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Rosuvastatin and atorvastatin are the most widely prescribed moderate- to high-intensity statins, but evidence on their efficacy and safety during actual use is limited. This study compared the real-world effectiveness and safety of rosuvastatin and atorvastatin among adults with data in the China Renal Data System and UK Biobank databases. Visual Abstract. Comparative Effectiveness and Safety of Atorvastatin Versus Rosuvastatin: Rosuvastatin and atorvastatin are the most widely prescribed moderate- to high-intensity statins, but evidence on their efficacy and safety during actual use is limited. This study compared the real-world effectiveness and safety of rosuvastatin and atorvastatin among adults with data in the China Renal Data System and UK Biobank databases. Background: Rosuvastatin and atorvastatin are the most widely prescribed moderate- to high-intensity statins. However, evidence on their efficacy and safety during actual use is limited. Objective: To compare the real-world effectiveness and safety of rosuvastatin and atorvastatin. Design: Active comparator cohort study using target trial emulation. Setting: The China Renal Data System (CRDS) and UK Biobank (UKB) databases. Participants: Adults newly prescribed rosuvastatin or atorvastatin. Measurements: The primary outcome was all-cause mortality. Cox proportional hazards regressions were used after 1:1 multilevel propensity score matching. Results: Among the 285 680 eligible participants in both databases, 6-year all-cause mortality was lower for rosuvastatin than for atorvastatin (2.57 vs. 2.83 per 100 person-years in the CRDS database and 0.66 vs. 0.90 per 100 person-years in the UKB database), with differences in cumulative incidence of −1.03% (95% CI, −1.44% to −0.46%) in the CRDS database and −1.38% (CI, −2.50% to −0.21%) in the UKB database. For secondary outcomes in both databases, rosuvastatin conferred lower risks for major adverse cardiovascular events and major adverse liver outcomes. In the UKB database, the risk for development of type 2 diabetes mellitus was higher with rosuvastatin, and the 2 medications carried similar risks for development of chronic kidney disease and other statin-related adverse effects. Limitation: Possible residual confounding. Conclusion: This study found differences in risks for some important outcomes associated with rosuvastatin and atorvastatin. The differences were relatively small, and many did not meet traditional standards for statistical significance. Further research is needed to understand whether these findings can be used with confidence in clinical practice. Primary Funding Source: National Key R&D Program of China and National Natural Science Foundation of China. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is a significant contributor to the development of end-stage renal disease and cardiovascular disease (CVD), with inflammation being a critical factor in its pathogenesis. The aim of this study is to examine the relationship between the neutrophil-to-lymphocyte ratio (NLR), a new inflammatory marker, and mortality from all causes and CVD in patients with DKD. Methods: This multicenter, retrospective cohort study utilized data from the China Renal Data System (CRDS) on patients with DKD hospitalized between January 1, 2000, and February 28, 2023. The patients' demographic information, along with their initial clinical and laboratory results, were collected and recorded. Follow-up continued until July 1, 2023, and patients were categorized into two groups based on the median baseline NLR. The Cox proportional hazards regression, Restricted cubic spline (RCS) curves, The Kaplan–Meier curve, Fine–Gray competing risk model, Time-dependent ROC and subgroup analysis were used to analyze the association between all-cause mortality and CVD mortality in patients having DKD with varying NLR. Results: This study included 11,427 patients who had been clinically diagnosed with DKD. Baseline NLR was associated with C-reactive protein, procalcitonin, high-sensitivity C-reactive protein, plasma D-dimer, cystatin C, creatinine, urea nitrogen, brain natriuretic peptide, and eGFR. We selected the demographic characteristics, differential factors from univariate analysis, and clinically DKD-related laboratory indicators as covariates for Cox analysis. Results indicated that NLR was an independent risk factor for both all-cause and CVD mortality after adjusting for the relevant variables. The risk of all-cause death and CVD death in the high NLR group was 4.688 and 2.141 times higher, respectively, compared to the low NLR group (HR = 4.688, 95% CI 1.153– 19.061, P = 0.031; HR = 2.141, 95% CI 1.257– 3.644, P = 0.005). However, potential confounding factors and biases, such as unmeasured variables and the influence of treatment interventions, could not be fully accounted for. Conclusion: NLR can independently predict the risk of all-cause and CVD mortality in patients with DKD. Identifying individuals with a high NLR and providing further intervention could be crucial measures to reduce both all-cause and CVD mortality. However, the results should be interpreted with caution due to the study's limitations. [ABSTRACT FROM AUTHOR]

