Biomarkers for intensive care unit-acquired weakness: a systematic review for prediction, diagnosis and prognosis
Bibliographic record
Abstract
BACKGROUND: Intensive care unit-acquired weakness (ICU-AW) is a common and debilitating complication in critically ill patients, significantly affecting both short- and long-term outcomes. The existing ICU-AW diagnostic methods are not widely accepted and have a narrow application window. Biomarkers offer potential for diagnosing, predicting, and prognosticating ICU-AW, but a comprehensive synthesis of the available evidence is still lacking. METHODS: We conducted a systematic search across PubMed, Cochrane Library, Embase, Web of Science, CNKI, Wanfang Database, China Science and Technology Journal Database (VIP Database), and China Biomedical Literature Database (SinoMed Database) from inception to January 23, 2025. Study quality was assessed using the revised Newcastle-Ottawa scale and the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Data extraction included basic characteristics of the included studies, name of biomarkers, objective, specimen types, sampling time, type of biomarker, ICU-AW diagnostic criteria, and outcomes. RESULTS: Out of 5,769 publications screened, 11 studies of moderate to high quality (scores ≥ 6) involving 1,176 critically ill patients were included. Ten biomarkers were identified and categorized into five mechanisms: muscle injury (myoglobin, N-titin, urinary titin), metabolic pathway (glucose transporter protein type-4), neurological injury (neurofilament light/heavy chain), stress response (growth differentiation factor-15), and inflammatory process (monocyte chemoattractant protein-1, NETs marker cfDNA, and miR-181a). Six biomarkers demonstrated strong predictive and diagnostic accuracy with AUC values exceeding 0.80. Notably, growth differentiation factor-15 exhibited excellent clinical utility across diagnostic, predictive, and prognostic applications (AUC ≥ 0.85). The remaining four biomarkers showed moderate performance, with AUC values ranging from 0.60 to 0.80. CONCLUSION: While ten biomarkers exhibit potential for ICU-AW assessment, their clinical utility remains inconsistent. This highlights the need for large-scale, prospective validation studies and the incorporation of advanced technologies to refine existing biomarkers and identify novel candidates for ICU-AW prediction, diagnosis and management. DATE OF REGISTRATION: Registered 1 August 2024. TRIAL REGISTRATION: PROSPERO ID: CRD42024574437.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".