ArticleScientific reports2018
Predicting Patient-ventilator Asynchronies with Hidden Markov Models.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.Critical care (London, England) · 2025Pooled it
- From genes to trajectories: mapping genetic influences on Huntington's disease progression.Bioinformatics (Oxford, England) · 2026Article
- An overview of patient-ventilator asynchrony in children.Expert review of respiratory medicine · 2025Review
- Construction and application of an ICU nursing electronic medical record quality control system in a Chinese tertiary hospital: a prospective controlled trial.BMC nursing · 2024Article
- A Markov network approach for reproducing purchase behaviours observed in convenience stores.Scientific reports · 2024Article
- Closing the Gap in Patient-Ventilator Discordance Recognition.Respiratory care · 2024Article
- Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony.Respiratory care · 2024Article
- Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation.Scientific reports · 2021Article
- Etiology, incidence, and outcomes of patient-ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation.Scientific reports · 2021Article
- The potential of real-time analytics to improve care for mechanically ventilated patients in the intensive care unit: an early economic evaluation.Cost effectiveness and resource allocation : C/E · 2020Article
- Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation.Scientific reports · 2020Article
- Artificial Intelligence in the Intensive Care Unit.Critical care (London, England) · 2020Review
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
- Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities.Intensive care medicine experimental · 2019Review
- Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients.Critical care (London, England) · 2019Observational
- Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations.Journal of primary care & community healthReview
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In mechanical ventilation, it is paramount to ensure the patient's ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) - z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.
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