cranial anatomy and brain function on the same 3D-printed model, as evidenced by a neurosurgical planning case study of a 29-year-old female patient with a low-grade frontal-lobe glioma. We herein report the rapid prototyping methodology utilized in conjunction with surgical navigation to prepare and plan a complex neurosurgery. The method introduced here combines CT and MRI images with DTI tractography, while using various image segmentation protocols to 3D model the skull base, tumor, and five eloquent fiber tracts. This 3D model is rapid-prototyped and coregistered with patient images and a reported surgical navigation system, establishing a clear link between the printed model and surgical navigation. This methodology highlights the potential for advanced neurosurgical preparation, which can begin before the patient enters the operation theatre. Moreover, the work presented here demonstrates the workflow developed at the National University Hospital of Iceland, Landspitali, focusing on the processes of anatomy segmentation, fiber tract extrapolation, MRI/CT registration, and 3D printing. Furthermore, we present a qualitative and quantitative assessment for fiber tract generation in a case study where these processes are applied in the preparation of brain tumor resection surgery.

Quantitative Computed Tomography and Image Analysis for Advanced Muscle Assessment.

Medical imaging is of particular interest in the field of translational myology, as extant literature describes the utilization of a wide variety of techniques to non-invasively recapitulate and quantity various internal and external tissue morphologies. In the clinical context, medical imaging remains a vital tool for diagnostics and investigative assessment. This review outlines the results from several investigations on the use of computed tomography (CT) and image analysis techniques to assess muscle conditions and degenerative process due to aging or pathological conditions. Herein, we detail the acquisition of spiral CT images and the use of advanced image analysis tools to characterize muscles in 2D and 3D. Results from these studies recapitulate changes in tissue composition within muscles, as visualized by the association of tissue types to specified Hounsfield Unit (HU) values for fat, loose connective tissue or atrophic muscle, and normal muscle, including fascia and tendon. We show how results from these analyses can be presented as both average HU values and compositions with respect to total muscle volumes, demonstrating the reliability of these tools to monitor, assess and characterize muscle degeneration.

Imaging Approaches in Functional Assessment of Implantable Myogenic Biomaterials and Engineered Muscle Tissue.

The fields of tissue engineering and regenerative medicine utilize implantable biomaterials and engineered tissues to regenerate damaged cells or replace lost tissues. There are distinct challenges in all facets of this research, but functional assessments and monitoring of such complex environments as muscle tissues present the current strategic priority. Many extant methods for addressing these questions result in the destruction or alteration of tissues or cell populations under investigation. Modern advances in non-invasive imaging modalities present opportunities to rethink some of the anachronistic methods, however, their standard employment may not be optimal when considering advancements in myology. New image analysis protocols and/or combinations of established modalities need to be addressed. This review focuses on efficacies and limitations of available imaging modalities to the functional assessment of implantable myogenic biomaterials and engineered muscle tissues.

3D False Color Computed Tomography for Diagnosis and Follow-Up of Permanent Denervated Human Muscles Submitted to Home-Based Functional Electrical Stimulation.

This report outlines the use of a customized false-color 3D computed tomography (CT) protocol for the imaging of the rectus femoris of spinal cord injury (SCI) patients suffering from complete and permanent denervation, as characterized by complete Conus and Cauda Equina syndrome. This muscle imaging method elicits the progression of the syndrome from initial atrophy to eventual degeneration, as well as the extent to which patients' quadriceps could be recovered during four years of home-based functional electrical stimulation (h-b FES). Patients were pre-selected from several European hospitals and functionally tested by, and enrolled in the EU Commission Shared Cost Project RISE (Contract n. QLG5-CT-2001-02191) at the Department of Physical Medicine, Wilhelminenspital, Vienna, Austria. Denervated muscles were electrically stimulated using a custom-designed stimulator, large surface electrodes, and customized progressive stimulation settings. Spiral CT images and specialized computational tools were used to isolate the rectus femoris muscle and produce 3D and 2D reconstructions of the denervated muscles. The cross sections of the muscles were determined by 2D Color CT, while muscle volumes were reconstructed by 3D Color CT. Shape, volume, and density changes were measured over the entirety of each rectus femoris muscle. Changes in tissue composition within the muscle were visualized by associating different colors to specified Hounsfield unit (HU) values for fat, (yellow: [-200; -10]), loose connective tissue or atrophic muscle, (cyan: [-9; 40]), and normal muscle, fascia and tendons included, (red: [41; 200]). The results from this analysis are presented as the average HU values within the rectus femoris muscle reconstruction, as well as the percentage of these tissues with respect to the total muscle volume. Results from this study demonstrate that h-b FES induces a compliance-dependent recovery of muscle volume and size of muscle fibers, as evidenced by the gain and loss in muscle mass. These results highlight the particular utility of this modality in the quantitative longitudinal assessment of the responses of skeletal muscle to long-term denervation and h-b FES recovery.

Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI.

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

Improving Planning and Post-Operative Assessment for Total Hip Arthroplasty.

UNLABELLED: Total Hip Arthroplasty (THA) remains the gold standard of treatment for patients who suffer with a variety of hip-related pathological degeneration or trauma. These patients often exhibit significantly less post-operative pain and an increase in the range of motion of the joint, but there are still relatively common instances of debilitating periprosthetic complications that call into question the method for pre-surgical implant choice. Currently, there are two principal options for THA prostheses: cemented or non cemented. Utilizing the cemented procedure ensures a faster acquisition of adequate implant stability than with the non cemented procedure, but can eventually lead to an increased periprosthetic fracture risk. Non cemented prosthetic stems are more frequently revised within the first few years following THA due to periprosthetic fracture, but non cemented revision surgeries generally result in fewer complications than those of cemented implants. Surgeons typically rely on experience or simple patient metrics such as age and sex to prescribe which implant procedure is optimal, and while this may work for most patients, there is a clear need to analyze more rigoriously patient conditions that correlate to optimal post-THA outcomes. The results from the investigation reported herein indicate that an understanding of how the percent composition and quality of a patient's quadriceps muscle in both healthy and operated legs may be a better indicator for prosthetic choice. Additionally, these data emphasize that the traditional metrics of age and sex inadequately predict changes in quadriceps composition and quality and thereby have comparatively minor utility in determining the patient-appropriate prosthetic type. KEY WORDS: Total Hip Arthroplasty, Prosthetic selection, Muscle size and quality, Anatomical modeling, Surgical planning.

Bone Mineral Density and Fracture Risk Assessment to Optimize Prosthesis Selection in Total Hip Replacement.

The variability in patient outcome and propensity for surgical complications in total hip replacement (THR) necessitates the development of a comprehensive, quantitative methodology for prescribing the optimal type of prosthetic stem: cemented or cementless. The objective of the research presented herein was to describe a novel approach to this problem as a first step towards creating a patient-specific, presurgical application for determining the optimal prosthesis procedure. Finite element analysis (FEA) and bone mineral density (BMD) calculations were performed with ten voluntary primary THR patients to estimate the status of their operative femurs before surgery. A compilation model of the press-fitting procedure was generated to define a fracture risk index (FRI) from incurred forces on the periprosthetic femoral head. Comparing these values to patient age, sex, and gender elicited a high degree of variability between patients grouped by implant procedure, reinforcing the notion that age and gender alone are poor indicators for prescribing prosthesis type. Additionally, correlating FRI and BMD measurements indicated that at least two of the ten patients may have received nonideal implants. This investigation highlights the utility of our model as a foundation for presurgical software applications to assist orthopedic surgeons with selecting THR prostheses.