Association of the blood urea nitrogen-to-left ventricular ejection fraction ratio with contrast-induced nephropathy in patients with acute coronary syndrome who underwent percutaneous coronary intervention

Effect of Serum Bilirubin Levels on Contrast-induced Acute Kidney Injury: A Systematic Evaluation and Meta-analysis

Contrast-induced acute kidney injury (CI-AKI) is an important complication following the use of iodinated contrast media. Bilirubin has a protective effect but may also aggravate CI-AKI. The purpose of this systematic review was to assess whether bilirubin is a risk factor for CI-AKI. We searched the databases PubMed, Embase, Web of Science, Cochrane Library, Scopus, Ovid Medline, CNKI (China National Knowledge Infrastructure), VPCS (Vip Paper Check System), Wanfang, and CBM (Chinese BioMedical Literature Database) from the initial date to May 6, 2023. We summarized the results by directly combining the effect-size odds ratio (OR) and 95% confidence interval (CI) and identified sources of heterogeneity through subgroup analysis, sensitivity analysis, and meta-regression analysis. A total of 10 studies (14 data sets) were included: 7 retrospective studies (10 data sets) and 3 prospective studies (4 data sets), involving 12776 participants. The incidence of CI-AKI of 16% (95% CI: 14-19%). Total bilirubin was positively associated with the occurrence of CI-AKI (OR = 1.80; 95% CI: 1.36-2.38). Both low and high bilirubin concentrations were risk factors for CI-AKI. The incidence of CI-AKI was higher in the low bilirubin group than in the high bilirubin group.

Blood-urea-nitrogen-to-serum-albumin ratio in predicting the value of patients with contrast-induced nephropathy for coronary heart disease

BACKGROUND

The blood-urea-nitrogen (BUN)-to-serum-albumin (ALB) ratio (BAR) has been identified as a novel indicator of both inflammatory and nutritional status, exhibiting a correlation with adverse cardiovascular outcomes. This study aims to investigate the potential predictive value of BAR levels at admission for the development of CIN in patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI).

METHODS

Retrospective data were collected from patients who were admitted and underwent CAG or PCI between January 2018 and December 2022 at the Cardiac Medical Center of Union Hospital of Fujian Medical University, and the patients were divided into CIN and non-CIN groups. The BAR was computed by dividing the BUN count by the ALB count. Using multiple variable logistic regression, risk variables associated with the development of CIN were found.

RESULTS

A total of 156 patients developed CIN (7.78%). The development of CIN was predicted by a BAR ratio > 4.340 with a sensitivity of 84.0% and a specificity of 70.2%, according to receiver operating characteristic (ROC) analysis. BAR, female gender, diuretic use, and statin medication use were found to be independent predictors of CIN using multifactorial analysis.

CONCLUSIONS

When patients are receiving CAG/PCI, BAR is a simple-to-use marker that can be used independently to predict the presence of CIN.

New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning

Acute ischemic stroke (AIS) is a syndrome characterized by high morbidity, prevalence, mortality, recurrence and disability. The longer the delay before proper treatment of a stroke, the greater the likelihood of brain damage and disability. Computed tomography and nuclear magnetic resonance are the primary choices for fast diagnosis of AIS in the early stage, which can provide certain information about infarction location and degree, and even the vascular distribution of lesions responsible for strokes. However, this is quite difficult to achieve in small clinics or at-home diagnoses. Hematology tests could quickly obtain a large number of pathology-related indicators, and offer an effective method for rapid AIS diagnosis when combined with the machine learning technique. To explore a reliable, predictable method for early clinical etiologic diagnosis of AIS, a retrospective study was deployed on 456 AIS patients at the early stage and 28 reference subjects without the symptoms of AIS, by means of the selected significant traits amongst 64 clinical and blood traits in conjunction with powerful machine learning strategies. Five representative biomarkers were closely related to cardioembolic (CE), 22 to large artery atherosclerosis (LAA), and 15 to small vessel occlusion (SVO) strokes, respectively. With these biomarkers, different etiologic subtypes of stroke patients were determined with high accuracy of >0.73, sensitivity of >0.73, and specificity of >0.70, which was comparable to the accuracy obtained in the emergency department by clinical diagnosis. The proposed method may offer an alternative strategy for the etiologic diagnosis of AIS at the early stage when integrating significant blood traits into machine learning.

A rapid and safe strategy was proposed for clinical etiologic diagnosis of acute ischemic stroke at the early stage using clinical hematology traits and machine learning. Blood biomarkers were effectively identified.