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.

This review comprehensively examines the evolution and current state of interbody cage technology for lumbar interbody fusion (LIF). This review highlights the biomechanical and clinical implications of the transition from traditional static cage designs to advanced expandable variants for spinal surgery. The review begins by exploring the early developments in cage materials, highlighting the roles of titanium and polyetheretherketone in the advancement of LIF techniques. This review also discusses the strengths and limitations of these materials, leading to innovations in surface modifications and the introduction of novel materials, such as tantalum, as alternative materials. Advancements in three-dimensional printing and surface modification technologies form a significant part of this review, emphasizing the role of these technologies in enhancing the biomechanical compatibility and osseointegration of interbody cages. In addition, this review explores the increase in biodegradable and composite materials such as polylactic acid and polycaprolactone, addressing their potential to mitigate long-term implant-related complications. A critical evaluation of static and expandable cages is presented, including their respective clinical and radiological outcomes. While static cages have been a mainstay of LIF, expandable cages are noted for their adaptability to the patient’s anatomy, reducing complications such as cage subsidence. However, this review highlights the ongoing debate and the lack of conclusive evidence regarding the superiority of either cage type in terms of clinical outcomes. Finally, this review proposes future directions for cage technology, focusing on the integration of bioactive substances and multifunctional coatings and the development of patient-specific implants. These advancements aim to further enhance the efficacy, safety, and personalized approach of spinal fusion surgeries. Moreover, this review offers a nuanced understanding of the evolving landscape of cage technology in LIF and provides insights into current practices and future possibilities in spinal surgery.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Background: The treatment success rate for tuberculosis (TB) has stagnated at 80–81% in South Korea, indicating unsatisfactory outcomes. Enhancing treatment success rate necessitates the development of individualized treatment approaches for each patient. This study aimed to identify the risk factors associated with unfavorable treatment outcomes to facilitate tailored TB care. Methods: We retrospectively analyzed the data of patients with active TB between January 2019 and December 2020 at a single tertiary referral center. We classified unfavorable treatment outcomes according to the 2021 World Health Organization guidelines as follows:“lost to follow-up” (LTFU), “not evaluated” (NE), “death,” and “treatment failure” (TF).Moreover, we analyzed risk factors for each unfavorable outcome using Cox proportional hazard regression analysis. Results: A total of 659 patients (median age 62 years; male 54.3%) were included in the study.The total unfavorable outcomes were 28.1%: 4.6% LTFU, 9.6% NE, 9.1% deaths, and 4.9% TF. Multivariate analysis showed that a culture-confirmed diagnosis of TB was associated with a lower risk of LTFU (adjusted hazard ratio [aHR], 0.25; 95% confidence interval [CI], 0.10–0.63), whereas the occurrence of adverse drug reactions (ADRs) significantly increased the risk of LTFU (aHR, 6.63; 95% CI, 2.63–16.69). Patients living far from the hospital (aHR, 4.47; 95% CI, 2.50–7.97) and those with chronic kidney disease (aHR, 3.21; 95% CI, 1.33–7.75) were at higher risk of being transferred out to other health institutions (NE). Higher mortality was associated with older age (aHR, 1.06; 95% CI, 1.04–1.09) and comorbidities. The ADRs that occurred during TB treatment were a risk factor for TF (aHR, 6.88; 95% CI, 2.24–21.13). Conclusion Unfavorable outcomes of patients with TB were substantial at a tertiary referral center, and the risk factors for each unfavorable outcome varied. To improve treatment outcomes, close monitoring and the provision of tailored care for patients with TB are necessary.