Background: This study aims to identify the incidence and risk factors of periprosthetic infections following endoprosthetic reconstruction of femoral metastatic bone disease (MBD). In this population with MBD, the marked impact of infection on the patient’s systemic treatment highlights the importance of understanding both the incidence and associated risk factors. Methods: This retrospective cohort study included a total of 140 patients who underwent endoprosthetic reconstruction for femoral MBD at a tertiary referral hospital in South Korea between 2009 and 2019. Infection-free survival was estimated using the Kaplan-Meier method, and Cox proportional hazards model analyses were performed to evaluate the risk factors associated with periprosthetic infection. Results: The incidence of periprosthetic infection in patients who underwent endoprosthetic reconstruction for femoral MBD was 9% (12 out of 140 patients). Risk factors for periprosthetic infection were hepatocellular carcinoma (HCC) as the primary tumor (hazard ratio [HR], 6.08; 95% CI, 1.63–22.6; p = 0.007) and low preoperative absolute neutrophil count (HR, 6.99; 95% CI, 1.79–27.4; p = 0.005). Conclusions Patients with femoral MBD had a 9% risk of developing a periprosthetic infection. Given their limited life expectancy, this translated to a substantial rate of 58.9 infections per 1,000 person-joint-years. Possible risk factors for periprosthetic infection were low preoperative absolute neutrophil count and HCC as the primary tumor. The high incidence of periprosthetic infection and its associated risk factors should be considered in patients undergoing endoprosthetic reconstruction for femoral MBD.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background: This study aims to identify the incidence and risk factors of periprosthetic infections following endoprosthetic reconstruction of femoral metastatic bone disease (MBD). In this population with MBD, the marked impact of infection on the patient’s systemic treatment highlights the importance of understanding both the incidence and associated risk factors. Methods: This retrospective cohort study included a total of 140 patients who underwent endoprosthetic reconstruction for femoral MBD at a tertiary referral hospital in South Korea between 2009 and 2019. Infection-free survival was estimated using the Kaplan-Meier method, and Cox proportional hazards model analyses were performed to evaluate the risk factors associated with periprosthetic infection. Results: The incidence of periprosthetic infection in patients who underwent endoprosthetic reconstruction for femoral MBD was 9% (12 out of 140 patients). Risk factors for periprosthetic infection were hepatocellular carcinoma (HCC) as the primary tumor (hazard ratio [HR], 6.08; 95% CI, 1.63–22.6; p = 0.007) and low preoperative absolute neutrophil count (HR, 6.99; 95% CI, 1.79–27.4; p = 0.005). Conclusions Patients with femoral MBD had a 9% risk of developing a periprosthetic infection. Given their limited life expectancy, this translated to a substantial rate of 58.9 infections per 1,000 person-joint-years. Possible risk factors for periprosthetic infection were low preoperative absolute neutrophil count and HCC as the primary tumor. The high incidence of periprosthetic infection and its associated risk factors should be considered in patients undergoing endoprosthetic reconstruction for femoral MBD.

Background: This study aims to identify the incidence and risk factors of periprosthetic infections following endoprosthetic reconstruction of femoral metastatic bone disease (MBD). In this population with MBD, the marked impact of infection on the patient’s systemic treatment highlights the importance of understanding both the incidence and associated risk factors. Methods: This retrospective cohort study included a total of 140 patients who underwent endoprosthetic reconstruction for femoral MBD at a tertiary referral hospital in South Korea between 2009 and 2019. Infection-free survival was estimated using the Kaplan-Meier method, and Cox proportional hazards model analyses were performed to evaluate the risk factors associated with periprosthetic infection. Results: The incidence of periprosthetic infection in patients who underwent endoprosthetic reconstruction for femoral MBD was 9% (12 out of 140 patients). Risk factors for periprosthetic infection were hepatocellular carcinoma (HCC) as the primary tumor (hazard ratio [HR], 6.08; 95% CI, 1.63–22.6; p = 0.007) and low preoperative absolute neutrophil count (HR, 6.99; 95% CI, 1.79–27.4; p = 0.005). Conclusions Patients with femoral MBD had a 9% risk of developing a periprosthetic infection. Given their limited life expectancy, this translated to a substantial rate of 58.9 infections per 1,000 person-joint-years. Possible risk factors for periprosthetic infection were low preoperative absolute neutrophil count and HCC as the primary tumor. The high incidence of periprosthetic infection and its associated risk factors should be considered in patients undergoing endoprosthetic reconstruction for femoral MBD.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background: This study aims to identify the incidence and risk factors of periprosthetic infections following endoprosthetic reconstruction of femoral metastatic bone disease (MBD). In this population with MBD, the marked impact of infection on the patient’s systemic treatment highlights the importance of understanding both the incidence and associated risk factors. Methods: This retrospective cohort study included a total of 140 patients who underwent endoprosthetic reconstruction for femoral MBD at a tertiary referral hospital in South Korea between 2009 and 2019. Infection-free survival was estimated using the Kaplan-Meier method, and Cox proportional hazards model analyses were performed to evaluate the risk factors associated with periprosthetic infection. Results: The incidence of periprosthetic infection in patients who underwent endoprosthetic reconstruction for femoral MBD was 9% (12 out of 140 patients). Risk factors for periprosthetic infection were hepatocellular carcinoma (HCC) as the primary tumor (hazard ratio [HR], 6.08; 95% CI, 1.63–22.6; p = 0.007) and low preoperative absolute neutrophil count (HR, 6.99; 95% CI, 1.79–27.4; p = 0.005). Conclusions Patients with femoral MBD had a 9% risk of developing a periprosthetic infection. Given their limited life expectancy, this translated to a substantial rate of 58.9 infections per 1,000 person-joint-years. Possible risk factors for periprosthetic infection were low preoperative absolute neutrophil count and HCC as the primary tumor. The high incidence of periprosthetic infection and its associated risk factors should be considered in patients undergoing endoprosthetic reconstruction for femoral MBD.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background: This study compared the costs associated with transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) in Korea by utilizing the National Health Insurance Service database. Methods: Between June 2015 and May 2019, 1,468 patients underwent primary isolated transfemoral TAVI, while 2,835 patients received primary isolated SAVR with a bioprosthesis. We assessed the costs of index hospitalization and subsequent healthcare utilization, categorizing the cohort into 6 age subgroups: <70, 70–74, 75–79, 80–84, 85–89, and ≥90 years. The median follow-up periods were 2.5 and 3.0 years in the TAVI and SAVR groups, respectively. Results: The index hospitalization costs were 41.0 million Korean won (KRW) (interquartile range [IQR], 39.1–44.7) for the TAVI group and 24.6 million KRW (IQR, 21.3–30.2) for the SAVR group (p<0.001). The TAVI group exhibited relatively constant index hospitalization costs across different age subgroups. In contrast, the SAVR group showed increasing index hospitalization costs with advancing age. The healthcare utilization costs were 5.7 million KRW per year (IQR, 3.3–14.2) for the TAVI group and 4.0 million KRW per year (IQR, 2.2–9.0) for the SAVR group (p<0.001). Healthcare utilization costs were higher in the TAVI group than in the SAVR group for the age subgroups of <70, 70–74, and 75–79 years, and were comparable in the age subgroups of 80–84, 85–89, and ≥90 years. Conclusion TAVI had much higher index hospitalization costs than SAVR. Additionally, the overall healthcare utilization costs post-discharge for TAVI were also marginally higher than those for SAVR in younger age subgroups.