Background Cardiorenal syndrome highlights the bidirectional relationship between kidney and heart dysfunction. N-terminal pro-B-type natriuretic peptide (NT-proBNP), which is the gold standard biomarker in heart failure (HF), may be an important biomarker for chronic kidney disease (CKD) progression. However, NT-proBNP is negatively related with estimated glomerular filtration rate (eGFR). In this study, we investigated the association of NT-proBNP, eGFR, and progression of kidney disease in CKD patients without HF. Methods This multicentric retrospective cohort study recruited 23 860 CKD patients without HF, who had at least one NT-proBNP record from China Renal Data System database. Linear regression model evaluated the relationship between eGFR and NT-proBNP. Cox regression analysis assessed the association between NT-proBNP and CKD progression. Sensitivity analysis examined the robustness of the main findings. Results This study involved 23 860 CKD patients without HF, distributed across different CKD stages: 10 526 in stages G1-2, 4665 in G3a, 3702 in G3b, 2704 in G4, and 2263 in G5. NT-proBNP was negatively correlated with eGFR, particularly in stages 4–5 CKD. A 15-unit decrease in eGFR was associated with increases in log (NT-proBNP) levels by 1.04-fold, 1.27-fold, 1.29-fold, 1.80-fold, and 3.50-fold for stages 1–2, 3a, 3b, 4, and 5, respectively. After excluding patients who developed CKD progression within 1 year, the Cox regression analysis revealed that the relationship between NT-proBNP and CKD progression was not significant in stages 4 and 5. However, for stages 1–3, each standard deviation increase in log (NT-proBNP) was associated with a 26%, 36%, and 28% higher risk of CKD progression, with P interaction ≤.001. The hazard ratios were 1.26 (95% confidence intervals (CI), 1.18 to 1.35), 1.36 (95% CI, 1.22 to 1.51), and 1.28 (95% CI, 1.14 to 1.43) for stages 1–2, stage 3a, and stage 3b, respectively. Conclusions Despite its strong inverse association with eGFR, NT-proBNP was positively associated with the risk of progression of kidney disease in CKD patients with stages 1–3 without HF. Future studies should investigate the effectiveness of NT-proBNP as a predictive biomarker for the progression of kidney disease across diverse racial groups and healthcare settings. [ABSTRACT FROM AUTHOR]

Author(s): Mengqi Xiong [sup.1] , Long Wang [sup.1] , Licong Su [sup.1] , Weihong Luo [sup.1] , Yanqin Li [sup.1] , Lu Li [sup.2] , Sheng Nie [sup.1] , Fan [...], Background Few studies to date have analyzed the epidemiology of acute kidney injury (AKI) in children with cancer in developing countries. The aim of this study was to assess the incidence, risk profile and outcomes of AKI in Chinese children hospitalized with cancer. Methods This multi-center study analyzed Chinese children hospitalized with cancer in 2013-2015. Electronic hospital and laboratory databases were screened to select pediatric patients with malignancy who had at least two Scr results within any 7-day window during their first 30 days of hospitalization. AKI events were identified and staged according to Kidney Disease Improving Global Outcomes (KDIGO) criteria. The incidence of and risk factors for AKI were analyzed, as were mortality rate, incidence of kidney recovery, and length of hospital stay. Results Of the 9828 children with cancer, 1657 (16.9%) experienced AKI events, including 549 (5.6%) community-acquired (CA-AKI) and 1108 (11.3%) hospital-acquired AKI (HA-AKI) events. The three types of cancer with the highest incidence of AKI were urinary system cancer (25.8%), hepatic cancer (19.4%), and retroperitoneal malignancies (19.1%). The risk factor profiles of CA-AKI and HA-AKI events differed, with many HA-AKI events due to treatment with nephrotoxic agents. In-hospital death rates were 5.4% (90 of 1657) in children with and 0.9% (74 of 8171) in children without AKI events. AKI events were also associated with longer hospitalization and higher daily costs. Conclusions AKI events are common among Chinese children hospitalized for cancer and are associated with adverse in-hospital outcomes.

