Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Allergen immunotherapy (AIT) has been used for over a century and has been demonstrated to be effective in treating patients with various allergic diseases. AIT allergens can be administered through various routes, including subcutaneous, sublingual, intralymphatic, oral, or epicutaneous routes. Sublingual immunotherapy (SLIT) has recently gained clinical interest, and it is considered an alternative treatment for allergic rhinitis (AR) and asthma. This review provides an overview of the current evidence-based studies that address the use of SLIT for treating AR, including (1) mechanisms of action, (2) appropriate patient selection for SLIT, (3) the current available SLIT products in Korea, and (4) updated information on its efficacy and safety. Finally, this guideline aims to provide the clinician with practical considerations for SLIT.

Allergen immunotherapy (AIT) is a causative treatment of allergic diseases in which allergen extracts are regularly administered in a gradually escalated doses, leading to immune tolerance and consequent alleviation of allergic diseases. The need for uniform practice guidelines in AIT is continuously growing as the number of potential candidates for AIT increases and new therapeutic approaches are tried. This updated version of the Korean Academy of Asthma Allergy and Clinical Immunology recommendations for AIT, published in 2010, proposes an expert opinion by specialists in allergy, pediatrics, and otorhinolaryngology. This guideline deals with the basic knowledge of AIT, including mechanisms, clinical efficacy, allergen standardization, important allergens in Korea, and special consideration in pediatrics. The article also covers the methodological aspects of AIT, including patient selection, allergen selection, schedule and doses, follow-up care, efficacy measurements, and management of adverse reactions. Although this guideline suggests the optimal dosing schedule, an individualized approach and modifications are recommended considering the situation for each patient and clinic.

Objective: This study aimed to evaluate the unexplored relationship between BDNF methylation, long-term outcomes, and its interaction with suicidal ideation (SI), which is closely associated with both BDNF expression and stroke outcomes. Methods: A total of 278 stroke patients were assessed for BDNF methylation status and SI using suicide-related item in the Montgomery–Åsberg Depression Rating Scale at 2 weeks post-stroke. We investigated the incidence of composite cerebro-cardiovascular events (CCVEs) during an 8−14-year period after the initial stroke as long-term stroke outcome.We conducted Cox regression models adjusted for covariates to evaluate the association between BDNF methylation status and CCVEs, as well as its interaction with post-stroke SI at 2 weeks. Results: Higher methylation status of CpG 1, 3, and 5, but not the average value, predicted a greater number of composite CCVEs during 8−14 years following the stroke. The associations between a higher methylation status of CpGs 1, 3, 5, and 8, as well as the average BDNF methylation value, and a greater number of composite CCVEs, were prominent in patients who had post-stroke SI at 2 weeks. Notably, a significant interaction between methylation status and SI on composite CCVEs was observed only for CpG 8. Conclusion The significant association between BDNF methylation and poor long-term stroke outcomes, particularly amplified in individuals who had post-stroke SI at 2 weeks, suggested that evaluating the biological marker status of BDNF methylation along with assessing SI during the acute phase of stroke can help predict long-term outcomes.

Background: and Purpose The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. Methods: We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3–6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3–6. Results: Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3–6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). Conclusion The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Background: New genome sequencing technologies with enhanced diagnostic efficiency have emerged. Rapid and timely diagnosis of treatable rare genetic diseases can alter their medical management and clinical course. However, multiple factors, including ethical issues, must be considered. We designed a targeted sequencing platform to avoid ethical issues and reduce the turnaround time. Methods: We designed an automated sequencing platform using dried blood spot samples and a NEOseq_ACTION panel comprising 254 genes associated with Mendelian diseases having curable or manageable treatment options. Retrospective validation was performed using data from 24 genetically and biochemically confirmed patients. Prospective validation was performed using data from 111 patients with suspected actionable genetic diseases. Results: In prospective clinical validation, 13.5% patients presented with medically actionable diseases, including short- or medium-chain acyl-CoA dehydrogenase deficiencies (N=6), hyperphenylalaninemia (N=2), mucopolysaccharidosis type IVA (N=1), alpha thalassemia (N=1), 3-methylcrotonyl-CoA carboxylase 2 deficiency (N=1), propionic acidemia (N=1), glycogen storage disease, type IX(a) (N=1), congenital myasthenic syndrome (N=1), and citrullinemia, type II (N=1). Using the automated analytic pipeline, the turnaround time from blood collection to result reporting was <4 days. Conclusions This pilot study evaluated the possibility of rapid and timely diagnosis of treatable rare genetic diseases using a panel designed by a multidisciplinary team. The automated analytic pipeline maximized the clinical utility of rapid targeted sequencing for medically actionable genes, providing a strategy for appropriate and timely treatment of rare genetic diseases.