Publication
Ultrasound-Derived Muscle Biomarkers as a Gateway toward Quantitative Pediatric Frailty in Palliative Care
Peñafiel J, Mingorence A, Mrtín V, Martino Alba R · Journal of Palliative Medicine · 2026
Background: Adult frailty scales rely on performance tests that are rarely feasible in children. Quantitative muscle ultrasound (US) could provide an objective bridge, but its pediatric utility is untested.Objective: To explore whether US-derived biomarkers Z-scores can cluster into recognizable patterns that serve as preliminary building blocks toward a quantitative definition of pediatric frailty.Design and Setting: Eight-month, single-center exploratory cross-sectional observational study in a pediatric palliative care unit.Participants: Twenty-four consecutively enrolled children (1?18 years) with advanced but clinically stable, life-limiting conditions (83.5% neurometabolic, 16.5% oncologic), recruited with parental informed consent.Procedures: Rectus femoris and vastus intermedius were scanned with standardized presets (64 dB gain; 4 cm depth). Age- or weight-adjusted Z-scores for muscle thickness, echo-intensity (EI), and subcutaneous-fat were derived from a published pediatric dataset. Each child was plotted in 3-D ?health cubes? (x = muscle-thickness z, y = EI z, z = subcutaneous-fat z).Results: All patients lay outside the ± 2 SD ?normality cube? for at least one reference set.Median (IQR) muscle-thickness z = ?4.9 (?5.6 to ?4.1); EI z = +3.7 (+2.1 to +5.8).Thickness and EI were inversely correlated (? = ?0.62, p = 0.002); lower thickness modestly predicted shorter time-to-death (? = ?0.55, p = 0.01).Conclusions: In this modest, single-center cohort, pediatric palliative patients consistently deviated from normative muscle US ranges, forming a distinct cluster outside the ?healthy? 3-D zone. While not diagnostic, these patterns support further, larger, longitudinal studies to develop composite US indices and formal cut-points for pediatric frailty. This visual framework may accelerate that process by highlighting multidimensional outliers at the bedside.
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