Predictive value of inflammatory prognostic index for contrast-induced nephropathy in patients undergoing coronary angiography and/or percutaneous coronary intervention.

The purpose of this study was to investigate the relationship between Inflammatory Prognostic Index (IPI) levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). A total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACEs), while the long-term outcome examined the all-cause readmission rate. Quartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile (P < 0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50-3.64; P = 0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35-0.69; P < 0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed (P < 0.001), which remained significant post-adjustment. For patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD).

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.

Association Between Remnant Cholesterol and Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and/or Percutaneous Coronary Intervention.

Contrast-induced nephropathy (CIN) is an acute renal complication that can occur after the use of iodinated contrast media. Remnant cholesterol (RC) is one of the markers of atherosclerotic cardiovascular disease risk. We evaluated the impact of RC on CIN and clinical outcomes after coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). Consecutive patients (n = 3332) undergoing CAG and/or PCI were assessed in this retrospective study. Patients were divided into four groups based on baseline RC levels. In the quartile analysis, RC were associated with a higher risk of CIN, especially when RC ≤0.20 or ≥0.38 mmol/L (P < .05). However, after adjustment, the association of RC with CIN was not significant. There was a significant correlation between RC and repeated revascularization in patients undergoing PCI (P < .001) and driven primarily by the highest quartile level. After adjustment, this remained statistically significant (adjusted odds ratio (aOR) 4.06; 95% CI 2.10-7.87; P < .001). This is the first large study to show a possible association between RC and the risk of CIN after CAG and/or PCI; however, this finding was not further confirmed after adjustment. The complex clinical risk profile of patients, rather than RC itself, may contribute to the risk of CIN in this high-risk subgroup.

Predictive Model of Functional Exercise Compliance of Patients with Breast Cancer Based on Decision Tree.

Objective: Regular functional exercise can help recover the functions of upper limb for patients with breast cancer. By finding the influencing factors of functional exercise compliance and constructing a predictive model, patients with a poor functional exercise compliance can be better identified. This study aims to find out the factors influencing the functional exercise compliance of patients with breast cancer and build a predictive model based on decision tree. Methods: Convenience sampling was used at two tertiary hospitals in Shantou from August 2020 to March 2021. Data of patients with breast cancer patient was obtained from questionnaires and based on demographics, Constant-Murley Score, Functional Exercise Compliance Scale for Postoperative Breast Cancer Patients, Champion Health Belief Model Scale, Social Support Rating Scale, Disease Perception Questionnaire and Family Care Index Questionnaire. Possible influencing factors of functional exercise compliance were analyzed using correlation analysis as well as univariate and binary logistic regression analysis through SPSS v25, and a CHAID decision tree was used to construct a predictive model on training, validation and test sets via SPSS Modeler v18 at a ratio of 6:2:2. Prediction accuracy, sensitivity, specificity and AUC were used to analyze the efficacy of the predictive model. Results: A total of 227 valid samples were collected, of which 145 were assessed with a poor compliance (63.9%). According to a logistic regression analysis, perceived benefits, time after surgery and self-efficacy were influencing factors. The prediction accuracy, sensitivity, specificity and AUC of the predictive model, based on a CHAID decision tree analysis, were 70.73%, 57.1%, 77.8% and 0.81 respectively. Conclusion: The predictive model, based on a CHAID decision tree analysis, had a moderate predictive efficacy, which could be used as a clinical auxiliary tool for clinical nurses to predict patients' functional exercise compliance.

Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study.

OBJECTIVE: To investigate the risk factors of cognitive frailty in elderly patients with chronic kidney disease (CKD), and to establish an artificial neural network (ANN) model. DESIGN: A cross-sectional design. SETTING: Two tertiary hospitals in southern China. PARTICIPANTS: 425 elderly patients aged ≥60 years with CKD. METHODS: Data were collected via questionnaire investigation, anthropometric measurements, laboratory tests and electronic medical records. The 425 samples were randomly divided into a training set, test set and validation set at a ratio of 5:3:2. Variables were screened by univariate and multivariate logistic regression analyses, then an ANN model was constructed. The accuracy, specificity, sensitivity, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the predictive power of the model. RESULTS: Barthel Index (BI) score, albumin, education level, 15-item Geriatric Depression Scale score and Social Support Rating Scale score were the factors influencing the occurrence of cognitive frailty (p<0.05). Among them, BI score was the most important factor determining cognitive frailty, with an importance index of 0.30. The accuracy, specificity and sensitivity of the ANN model were 86.36%, 88.61% and 80.65%, respectively, and the AUC of the constructed ANN model was 0.913. CONCLUSION: The ANN model constructed in this study has good predictive ability, and can provide a reference tool for clinical nursing staff in the early prediction of cognitive frailty in a high-risk population.