ArticleCritical care (London, England)2021
Early prediction of hemodynamic interventions in the intensive care unit using machine learning.
Article in Critical care (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 31 citations in OpenAlex.
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 2026Review
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Development and external validation of a machine learning model for predicting severe immediate postoperative complications in adults undergoing cardiac surgery.BMC anesthesiology · 2026Article
- Stratifying mortality risk in hypotension using outcome-oriented clustering of vital sign trajectories.iScience · 2026Article
- Temporal Analysis of Diastolic Shock Index in Patients with Septic Shock and Its Correlation with Clinical Outcomes in an Indian Setting: A Prospective Observational Study.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Pre-CRRT furosemide and mortality in sepsis-associated AKI: A retrospective cohort study.PloS one · 2026Article
- Tele-Intensive Care Unit-associated Early Recognition of In-hospital Hemodynamic Events and Clinical Outcomes: A Multicenter Observational Study.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Development and validation of a dynamic early warning system with time-varying machine learning models for predicting hemodynamic instability in critical care: a multicohort study.Critical care (London, England) · 2025Article
- Fluid management strategies in critically ill patients with ARDS: a narrative review.European journal of medical research · 2025Review
- Large Language Model-Based Critical Care Big Data Deployment and Extraction: Descriptive Analysis.JMIR medical informatics · 2025Article
- Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Clinical Features and Physiological Signals Fusion Network for Mechanical Circulatory Support Need Prediction in Pediatric Cardiac Intensive Care Unit.IEEE journal of biomedical and health informatics · 2025Article
- Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.Journal of intensive medicine · 2024Review
- Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study.Frontiers in cardiovascular medicine · 2024Article
- Article
- Possibilities and challenges for artificial intelligence and machine learning in perioperative care.BJA education · 2023Review
- Dynamic Arterial Elastance to Predict Mean Arterial Pressure Decrease after Reduction of Vasopressor in Septic Shock Patients.Life (Basel, Switzerland) · 2022Article
- External validation of a machine learning model to predict hemodynamic instability in intensive care unit.Critical care (London, England) · 2022Article
- Machine Learning and Antibiotic Management.Antibiotics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions.
methodsWe used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings.
resultsHSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold.
conclusionsThe HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.