AIMC Topic: Respiratory Insufficiency

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Prediction of Respiratory Decompensation in Patients Receiving Home Mechanical Ventilation: Machine Learning Model Development and Validation Study.

JMIR formative research
BACKGROUND: Chronic respiratory diseases often require long-term ventilatory support, leading to a growing number of patients treated with home mechanical ventilation (HMV). Despite advancements in telemonitoring with real-time tracking of noninvasiv...

Development and external validation of a machine learning model to predict high flow nasal cannula failure.

BMJ open respiratory research
INTRODUCTION: High-flow nasal cannula (HFNC) is an important treatment option for acute hypoxic respiratory failure and can improve outcomes. However, patients on a prolonged duration of HFNC have worse clinical outcomes and increased mortality. It i...

Development of a machine learning-based prediction model for acute kidney injury associated with respiratory failure in the intensive care unit.

Clinical and experimental medicine
Acute kidney injury (AKI) is a frequent and severe complication in intensive care unit (ICU) patients with respiratory failure, associated with high mortality, prolonged hospitalization, and substantial healthcare burden. Conventional risk scores, su...

Machine learning-based prediction of respiratory depression during sedation for liposuction.

Scientific reports
Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation...

Monitoring effort and respiratory drive in patients with acute respiratory failure.

Current opinion in critical care
PURPOSE OF REVIEW: Accurate monitoring of respiratory drive and inspiratory effort is crucial for optimizing ventilatory support during acute respiratory failure. This review evaluates current and emerging bedside methods for assessing respiratory dr...

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

Clinical and translational gastroenterology
INTRODUCTION: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our a...

Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.

PloS one
BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increase...

Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.

Critical care (London, England)
BACKGROUND: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed...

A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.

PloS one
BACKGROUND: Patients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increased mortality rates. This study aimed to develop a robust machine learning-based risk score to predi...