A 28-year-old woman with no underlying health issues was injured in a motorcycle accident and taken to our hospital by ambulance when she was 26 years old. Though she was diagnosed with multiple trauma, upon arrival at the hospital neither cardiac murmurs nor cardiac abnormalities on transthoracic echocardiography were detected. She was managed conservatively, and discharged on hospital day 16. She experienced dyspnea upon mild effort, and an early diastolic murmur appeared. She was again referred to our hospital, and diagnosed with severe aortic regurgitation. We scheduled an aortic valve replacement using an bioprosthetic valve because she intended to give birth. We also considered simultaneous aortic root enlargement as her aortic annulus was small. We performed the surgery 2 years after the initial motorcycle accident. Perioperatively, we noticed that her non-coronary cusp was torn. We converted the procedure to an aortic valve repair using an autologous pericardial patch. Her aortic regurgitation disappeared after the operation, and she was discharged on postoperative day 14. We successfully preserved the aortic valve cusps and avoided the need for anticoagulant therapy.

The number of operations performed for cardiovascular disease has increased since recent improvements in diagnostic and the therapeutic technology have led to a remarkable increase in the life expectancy of patients with Marfan syndrome. On the other hand, operative procedures can be difficult when patients have complications of connective tissue abnormalities such as thoracic deformities, lung diseases and ophthalmic lesions. Although recent surgical outcomes have improved, those of secondary surgery are more difficult. We describe aortic root replacement to treat perivalvular leakage after aortic valve replacement in a patient with Marfan syndrome with a severe thoracic deformity.

Objective: Gait speed is regulated by varying gait parameters depending on the diverse contexts of the environment. People with Parkinson’s disease (PwPD) have difficulty adapting to gait control in their environment; however, the relationships between gait speed and spatiotemporal parameters in free-living environments have not been clarified. This study aimed to compare gait parameters according to gait speed in clinics and free-living environments. Methods: PwPD were assessed at the clinic and in a free-living environment using an accelerometer on the lower back. By fitting a bimodal Gaussian model to the gait speed distribution, gait speed was divided into lower and higher speeds. We compared the spatiotemporal gait parameters using a 2 × 2 (environment [clinic/free-living] × speed [lower/higher]) repeated-measures analysis of variance. Associations between Parkinson’s disease symptoms and gait parameters were evaluated using Bayesian Pearson’s correlation coefficients. Results: In the 41 PwPD included in this study, spatiotemporal gait parameters were significantly worse in free-living environments than in clinics and at lower speeds than at higher speeds. The fit of the walking speed distribution to the bimodal Gaussian model (adjustability of gait speed) in free-living environments was related to spatiotemporal gait parameters, severity of Parkinson’s disease, number of falls, and quality of life. Conclusion The findings suggest that gait control, which involves adjusting gait speed according to context, differs between clinics and free-living environments in PwPD. Gait assessments for PwPD in both clinical and free-living environments should interpret gait impairments in a complementary manner.

Objective: Gait speed is regulated by varying gait parameters depending on the diverse contexts of the environment. People with Parkinson’s disease (PwPD) have difficulty adapting to gait control in their environment; however, the relationships between gait speed and spatiotemporal parameters in free-living environments have not been clarified. This study aimed to compare gait parameters according to gait speed in clinics and free-living environments. Methods: PwPD were assessed at the clinic and in a free-living environment using an accelerometer on the lower back. By fitting a bimodal Gaussian model to the gait speed distribution, gait speed was divided into lower and higher speeds. We compared the spatiotemporal gait parameters using a 2 × 2 (environment [clinic/free-living] × speed [lower/higher]) repeated-measures analysis of variance. Associations between Parkinson’s disease symptoms and gait parameters were evaluated using Bayesian Pearson’s correlation coefficients. Results: In the 41 PwPD included in this study, spatiotemporal gait parameters were significantly worse in free-living environments than in clinics and at lower speeds than at higher speeds. The fit of the walking speed distribution to the bimodal Gaussian model (adjustability of gait speed) in free-living environments was related to spatiotemporal gait parameters, severity of Parkinson’s disease, number of falls, and quality of life. Conclusion The findings suggest that gait control, which involves adjusting gait speed according to context, differs between clinics and free-living environments in PwPD. Gait assessments for PwPD in both clinical and free-living environments should interpret gait impairments in a complementary manner.

Objective: Gait speed is regulated by varying gait parameters depending on the diverse contexts of the environment. People with Parkinson’s disease (PwPD) have difficulty adapting to gait control in their environment; however, the relationships between gait speed and spatiotemporal parameters in free-living environments have not been clarified. This study aimed to compare gait parameters according to gait speed in clinics and free-living environments. Methods: PwPD were assessed at the clinic and in a free-living environment using an accelerometer on the lower back. By fitting a bimodal Gaussian model to the gait speed distribution, gait speed was divided into lower and higher speeds. We compared the spatiotemporal gait parameters using a 2 × 2 (environment [clinic/free-living] × speed [lower/higher]) repeated-measures analysis of variance. Associations between Parkinson’s disease symptoms and gait parameters were evaluated using Bayesian Pearson’s correlation coefficients. Results: In the 41 PwPD included in this study, spatiotemporal gait parameters were significantly worse in free-living environments than in clinics and at lower speeds than at higher speeds. The fit of the walking speed distribution to the bimodal Gaussian model (adjustability of gait speed) in free-living environments was related to spatiotemporal gait parameters, severity of Parkinson’s disease, number of falls, and quality of life. Conclusion The findings suggest that gait control, which involves adjusting gait speed according to context, differs between clinics and free-living environments in PwPD. Gait assessments for PwPD in both clinical and free-living environments should interpret gait impairments in a complementary manner.

Objective: Gait speed is regulated by varying gait parameters depending on the diverse contexts of the environment. People with Parkinson’s disease (PwPD) have difficulty adapting to gait control in their environment; however, the relationships between gait speed and spatiotemporal parameters in free-living environments have not been clarified. This study aimed to compare gait parameters according to gait speed in clinics and free-living environments. Methods: PwPD were assessed at the clinic and in a free-living environment using an accelerometer on the lower back. By fitting a bimodal Gaussian model to the gait speed distribution, gait speed was divided into lower and higher speeds. We compared the spatiotemporal gait parameters using a 2 × 2 (environment [clinic/free-living] × speed [lower/higher]) repeated-measures analysis of variance. Associations between Parkinson’s disease symptoms and gait parameters were evaluated using Bayesian Pearson’s correlation coefficients. Results: In the 41 PwPD included in this study, spatiotemporal gait parameters were significantly worse in free-living environments than in clinics and at lower speeds than at higher speeds. The fit of the walking speed distribution to the bimodal Gaussian model (adjustability of gait speed) in free-living environments was related to spatiotemporal gait parameters, severity of Parkinson’s disease, number of falls, and quality of life. Conclusion The findings suggest that gait control, which involves adjusting gait speed according to context, differs between clinics and free-living environments in PwPD. Gait assessments for PwPD in both clinical and free-living environments should interpret gait impairments in a complementary manner.