The Pediatric Recruitment Engine: A Transparent Computational Model for Inspecting Assumptions During Positive End-Expiratory Pressure Titration
- 1General and Pediatric Anesthesia and Intensive Care Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Azienda Ospedaliero-Universitaria di Bologna Policlinico di Sant’Orsola, Bologna, ITA
Abstract
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.
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BibTeX
@article{taffache2026pediatric,
title = {{The Pediatric Recruitment Engine: A Transparent Computational Model for Inspecting Assumptions During Positive End-Expiratory Pressure Titration}},
author = {Paolo Taffache},
journal = {Cureus},
year = {2026},
month = oct,
doi = {10.7759/cureus.117389},
url = {https://europepmc.org/article/PMC/PMC13726375},
note = {CC BY},
}
RIS
TY - JOUR
TI - The Pediatric Recruitment Engine: A Transparent Computational Model for Inspecting Assumptions During Positive End-Expiratory Pressure Titration
AU - Paolo Taffache
PY - 2026
DA - 2026/10/03
JO - Cureus
DO - 10.7759/cureus.117389
UR - https://europepmc.org/article/PMC/PMC13726375
AB - 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.
SN - 2168-8184
ER -