Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning.

(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model's robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years.

Association between Cognitive Abilities before the Age of 3 Years and Those at Least 1 Year Later in Children with Developmental Delay.

OBJECTIVE: This study investigated whether early cognitive assessment in children with developmental delay (DD) predicts cognitive development. We investigated the correlation between cognitive and language development in children with DD, cerebral palsy (CP), and autism spectrum disorder (ASD). METHODS: Data were collected from children diagnosed with DD who visited the hospital between 2015 and 2023. The assessments included the Korean Bayley Scales of Infant and Toddler Development Second Edition (K-BSID-II) and the Korean Wechsler Preschool Primary Scale of Intelligence Fourth Edition (K-WPPSI-IV). Language development was evaluated using the Sequenced Language Scale for Infants (SELSI) and Preschool Receptive-Expressive Language Scale (PRES). The statistical analysis involved a correlation analysis. RESULTS: Among 95 children in the study, a significant correlation was discovered between early cognitive assessments (the Mental Developmental Index from the K-BSID-II) and later cognitive development (the Full-Scale Intelligence Quotient from the K-WPPSI-IV) in the DD and CP groups, but not in the ASD group. The DD and CP groups exhibited significant correlations in language development between the SELSI and PRES, whereas the ASD group did not. CONCLUSION: Early cognitive assessments can predict later cognitive development in children with DD and CP, but not in those with ASD, according to this study. There was a strong correlation between language and cognitive development in the DD and CP groups, highlighting the importance of early intervention and assessment for these children. Further investigation is necessary to address these limitations and refine demographic data.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

A Method for Straightening Distorted Giga-Cast Large Thin-Walled Components.

Giga-casting, a revolutionary approach for manufacturing large, single-piece car body components from aluminium, has emerged as a potential game-changer in the automotive industry. However, these large, thin-walled castings are prone to distortions during solidification and heat treatment processes. Straightening these distortions is crucial to ensure structural integrity, facilitate downstream assembly, and maintain aesthetic qualities. This paper proposes a novel method for straightening giga-cast components using a multi-pin straightening machine. The machine's versatility stems from its ability to adapt to various geometries through multiple strategically controlled straightening pins. This paper introduces the concept of a "straightening stroke decision algorithm" to achieve precise straightening and overcome the challenges of complex shapes. This algorithm determines the stroke length for each pin, combining a polynomial model representing the global stiffness of the component with a machine learning model that captures the stiffness changes arising from the current geometry. The effectiveness of the proposed approach is evaluated through comprehensive numerical experiments using finite element analyses. The straightening performance is assessed for the straightening algorithm with different machine learning models (deep neural network and XGBoost) and compared to a traditional optimisation method. The proposed surrogate models decided the straightening strokes so that the maximum remaining distortion became 0.02% of the largest dimension of each target geometry. The results of the numerical experiment showed that the proposed straightening method is suitable for straightening distortion in large thin-walled components.

Evaluation of clinical factors predicting dysphagia in patients with traumatic and non-traumatic cervical spinal cord injury: a retrospective study.

Introduction: Dysphagia is a common complication in patients with cervical spinal cord injury (C-SCI) and can cause various pulmonary complications, such as aspiration pneumonia and mechanical airway obstruction increasing mortality and morbidity. This study evaluated the clinical factors that predict dysphagia in patients with traumatic and non-traumatic C-SCI. Methods: Ninety-eight patients with C-SCI were retrospectively enrolled in this study and were divided into those with and without dysphagia. Clinical factors such as age, sex, tracheostomy, spinal cord independence measure, pulmonary function test (PFT) including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and FVC/FEV1, American Spinal Cord Injury Association score, Berg Balance Scale, and surgical approach were investigated retrospectively. Results: Multivariate logistic regression analysis revealed that FVC and the presence of tracheostomy were significantly correlated with dysphagia in patients with C-SCI (p < 0.05). FVC and the presence of tracheostomy are useful tools for detecting dysphagia in patients with C-SCI. Conclusion: Considering the results of our study, early PFTs, especially FVC, in patients with C-SCI and early initiation of dysphagia management and treatment in patients with C-SCI and tracheostomy will be advantageous in lowering the mortality and morbidity due to pulmonary aspiration in these patients.

