Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Background and Objectives: Outcomes of deferring percutaneous coronary intervention (PCI) without invasive physiologic assessment for intermediate coronary lesions is uncertain.We sought to compare long-term outcomes between medical treatment and PCI of intermediate lesions without invasive physiologic assessment. Methods: A total of 899 patients with intermediate coronary lesions between 50% and 70% diameter-stenosis were randomized to the conservative group (n=449) or the aggressive group (n=450). For intermediate lesions, PCI was performed in the aggressive group, but was deferred in the conservative group. The primary endpoint was major adverse cardiac events (MACE, a composite of all-cause death, myocardial infarction [MI], or ischemia-driven any revascularization) at 3 years. Results: The number of treated lesions per patient was 0.8±0.9 in the conservative group and 1.7±0.9 in the aggressive group (p=0.001). At 3 years, the conservative group had a significantly higher incidence of MACE than the aggressive group (13.8% vs. 9.3%; hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.00–2.21; p=0.049), mainly driven by revascularization of target intermediate lesion (6.5% vs. 1.1%; HR, 5.69; 95% CI, 2.20–14.73;p<0.001). Between 1 and 3 years after the index procedure, compared to the aggressive group, the conservative group had significantly higher incidence of cardiac death or MI (3.2% vs.0.7%; HR, 4.34; 95% CI, 1.24–15.22; p=0.022) and ischemia-driven any revascularization. Conclusions For intermediate lesions, medical therapy alone, guided only by angiography, was associated with a higher risk of MACE at 3 years compared with performing PCI, mainly due to increased revascularization.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background and Objectives: Outcomes of deferring percutaneous coronary intervention (PCI) without invasive physiologic assessment for intermediate coronary lesions is uncertain.We sought to compare long-term outcomes between medical treatment and PCI of intermediate lesions without invasive physiologic assessment. Methods: A total of 899 patients with intermediate coronary lesions between 50% and 70% diameter-stenosis were randomized to the conservative group (n=449) or the aggressive group (n=450). For intermediate lesions, PCI was performed in the aggressive group, but was deferred in the conservative group. The primary endpoint was major adverse cardiac events (MACE, a composite of all-cause death, myocardial infarction [MI], or ischemia-driven any revascularization) at 3 years. Results: The number of treated lesions per patient was 0.8±0.9 in the conservative group and 1.7±0.9 in the aggressive group (p=0.001). At 3 years, the conservative group had a significantly higher incidence of MACE than the aggressive group (13.8% vs. 9.3%; hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.00–2.21; p=0.049), mainly driven by revascularization of target intermediate lesion (6.5% vs. 1.1%; HR, 5.69; 95% CI, 2.20–14.73;p<0.001). Between 1 and 3 years after the index procedure, compared to the aggressive group, the conservative group had significantly higher incidence of cardiac death or MI (3.2% vs.0.7%; HR, 4.34; 95% CI, 1.24–15.22; p=0.022) and ischemia-driven any revascularization. Conclusions For intermediate lesions, medical therapy alone, guided only by angiography, was associated with a higher risk of MACE at 3 years compared with performing PCI, mainly due to increased revascularization.

Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Objective: The association of lipid parameters with cardiovascular disease (CVD) and the impact of kidney function on this association have not been thoroughly evaluated in patients with type 2 diabetes mellitus (T2DM). Methods: Using the Korean National Health Insurance Service Cohort database, we identified 2,343,882 subjects with T2DM in 2015–2016. Baseline lipid levels and kidney function were evaluated and followed up until December 2020. Subjects were classified into three groups according to their estimated glomerular filtration rate (eGFR): ≥60, 30–59, or <30 mL/min/ 1.73 m2 . We analyzed the diabetes group with eGFR ≥60 and low-density lipoprotein cholesterol (LDL-C) <70 mg/dL as a reference group. Results: The risk of CVD began to increase at LDL-C ≥100 mg/dL in the eGFR ≥60 mL/min/m2group. The risk of CVD in the eGFR 30–59 mL/min/m2 group was increased by 43%, even in the LDL-C <70 mg/dL, and the risk increased progressively with LDL-C category. Among subjects with eGFR 30–59 mL/min/m2 , LDL-C 70–99, 100–129, 130–159, and ≥160 mg/ dL were significantly associated with the risk of CVD, with hazard ratio (95% confidence interval) of 1.48 (1.43–1.53), 1.54 (1.49–1.60), 1.55 (1.48–1.63), and 1.88 (1.77–2.00), respectively. In the eGFR <30 mL/min/m2 group, a 3.3-fold increased risk of CVD was seen, even at LDL-C <70 mg/dL. Conclusion The cutoff levels of LDL-C that increase CVD risk in patients with T2DM depend on kidney function, which influences the relationship between LDL-C and CVD risk in patients with T2DM.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.