• Innovation
    Promotion of technological and clinical innovation in critical care
  • Grip
    In-vitro and in-vivo research adherent to clinical practice and relevant for ICU every-day activities
  • Sharing
    International spread of ideas, innovation and research

Our philosophy

The GRIP (Group for Research in Intensive care in Pavia) is founded in 2015 by a group of intensivists working at Intensive Care Units of Policlinico S. Matteo in Pavia. We are a group of young doctors and researchers who dedicated in the last years great energy, enthusiasm and time to develop new ideas, improve technology and optimize quality of care for critical patients. Our group is characterized by strong international connections for both clinical research and technological developement. Our missions are:


  1. 1

    Intensive care units are highly technological; therefore, development of innovative instruments and optimization of existing ones can have a deep clinical impact. We have strong national and international collaborations with research and development sections of industries involved in the field and with many universities in order to push technology forward.

  2. 2

    Our aim is to promote and support a research projects gripping the real world. First, this means we support research with high clinical impact and strong everyday applicability. Second, we support researchers, offering work possibilities for young professionals.

  3. 3

    We aim to share our ideas, projects and results with scientific community; we have strong national and international research cooperation and  researcher exchange programs with multiple university centers.

Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated data

Collecting the results of international fellows coming to Pavia from Eindhoven Technical University for research on respiratory patients: machine learning for detection and classification of patient-ventilator asynchronies. Free full text here on Computer Methods and Programs in Biomedicine 1-s2-0-s0169260722007143-gr1


Background and objective

Mechanical ventilation is a lifesaving treatment for critically ill patients in an Intensive Care Unit (ICU) or during surgery. However, one potential harm of mechanical ventilation is related to patient–ventilator asynchrony (PVA). PVA can cause discomfort to the patient, damage to the lungs, and an increase in the length of stay in the ICU and on the ventilator. Therefore, automated detection algorithms are being developed to detect and classify PVAs, with the goal of optimizing mechanical ventilation. However, the development of these algorithms often requires large labeled datasets; these are generally difficult to obtain, as their collection and labeling is a time-consuming and labor-intensive task, which needs to be performed by clinical experts.


In this work, we aimed to develop a computer algorithm for the automatic detection and classification of PVA. The algorithm employs a neural network for the detection of the breath of the patient. The development of the algorithm was aided by simulations from a recently published model of the patient-ventilator interaction.


The proposed method was effective, providing an algorithm with reliable detection and classification results of over 90% accuracy. Besides presenting a detection and classification algorithm for a variety of PVAs, here we show that using simulated data in combination with clinical data increases the variability in the training dataset, leading to a gain in performance and generalizability.


In the future, these algorithms can be utilized to gain a better understanding of the clinical impact of PVAs and help clinicians to better monitor their ventilation strategies.

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