Relationship between sleep disturbance and developmental status in preschool-aged children with developmental disorder.

BACKGROUND: Sleep has been known to affect childhood development. Sleep disturbance is likely more common in children with developmental delay (DD) than in typical development. There are few studies on the correlation between sleep disturbance and developmental features in children with DD. Therefore, this study aimed to evaluate the associations between the two in children with DD. METHODS: A total of 45 children (age range 27.0 ± 11.1) with DD were recruited and evaluated using the Sleep Disturbance Scale for Children (SDSC) and Bayley Scales of Infant and Toddler Development (BSID-III). The outcomes are expressed as means and standard deviations. The correlation between SDSC and BSID-III was assessed using Spearman's rank correlation test. Multiple regression analysis was performed to investigate the relationship between BSID-III domains and SDSC questionnaire subscales. Statistical significance was set at p < 0.05. RESULTS: Based on the correlation analysis and subsequent hierarchical regression analysis, cognition and socio-emotional domains of BSID-III were significantly associated with the DOES subscale of the SDSC questionnaire. In addition, the expressive language domain of the BSID-III was found to be associated with the DA subscale of the SDSC questionnaire. It seems that excessive daytime sleepiness might negatively affect emotional and behavioral problems and cognitive function. Also, arousal disorders seem to be related to memory consolidation process, which is thought to affect language expression. CONCLUSION: This study demonstrated that DA and DOES subscales of the SDSC questionnaire were correlated with developmental aspects in preschool-aged children with DD. Sleep problems in children with DD can negatively affect their development, thereby interfering with the effectiveness of rehabilitation. Identifying and properly managing the modifiable factors of sleep problems is also crucial as a part of comprehensive rehabilitation treatment. Therefore, we should pay more attention to sleep problems, even in preschool-aged children with DD.

Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.

PURPOSE: The Cobb angle is a standard measurement to qualify and track the progression of scoliosis. However, the Cobb angle has high inter- and intra-observer variability. Consequently, its measurement varies with vertebrae and may even differ when the same vertebra is measured. Therefore, it is not constant and differs with measurements. This study aimed to develop a deep learning model that automatically measures the Cobb angle. The deep learning model for identifying vertebrae on spine radiographs was developed. METHODS: The dataset consisted of 297 images that were divided into two subsets for training and validation. Two hundred and twenty-seven images (76.4%) were used to train the model, while 70 images (23.6%) were used as the validation dataset. Absolut error between the measurements by the observer and developed deep learning model and intraclass correlation coefficient (ICC). RESULTS: The average absolute error between the measurements was 1.97° with a standard deviation of 1.57°. In addition, 95.9% of the angles had an absolute error of less than 5°. The ICC was calculated to assess the model's reliability further. The ICC was 0.981, indicating excellent reliability. CONCLUSIONS: The authors believe the model will be useful in clinical practice by relieving clinicians of the burden of having to manually compute the Cobb angle. Further studies are needed to enhance the accuracy and versatility of this deep learning model.

Experience from a single-center study on multimodal medication therapy for patients with complex regional pain syndrome.