Background The diagnosis of acute myocardial infarction (AMI) using high-sensitivity cardiac troponin T (hs-cTnT) remains challenging in patients with kidney dysfunction. Methods In this large, multicenter cohort study, a total of 20 912 adults who underwent coronary angiography were included. Kidney function–specific cut-off values of hs-cTnT were determined to improve the specificity without sacrificing sensitivity, as compared with that using traditional cut-off value (14 ng/L) in the normal kidney function group. The diagnostic accuracy of the novel cut-off values was validated in an independent validation cohort. Results In the derivation cohort (n  = 12 900), 3247 patients had an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Even in the absence of AMI, 50.2% of participants with eGFR <60 mL/min/1.73 m2 had a hs-cTnT concentration ≥14 ng/L. Using 14 ng/L as the threshold of hs-cTnT for diagnosing AMI led to a significantly reduced specificity and positive predictive value in patients with kidney dysfunction, as compared with that in patients with normal kidney function. The kidney function–specific cut-off values were determined as 14, 18 and 48 ng/L for patients with eGFR >60, 60–30 and <30 mL/min/1.73 m2, respectively. Using the novel cut-off values, the specificities for diagnosing AMI in participants with different levels of kidney dysfunction were remarkably improved (from 9.1%–52.7% to 52.8–63.0%), without compromising sensitivity (96.6%–97.9%). Similar improvement of diagnostic accuracy was observed in the validation cohort (n  = 8012). Conclusions The kidney function–specific cut-off values of hs-cTnT may help clinicians to accurately diagnose AMI in patients with kidney dysfunction and avoid the potential overtreatment in practice. [ABSTRACT FROM AUTHOR]

Right ventricle (RV) segmentation is essential for assessing cardiac function in patients with pulmonary hypertension, tetralogy of Fallot, and so on. However, it remains difficult due to the complex structure of heart, thin myocardium and large variability of the RV, as well as the interferences from the fat nearby. Due to its high temporal and spatial resolution, cardiac cine magnetic resonance imaging is widely used for functional evaluation of the heart. This article reviews the commonly-used methods for RV segmentation from the cardiac cine magnetic resonance images. The traditional algorithms are described first, followed by the novel multi-atlas and deep learning methods. Lastly, the evaluation standards for RV segmentation are introduced. It is concluded that the deep learning-based segmentation methods may have the potential to become the method of choice in clinical settings due to their high efficiency and accuracy in the diagnosis and prognosis of the heart-related diseases.

Introduction Glaucoma is a major chronic eye disease characterized by optic nerve damage and visual field defects (1, 2). Its onset is often insidious, with a risk of irreversible visual [...], BACKGROUND. Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. METHODS. We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. RESULTS. The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively. CONCLUSION. Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING. National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

Cardiac right ventricle (RV) segmentation plays an essential role in the functional analysis of heart diseases, such as pulmonary hypertension. The myocardium of RV is thin and irregular-shaped, making the traditional segmentation methods less effective. To improve RV segmentation, a COLLATE (Consensus Level, Labeler Accuracy and Truth Estimation) fusion-based multi-atlas method was developed. The preprocessed target image was first registered to atlas images with a B-spline algorithm optimizing normalized mutual information. The registration coefficients obtained were then used to get a rough RV segmentation for COLLATE fusion. Shape-constrained region growing algorithm was used to correct the segmentation errors. Ten cardiac magnetic resonance datasets were blindly selected to compare the performance of RV segmentation between the method developed and a method based on deep learning. The results of manual segmentation were used as the golden standard. Ejection fraction (EF) calculated with the proposed segmentation method showed better correlation and consistency with the golden standard, relative to the results calculated with the deep learning method.

Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data. The compressed sensing algorithm models image reconstruction as a linear combination minimization problem that includes data fidelity terms, sparse priors, and total variation terms. Sparse representation is a key assumption of the compressed sensing theory, and the quality of reconstruction largely depends on sparse transformation. In this article, we proposed a compressed sensing low-field MRI reconstruction algorithm that combined dual-tree wavelet transform and wavelet tree sparsity. Experimental results demonstrated that the proposed algorithm had certain advantages over the conventional reconstruction algorithm, in terms of certain objective evaluation indicators.

Low-field NMR often uses permanent magnet. The signals obtained contain high level of white Gaussian noises, and have low signal-to-noise ratio (SNR). In recent years, many denoising methods have been proposed for low-field NMR measurements. Most of these methods can remove noises without losing useful information contained in the original signals. Wavelet transform is the most popular denoising method among them. In this paper, we first introduced the theory of wavelet transform analysis, followed by review of three wavelet transform denoising methods for low-field NMR, namely the wavelet threshold method, the wavelet transform modulus maximum method and the correlation of wavelet coefficient method. Finally, we showed that four parameters could be calculated to evaluate the denoising performance.