Nephrology

Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models.

BACKGROUND: Aortic coarctation (CoA) is a major cause of arterial hypertension in young individuals, with recurrence occurring in up to one-third of patients throughout life despite guideline-directed management. METHODS: We conducted a development and validation study at a single centre in Berlin, Germany, utilising routinely collected electronic health records, cardiovascular magnetic resonance ...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities may be associated with stone formation, yet their combined association with KSD has not been fully explored. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2018, we conducted a cross-sectional study including adults...

A Machine-Learning Model for Accurate Diuresis Prediction in Acute Heart Failure (DRC-AHF).

AIMS: We aimed to develop a machine learning-based tool for accurate quantitative prediction of diuretic response in acute heart failure (AHF). METHOD...

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for Intra-Procedural Decision Support.

OBJECTIVE: To evaluate determinants of first-pass reperfusion and to develop an explainable machine learning framework for intra-procedural decision s...

Interpretable machine learning reveals the synergistic impact of essential hypertension on early recurrence in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) patients often ential hypertension, yet how systemic vascular stress synergizes with tumor aggressiveness to driv...

Maternal circulating lipid metabolic heterogeneity in preeclampsia: machine learning identifies clinically distinct subtypes.

BACKGROUND: Preeclampsia is associated with dyslipidemia. Maternal circulating lipid profiles may reflect underlying placental-metabolic interactions....

Artificial intelligence-based prediction of diseases among homeless populations in Bogotá: Implications for targeted interventions.

BACKGROUND: The homeless population in Bogotá exhibits complex social and health vulnerabilities, with a high prevalence of chronic and communicable d...

Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney transplantation. Both induction and maintenance ther...

Development and validation of an explainable machine learning model for predicting acute kidney injury in critically ill patients with primary peritonitis: a multicenter cohort study.

Primary peritonitis-associated acute kidney injury (PPA-AKI) represents a complex complication that substantially elevates mortality risk. Currently, ...

The evolving landscape of acute kidney injury: research directions and changes in clinical practice.

Acute kidney injury (AKI) is a common and serious disease, needing a comprehensive and management-based approach. While traditional practice focused o...

Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.

PURPOSE: To develop and validate machine learning models for predicting systemic inflammatory response syndrome (SIRS) after percutaneous nephrolithot...

Development of a LASSO-based machine learning model for relapse prediction in lupus nephritis.

OBJECTIVE: To identify the predictors of renal relapse in patients with lupus nephritis (LN) and develop a predictive model. METHODS: The patients wit...

Skin microbiome signatures track uremic pruritus severity in hemodialysis patients.

Chronic kidney disease-associated pruritus (CKD-aP) is frequent in patients receiving maintenance hemodialysis and is often difficult to control. Whil...

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acqui...

Impact of machine learning on spare parts availability for critical medical devices: a supervised machine learning perspective.

PURPOSE: This paper seeks to improve the reliability and quality of operation of the critical medical equipment methods through the combination of fai...

A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.

OBJECTIVES: Precise perioperative risk stratification for upper tract urothelial carcinoma (UTUC) is essential. We developed a multimodal prognostic m...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference stand...

High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.

OBJECTIVE: To develop and validate a high-fidelity super-resolution (SR)-enhanced radiomics framework using a Residual Channel Attention Network (RCAN...

Right Ventricular Metrics as End Points in Clinical Trials: A Review.

IMPORTANCE: Clinical trials in cardiovascular medicine aim to deliver high-quality evidence with greater efficiency, including smaller sample sizes an...

Human donor liver viability evaluation with polarization-sensitive optical coherence tomography.

Human liver transplantation is constrained by a critical shortage of viable donor livers. In response to this shortage, marginal livers from extended ...