Efficacy of Mechanical Insufflation-Exsufflation Devices as Analyzed in Lung Models: Systematic Review and Network Meta-Analysis of Peak Expiratory Flow Data

BACKGROUND: Mechanical insufflation-exsufflation (MI-E) devices support airway clearance by generating positive and negative pressure cycles that simulate a cough. Bench studies using lung models provide a controlled environment to investigate device performance and optimize settings; however, the lack of methodological standardization limits reproducibility and clinical translation. METHODS: A systematic review and network meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in PROSPERO (International Prospective Register of Systematic Reviews) (CRD42023486537). Experimental studies using adult mechanical lung models were identified from MEDLINE, Embase, and Web of Science (search updated November 2024). Two reviewers independently extracted data and assessed methodological quality using a customized risk-of-bias tool specifically developed for bench studies of respiratory devices. The primary outcome was peak expiratory flow (PEF); secondary outcomes included insufflation and exsufflation parameters. Random-effects meta-analyses were performed. RESULTS: O) produced significantly greater PEF compared with lower pressures, particularly in obstructive models (mean difference = 134 L/min, 95% CI 2-267). Methodological quality varied widely, with limited information on calibration, sensor characteristics, and study reproducibility. CONCLUSIONS: Higher pressure differentials generally enhance PEF, but optimal MI-E settings are not universal. MI-E performance depends on pressure settings and simulated respiratory mechanics. Clinically, these findings support individualized adjustment of MI-E parameters based on patient-specific characteristics. The lack of methodological standardization and calibration reporting limits comparability. A structured protocol for MI-E bench studies is proposed to enhance reproducibility, transparency, and translational relevance.