Prevention of Contrast-induced Nephropathy in Patients Undergoing Percutaneous Coronary Intervention

Contrast-Induced Nephropathy (CIN) After Invasive Treatment of Acute Coronary Syndromes-Predictors, Short and Long-Term Outcome

Background/Objectives: Contrast-induced nephropathy (CIN) is a well-documented complication following coronary angiography and percutaneous coronary intervention (PCI). This study aims to evaluate the predictors of CIN and its effect on outcomes in patients with acute coronary syndrome (ACS). Methods: A retrospective study included 1579 patients who underwent coronary angiography or PCI. Results: The overall incidence of in-hospital CIN was 6.8%, with the highest incidence in the group with eGFR <30 mL/min/1.73 m2 at 21.6%. Non-radial vascular access was an independent predictor of CIN occurrence (OR = 2.06 [1.37-3.08]; p < 0.001). The risk of death within 30 days was influenced by history of stroke (OR = 4.94 [1.58-15.51]; p = 0.006), glucose level on admission (per 10-unit increase) (OR = 1.07 [1.04-1.1]; p < 0.001), occurrence of CIN (OR = 5.64 [2.49-12.79]; p < 0.001), and hemoglobin level (OR = 0.77 [0.65-0.92]; p = 0.003). The risk of death within 365 days was increased by age (OR = 1.05 [1.02-1.07]; p < 0.001), history of stroke (OR = 2.45 [1.02-5.89]; p = 0.046), glucose levels on admission (per 10-unit increase) (OR = 1.05 [1.03-1.08]; p < 0.001), occurrence of CIN (OR = 2.62 [1.42-4.84]; p = 0.002), and hemoglobin concentration (OR = 0.78 [0.7-0.88]; p = 0.003). An independent predictor of hospitalization for acute or exacerbation of chronic renal failure was baseline creatinine concentration (OR = 3.44 [2.4-4.93]; p < 0.001). Conclusions: The incidence of CIN is significant, particularly in patients with severe pre-existing renal impairment. Non-radial vascular access is an independent predictor of CIN. The occurrence of CIN is a strong independent predictor of both short-term and long-term mortality.

Segmentation of renal vessels on non-enhanced CT images using deep learning models

OBJECTIVE

To evaluate the possibility of performing renal vessel reconstruction on non-enhanced CT images using deep learning models.

MATERIALS AND METHODS

177 patients' CT scans in the non-enhanced phase, arterial phase and venous phase were chosen. These data were randomly divided into the training set (n = 120), validation set (n = 20) and test set (n = 37). In training set and validation set, a radiologist marked out the right renal arteries and veins on non-enhanced CT phase images using contrast phases as references. Trained deep learning models were tested and evaluated on the test set. A radiologist performed renal vessel reconstruction on the test set without the contrast phase reference, and the results were used for comparison. Reconstruction using the arterial phase and venous phase was used as the gold standard.

RESULTS

Without the contrast phase reference, both radiologist and model could accurately identify artery and vein main trunk. The accuracy was 91.9% vs. 97.3% (model vs. radiologist) in artery and 91.9% vs. 100% in vein, the difference was insignificant. The model had difficulty identify accessory arteries, the accuracy was significantly lower than radiologist (44.4% vs. 77.8%, p = 0.044). The model also had lower accuracy in accessory veins, but the difference was insignificant (64.3% vs. 85.7%, p = 0.094).

CONCLUSION

Deep learning models could accurately recognize the right renal artery and vein main trunk, and accuracy was comparable to that of radiologists. Although the current model still had difficulty recognizing small accessory vessels, further training and model optimization would solve these problems.

Efficacy of High-Dose Statins in Preventing Contrast-Induced Nephropathy Post-cardiac Catheterization

Contrast-induced nephropathy (CIN) is defined as an increase in serum creatinine (s-CR) of at least 0.5 mg/dL or a 25% or greater increase from baseline within 48-72 hours of contrast agent administration. High-dose statins have been proposed as a prophylactic measure against CIN. This updated systematic review and meta-analysis aimed to assess the efficacy of peri-procedurally administered high-dose statins in preventing CIN in patients who underwent cardiac catheterization. A comprehensive literature review of PubMed and Embase was conducted with a distinct focus on studies published in the last 15 years up to June 2024. The study included randomized controlled trials (RCTs) that assessed the impact of high-dose statin treatment during or around the time of cardiac catheterization on the rate of contrast-induced nephropathy (CIN). It excluded trials comparing low-dose statins or statins used in non-cardiac surgeries, as well as studies with missing data or unclear end points on CIN. The primary objective was to evaluate the efficacy of high-dose statins administered peri-angiography to prevent CIN. Random effects were used using the DerSimonian and Laird method. A subgroup analysis was performed to assess the effectiveness of high-dose statins in preventing CIN in patients on chronic statin therapy. Our pooled analysis of 2,312 participants revealed a significantly lower incidence of CIN (odds ratio (OR): 0.47, 95% confidence interval (CI): 0.30-0.72, P = 0.0007, I2: 38%) in the high-dose statin group compared to the control group. However, the subgroup analysis showed no benefit of high-dose statins in decreasing the incidence of CIN in individuals already on chronic statin therapy (OR: 1.03, 95% CI: 0.33-3.18, P = 0.97). Our study highlights the beneficial effect of high-dose statins in preventing CIN in statin-naive patients. However, no benefit was observed in patients who were on chronic statin therapy.

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans

This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans with normal appearance of the left UUT were chosen for this study. The dataset was divided into training (n = 130) and validation sets (n = 20). The test set contained 29 randomly chosen cases with computed tomography urography (CTU) and NECT scans, all with normal appearance of the left UUT. An experienced radiologist marked out the left renal pelvis and ureter on each scan. Two types of frameworks (entire and sectional) with three types of DL models (basic UNet, UNet3 + and ViT-UNet) were developed, and evaluated. The sectional framework with basic UNet model achieved the highest mean precision (85.5%) and mean recall (71.9%) on the test set compared to all other tested methods. Compared with CTU scans, this method had higher axial UUT recall than CTU (82.5% vs 69.1%, P < 0.01). This method achieved similar or better visualization of UUT than CTU in many cases, however, in some cases, it exhibited a non-ignorable false-positive rate. The proposed DL method demonstrates promising potential in automated 3D UUT segmentation on NECT scans. The proposed DL models could remarkably improve the efficiency of UUT reconstruction, and have the potential to save many patients from invasive examinations such as CTU. DL models could also serve as a valuable complement to CTU.