Al Prediction of Sarcopenia Risk in Neurocritical ICU Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Prospective Observational Assessment.
- Who it may be relevant to
- Registry conditions: Intracerebral Hemorrhage, Subarachnoid Hemorrhage, Subdural Hematoma, Epidural Hematoma, Ischemic Stroke, Brain Neoplasms. Basic parameters: 18 years — 65 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Turkey (Türkiye)
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Artificial Intelligence-Based Prediction of Sarcopenia Risk in Intensive Care Unit Patients With Intracranial Pathology
Overview
This prospective observational study aims to evaluate sarcopenia in intensive care patients with intracranial pathologies using ultrasound and to compare the predictive performance of different artificial intelligence models. Rectus femoris muscle thickness will be measured by ultrasound on ICU admission (Day 0) and Day 7. Prealbumin levels will be assessed on Days 0, 3, and 7, and the modified Nutrition Risk in Critically Ill (mNUTRIC) score will be calculated on the first day of ICU admission. Clinical, laboratory, and ultrasonographic data will be integrated into different artificial intelligence models to predict sarcopenia status on Day 7. The study aims to determine the effectiveness of artificial intelligence in the early identification of sarcopenia and to support future clinical decision-making in intensive care practice.
Detailed description
This study is designed as a prospective observational study. Patients admitted to the Level III Intensive Care Units of Trabzon University Faculty of Medicine, Kanuni Training and Research Hospital, Kaşüstü Campus, due to intracranial pathologies between January 1, 2026, and June 30, 2026, will be included. Approximately 100-150 patients are planned to be evaluated.
Demographic data of the enrolled patients will be recorded, and the modified Nutrition Risk in Critically Ill (mNUTRIC) score will be calculated on the first day of intensive care unit admission. Rectus femoris muscle thickness will be evaluated by ultrasonography on Day 0 and Day 7 of ICU admission. All ultrasonographic measurements will be performed using the same ultrasound device and by the same investigator according to a standardized protocol. During the measurements, the patient will be positioned supine, the knee will be kept in extension, and the muscle will be evaluated in a relaxed position. Three repeated measurements will be obtained at each assessment, and the mean value will be recorded.
As part of the laboratory assessment, prealbumin levels will be measured on Days 0, 3, and 7. Biochemical parameters evaluated during routine clinical follow-up will be recorded from the hospital information system.
No intervention, additional procedure, or treatment modification will be performed as part of this study. All data will consist of observational data obtained during routine clinical follow-up. Data collection will be conducted by a resident physician from the Department of Anesthesiology and Reanimation with experience in intensive care.
The collected clinical, laboratory, and ultrasonographic data will be provided to different artificial intelligence models, and their accuracy and performance in predicting sarcopenia development on Day 7 will be evaluated. The primary objective of the study is to assess the predictive performance of artificial intelligence models, including ChatGPT, Gemini, and Claude, for Day 7 sarcopenia development in intensive care patients with intracranial pathologies. Secondary objectives include comparing artificial intelligence predictions with clinical assessments, comparing predictive performance among different artificial intelligence models, and evaluating the potential usability of artificial intelligence models as clinical decision-support tools in intensive care practice.
All data will be de-identified before analysis, and patient confidentiality will be maintained. Study data will be stored in a secure digital environment accessible only to the research team.
Interventions
- Other Prospective Observational Assessment
Prospective observational assessment including rectus femoris ultrasonography, prealbumin measurements, mNUTRIC scoring, and collection of routine clinical data. No experimental intervention or treatment modification will be performed.
Primary outcome measures
- Accuracy of Artificial Intelligence Models in Predicting Day-7 Sarcopenia [Time frame: 7 Days]
Secondary outcome measures (2)
- Comparison of Predictive Performance Among AI Models [Time frame: 7 Days]
- Agreement Between AI Predictions and Clinical Assessment [Time frame: 7 Days]
Eligibility 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Turkey (Türkiye) · 1 center
- Trabzon University Faculty of Medicine, Kanuni Training and Research Hospital, Trabzon, 61 — Trabzon
Publications
- Phongpreecha T, Ghanem M, Reiss JD, Oskotsky TT, Mataraso SJ, De Francesco D, Reincke SM, Espinosa C, Chung P, Ng T, Costello JM, Sequoia JA, Razdan S, Xie F, Berson E, Kim Y, Seong D, Szeto MY, Myers F, Gu H, Feister J, Verscaj CP, Rose LA, Sin LWY, Oskotsky B, Roger J, Shu CH, Shome S, Yang LK, Tan Y, Levitte S, Wong RJ, Gaudilliere B, Angst MS, Montine TJ, Kerner JA, Keller RL, Shaw GM, Sylvest PMID 40133525
- Lopez-Gomez JJ, Sanchez-Lite I, Fernandez-Velasco P, Izaola-Jauregui O, Cebria A, Perez-Lopez P, Gonzalez-Gutierrez J, Estevez-Asensio L, Primo-Martin D, Gomez-Hoyos E, Jorge-Godoy E, De Luis-Roman DA. Artificial intelligence-assisted rectus femoris ultrasound vs. L3 computed tomography for sarcopenia assessment in oncology patients: establishing diagnostic cut-offs for muscle mass and quality. Fr PMID 41080186
- Choi YH, Kim DH, Jeon ET, Lee HJ, Park TY, Yoon SH, Jin KN, Lee HW. Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia. Sci Rep. 2024 Jul 23;14(1):16912. doi: 10.1038/s41598-024-67625-2. PMID 39043882
Identifiers
NCT: NCT07712198 · 10496660-2026-25030