Dataset Information

An investigation of jogging biomechanics using the full-body lumbar spine model: Model development and validation.

ABSTRACT: The ability of a biomechanical simulation to produce results that can translate to real-life situations is largely dependent on the physiological accuracy of the musculoskeletal model. There are a limited number of freely-available, full-body models that exist in OpenSim, and those that do exist are very limited in terms of trunk musculature and degrees of freedom in the spine. Properly modeling the motion and musculature of the trunk is necessary to most accurately estimate lower extremity and spinal loading. The objective of this study was to develop and validate a more physiologically accurate OpenSim full-body model. By building upon three previously developed OpenSim models, the full-body lumbar spine (FBLS) model, comprised of 21 segments, 30 degrees-of-freedom, and 324 musculotendon actuators, was developed. The five lumbar vertebrae were modeled as individual bodies, and coupled constraints were implemented to describe the net motion of the spine. The eight major muscle groups of the lumbar spine were modeled (rectus abdominis, external and internal obliques, erector spinae, multifidus, quadratus lumborum, psoas major, and latissimus dorsi), and many of these muscle groups were modeled as multiple fascicles allowing the large muscles to act in multiple directions. The resulting FBLS model?s trunk muscle geometry, maximal isometric joint moments, and simulated muscle activations compare well to experimental data. The FBLS model will be made freely available (https://simtk.org/home/fullbodylumbar) for others to perform additional analyses and develop simulations investigating full-body dynamics and contributions of the trunk muscles to dynamic tasks.

SUBMITTER: Raabe ME

PROVIDER: S-EPMC4851590 | biostudies-other | 2016 May

REPOSITORIES: biostudies-other

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An investigation of jogging biomechanics using the full-body lumbar spine model: Model development and validation.

Raabe Margaret E ME Chaudhari Ajit M W AMW

Journal of biomechanics 20160227 7

The ability of a biomechanical simulation to produce results that can translate to real-life situations is largely dependent on the physiological accuracy of the musculoskeletal model. There are a limited number of freely-available, full-body models that exist in OpenSim, and those that do exist are very limited in terms of trunk musculature and degrees of freedom in the spine. Properly modeling the motion and musculature of the trunk is necessary to most accurately estimate lower extremity and ...[more]

PMID: 26947033

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Project description:ImportanceFunctional impairment and pain are common indications for the initiation of lumbar spine surgery, but information about expected improvement in these patient-reported outcome (PRO) domains is not readily available to most patients and clinicians considering this type of surgery.ObjectiveTo assess population-level PRO response after lumbar spine surgery, and develop/validate a prediction tool for PRO improvement.Design, setting, and participantsThis statewide multicenter cohort was based at 15 Washington state hospitals representing approximately 75% of the state's spine fusion procedures. The Spine Surgical Care and Outcomes Assessment Program and the survey center at the Comparative Effectiveness Translational Network prospectively collected clinical and PRO data from adult candidates for lumbar surgery, preoperatively and postoperatively, between 2012 and 2016. Prediction models were derived for PRO improvement 1 year after lumbar fusion surgeries on a random sample of 85% of the data and were validated in the remaining 15%. Surgical candidates from 2012 through 2015 were included; follow-up surveying continued until December 31, 2016, and data analysis was completed from July 2016 to April 2017.Main outcomes and measuresFunctional improvement, defined as a reduction in Oswestry Disability Index score of 15 points or more; and back pain and leg pain improvement, defined a reduction in Numeric Rating Scale score of 2 points or more.ResultsA total of 1965 adult lumbar surgical candidates (mean [SD] age, 61.3 [12.5] years; 944 [59.6%] female) completed baseline surveys before surgery and at least 1 postoperative follow-up survey within 3 years. Of these, 1583 (80.6%) underwent elective lumbar fusion procedures; 1223 (77.3%) had stenosis, and 1033 (65.3%) had spondylolisthesis. Twelve-month follow-up participation rates for each outcome were between 66% and 70%. Improvements were reported in function, back pain, and leg pain at 12 months by 306 of 528 surgical patients (58.0%), 616 of 899 patients (68.5%), and 355 of 464 patients (76.5%), respectively, whose baseline scores indicated moderate to severe symptoms. Among nonoperative patients, 35 (43.8%), 47 (53.4%), and 53 (63.9%) reported improvements in function, back pain, and leg pain, respectively. Demographic and clinical characteristics included in the final prediction models were age, sex, race, insurance status, American Society of Anesthesiologists score, smoking status, diagnoses, prior surgery, prescription opioid use, asthma, and baseline PRO scores. The models had good predictive performance in the validation cohort (concordance statistic, 0.66-0.79) and were incorporated into a patient-facing, web-based interactive tool (https://becertain.shinyapps.io/lumbar_fusion_calculator).Conclusions and relevanceThe PRO response prediction tool, informed by population-level data, explained most of the variability in pain reduction and functional improvement after surgery. Giving patients accurate information about their likelihood of outcomes may be a helpful component in surgery decision making.