BACKGROUND: Despite the application of various therapeutic methods, pain caused by complex regional pain syndrome (CRPS) is not sufficiently managed and often progresses to a chronic stage. For the systematic and effective treatment of CRPS, we developed an algorithm for multimodal medication therapy based on the established pathophysiology of CRPS to control CRPS-related pain. OBJECTIVE: In this study, we present the outcomes of our novel algorithm for multimodal medication therapy for patients with CRPS, consisting of three major components: multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapy. METHODS: We retrospectively investigated patients with CRPS who received multimodal therapy. Pain severity scores were evaluated using a numerical rating scale at four time points (P1, pain at initial consultation; P2, pain after oral medication; P3, pain after ketamine treatment; and P4, pain after lidocaine treatment). The effect of the multimodal medication therapy algorithm on pain management was evaluated at each time point. RESULTS: In patients with CRPS, multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapies led to significantly improved pain control (p< 0.05). Additionally, the combination of these three therapies (through the multimodal medication therapy algorithm) resulted in significant pain relief in patients with CRPS (p< 0.05). CONCLUSIONS: Our multimodal medication therapy algorithm effectively controlled pain in patients with CRPS. However, further prospective studies with large sample sizes and randomized controlled trials are needed for more accurate generalization.

The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review.

Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.

Content Analysis of YouTube Videos on the Effect of Vitamin C on Common Cold.

BACKGROUND: With the growth of the Internet, social media platforms have emerged as major sources of medical information. We assessed the reliability, quality, and accuracy of the most-viewed YouTube videos containing information on the effect of vitamin C on the common cold. METHODS: The YouTube videos were searched on August 1, 2022, using the keywords: ("ascorbic acid" OR "vitamin C" OR "Sodium Ascorbate" OR "L-ascorbic") AND "common cold". The 30 most-viewed videos were included in our study. The reliability and quality of the videos were analyzed using modified DISCERN and Global Quality Scales, respectively. When the videos included at least one correct or inaccurate scientific statement about the effect of vitamin C on the common cold, they were classified as accurate or misleading videos, respectively; those without any pertinent information were considered neither accurate nor misleading. If a video contained both accurate and inaccurate statements, it was classified as misleading. RESULTS: Of the 30 most-viewed videos, 73% were unreliable, and 67% contained misleading information and were of a poor quality. Of these 30 videos, 14 videos were produced and posted by customers who were not specialized in medicine or nutrition. Moreover, these videos were of significantly lower reliability, quality, and accuracy than those produced by nutrition or fitness channels or by medical or nutrition professionals. CONCLUSION: The reliability, quality, and accuracy of videos uploaded by non-professionals were low. Therefore, video creators should upload reliable, high-quality videos to ensure the dissemination of accurate medical information.

Prediction of motor outcome based on brain perfusion single photon emission computed tomography in corona radiata infarct.

BACKGROUND: There is limited information on brain perfusion single-photon emission computed tomography (SPECT) findings related to motor outcomes in patients with stroke. We aimed to investigate whether brain SPECT can be used to determine motor outcomes after corona radiata infarction. METHODS: Eighty-nine patients were recruited in this study. Brain SPECT and diffusion tensor tractography (DTT) were conducted to evaluate the state of the corticospinal tract (CST) within 7-30 days of corona radiata infarct. Motor outcome was measured 6 months after infarct onset and was evaluated using the modified Brunnstrom classification (MBC) and functional ambulation category (FAC) for motor function of the upper and lower extremities, respectively. The presence of hypoperfusion on brain SPECT was evaluated in the frontal lobe, temporal lobe, parietal lobe, basal ganglia, thalamus, and cerebellum on both the ipsilesional and contralesional sides. Statistical analysis was performed using multivariate logistic regression, comparing patients in which CST was spared versus interrupted. RESULTS: Hypoperfusion in the contralesional cerebellum was indicative of poor recovery in both the upper and lower extremities after corona radiata infarction when the CST was interrupted. Additionally, when the CST was preserved, hypoperfusion in the ipsilesional thalamus was indicative of poor recovery of the lower extremities. CONCLUSION: Brain SPECT evaluation was shown to be a useful tool for predicting motor outcomes in patients with corona radiata infarcts.

Kinematic mechanism of the rehabilitative effect of 4-channel NMES: post-hoc analysis of a prospective randomized controlled study.

