Lower Leg Injury Reference Values and Risk Curves from Survival Analysis for Male and Female Dummies: Meta-analysis of Postmortem Human Subject Tests

Yoganandan, Narayan; Arun, Mike W. J.; Pintar, Frank A.; Banerjee, Anjishnu

doi:10.1080/15389588.2015.1015118

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…Interval censoring has also been used in an analysis of foot-ankle fracture data from different experimental sources. 38,39 The reduction of the fracture threshold due to interval censoring reflects the conservativeness of the estimate in occupant safety.…”

Section: Discussionmentioning

confidence: 99%

Foot–Ankle Fractures and Injury Probability Curves from Post-mortem Human Surrogate Tests

et al. 2016

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This purpose of this study was to replicate foot-ankle injuries seen in the military and derive human injury probability curves using the human cadaver model. Lower legs were isolated below knee from seventeen unembalmed human cadavers and they were aligned in a 90-90 posture (plantar surface orthogonal to leg). The specimens were loaded along the tibia axis by applying short-time duration pulses, using a repeated testing protocol. Injuries were documented using pre- and post-test X-rays, computed tomography scans, and dissection. Peak force-based risk curves were derived using survival analysis and accounted for data censoring. Fractures were grouped into all foot-ankle (A), any calcaneus (B), and any tibia injuries (C), respectively. Calcaneus and/or distal tibia/pilon fractures occurred in fourteen tests. Axial forces were the greatest and least for groups C and B, respectively. Times attainments of forces for all groups were within ten milliseconds. The Weibull function was the optimal probability distribution for all groups. Age was significant (p < 0.05) for groups A and C. Survival analysis-based probability curves were derived for all groups. Data are given in the body of paper. Age-based, risk-specific, and continuous distribution probability curves/responses guide in the creation of an injury assessment capability for military blast environments.

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Section: Discussionmentioning

confidence: 99%

Foot–Ankle Fractures and Injury Probability Curves from Post-mortem Human Surrogate Tests

et al. 2016

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“…This is because the true shape of the injury risk curve for is not known. Commonly assumed shapes in the parametric analysis for impact biomechanics experiments are the Weibull, lognormal, or log-logistic [26,[42][43][44]. In other words, different types of experiment datasets can have different shapes with the Bayesian model.…”

Section: Discussionmentioning

confidence: 99%

Human Pelvis Bayesian Injury Probability Curves From Whole Body Lateral Impact Experiments

Yoganandan

DeVogel

Pintar

et al. 2020

Journal of Engineering and Science in Medical Diagnostics and Therapy

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Injury criteria are used in military, automotive, and aviation environments to advance human safety. While injury risk curves (IRCs) for the human pelvis are published under vertical loading, there is a paucity of analysis that describe IRCs under lateral impact. The objective of the present study is to derive IRCs under this mode. Published data were used from 60 whole-body postmortem human surrogate (PMHS) tests that used repeated testing protocols. In the first analysis, from single impact tests, all injury data points were considered as left censored and noninjury points were considered as right censored, while repeated testing results were treated as interval censored data. In the second analysis, injury data were treated uncensored. Peak force was used as the response variable. Age, total body mass, gender, and body mass index (BMI) were used as covariates in different combinations. Bayesian survival analysis model was used to derive the IRCs. Plus-minus 95% credible intervals (CI) and their normalized CI sizes (NCIS) were obtained. This is the first study to develop IRCs in whole body PMHS tests to describe the human pelvic tolerance under lateral impact using Bayesian models.

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“…Modern safety-related advancements are targeted at lower injury risks. For example, lower leg injury criterion is specified at 10% risk level in the military and the trend is along similar lines in motor vehicle-related crashworthiness research (NATO, 2007 ; Prasad et al, 2010 ; Yoganandan et al, 2014a , 2015a ). Because of stated biomechanical rationale, the infusion approach accounting for the age variable better describes the underlying biomechanical phenomena at different probability levels.…”

Section: Comments On the Age-infusion Methodologymentioning

confidence: 99%

Age-Infusion Approach to Derive Injury Risk Curves for Dummies from Human Cadaver Tests

Yoganandan

Banerjee

Pintar

2015

Front. Bioeng. Biotechnol.

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Injury criteria and risk curves are needed for anthropomorphic test devices (dummies) to assess injuries for improving human safety. The present state of knowledge is based on using injury outcomes and biomechanical metrics from post-mortem human subject (PMHS) and mechanical records from dummy tests. Data from these models are combined to develop dummy injury assessment risk curves (IARCs)/dummy injury assessment risk values (IARVs). This simple substitution approach involves duplicating dummy metrics for PMHS tested under similar conditions and pairing with PMHS injury outcomes. It does not directly account for the age of each specimen tested in the PMHS group. Current substitution methods for injury risk assessments use age as a covariate and dummy metrics (e.g., accelerations) are not modified so that age can be directly included in the model. The age-infusion methodology presented in this perspective article accommodates for an annual rate factor that modifies the dummy injury risk assessment responses to account for the age of the PMHS that the injury data were based on. The annual rate factor is determined using human injury risk curves. The dummy metrics are modulated based on individual PMHS age and rate factor, thus “infusing” age into the dummy data. Using PMHS injuries and accelerations from side-impact experiments, matched-pair dummy tests, and logistic regression techniques, the methodology demonstrates the process of age-infusion to derive the IARCs and IARVs.

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Lower Leg Injury Reference Values and Risk Curves from Survival Analysis for Male and Female Dummies: Meta-analysis of Postmortem Human Subject Tests

Cited by 23 publications

References 17 publications

Foot–Ankle Fractures and Injury Probability Curves from Post-mortem Human Surrogate Tests

Foot–Ankle Fractures and Injury Probability Curves from Post-mortem Human Surrogate Tests

Human Pelvis Bayesian Injury Probability Curves From Whole Body Lateral Impact Experiments

Age-Infusion Approach to Derive Injury Risk Curves for Dummies from Human Cadaver Tests

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