ReviewCurrent opinion in critical care2018
Predicting adverse hemodynamic events in critically ill patients.
Review in Current opinion in critical care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Predictive Value of the CRP/Albumin Ratio in Mortality Prediction in Hip Fracture Surgery Patients Admitted to Intensive Care.Life (Basel, Switzerland) · 2026Article
- Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.Journal of anesthesia, analgesia and critical care · 2026Review
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Role of artificial intelligence in pediatric intensive care: a survey of healthcare staff perspectives in Saudi Arabia.Frontiers in pediatrics · 2025Article
- Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning.World journal of critical care medicine · 2024Review
- Early Physiologic Numerical and Waveform Characteristics of Simulated Hemorrhagic Events With Healthy Volunteers Donating Blood.Critical care explorations · 2024Article
- Artificial intelligence in critical illness and its impact on patient care: a comprehensive review.Frontiers in medicine · 2023Review
- Artificial Intelligence in Critical Care Medicine.Critical care (London, England) · 2022Review
- Ground truth labels challenge the validity of sepsis consensus definitions in critical illness.Journal of translational medicine · 2022Article
- The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.Frontiers in pediatrics · 2022Article
- The importance of a sepsis layered early warning system for critical patients.American journal of translational research · 2022Review
- Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.Critical care (London, England) · 2020Article
- Predicting tachycardia as a surrogate for instability in the intensive care unit.Journal of clinical monitoring and computing · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
purpose of reviewThe art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. RECENT
findingsData mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. SUMMARY: Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.