Purpose: Hereditary spastic paraplegia (HSP) refers to a group of genetic neurodegenerative diseases marked by gradually worsening spasticity and hyperreflexia in the lower extremities. This study aimed to describe the clinical and genetic characteristics of Korean patients with spastic paraplegia. Materials and Methods: We retrospectively reviewed medical records of 69 patients with spastic paraplegia from 54 unrelated families between 2002 and 2024. Genetic, clinical, electrophysiological, and radiological features were comprehensively analyzed. Results: Causative genes were identified in 34 (63%) of 54 unrelated families; SPAST, detected in 26 families, was the most prevalent. Seven novel pathogenic variants were identified. Clinically, the median age of symptom onset was 25 years [14.0–37.0]. Out of 69 patients with spastic paraplegia, 51 (74%) presented with the pure form of spastic paraplegia, which included all patients with SPG4. Spastic gait was a universal feature in all patients. Urinary dysfunction was present in 42 (61%) patients. Additional neurologic manifestations included peripheral neuropathy 9 (13%), cognitive impairment 5 (7%), upper limb weakness 4 (6%), dysarthria 4 (6%), dysphagia 3 (4%), ataxia 3 (4%), and scoliosis 1 (3%). Brain MRI findings demonstrated a thin corpus callosum in two patients with SPG11; all patients with SPG4 had normal findings. Spine MRI revealed spinal cord atrophy in 16 (27%) patients, including 6 (21%) patients with SPG4. Conclusion The study comprehensively reviewed genetic and clinical spectra of spastic paraplegia in Korean patients, emphasizing the predominance of SPAST as the causative gene and underscoring the genetic and phenotypic heterogeneity of spastic paraplegia.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder characterized by the presence of eosinophilic nuclear inclusions in neurons and somatic cells. It clinically manifests as cognitive decline, seizures, and autonomic dysfunction. A 44-year-old man presented with a transient visual field defect and hemiparesis. Based on characteristic imaging findings and pathological findings, NIID was suspected and diagnosed through genetic testing. This case emphasizes the importance of comprehensive clinical phenotype analysis and accurate genetic diagnosis.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder characterized by the presence of eosinophilic nuclear inclusions in neurons and somatic cells. It clinically manifests as cognitive decline, seizures, and autonomic dysfunction. A 44-year-old man presented with a transient visual field defect and hemiparesis. Based on characteristic imaging findings and pathological findings, NIID was suspected and diagnosed through genetic testing. This case emphasizes the importance of comprehensive clinical phenotype analysis and accurate genetic diagnosis.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder characterized by the presence of eosinophilic nuclear inclusions in neurons and somatic cells. It clinically manifests as cognitive decline, seizures, and autonomic dysfunction. A 44-year-old man presented with a transient visual field defect and hemiparesis. Based on characteristic imaging findings and pathological findings, NIID was suspected and diagnosed through genetic testing. This case emphasizes the importance of comprehensive clinical phenotype analysis and accurate genetic diagnosis.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

High-intensity interval training (HIIT), characterized by alternating intense exercise with short recovery periods, has emerged as an effective and time-efficient approach for diabetes management. HIIT is particularly recommended for individuals who are physically capable and have limited time, as per guidelines from the Korean Diabetes Association (KDA) and the American Diabetes Association (ADA). Studies indicate that HIIT can enhance aerobic capacity, reduce insulin resistance, and improve glycemic control while potentially decreasing cardiovascular risks, such as high blood pressure and lipid imbalances. Moreover, HIIT may help mitigate diabetes-related complications by improving vascular function and insulin sensitivity. However, HIIT carries certain risks, such as episodes of hyperglycemia, hypoglycemia, and an increased risk of musculoskeletal injury, particularly in older adults or those with lower fitness levels. Therefore, it is crucial that HIIT regimens be individualized and guided by healthcare professionals to ensure safe and effective integration into diabetes management.