Video Experimental Relacionado

Updated: Feb 4, 2026

Assessment of Vascular Function in Patients With Chronic Kidney Disease

Assessment of Vascular Function in Patients With Chronic Kidney Disease

Published on: June 16, 2014

Predicción de factores de riesgo y cambios electrocardiográficos en pacientes con enfermedad renal crónica

Sanjaya Kumar Panigrahi¹, Madhuchhanda Pattnaik², Aruna Acharya³

¹Department of Physiology, 627109 MKCG Medical College and Hospital, Berhampur, Ganjam, 760004, India.

Journal of basic and clinical physiology and pharmacology

|February 3, 2026

Abstracto

Objectives:

Chronic kidney disease (CKD) is a global health issue with significant morbidity and mortality, particularly due to cardiovascular events. Early identification and management of risk factors are crucial to prevent CKD progression and complications. CKD is heterogeneous with diverse etiologies and presentations, generalizing across populations challenging. This study aims to develop accurate predictive models for cardiovascular events in CKD patients.

Methods:

Biosensors capture key parameters, including SpO₂ (Oxygen saturation), PR (Pulse rate), Pi (Perfusion index), RRp (Respiration rate), and PVi (Pleth variability index), enabling comprehensive evaluation of physiological dynamics in CKD patients. Stacked Auto-Encoders (SAEs) are applied for diagnostics. Genetic risk score (GRS) and nongenetic risk score (NGRS) models are developed using natural logarithms of odds ratios (OR) of risk factors.

Results:

The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).

Conclusions:

The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).

Palabras clave:

aprendizaje automático automático enfermedad renal crónica (ERC)puntuación de riesgo genético optimización de hiperparámetros puntuación de riesgo no genético autoencoders apilados

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