Prognostic Value of Multifrequency Bioelectrical Impedance Analysis in Chronic Obstructive Pulmonary Disease: Systematic Review
Bibliographic record
Abstract
Background and Objectives: Chronic obstructive pulmonary disease (COPD) is a systemic condition in which muscle wasting, malnutrition, and altered fluid balance strongly influence prognosis. While spirometry remains essential for diagnosis and staging, it often fails to reflect the heterogeneity of outcomes. Multifrequency bioelectrical impedance analysis (MF-BIA) enables the assessment of body composition and fluid distribution, offering additional prognostic information. This systematic review aimed to evaluate the prognostic significance of MF-BIA in COPD, with emphasis on outcomes such as mortality, exacerbations, and hospital admissions. Materials and Methods: We systematically searched PubMed, Web of Science and Scopus from inception to 29 April 2025. The earliest record retrieved was published in 1996 but was excluded during screening. Studies including COPD patients in whom MF-BIA-derived parameters were related to clinical outcomes were eligible. Risk of bias was assessed using the Newcastle–Ottawa Scale. Data on design, population, methodology, and endpoints were extracted and narratively synthesized due to heterogeneity. The review protocol was not registered. Results: Eight studies were included. Phase angle (PhA) consistently showed prognostic value, being inversely related to mortality and rehospitalizations. Fat-free mass index (FFMI) was integrated into multidimensional models, but its independent role was inconsistent. Parameters describing fluid distribution, such as Extracellular Water/Total Body Water ratio, also appeared relevant, though interpretation was often limited by the absence of consistent consideration of underlying cardiac disease. Conclusions: MF-BIA provides useful prognostic insights in COPD patients, particularly through PhA. It may refine risk stratification beyond spirometry, yet further prospective studies with standardized methods are needed to confirm its independent value. Heterogeneity of methods and small sample sizes remain important limitations.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".