{
    "abstract": "Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics.\nChanges in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, E\nRoot mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. E\nExperimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.",
    "authors": [
        {
            "affiliation": "School of Civil Aviation & Yangtze River Delta Research Institute, Northwestern Polytechnical University, Xian, China. cong.zhou@nwpu.edu.cn.\nDept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand. cong.zhou@nwpu.edu.cn.",
            "firstname": "Cong",
            "initials": "C",
            "lastname": "Zhou"
        },
        {
            "affiliation": "Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.",
            "firstname": "J Geoffrey",
            "initials": "JG",
            "lastname": "Chase"
        },
        {
            "affiliation": "Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.",
            "firstname": "Qianhui",
            "initials": "Q",
            "lastname": "Sun"
        },
        {
            "affiliation": "Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.",
            "firstname": "Jennifer",
            "initials": "J",
            "lastname": "Knopp"
        },
        {
            "affiliation": "Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.",
            "firstname": "Merryn H",
            "initials": "MH",
            "lastname": "Tawhai"
        },
        {
            "affiliation": "GIGA-In Silico Medicine, Institute of Physics, University of Liege, Liege, Belgium.",
            "firstname": "Thomas",
            "initials": "T",
            "lastname": "Desaive"
        },
        {
            "affiliation": "Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.",
            "firstname": "Knut",
            "initials": "K",
            "lastname": "M\u00f6ller"
        },
        {
            "affiliation": "Dept of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.",
            "firstname": "Geoffrey M",
            "initials": "GM",
            "lastname": "Shaw"
        },
        {
            "affiliation": "School of Engineering, Monash University, Subang Jaya, Malaysia.",
            "firstname": "Yeong Shiong",
            "initials": "YS",
            "lastname": "Chiew"
        },
        {
            "affiliation": "Dept of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary.",
            "firstname": "Balazs",
            "initials": "B",
            "lastname": "Benyo"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2022. The Author(s).",
    "doi": "10.1186/s12938-022-00986-9",
    "journal": "Biomedical engineering online",
    "keywords": [
        "Asynchrony",
        "Hysteresis loop model",
        "Hysteretic lung mechanics",
        "Lung mechanics",
        "Mechanical ventilation",
        "Virtual patient"
    ],
    "methods": null,
    "publication_date": "2022-03-09",
    "pubmed_id": "35255922\n30419903\n34079951\n22196749\n30927170\n32798977\n33360683\n36911536\n12945994\n16896854\n30762325\n9404759\n11373508\n21235836\n30020347\n29729488\n28108684\n18091543\n19963896\n33836375\n33588912\n32250853\n29477430\n27208532\n27697371\n28196936\n31993892\n15691392\n33017952\n31853811\n29463246\n29629989\n34271958\n31715280\n30419903\n32798977\n32809842\n22809585\n22955868\n21198117\n26337953\n32007099\n25270094",
    "results": "Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. E",
    "title": "Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.",
    "xml": "<Element 'PubmedArticle' at 0x788503c6f880>"
}