Purpose: This study assessed the reproducibility and clinical value of the vascular index (VI), derived from superb microvascular imaging (SMI) using Doppler ultrasonography, for evaluating renal function in transplanted kidneys. Methods: This retrospective study included 63 renal transplant patients who underwent grayscale and Doppler ultrasonography with SMI from January 2022 to February 2023. The VI of the transplanted kidneys was measured using three methods (VI_box, VI_F1, VI_F2). The VI was compared across chronic kidney disease (CKD) groups categorized by estimated glomerular filtration rate (eGFR) and Kidney Disease: Improving Global Outcomes (KDIGO) CKD risk groups based on eGFR and albuminuria. The correlation between VI and renal function was evaluated. Univariate and multivariate linear regression analyses were used to identify predictors of eGFR. Results: Significant differences in VI were observed among CKD groups based on eGFR (VI_box, P=0.001; VI_F1, P<0.001; VI_F2, P<0.001) and KDIGO CKD groups based on eGFR and albuminuria (VI_box, P=0.039; VI_F1, P=0.001; VI_F2, P<0.001). VI_F1 and VI_F2 demonstrated moderate/high correlations with eGFR (r=0.627, P<0.001 and r=0.657, P<0.001, respectively) and serum creatinine (r=-0.626, P<0.001 and r=-0.649, P<0.001, respectively). VI_box moderately correlated with eGFR (r=0.445, P<0.001). Multivariate regression identified the urine albumincreatinine ratio (ACR) (adjusted odds ratio [aOR], 1.122; 95% confidence interval [CI], -0.007 to, 0.000; P=0.030) and VI_F2 (aOR, 1.114; 95% CI, 0.466 to 1.235; P<0.001) were independently associated with eGFR. Conclusion The VI measured by drawing a region of interest along the border of the transplanted kidney in SMI (VI_F2) is highly reproducible and correlates well with eGFR. Both VI_F2 and ACR independently predict eGFR.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the reproducibility and clinical value of the vascular index (VI), derived from superb microvascular imaging (SMI) using Doppler ultrasonography, for evaluating renal function in transplanted kidneys. Methods: This retrospective study included 63 renal transplant patients who underwent grayscale and Doppler ultrasonography with SMI from January 2022 to February 2023. The VI of the transplanted kidneys was measured using three methods (VI_box, VI_F1, VI_F2). The VI was compared across chronic kidney disease (CKD) groups categorized by estimated glomerular filtration rate (eGFR) and Kidney Disease: Improving Global Outcomes (KDIGO) CKD risk groups based on eGFR and albuminuria. The correlation between VI and renal function was evaluated. Univariate and multivariate linear regression analyses were used to identify predictors of eGFR. Results: Significant differences in VI were observed among CKD groups based on eGFR (VI_box, P=0.001; VI_F1, P<0.001; VI_F2, P<0.001) and KDIGO CKD groups based on eGFR and albuminuria (VI_box, P=0.039; VI_F1, P=0.001; VI_F2, P<0.001). VI_F1 and VI_F2 demonstrated moderate/high correlations with eGFR (r=0.627, P<0.001 and r=0.657, P<0.001, respectively) and serum creatinine (r=-0.626, P<0.001 and r=-0.649, P<0.001, respectively). VI_box moderately correlated with eGFR (r=0.445, P<0.001). Multivariate regression identified the urine albumincreatinine ratio (ACR) (adjusted odds ratio [aOR], 1.122; 95% confidence interval [CI], -0.007 to, 0.000; P=0.030) and VI_F2 (aOR, 1.114; 95% CI, 0.466 to 1.235; P<0.001) were independently associated with eGFR. Conclusion The VI measured by drawing a region of interest along the border of the transplanted kidney in SMI (VI_F2) is highly reproducible and correlates well with eGFR. Both VI_F2 and ACR independently predict eGFR.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the reproducibility and clinical value of the vascular index (VI), derived from superb microvascular imaging (SMI) using Doppler ultrasonography, for evaluating renal function in transplanted kidneys. Methods: This retrospective study included 63 renal transplant patients who underwent grayscale and Doppler ultrasonography with SMI from January 2022 to February 2023. The VI of the transplanted kidneys was measured using three methods (VI_box, VI_F1, VI_F2). The VI was compared across chronic kidney disease (CKD) groups categorized by estimated glomerular filtration rate (eGFR) and Kidney Disease: Improving Global Outcomes (KDIGO) CKD risk groups based on eGFR and albuminuria. The correlation between VI