The sequential 4-channel neuromuscular electrical stimulation (NMES), based on the normal contractile sequences of swallowing-related muscles, is a new rehabilitative treatment. The objective of this study was to explore the mechanism of the rehabilitative effect of the 4-channel NMES using kinematic analysis of videofluoroscopic swallowing study (VFSS) data. For this post-hoc analysis, we included a subset of participants from the prospective randomized controlled study on the clinical effectiveness of the sequential 4-channel NMES compared with that of the conventional 2-channel NMES. Seventeen subjects (11 and six in the 4- and 2-channel NMES groups, respectively) were eligible for the kinematic analysis of VFSS data. The hyoid bone movement was analyzed by evaluating the distance and time parameters with four peak points (A, B, C, D). The 4-channel NMES group showed significant improvement in vertical distances (A-C), horizontal distance (A-B, A-C), time interval (A-B-C) and total time, compared with their pretreatment data. The 2-channel NMES group showed significant improvements in time interval (A-B); however, the Euclidean distance (A-D) and mean velocity of the Euclidean distance (A-C) were significantly decreased. When the two groups were directly compared, the 4-channel group showed significantly greater improvement in horizontal distance (A-B), Euclidean distance (A-D), time interval (A-B-C), and mean velocity the Euclidean distance (A-D). The results in this study suggest that the sequential 4-channel NMES might lead to the physiologic circular movement of the hyoid bone during swallowing, and therefore be an effective treatment for dysphagia.Trial registration: Clinicaltrials.gov, registration number: NCT03670498.

Algorithm for multimodal medication therapy in patients with complex regional pain syndrome.

Complex regional pain syndrome (CRPS), previously known as reflex sympathetic dystrophy and causalgia, is a clinical entity characterized by classic neuropathic pain, autonomic involvement, motor symptoms, and trophic changes in the skin, nails, and hair. Although various therapeutic modalities are used to control CRPS-related pain, severe pain due to CRPS often persists and progresses to the chronic phase. In this study, we constructed an algorithm for multimodal medication therapy for CRPS based on the established pathology of CRPS. Oral steroid pulse therapy is recommended for initial pain management in patients with CRPS. Oral steroid therapy can reduce peripheral and central neuroinflammation, contributing to the development of neuropathic pain during the acute and chronic phases. If steroid pulse therapy offers poor relief or is ineffective, treatment to control central sensitization in the chronic phase should be initiated. If pain persists despite all drug adjustments, ketamine with midazolam 2 mg before and after ketamine injection can be administered intravenously to inhibit the N-methyl D-aspartate receptor. If this treatment fails to achieve sufficient efficacy, intravenous lidocaine can be administered for 2 weeks. We hope that our proposed drug treatment algorithm to control CRPS pain will help clinicians appropriately treat patients with CRPS. Further clinical studies assessing patients with CRPS are warranted to establish this treatment algorithm in clinical practice.

The effect of intensive rehabilitation treatment on sleep disorder in children with motor delays.

BACKGROUND: Although the importance of sleep problems has been increasingly emphasized due to the effects on children's development and children's and families' daytime behaviors, physical health, and quality of life, they have been overlooked in clinical practice. However, there have been few studies on the effects of rehabilitation on sleep problems. Therefore, in this study, we investigated the effects of an intensive rehabilitation program on sleep problems in children with developmental delays (DD). METHODS: We included 36 children with DD (30 outpatients, 6 inpatients) and their caregivers who completed all items on the Sleep Disturbance Scale for Children. Of the children with DD, 19 (59.3%) had cerebral palsy (CP) and 13 (40.7%) had DD of non-CP origins, of which 6 (18.8%) had prematurity, 4 (12.5%) had genetic causes, and 3 (9.4%) had an unknown origin. Changes in sleep problems after the intensive rehabilitation program were evaluated using a paired or unpaired t-test, depending on the distribution of the continuous variables. RESULTS: After the intensive rehabilitation program, in 36 children with DD, there was a significant improvement in the difficulty in initiating and maintaining sleep (DIMS) sub-score (p < 0.05). However, there was no significant improvement in the total score or other sub-scores, such as those for sleep breathing disorders (SBD), disorders of arousal (DA), sleep-wake transition disorders (SWTD), disorders of excessive somnolence (DOES), and sleep hyperhidrosis (SH). In the subgroup analysis according to the cause of DD, children with CP had a significant improvement in DIMS and DOES sub-scores (p < 0.05). CONCLUSION: The intensive rehabilitation program, consisting of more than two sessions per day, effectively alleviated sleep problems in children with DD, especially in those with CP. Among the sleep problems, the intensive rehabilitative program was most effective at improving the DIMS. However, further prospective studies with a larger number of patients with DD and a more standardized protocol are necessary to generalize this effect.

