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Displaying 571 - 580 of 6736 in Annals of Internal Medicine
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Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs: A Risk Prediction Study: Annals of Internal Medicine: Vol 177, No 4
Background: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. Objective: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. Design: Risk prediction study. Setting: Outpatients potentially eligible for primary cardiovascular prevention. Participants: The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. Measurements: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. Results: Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). Limitation: Retrospective study design using electronic medical records. Conclusion: On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. Primary Funding Source: None.
Sodium–Glucose Cotransporter-2 Inhibitors and the Risk for Dialysis and Cardiovascular Disease in Patients With Stage 5 Chronic Kidney Disease
Background: No studies have reported the long-term outcomes of initiating sodium–glucose cotransporter-2 inhibitors (SGLT2is) in patients with estimated glomerular filtration rates less than 20 mL/min/1.73 m2 to predialysis. Objective: To compare the risk for dialysis, cardiovascular events, and death between SGLT2i users and nonusers in patients with type 2 diabetes (T2D) and stage 5 chronic kidney disease (CKD). Design: Target trial emulation study. Setting: Taiwan’s National Health Insurance Research Database (NHIRD). Participants: By applying sequential target trial emulation principle, 23 854 SGLT2i users and 23 892 SGLT2i nonusers were selected from the NHIRD for patients with T2D and stage 5 CKD from 1 May 2016 to 31 October 2021. Measurements: Conditional Cox proportional hazards models were used to compare the risks for dialysis, hospitalization for heart failure, acute myocardial infarction (AMI), diabetic ketoacidosis (DKA), acute kidney injury (AKI), and all-cause mortality between SGLT2i users and nonusers. Results: In the intention-to-treat model, compared with no SGLT2i use, SGLT2i use was associated with lower risks for dialysis (hazard ratio [HR], 0.34 [95% CI, 0.27 to 0.43]), hospitalization for heart failure (HR, 0.80 [CI, 0.73 to 0.86]), AMI (HR, 0.61 [CI, 0.52 to 0.73]), DKA (HR, 0.78 [CI, 0.71 to 0.85]), and AKI (HR, 0.80 [CI, 0.70 to 0.90]), but there was no difference in the risk for all-cause mortality (HR, 1.11 [CI, 0.99 to 1.24]). The Kaplan–Meier curves and subgroup analyses also showed that initiation of an SGLT2i in stage 5 CKD was associated with a lower risk for long-term dialysis than no SGLT2i use. Limitation: This result may not apply to patients without T2D. Conclusion: This emulated target trial showed that SGLT2i use was associated with a lower risk for dialysis, cardiovascular events, DKA, and AKI than no SGLT2i use in patients with T2D and stage 5 CKD. Primary Funding Source: National Health Research Institutes, Taiwan.