Subarachnoid hemorrhage (SAH) is a stroke subtype with high mortality and poor functional outcomes. Prompt occlusion of a ruptured aneurysm at an early stage is crucial to prevent rebleeding, which can result in even higher mortality and more severe disabilities. The most critical initial decision in SAH management is the choice of treatment method with surgical clipping or endovascular coiling. We aimed to develop an evidence-based clinical guideline to select the optimal initial treatment in patients with SAH. We developed this guideline based on evidence from systematic reviews and meta-analyses via a de novo process. A systematic literature review was conducted across four databases (MEDLINE, Embase, Cochrane, and KoreaMed) to answer two population, intervention, comparison, outcome questions comparing clipping and coiling. The risk of bias was assessed using ROB 2.0 and the Newcastle-Ottawa Scale. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagrams and meta-analyses were generated for functional outcome and mortality. We included six randomized control trials (RCTs) and 58 observational studies. Meta-analysis of RCTs showed that coiling improved functional outcomes compared to clipping (odds ratio [OR], 0.91; 95% confidence interval [CI], 0.86–0.97). No significant mortality difference was observed in RCTs (OR, 1.38; 95% CI, 0.91–2.09), but non-RCTs favored clipping for reduced mortality (OR, 0.77; 95% CI, 0.69–0.86). However, it is difficult to generalize these findings to all clinical situations, as patients with SAH have a highly variable clinical course. Final treatment decision should be tailored to the individual patient’s status, including aneurysm location, morphology, and the expertise available at the treatment center. Such decisions are best made by specialists such as a board-certified physician and should be explained to the patient and their caregivers, along with the rationale for selecting the most appropriate treatment at the given hospital. Korea has many certified endovascular neurosurgeons, cerebrovascular surgeons, and certified cerebrovascular centers. Proper selection of the most suitable treatment method by certified physicians and centers would greatly benefit patient outcomes and healthcare professionals.

Accurate and early identification of stroke and large vessel occlusion (LVO) in emergency settings is essential for improving patient outcomes and ensuring the efficient allocation of medical resources. This clinical practice guideline systematically reviews domestic and international literature and conducts meta-analyses to evaluate the utility and diagnostic accuracy of stroke assessment tools used in prehospital emergency medical services (EMS). We developed a guideline based on evidence from systematic reviews and meta-analyses via a de novo process. A systematic literature review was conducted to evaluate the usefulness of diagnostic EMS assessment tools for diagnosing stroke and LVO. Overall, 70 non-randomized control studies were selected for this study. A meta-analysis was conducted with a subgroup analysis to distinguish between patients with stroke and those with LVO. EMS tools demonstrated high sensitivity but low specificity for diagnosing stroke. In the prehospital setting, using validated EMS stroke assessment tools is recommended for the early identification of stroke and LVO. Upon hospital arrival, stroke specialists should conduct further evaluation and triage to confirm the diagnosis and guide appropriate management. Delays in diagnosing LVO are frequently unacceptable. While experts advocate for the use of EMS assessment tools to facilitate early identification of LVO, these tools alone lack adequate sensitivity. Therefore, further diagnostic evaluations and consultation with stroke specialists upon hospital arrival are recommended.

The mothership (MS) model, where patients are directly transferred to a thrombectomy-capable center, and the drip-and-ship (DS) model, where thrombolysis is initiated at the nearest primary stroke center before transfer for thrombectomy, are the primary transport modes for patients with stroke. We aimed to establish guidelines for selecting the appropriate transfer strategy based on emergent large vessel occlusion (LVO). We developed this guideline based on evidence from systematic reviews and meta-analyses via a de novo process. A systematic literature review was conducted across four databases (MEDLINE, Embase, Cochrane, and KoreaMed) to answer three Population, Intervention, Comparison, and Outcome questions comparing MS and DS models. The risk of bias was assessed using the Newcastle-Ottawa Scale. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagrams and meta-analyses were generated for functional outcomes, mortality, and successful recanalization. Twenty-six non-randomized controlled studies showed that the MS model improved good functional outcomes by approximately 14% compared with the DS model (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.00–1.30). Fifteen studies reported that mortality in the MS and DS models showed no significant differences (OR, 0.97; 95% CI, 0.84–1.11). Twenty-four studies revealed no significant difference in successful recanalization between the MS and DS models (OR, 0.87; 95% CI, 0.68–1.10). The MS model should be considered first to improve the functional outcome of patients with LVO. However, if thrombectomy cannot be performed immediately after thrombolysis, or if a thrombectomy-enabled hospital is not nearby, the DS model should be considered by stroke specialists depending on transportation time and regional factors. We suggest a mixed approach with the DS model based on specific circumstances or regions to ensure the optimum treatment of patients with acute ischemic stroke (AIS). Appropriate transport for patients with LVO improves the prognosis of AIS.

Objective: This study aims to investigate the clinical relevance of occipital hypoperfusion in patients with Parkinson’s disease (PD) with respect to clinical phenotype and the risk of dementia conversion. Methods: We enrolled 349 patients with newly diagnosed PD and 48 healthy controls who underwent dual-phase 18F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane (18F-FP-CIT) positron emission tomography (PET). Patients with PD were classified into three groups based on posterior cortical perfusion patterns on early-phase 18F-FP-CIT PET images: PD with preserved posterior cortical perfusion (n=186), PD with parieto-temporal hypoperfusion (n=84), and PD with parieto-temporo-occipital hypoperfusion (n=79). Baseline clinical features and dementia conversion risk were compared across PD groups. Results: Patients with preserved posterior cortical perfusion were younger than those in the other PD groups. Compared with the other groups, the parieto-temporo-occipital hypoperfusion group tended to have lower Cross-Cultural Smell Identification Test scores, a higher prevalence of rapid eye movement sleep behavior disorder, higher Unified PD Rating Scale motor scores, and more severe reductions in striatal dopamine transporter availability. The risk of dementia conversion was lower in patients with preserved posterior cortical perfusion than in those with posterior cortical hypoperfusion. However, the risk of dementia conversion did not differ between the parieto-temporal and parieto-temporo-occipital hypoperfusion groups. Conclusion Additional occipital hypoperfusion was not associated with an imminent risk of dementia conversion in patients with PD with posterior cortical hypoperfusion. Nonetheless, occipital involvement may serve as an indicator of the diffuse malignant subtype of PD.

Objective: Cognitive impairment is common in patients with Parkinson’s disease (PD), and few pharmacological options are available for treating this condition. We evaluated the effects of a digital cognitive training program (SUPERBRAIN), which was previously shown to be effective in populations at risk of Alzheimer’s disease, on cognitive function in individuals with PD. Methods: Twenty-three individuals with PD and mild cognitive impairment (PD-MCI) from four clinics were randomized to the intervention (n=16) or control (n=7) groups. The intervention group completed a 12-week, home-based, tablet-based cognitive training program (25–30 min/day, 7 days/week). Cognitive outcomes were assessed using the Seoul Neuropsychological Screening Battery pre- and post-intervention. Results: The adherence rate was 79.36%. The intervention group showed significant improvements in the Seoul Verbal Learning Test (SVLT) delayed recall and the Controlled Oral Word Association Test, while no changes were observed in the control group. Analysis of covariance confirmed greater SVLT improvement in the intervention group (F statistic=7.15, p=0.015, partial η2=0.28). Conclusion SUPERBRAIN is feasible and can improve cognitive function in individuals with PD-MCI.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.