Association between Anterior Surgical Approach and Dysphagia Severity in Patients with Cervical Spinal Cord Injury.

INTRODUCTION: Early detection and management of dysphagia are essential for preventing aspiration pneumonia and reducing mortality in patients with cervical spinal cord injury (C-SCI). In this study, we identified risk factors for dysphagia in patients with C-SCI by analyzing the correlation between the clinical factors and the severity of dysphagia, not the presence or absence of dysphagia. Combined with the analysis results of previous studies, we thought that this additional analysis method could more accurately reveal the risk factors for dysphagia in patients with C-SCI. METHODS: The presence and severity of dysphagia in patients with C-SCI was evaluated using a modified videofluoroscopic dysphagia scale (mVDS) and penetration-aspiration scale (PAS). All included patients with C-SCI performed a video fluoroscopic swallowing study (VFSS). Clinical factors such as age, sex, the presence of tracheostomy, spinal cord independence measure (SCIM), pulmonary function test (PFT), including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FVC/FEV1, maximal inspiratory pressure (MIP), and maximal expiratory pressure (MEP), American Spinal Cord Injury Association (ASIA) score, Berg Balance Scale (BBS), and operation method were investigated. RESULTS: In the multivariate regression analysis, the anterior surgical approach was the only clinical factor that had a significant correlation in both mVDS and PAS, which represents the severity of dysphagia in C-SCI patients (p < 0.05). CONCLUSION: The anterior surgical approach was correlated with the severity of dysphagia in patients with C-SCI. Considering this, as one of the risk factors affecting dysphagia in patients with C-SCI, surgical method may also need to be considered. Additionally, we recommend that clinicians should pay particular attention to the potential for development of dysphagia in patients who received anterior cervical surgery. However, further prospective studies with larger sample sizes are needed for more accurate generalization.

Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs.

Deep learning is an advanced machine learning technique that is used in several medical fields to diagnose diseases and predict therapeutic outcomes. In this study, using anteroposterior ankle radiographs, we developed a convolutional neural network (CNN) model to diagnose osteochondral lesions of the talus (OLTs) using ankle radiographs as input data. We evaluated whether a CNN model trained on anteroposterior ankle radiographs could help diagnose the presence of OLT. We retrospectively collected 379 cases (OLT cases = 133, non-OLT cases = 246) of anteroposterior ankle radiographs taken at a university hospital between January 2010 and December 2020. The OLT was diagnosed using ankle magnetic resonance images of each patient. Among the 379 cases, 70% of the included data were randomly selected as the training set, 10% as the validation set, and the remaining 20% were assigned to the test set to evaluate the model performance. To accurately classify OLT and non-OLT, we cropped the area of the ankle on anteroposterior ankle radiographs, resized the image to 224 × 224, and used it as the input data. We then used the Visual Geometry Group Network model to determine whether the input image was OLT or non-OLT. The performance of the CNN model for the area under the curve, accuracy, positive predictive value, and negative predictive value on the test data were 0.774 (95% confidence interval [CI], 0.673-0.875), 81.58% (95% CI, 0.729-0.903), 80.95% (95% CI, 0.773-0.846), and 81.82% (95% CI, 0.804-0.832), respectively. A CNN model trained on anteroposterior ankle radiographs achieved meaningful accuracy in diagnosing OLT and demonstrated that it could help diagnose OLT.

