Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models

Fibrinogen/Albumin Ratio is Associated with the Occurrence of Contrast-Induced Acute Kidney Injury in Patients with Congestive Heart Failure

Purpose

Patients with congestive heart failure (CHF) are associated with an elevated risk of mortality and poor prognosis. Contrast-induced acute kidney injury (CI-AKI), a common complication in CHF patients undergoing contrast-enhanced procedures, exacerbates renal dysfunction and contributes to adverse outcomes. However, the relationship between the preoperative fibrinogen/albumin ratio (FAR) and the risk of CI-AKI or all-cause mortality in CHF remains unclear. This study analyzed the correlation of FAR with the risk of CI-AKI and all-cause mortality in patients with CHF.

Patients and Methods

In this retrospective observational study, CHF patients undergoing coronary angiography (CAG) were enrolled and grouped according to their FAR quartiles. The association between FAR and clinical outcomes was assessed using the multivariate logistic regression and restricted cubic spline (RCS) analyses.

Results

This study included 7,235 CHF patients with a mean age of 65.8 ± 11.7 years. Among these, 2,100 were female (29.0%), and 1,094 (15.1%) experienced CI-AKI. FAR showed a non-linear relationship with CI-AKI (p < 0.001). The risk of CI-AKI was significantly higher with increasing FAR. After adjusting for all the potential confounding variables, the risk of CI-AKI was highest in patients with FAR >0.150 (OR = 1.572, 95% CI 1.237-2.004, p < 0.001). Multivariate COX proportional risk model showed that the risk of all-cause mortality was highest in CHF patients with FAR > 0.150 (HR = 1.20, 95% CI 1.04-1.38, p = 0.014).

Conclusion

FAR is an independent risk factor for the occurrence of CI-AKI in patients with CHF.

Biomarkers in Contrast-Induced Nephropathy: Advances in Early Detection, Risk Assessment, and Prevention Strategies

Contrast-induced nephropathy (CIN) represents a significant complication associated with the use of iodinated contrast media (ICM), especially in individuals with preexisting renal impairment. The pathophysiology of CIN encompasses oxidative stress, inflammation, endothelial dysfunction, and hemodynamic disturbances, resulting in acute kidney injury (AKI). Early detection is essential for effective management; however, conventional markers like serum creatinine (sCr) and estimated glomerular filtration rate (eGFR) exhibit limitations in sensitivity and timeliness. This review emphasizes the increasing significance of novel biomarkers in enhancing early detection and risk stratification of contrast-induced nephropathy (CIN). Recent advancements in artificial intelligence and computational analytics have improved the predictive capabilities of these biomarkers, enabling personalized risk assessment and precision medicine strategies. Additionally, we discuss mitigation strategies, including hydration protocols, pharmacological interventions, and procedural modifications, aimed at reducing CIN incidence. Incorporating biomarker-driven assessments into clinical decision-making can enhance patient management and outcomes. Future research must prioritize the standardization of biomarker assays, the validation of predictive models across diverse patient populations, and the exploration of novel therapeutic targets. Utilizing advancements in biomarkers and risk mitigation strategies allows clinicians to improve the safety of contrast-enhanced imaging and reduce the likelihood of renal injury.

Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis

BACKGROUND

Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies.

METHODS

A comprehensive literature search following PRISMA guidelines was conducted in PubMed, Scopus, and Embase from inception to June 11, 2024. Study characteristics, ML models, performance metrics (AUC, accuracy, sensitivity, specificity, precision), and risk-of-bias assessment using the PROBAST tool were extracted. Statistical analysis used a random-effects model to pool AUC values, with heterogeneity assessed via the I2 statistic.

RESULTS

From 431 initial studies, 14 met the inclusion criteria. Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models showed the highest pooled AUCs of 0.87 (95% CI: 0.82-0.92) and 0.85 (95% CI: 0.80-0.90), respectively, with low heterogeneity (I2 < 30%). Random Forest (RF) had a similar AUC of 0.85 (95% CI: 0.78-0.92) but significant heterogeneity (I2 > 90%). Multilayer perceptron (MLP) and XGBoost models had moderate pooled AUCs of 0.79 (95% CI: 0.74-0.84) with high heterogeneity. RF showed strong accuracy (0.83, 95% CI: 0.70-0.96), while SVM had balanced sensitivity (0.69, 95% CI: 0.63-0.75) and specificity (0.73, 95% CI: 0.60-0.86). Age, serum creatinine, left ventricular ejection fraction, and hemoglobin consistently influenced model efficacy.

CONCLUSIONS

GBM and SVM models, with robust AUCs and low heterogeneity, are effective in predicting AKI and CIN post-PCI/CAG. RF, MLP, and XGBoost, despite competitive AUCs, showed considerable heterogeneity, emphasizing the need for further validation.

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

Introduction

Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.

Methods

A systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.

Results

A total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65-0.66), 0.89 (95% CI: 0.89-0.90), 12.11 (95% CI: 8.17-17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.

Discussion

Future research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.