ArticleBiomedical engineering online2011
Model-based PEEP optimisation in mechanical ventilation.
Article in Biomedical engineering online, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 147 citations in OpenAlex.
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- A single compartment model to describe lung functionality: A comprehensive study.Physiological reports · 2026Review
- Monitoring of invasive assisted mechanical ventilation: a good clinical practice document by the Italian Society of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIAARTI).Journal of anesthesia, analgesia and critical care · 2025Review
- Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.Frontiers in medicine · 2025Review
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- Pulmonary function testing dataset of pressure and flow, dynamic circumference, heart rate, and aeration monitoring.Data in brief · 2024Article
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- Respiratory pressure and split flow data collection device with rapid occlusion attachment.HardwareX · 2023Article
- Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses.Biomedical engineering online · 2023Article
- Airflow and dynamic circumference of abdomen and thorax for adults at varied continuous positive airway pressure ventilation settings and breath rates.Scientific data · 2023Article
- Non-invasive over-distension measurements: data driven vs model-based.Journal of clinical monitoring and computing · 2023Article
- Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.Biomedical engineering online · 2022Article
- Quantifying neonatal patient effort using non-invasive model-based methods.Medical & biological engineering & computing · 2022Article
- Stochastic integrated model-based protocol for volume-controlled ventilation setting.Biomedical engineering online · 2022Article
- Model-based patient matching for in-parallel pressure-controlled ventilation.Biomedical engineering online · 2022Article
- Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU.Bioengineering (Basel, Switzerland) · 2021Article
- Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients.Annals of biomedical engineering · 2021Observational
- Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support.IFAC-PapersOnLine · 2021Article
- A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms.Frontiers in physiology · 2021Article
- Measuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion.Biomedical engineering online · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.
methodsA study of 10 patients diagnosed with ALI/ARDS whom underwent recruitment manoeuvre is carried out. Airway pressure and flow data are used to identify patient-specific constant lung elastance (E lung) and time-variant dynamic lung elastance (E drs) at each PEEP level (increments of 5 cm H2O), for a single compartment linear lung model using integral-based methods. Optimal PEEP is estimated using E lung versus PEEP, Edrs-Pressure curve and E drs Area at minimum elastance (maximum compliance) and the inflection of the curves (diminishing return). Results are compared to clinically selected PEEP values. The trials and use of the data were approved by the New Zealand South Island Regional Ethics Committee.
resultsMedian absolute percentage fitting error to the data when estimating time-variant E drs is 0.9% (IQR = 0.5-2.4) and 5.6% [IQR: 1.8-11.3] when estimating constant E lung. Both E lung and E drs decrease with PEEP to a minimum, before rising, and indicating potential over-inflation. Median E drs over all patients across all PEEP values was 32.2 cmH2O/l [IQR: 26.1-46.6], reflecting the heterogeneity of ALI/ARDS patients, and their response to PEEP, that complicates standard approaches to PEEP selection. All E drs-Pressure curves have a clear inflection point before minimum E drs, making PEEP selection straightforward. Model-based selected PEEP using the proposed metrics were higher than clinically selected values in 7/10 cases.
conclusionContinuous monitoring of the patient-specific E lung and E drs and minimally invasive PEEP titration provide a unique, patient-specific and physiologically relevant metric to optimize PEEP selection with minimal disruption of MV therapy.
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