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.

Background: Pulse pressure, reflecting arterial wall stiffness, is a cardiovascular disease risk factor but is highly variable. This study investigated the association of the pulse pressure index (PPI) with 10-year cardiovascular disease risk and its clinical utility by examining its correlations with diabetes mellitus (DM) and chronic kidney disease (CKD). Methods: This cross-sectional study included 3,604 hypertensive adults (30–79 years) from the 2019–2021 Korea National Health and Nutrition Examination Survey. We categorized PPI as <30, 30 to 40, 40 to 50, and ≥50. The Framingham risk score assessed 10-year cardiovascular disease risk, and multiple regressions analyzed its relationship with the PPI category. Binary logistic regressions evaluated the relationship between PPI, DM, and CKD. Results: Adjusted for other variables, higher PPI levels are associated with an increased 10-year cardiovascular disease risk (P<0.001). PPI ≥50 was associated with CKD, and all PPI groups, except <30, were associated with DM. Conclusion PPI predicts 10-year CVD risk and is associated with DM presence. PPI can be considered a risk factor for both cardiovascular disease and DM. Additionally, PPI ≥50 is associated with CKD.

Background: Pulse pressure, reflecting arterial wall stiffness, is a cardiovascular disease risk factor but is highly variable. This study investigated the association of the pulse pressure index (PPI) with 10-year cardiovascular disease risk and its clinical utility by examining its correlations with diabetes mellitus (DM) and chronic kidney disease (CKD). Methods: This cross-sectional study included 3,604 hypertensive adults (30–79 years) from the 2019–2021 Korea National Health and Nutrition Examination Survey. We categorized PPI as <30, 30 to 40, 40 to 50, and ≥50. The Framingham risk score assessed 10-year cardiovascular disease risk, and multiple regressions analyzed its relationship with the PPI category. Binary logistic regressions evaluated the relationship between PPI, DM, and CKD. Results: Adjusted for other variables, higher PPI levels are associated with an increased 10-year cardiovascular disease risk (P<0.001). PPI ≥50 was associated with CKD, and all PPI groups, except <30, were associated with DM. Conclusion PPI predicts 10-year CVD risk and is associated with DM presence. PPI can be considered a risk factor for both cardiovascular disease and DM. Additionally, PPI ≥50 is associated with CKD.

Background: Pulse pressure, reflecting arterial wall stiffness, is a cardiovascular disease risk factor but is highly variable. This study investigated the association of the pulse pressure index (PPI) with 10-year cardiovascular disease risk and its clinical utility by examining its correlations with diabetes mellitus (DM) and chronic kidney disease (CKD). Methods: This cross-sectional study included 3,604 hypertensive adults (30–79 years) from the 2019–2021 Korea National Health and Nutrition Examination Survey. We categorized PPI as <30, 30 to 40, 40 to 50, and ≥50. The Framingham risk score assessed 10-year cardiovascular disease risk, and multiple regressions analyzed its relationship with the PPI category. Binary logistic regressions evaluated the relationship between PPI, DM, and CKD. Results: Adjusted for other variables, higher PPI levels are associated with an increased 10-year cardiovascular disease risk (P<0.001). PPI ≥50 was associated with CKD, and all PPI groups, except <30, were associated with DM. Conclusion PPI predicts 10-year CVD risk and is associated with DM presence. PPI can be considered a risk factor for both cardiovascular disease and DM. Additionally, PPI ≥50 is associated with CKD.

Background: Pulse pressure, reflecting arterial wall stiffness, is a cardiovascular disease risk factor but is highly variable. This study investigated the association of the pulse pressure index (PPI) with 10-year cardiovascular disease risk and its clinical utility by examining its correlations with diabetes mellitus (DM) and chronic kidney disease (CKD). Methods: This cross-sectional study included 3,604 hypertensive adults (30–79 years) from the 2019–2021 Korea National Health and Nutrition Examination Survey. We categorized PPI as <30, 30 to 40, 40 to 50, and ≥50. The Framingham risk score assessed 10-year cardiovascular disease risk, and multiple regressions analyzed its relationship with the PPI category. Binary logistic regressions evaluated the relationship between PPI, DM, and CKD. Results: Adjusted for other variables, higher PPI levels are associated with an increased 10-year cardiovascular disease risk (P<0.001). PPI ≥50 was associated with CKD, and all PPI groups, except <30, were associated with DM. Conclusion PPI predicts 10-year CVD risk and is associated with DM presence. PPI can be considered a risk factor for both cardiovascular disease and DM. Additionally, PPI ≥50 is associated with CKD.

Purpose: Gastric cancer is one of the most common cancers in Korea, and the proportion of upper-third gastric cancers has been steadily increasing over the last two decades. This study aimed to evaluate the effect of tumor location on gastric cancer prognosis. Materials and Methods: We retrospectively reviewed 2,466 patients who underwent gastrectomy for pathologically proven gastric cancer between January 2011 and December 2016.The patients were divided into an upper-third group (U group; n=419, 17.0%) and a middle- and lower-third group (ML group; n=2,047, 83.0%). Clinicopathological characteristics, overall survival (OS), and recurrence-free survival (RFS) after surgery were compared. Results: The U group had more advanced disease than the ML group and a higher incidence of N3b disease for T3 (12.0% vs. 4.9%, p=0.023) and T4 tumors (33.3% vs. 17.5%, p=0.001).The 5-year RFS rate for stage III disease was marginally lower in the U group than that in the ML group (47.1% vs. 56.7%, p=0.082). The upper third location was an independent prognostic factor for both OS (hazard ratio [HR], 1.350; 95% confidence interval [CI], 1.065–1.711) and RFS (HR, 1.430; 95% CI, 1.080–1.823). Conclusions Upper-third gastric cancer shows extensive node metastasis compared to those located more distally in ≥T3 tumors. The upper third location is an independent prognostic factor for both OS and RFS and may have an adverse impact on RFS, particularly in patients with stage III gastric cancer.