Toward interpretative ranges for enzymatically isolated human stromal vascular fraction: a systematic review and meta-analysis

Background Enzymatically isolated human stromal vascular fraction (SVF) is increasingly investigated in regenerative and translational medicine. However, substantial variability in reported cellular composition, viability, and cellular yield complicates biological characterization, inter-study comparability, and translational interpretation. To date, no broadly accepted interpretative ranges for SVF biological parameters have been established. Methods A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Studies reporting quantitative characterization of enzymatically isolated human stromal vascular fraction were included. Primary outcomes comprised cell viability, viable nucleated cell yield, and cellular subset distribution. Random-effects meta-analyses were performed to estimate pooled means, confidence intervals, predictive intervals, and heterogeneity metrics. An independent large-scale GMP-compliant manufacturing cohort was analyzed separately for external comparison. Results Substantial variability was observed across pooled SVF datasets. Despite significant inter-study heterogeneity, pooled analyses revealed relatively stable central tendencies for viability, cellular yield, and principal cellular subsets. Viability demonstrated a pooled estimate of 84% (95% CI: 82%–86%), while VNC yield reached approximately 2.5 × 10 5 cells/mL adipose tissue (95% CI: 2.0–3.0 × 10 5 ). Predictive intervals described broad variability distributions across heterogeneous published datasets. External comparison with 364 GMP-compliant SVF productions demonstrated concordant central estimates while showing reduced variance under standardized manufacturing conditions. Conclusion Current evidence suggests that observed SVF variability reflects the combined influence of methodological heterogeneity and intrinsic donor-dependent biological variability. Pooled quantitative distributions and predictive intervals may provide useful exploratory contextualization of currently observed SVF biological variability without defining physiological limits or rigid specification thresholds. Larger harmonized multicenter datasets integrating donor characteristics and standardized analytical methodologies will be required to establish more refined biologically informed characterization frameworks.