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Pelvis injury risk curves in side impacts from human cadaver experiments using survival analysis and Brier score metrics.

ABSTRACT: Objectives: Post Mortem Human Surrogate (PMHS) experiments are used for describing tolerance and improve safety. For nearside impacts, the United States Standard Federal Motor Vehicle Safety Standards (FMVSS-214) used PMHS tests and binary regression methods to achieve these goals. Since this promulgation, Parametric Statistical Survival Modeling (PSSM) has become a de facto standard for developing injury risk curves (IRCs). This study is focused on pelvic injuries from side impacts. The objectives are as follows. Analyze impactor-based intact PMHS tests and develop IRCs at different AIS levels using the force metric and examine the effectiveness of other force-related variables on IRCs.Methods: Impactor-driven pelvic tests conducted using whole body PMHS were selected from published studies. The dataset had 63 tests. Peak force, 3-ms clip force, and impulse were used to develop IRCs for Abbreviated Injury Scores (AIS) AIS2+ and AIS3+, i.e., groups A and B. Brier Score Metric (BSM) was used for ranking metrics. 95% confidence intervals were computed, Normalized Confidence Interval Sizes (NCIS) were determined, and quality of the IRCs were obtained.Results: Impulse best described the underlying response of the pelvis. BSMs were the lowest for the impulse for both groups. At 10% and 50% probabilities, impulses were 71 Ns and 125 Ns for group A and 79 Ns and 160 Ns for group B; peak forces were 3.8 kN and 7.1 kN and 4 kN and 10 kN for groups A and B; and clip forces were 2.7 kN and 6.5 kN and 3.6 kN and 8.6 kN, for groups A and B. NCIS at discrete probability levels, qualities of risk curves, and individual IRCs are given.Conclusion: This study underscores the importance of using impulse to describe pelvis injury criteria in lateral impacts. These findings are applicable to anthropomorphic test devices, as matched pair tests are done to determine dummy-based injury criteria/injury assessment risk curves (IARCs). Although IRCs have been developed for WorldSID, it may be appropriate to use impulse-based IARCs. Because THOR is a potential device for automated vehicle environments, it may be appropriate to develop THOR-based IARCS. The present IRCs act as fundamental human-based injury criteria. These responses can also be used in human body and subsystem computational models.

SUBMITTER: Yoganandan N

PROVIDER: S-EPMC8325432 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveDerive optimum injury probability curves to describe human tolerance of the lower leg using parametric survival analysis.MethodsThe study reexamined lower leg postmortem human subjects (PMHS) data from a large group of specimens. Briefly, axial loading experiments were conducted by impacting the plantar surface of the foot. Both injury and noninjury tests were included in the testing process. They were identified by pre- and posttest radiographic images and detailed dissection following the impact test. Fractures included injuries to the calcaneus and distal tibia-fibula complex (including pylon), representing severities at the Abbreviated Injury Score (AIS) level 2+. For the statistical analysis, peak force was chosen as the main explanatory variable and the age was chosen as the covariable. Censoring statuses depended on experimental outcomes. Parameters from the parametric survival analysis were estimated using the maximum likelihood approach and the dfbetas statistic was used to identify overly influential samples. The best fit from the Weibull, log-normal, and log-logistic distributions was based on the Akaike information criterion. Plus and minus 95% confidence intervals were obtained for the optimum injury probability distribution. The relative sizes of the interval were determined at predetermined risk levels. Quality indices were described at each of the selected probability levels.ResultsThe mean age, stature, and weight were 58.2±15.1 years, 1.74±0.08 m, and 74.9±13.8 kg, respectively. Excluding all overly influential tests resulted in the tightest confidence intervals. The Weibull distribution was the most optimum function compared to the other 2 distributions. A majority of quality indices were in the good category for this optimum distribution when results were extracted for 25-, 45- and 65-year-olds at 5, 25, and 50% risk levels age groups for lower leg fracture. For 25, 45, and 65 years, peak forces were 8.1, 6.5, and 5.1 kN at 5% risk; 9.6, 7.7, and 6.1 kN at 25% risk; and 10.4, 8.3, and 6.6 kN at 50% risk, respectively.ConclusionsThis study derived axial loading-induced injury risk curves based on survival analysis using peak force and specimen age; adopting different censoring schemes; considering overly influential samples in the analysis; and assessing the quality of the distribution at discrete probability levels. Because procedures used in the present survival analysis are accepted by international automotive communities, current optimum human injury probability distributions can be used at all risk levels with more confidence in future crashworthiness applications for automotive and other disciplines.

Project description:The study was conducted to determine the association between vehicle-, crash-, and demographic-related factors and injuries to front seat far-side occupants in modern environments.Field data were obtained from the NASS-CDS database for the years 2009-2012. Inclusion factors included the following: adult restrained front outboard-seated occupants, no ejection or rollovers, and vehicle model years less than 10 years old at the time of crash. Far-side crashes were determined by using collision deformation classification. Injuries were scored using the Abbreviated Injury Scale (AIS). Injuries (MAIS 2+, MAIS 3+, M denotes maximum score) were examined based on demographics, change in velocity, vehicle type, direction of force, extent zone, collision partner, and presence of another occupant in the front seat. Only weighted data were used in the analysis. Injuries to the head and face, thorax, abdomen, pelvis, and upper and lower extremity regions were studied. Odds ratios and upper and lower confidence intervals were estimated from multivariate analysis.Out of 519,195 far-side occupants, 17,715 were MAIS 2+ and 4,387 were MAIS 3+ level injured occupants. The mean age, stature, total body mass, and body mass index (BMI) were 40.7 years, 1.7 m, 77.2 kg, and 26.8 kg/m2, respectively. Of occupants with MAIS 2+ injuries, 51% had head and 19% had thorax injuries. Of occupants with MAIS 3+ injuries, 50% had head and 69% had thorax injuries. The cumulative distribution of changes in velocities at the 50th percentile for the struck vehicle for all occupants and occupants with MAIS 2+ and MAIS 3+ injuries were 19, 34, and 42 km/h, respectively. Furthermore, 73% of MAIS 2+ injuries and 86% of MAIS 3+ injuries occurred at a change in velocity of 24 km/h or greater. Odds of sustaining MAIS 2+ and MAIS 3+ injuries increased with each unit increase in change in velocity, stature, and age, with one exception. Odds of sustaining injuries were higher with the presence of an occupant in the front seat at the MAIS 3+ level, although it was reversed at the lower level. The extent zone of 3+ increased the odds compared to the extent zones of 1 to 2 at both MAIS 2+ and MAIS 3+ injuries. Odds ratios and confidence intervals are given.The findings are as follows: head and thorax are the more frequently injured body regions, and the prevalence of cranium injuries is similar at both injury severities; thoracic injuries are more prevalent at the MAIS 3+ level; the presence of another front seat occupant plays a role in MAIS 3+ trauma; injuries continue to occur at changes in velocity representative of side impact environments; and mean demographic factors are close to mid-size automotive anthropometry, indicating the need to pursue this line of study. Because data were gathered from only 4 years, it would be important to include additional NASS-CDS database years, rescore injuries from previous years, and analyze other international databases to reinforce these findings for advancing safety for far-side occupants.

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Pelvis injury risk curves in side impacts from human cadaver experiments using survival analysis and Brier score metrics.

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