RecruitingNCT07712198

Artificial Intelligence-Based Prediction of Sarcopenia Risk in Intensive Care Unit Patients With Intracranial Pathology

Sponsor: Trabzon Kanuni Education and Research Hospital

Trial Details

Condition	Intracerebral Hemorrhage, Subarachnoid Hemorrhage, Subdural Hematoma, Epidural Hematoma, Ischemic Stroke, Brain Neoplasms
Intervention	Prospective Observational Assessment
Status	Recruiting
Participants	100
Est. Completion	30 Aug 2026
Last Updated	17 July 2026

Primary Outcome

Accuracy of Artificial Intelligence Models in Predicting Day-7 Sarcopenia

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * Age between 18 and 65 years * Admission to the intensive care unit due to intracranial pathology (intracerebral hemorrhage, epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, intracranial tumors, or ischemic stroke) * Informed consent obtained from the patient or legally authorized representative Exclusion Criteria: * Age \<18 years or \>65 years * Failure to achieve nutritional targets according to ESPEN guidelines * Palliative care or home care patients * Morbid obesity (BMI ≥40 kg/m²) * History of neuromuscular disease * Lower extremity amputation * History of trauma affecting the thigh region * Pregnancy

Trial Sites

Countries

Turkey (Türkiye)

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Related Analysis

Predicting ICU-Acquired Sarcopenia in Intracranial Pathology: Turkish Trial Evaluates AI Models Against Ultrasound Muscle Metrics

A prospective observational trial at Trabzon Kanuni Education and Research Hospital is evaluating whether AI models can predict ICU-acquired sarcopenia by Day 7 in patients admitted with acute intracranial pathologies including stroke, haemorrhage, and brain tumours. The study integrates serial rectus femoris ultrasound, prealbumin levels, and mNUTRIC scoring to train and compare multiple AI architectures. Findings could inform early nutritional and rehabilitative decision-making in one of critical care's most functionally vulnerable patient groups.

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