and renal function was evaluated. Univariate and multivariate linear regression analyses were used to identify predictors of eGFR. Results: Significant differences in VI were observed among CKD groups based on eGFR (VI_box, P=0.001; VI_F1, P<0.001; VI_F2, P<0.001) and KDIGO CKD groups based on eGFR and albuminuria (VI_box, P=0.039; VI_F1, P=0.001; VI_F2, P<0.001). VI_F1 and VI_F2 demonstrated moderate/high correlations with eGFR (r=0.627, P<0.001 and r=0.657, P<0.001, respectively) and serum creatinine (r=-0.626, P<0.001 and r=-0.649, P<0.001, respectively). VI_box moderately correlated with eGFR (r=0.445, P<0.001). Multivariate regression identified the urine albumincreatinine ratio (ACR) (adjusted odds ratio [aOR], 1.122; 95% confidence interval [CI], -0.007 to, 0.000; P=0.030) and VI_F2 (aOR, 1.114; 95% CI, 0.466 to 1.235; P<0.001) were independently associated with eGFR. Conclusion The VI measured by drawing a region of interest along the border of the transplanted kidney in SMI (VI_F2) is highly reproducible and correlates well with eGFR. Both VI_F2 and ACR independently predict eGFR.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Obstructed hemivagina and ipsilateral renal anomaly (OHVIRA) syndrome is a rare Müllerian duct anomaly, commonly characterized by uterus didelphys, obstructed hemivagina, and ipsilateral renal agenesis. While these are the three most common genitourinary anomalies in OHVIRA syndrome, a spectrum of urogenital anomalies can be present. Knowledge of this spectrum is crucial for proper patient management and treatment planning. In this case series, we report on five patients with OHVIRA syndrome, each presenting with a urogenital anomaly other than the typical renal agenesis or uterus didelphys. We highlight the gynecological complications encountered, which clinicians and radiologists should be aware of.

Obstructed hemivagina and ipsilateral renal anomaly (OHVIRA) syndrome is a rare Müllerian duct anomaly, commonly characterized by uterus didelphys, obstructed hemivagina, and ipsilateral renal agenesis. While these are the three most common genitourinary anomalies in OHVIRA syndrome, a spectrum of urogenital anomalies can be present. Knowledge of this spectrum is crucial for proper patient management and treatment planning. In this case series, we report on five patients with OHVIRA syndrome, each presenting with a urogenital anomaly other than the typical renal agenesis or uterus didelphys. We highlight the gynecological complications encountered, which clinicians and radiologists should be aware of.

Purpose: This study assessed the reproducibility and clinical value of the vascular index (VI), derived from superb microvascular imaging (SMI) using Doppler ultrasonography, for evaluating renal function in transplanted kidneys. Methods: This retrospective study included 63 renal transplant patients who underwent grayscale and Doppler ultrasonography with SMI from January 2022 to February 2023. The VI of the transplanted kidneys was measured using three methods (VI_box, VI_F1, VI_F2). The VI was compared across chronic kidney disease (CKD) groups categorized by estimated glomerular filtration rate (eGFR) and Kidney Disease: Improving Global Outcomes (KDIGO) CKD risk groups based on eGFR and albuminuria. The correlation between VI and renal function was evaluated. Univariate and multivariate linear regression analyses were used to identify predictors of eGFR. Results: Significant differences in VI were observed among CKD groups based on eGFR (VI_box, P=0.001; VI_F1, P<0.001; VI_F2, P<0.001) and KDIGO CKD groups based on eGFR and albuminuria (VI_box, P=0.039; VI_F1, P=0.001; VI_F2, P<0.001). VI_F1 and VI_F2 demonstrated moderate/high correlations with eGFR (r=0.627, P<0.001 and r=0.657, P<0.001, respectively) and serum creatinine (r=-0.626, P<0.001 and r=-0.649, P<0.001, respectively). VI_box moderately correlated with eGFR (r=0.445, P<0.001). Multivariate regression identified the urine albumincreatinine ratio (ACR) (adjusted odds ratio [aOR], 1.122; 95% confidence interval [CI], -0.007 to, 0.000; P=0.030) and VI_F2 (aOR, 1.114; 95% CI, 0.466 to 1.235; P<0.001) were independently associated with eGFR. Conclusion The VI measured by drawing a region of interest along the border of the transplanted kidney in SMI (VI_F2) is highly reproducible and correlates well with eGFR. Both VI_F2 and ACR independently predict eGFR.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.