The association between cognition and gait disturbance in central nervous system demyelinating disorder with mild disability.

INTRODUCTION: Gait disturbance in central nervous system (CNS) demyelinating disorders, including multiple sclerosis (MS) and neuromyelitis optica (NMO) is one of the most troublesome problems that has a direct impact on the quality of life. However, the associations between gait disturbance and other clinical variables of these two diseases have not been fully elucidated. OBJECTIVE: This study aimed to evaluate gait disturbance using a computerized gait analysis system and its association with various clinical variables in patients with MS and NMO. METHODS: A total of 33 patients (14 with MS and 19 with NMO) with minor disabilities, who were able to walk independently and had passed their acute phase, were enrolled in the study. Gait analysis were performed using a computer-based instrumented walkway system. (Walk-way MG-1000, Anima, Japan) Clinical variables, such as disease duration, medication, body mass index (BMI), hand grip power, and muscle mass were recorded. The Montreal Cognitive Assessment (MOCA), Beck Depression Inventory score-II (BDI), and fatigue scale were measured using the Functional Assessment of Chronic Illness Therapy-fatigue scale (FACIT-fatigue) scale. A trained neurologist scored the Expanded Disability Status Scale (EDSS). RESULTS: Gait speed was the single parameter that showed a significant positive correlation with MOCA (p < 0.001). The stance phase time was the single parameter that showed a significant negative correlation with EDSS (p < 0.001). Hand grip strength showed a significant positive correlation with skeletal muscle mass as assessed by bioimpedance analysis (p < 0.05). The FACIT-fatigue scale score showed a significant negative correlation with the BDI (p < 0.001). CONCLUSION: In our patients with MS/NMO with mild disability, cognitive impairment was significantly correlated with gait speed, and the degree of disability was significantly correlated with stance phase time. Our findings may imply that early detection of a decrease in gait speed and an increase in stance phase time can predict the progression of cognitive impairment in patients with MS/NMO with mild disability.

Clinical and electrophysiological characteristics of respiratory onset amyotrophic lateral sclerosis: a single-centre study.

BACKGROUND: We compared the clinical characteristics of patients with respiratory, bulbar and limb onset amyotrophic lateral sclerosis (ALS) who visited a single tertiary centre for 8 years. METHODS: Total of 115 ALS patients with respiratory, bulbar and limb onset ALS, including sex, body mass index (BMI), presence of lung disease, age at diagnosis, disease duration after initial symptoms, ALS Functional Rating Scale (ALSFRS-R) and progression rate (Delta-FS), pulmonary function, amplitude and distal latency (DL) of the phrenic nerves and blood creatine kinase (CK) and uric acid levels were collected. RESULTS: The prevalence of respiratory, bulbar and limb onset ALS were 5.2%, 28.7% and 66.1%, respectively. The mean age at diagnosis and ALSFRS-R were 67.8 ± 5.5, 63.8 ± 10.1 and 59.2 ± 11.7 in the descending order. The mean amplitude (0.18 ± 0.10 mV) and DL (9.5 ± 1.7 ms) of the phrenic nerves were significantly decreased and prolonged in respiratory onset ALS compared with other types of ALS patients. Patients with respiratory onset ALS had normal creatine kinase (CK) levels, whereas patients with other types of ALS had increased CK levels. CONCLUSIONS: Although rare, respiratory onset ALS may occur and should be considered during the initial differential diagnosis. In this study, patients with respiratory onset ALS were characterised by male predominance, with a higher baseline ALSFRS-R, lower BMI and phrenic nerve study well discriminated respiratory onset ALS from bulbar or limb onset ALS patients.