About this Research Topic
Noninvasive mechanical ventilation is an evolving field marking significant progress for both acute and chronic respiratory failure scenarios. This technology has been pivotal in managing patient-ventilator interactions while reducing asynchronies that could negate therapeutic benefits. A critical aspect of these advancements is the development and refinement of predictive models designed to forecast short-term and long-term clinical outcomes. These models, especially with recent additions of machine learning algorithms, attempt to gauge patient responses with greater accuracy. With the focus on disorders such as hypoxemic and hypercapnic respiratory failures, past efforts have considerably centered on the technical delivery of such care. However, despite progress, there is still a substantial gap in systematically analyzing predictive models by contexts of application and specific patient conditions, leading to potential inconsistencies in clinical outcomes.
This Research Topic aims to rigorously investigate and enhance the predictive capacity of non-invasive mechanical ventilation models through integrating nuanced machine learning methodologies. The goal is to assess how these sophisticated tools can improve response predictions and, consequently, patient outcomes across various clinical scenarios including prehospital settings, intensive care units, and postoperative care. We seek to identify the technological and methodological determinants that significantly impact the risk and response of patients, facilitating improved management strategies for those at heightened risk of ventilation asynchrony.
To refine the integration of clinical prediction with technological advancement, this research topic will primarily center on diverse environments where noninvasive mechanical ventilation is utilized, extending from hospital settings to less controlled environments like prehospital care. We welcome the submission of articles including original research, hypothesis & theory, mini review, brief research report, case report, clinical trial, general commentary, and study protocol addressing, but not limited to, the following themes:
- Impact of machine learning on predicting patient response in non-invasive ventilation.
- Analysis of patient-ventilator asynchrony risks and mitigative strategies through technological improvements (ventilatory modes, new algorithms, neurally adjusted ventilatory assist).
- Tailoring ventilation modes to specific populations (hypoxemic, COPD, obese).
- Strategic uses of noninvasive ventilation, extubation respiratory failure, postoperative high-risk vulnerable patients (elderly, chronic critical ill patients).
- Brief Research Report, novel or technology advances, research proposals, hypothesis and bench, clinical trials and comparative studies examining different models of ventilation support.
Collectively, these focus areas are intended to advance our understanding and application of mechanical ventilation, making it more adaptive and responsive to individual patient needs.
This Research Topic aims to rigorously investigate and enhance the predictive capacity of non-invasive mechanical ventilation models through integrating nuanced machine learning methodologies. The goal is to assess how these sophisticated tools can improve response predictions and, consequently, patient outcomes across various clinical scenarios including prehospital settings, intensive care units, and postoperative care. We seek to identify the technological and methodological determinants that significantly impact the risk and response of patients, facilitating improved management strategies for those at heightened risk of ventilation asynchrony.
To refine the integration of clinical prediction with technological advancement, this research topic will primarily center on diverse environments where noninvasive mechanical ventilation is utilized, extending from hospital settings to less controlled environments like prehospital care. We welcome the submission of articles including original research, hypothesis & theory, mini review, brief research report, case report, clinical trial, general commentary, and study protocol addressing, but not limited to, the following themes:
- Impact of machine learning on predicting patient response in non-invasive ventilation.
- Analysis of patient-ventilator asynchrony risks and mitigative strategies through technological improvements (ventilatory modes, new algorithms, neurally adjusted ventilatory assist).
- Tailoring ventilation modes to specific populations (hypoxemic, COPD, obese).
- Strategic uses of noninvasive ventilation, extubation respiratory failure, postoperative high-risk vulnerable patients (elderly, chronic critical ill patients).
- Brief Research Report, novel or technology advances, research proposals, hypothesis and bench, clinical trials and comparative studies examining different models of ventilation support.
Collectively, these focus areas are intended to advance our understanding and application of mechanical ventilation, making it more adaptive and responsive to individual patient needs.
Keywords: Noninvasive Mechanical Ventilation, Predictive Models, Machine Learning Algorithms, Patient-Ventilator Asynchrony, Respiratory Failure Management, Response, Outcome
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
