Louis Puybasset

Reconstructing traumatic brain injury loading conditions remains challenging in forensic biomechanics. This study presents a proof-of-concept inverse-analysis framework for identifying plausible head impacts from post-traumatic brain imaging. It was applied to a pedestrian struck by a car who sustained severe brain injury and a malar fracture. Cerebral microbleeds on T1-weighted MRI and regions of abnormal fractional anisotropy on diffusion tensor imaging served as injury markers. A partially personalized finite element head model was subjected to 120 impacts compatible with the facial fracture, varying velocity, orientation, and contact surface. Predicted regions of elevated maximum principal strain (>0.2) were compared with observed injury locations using the Matthews correlation coefficient (MCC). With T1-weighted lesions alone, the best simulation achieved an MCC of 0.11, with high specificity (0.84) but low sensitivity (0.50), reflecting the difficulty of reproducing sparse, dispersed microbleeds. Including regions of abnormal fractional anisotropy improved performance, yielding an MCC of 0.34 and a sensitivity of 0.884. The best scenarios involved impacts to the lower posterior maxillary region, near the mandible and zygomatic bone, at 4–7 m/s. These findings support lesion-informed inverse finite element modeling for forensic impact reconstruction and suggest that fractional anisotropy alterations may provide complementary information on brain deformation.

Rationale: The ability to anticipate ICU beds mobilizable through elective surgical postponements during health system shocks remains poorly quantified. Aims and Objectives: Using real‐world COVID‐19 deprogramming data, we combined a classification of acceptable surgical deferral times with predictive models of expected surgical activity to estimate ICU capacity that can be generated in crisis scenarios. Method: We conducted a retrospective time‐series analysis of all surgical procedures performed from 2017 to 2021 in three AP‐HP university hospitals. Procedures were classified as non‐deferrable (red), deferrable < 1 month (orange), or > 1 month (green). Expected weekly surgical volumes for 2020–2021 without pandemic disruption were predicted using models trained on 2017–2018 and validated on 2019 (XGBoost, Holt–Winters). Multivariable linear regression quantified the association between postponed procedures and ICU beds opened, enabling projection of capacity gains under different postponement scenarios. Results: Among 196,469 procedures, 59% were red, 20% orange, and 21% green. Surgical activity declined by 13% in 2020% and 8% in 2021, while ICU capacity increased by up to 85 and 46 beds, respectively. Reductions were mainly driven by postponement of green procedures (IRR 0.78 [0.75–0.80]). Only postponed green procedures were significantly associated with ICU bed expansion ( β = 0.75, 95% CI 0.53–0.97; p < 0.001), accounting for 34.6% of ICU bed demand. Conclusion: Clinically acceptable postponement of low‐priority elective surgery contributes substantially to ICU surge capacity. Integrating prioritization and predictive modeling into preparedness strategies may support balanced, data‐driven responses to future healthcare system shocks.

Discomfort of noninvasive respiratory supports remain controversial. In the present study, the discomfort of high flow nasal cannula oxygen, noninvasive ve

A reliable outcome prognostication tool for patients in coma of various etiologies would facilitate ICU treatment by providing objective information to car

Moderate to severe traumatic brain injury (TBI) should be considered as a chronic health condition. The corpus callosum is the brain region that suffers most from diffuse axonal injury, leading to long-term functional deficits. Few studies have considered the relationships between inter- and intrahemispheric functional connectivity and structural damages to the corpus callosum in chronic TBI patients. We examined how callosal functional connectivity and white matter alterations relate to clinical outcome using multimodal magnetic resonance imaging (MRI): structural MRI estimates callosal volume, diffusion-weighted MRI enables white matter integrity quantification, resting-state functional MRI assesses neural dysfunction. Seventy-four patients underwent a multimodal MRI session on average 5 years after a moderate-to-severe TBI. Multiple factorial analysis analyzed the relationships between clinical outcome (from severe disability to good recovery, assessed by the Glasgow Outcome Scale extended GOSE), callosal volume, diffusion metrics (fractional anisotropy and mean, axial, and radial diffusivity), and inter- and intrahemispheric functional connectivity. Multiple factorial analysis confirmed that patients with severe disability (GOSE 3-4) had more structural alterations in the corpus callosum than patients with a good recovery (GOSE 7-8). Most importantly, patients able to live independently but unable to work/study in a standard environment (GOSE 5-6) could not be described solely by structural features. They exhibited a lower interhemispheric connectivity between cortical regions mediated by the corpus callosum than patients with a good recovery, and a tendency towards a decrease in intrahemispheric connectivity compared with severely disabled patients. These findings suggest a complex long-term functional impact of moderate-to-severe TBI.

Objective This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. Methods We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. Results The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). Conclusions Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.