Project description:BACKGROUND:Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. METHODS:Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. RESULTS:Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84-3.50) and 2.48 (95% CrI: 1.27-5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37-10.61). CONCLUSIONS:For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.

Project description:Length-of-hospital stay (LOS) after lumbar spine surgery (LSS) can be affected by many factors. However, few studies have evaluated predictors of LOS, and all have used limited number of variables as predictors.The purpose of the study was to identify presurgical, surgical, and postsurgical predictors of LOS after LSS.Retrospective review of consecutive patients who had LSS at the University of Kansas Hospital from October 2008 to April, 2012.Five hundred ninety-three patients underwent LSS consisting of laminotomy, laminectomy, or arthrodesis.LOS. Multiple presurgical, surgical, and postsurgical variables were extracted from the patients' medical records and considered as possible predictors (independent variables) of LOS.Potential predictors that were significantly correlated with LOS were used as indicators to construct three latent factors presurgical, surgical, and postsurgical, which were in turn used to predict LOS in a structural equation model.The average LOS was 4.01±2.73 days. The presurgical factor was indicated by age (61.97±14.49 years), previous level of function (60.5% were totally independent), previous hemoglobin level (13.70±1.36 mg/dL), and use of assistive devices (60% were assistive device users). The surgical factor was indicated by severity of illness (50.2% had minor disease severity), presence of complications (1.9%), and stay in an intensive care unit (4.0%). The postsurgical factor was indicated by postsurgical walking distance (166.43±175.75 ft), level of assistance during walking (5.18±0.81 out of 7 points), balance scores (6.18±1.82 out of 10 points), and bed mobility and transfer dependency scores (9.81±1.99 out of 14 points). These three latent factors explained 47% of variation in LOS.Postsurgical factors predicted the highest variation in LOS in comparison with presurgical and surgical factors and should be taken into consideration for discharge planning. Postsurgical factors are related to the patient's function, modifiable with rehabilitation, and can be improved to shorten LOS. Inclusion of more reliable and standardized presurgical variables could improve the predictability of the model.

Project description:PurposeTo characterize the inter- and intraobserver variability of qualitative, non-disk contour degenerative findings of the lumbar spine at magnetic resonance (MR) imaging.Materials and methodsThe case accrual method used to perform this institutional review board-approved, HIPAA-compliant retrospective study was the random selection of 111 interpretable MR examination cases of subjects from the Spine Patient Outcomes Research Trial. The subjects were aged 18-87 years (mean, 53 years +/- 16 [standard deviation]). Four independent readers rated the cases according to defined criteria. A subsample of 40 MR examination cases was selected for reevaluation at least 1 month later. The following findings were assessed: spondylolisthesis, disk degeneration, marrow endplate abnormality (Modic changes), posterior anular hyperintense zone (HIZ), and facet arthropathy. Inter- and intraobserver agreement in rating the data was summarized by using weighted kappa statistics.ResultsInterobserver agreement was good (kappa = 0.66) in rating disk degeneration and moderate in rating spondylolisthesis (kappa = 0.55), Modic changes (kappa = 0.59), facet arthropathy (kappa = 0.54), and posterior HIZ (kappa = 0.44). Interobserver agreement in rating the extent of Modic changes was moderate: kappa Values were 0.43 for determining superior anteroposterior extent, 0.47 for determining superior craniocaudal extent, 0.57 for determining inferior anteroposterior extent, and 0.48 for determining inferior craniocaudal extent. Intraobserver agreement was good in rating spondylolisthesis (kappa = 0.66), disk degeneration (kappa = 0.74), Modic changes (kappa = 0.64), facet arthropathy (kappa = 0.69), and posterior HIZ (kappa = 0.67). Intraobserver agreement in rating the extent of Modic changes was moderate, with kappa values of 0.54 for superior anteroposterior, 0.60 for inferior anteroposterior, 0.50 for superior craniocaudal, and 0.60 for inferior craniocaudal extent determinations.ConclusionThe interpretation of general lumbar spine MR characteristics has sufficient reliability to warrant the further evaluation of these features as potential prognostic indicators.

Dataset Information

An investigation of jogging biomechanics using the full-body lumbar spine model: Model development and validation.

Publications

An investigation of jogging biomechanics using the full-body lumbar spine model: Model development and validation.

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