ArticleCritical care (London, England)2020
Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.
Article in Critical care (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- Extrapolation of MAP trends predicts hypotension and vasopressor up-titration in the ICU.Critical care (London, England) · 2026Article
- Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study.PLoS medicine · 2026Article
- Continuous Evaluation Frameworks for Retrospective Evaluation of Clinical Machine Learning Models.Critical care (London, England) · 2026Article
- PhysioJEPA: Joint Embedding Representations of Physiological Signals for Real Time Risk Estimation in the Intensive Care Unit.Proceedings of machine learning research · 2025Article
- Predicting hypotension, syncope, and fracture risk in patients indicated for antihypertensive treatment: the STRATIFY models.Nature communications · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.BMC medical informatics and decision making · 2025Article
- Pediatric Sepsis Phenotype in a Single-Center Cohort Covering 2010-2020: Evolution in Day 1-Day 3 Trajectory and Potential Prognostic Value.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- Fluid management strategies in critically ill patients with ARDS: a narrative review.European journal of medical research · 2025Review
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- The development of a non-contact tachycardia and tachypnea monitoring system, a clinical trial on shock patients.Critical care (London, England) · 2025Article
- Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Review
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.Biomedical signal processing and control · 2024Article
- Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients.Journal of clinical medicine · 2024Article
- Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.Applied clinical informatics · 2023Article
- Current challenges in adopting machine learning to critical care and emergency medicine.Clinical and experimental emergency medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundEven brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients and designed an alert system for bedside implementation. MATERIALS AND
methodsFrom the Medical Information Mart for Intensive Care III (MIMIC-3) dataset, minute-by-minute vital signs were extracted. A hypotension event was defined as at least five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train the model, and the second half was used to validate the trained model. The model's performance was measured with area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Hypotension alerts were generated using risk score time series, a stacked RF model. A lockout time were applied for real-life implementation.
resultsWe identified 1307 subjects (1580 ICU stays) as the hypotension group and 1619 subjects (2279 ICU stays) as the non-hypotension group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 min, respectively, before hypotension, and AUPRC of 0.77 at 60 min before. Risk score trajectories revealed 80% and > 60% of hypotension predicted at 15 and 60 min before the hypotension, respectively. The stacked model with 15-min lockout produced on average 0.79 alerts/subject/hour (sensitivity 92.4%).
conclusionClinically significant hypotension events in the ICU can be predicted at least 1 h before the initial hypotension episode. With a highly sensitive and reliable practical alert system, a vast majority of future hypotension could be captured, suggesting potential real-life utility.
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