Clinical trial · Observational
Al Prediction of Sarcopenia Risk in Neurocritical ICU Patients
Artificial Intelligence-Based Prediction of Sarcopenia Risk in Intensive Care Unit Patients With Intracranial Pathology
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 26, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260926-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Brain Neoplasms | Brain Neoplasm | ONTOLOGY_EXACT | 0.90 |
| Epidural Hematoma | — | UNRESOLVED | — |
| Intracerebral Hemorrhage | — | UNRESOLVED | — |
| Ischemic Stroke | — | UNRESOLVED | — |
| Subarachnoid Hemorrhage | — | UNRESOLVED | — |
| Subdural Hematoma | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Prospective Observational Assessment | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Intracranial Pathology ICU Patients
- description
- Adult patients admitted to the intensive care unit with intracranial pathologies, including intracerebral hemorrhage, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, intracranial tumors, and ischemic stroke. Participants will be prospectively observed. Rectus femoris muscle thickness will be measured by ultrasonography on days 0 and 7, and prealbumin levels will be assessed on days 0, 3, and 7. No experimental intervention or treatment modification will be performed.
- interventionNames
- Other: Prospective Observational Assessment
Primary outcomes (1)
- measure
- Accuracy of Artificial Intelligence Models in Predicting Day-7 Sarcopenia
- timeFrame
- 7 Days
- description
- Evaluation of the predictive performance of ChatGPT, Gemini, and Claude models for day-7 sarcopenia in ICU patients with intracranial pathology using rectus femoris muscle thickness, prealbumin levels, and clinical data.
Secondary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 65 Years
Show eligibility criteria text
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
References
Publications (3)
- BACKGROUNDPhongpreecha 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, Sylvester KG, Fuerch J, Chock V, Gaskari S, Stevenson DK, Sirota M, Prince LS, Aghaeepour N. AI-guided precision parenteral nutrition for neonatal intensive care units. Nat Med. 2025 Jun;31(6):1882-1894. doi: 10.1038/s41591-025-03601-1. Epub 2025 Mar 25. PMID 40133525
- BACKGROUNDLopez-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. Front Nutr. 2025 Sep 25;12:1678989. doi: 10.3389/fnut.2025.1678989. eCollection 2025. PMID 41080186
- BACKGROUNDChoi 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