Abstract / Summary
Positive end-expiratory pressure (PEEP) can reduce lung collapse, while higher pressures may cause overdistension and unfavorable dead space or pressure effects.The Pediatric Recruitment Engine (PRE) v0.7 is an open-source computational model for supervised teaching about these competing assumptions.It represents 2,560 heterogeneous virtual lung units in 16 conceptual regions.A fixed sequence of increasing and decreasing PEEP updates each unit's probability of remaining open and produces model-derived indices of recruitment, collapse, mechanics, shunt, oxygenation, dead space, carbon dioxide clearance, and regional balance.In one seeded scenario for a 2-year-old, 12-kg child under the moderate impairment preset, several balance and clearance indices selected 16 cmH2O, while the intentionally monotonic oxygenation surrogate selected the upper tested value of 24 cmH2O.Re-running the frozen code reproduced its archived numerical outputs, and 10 scripted execution and expected-behavior checks passed.These checks do not establish independent code verification or physiologic validity.A focused exploratory sensitivity analysis showed that some selected indices changed when illustrative model assumptions were varied.PRE uses no patient-level data, has not been calibrated or externally validated, and cannot recommend patient-specific PEEP.Its code and archived release are publicly available; the full mathematical specification and new exploratory analyses accompany this revision.