Observational studyCritical care medicine2016
Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.
Observational study in Critical care medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.Scientific reports · 2026Trial
- Predictive analytics characterize cardiovascular function and outcomes following cardiac surgery.Pediatric research · 2026Article
- Predicting pulmonary hemorrhage in very low birth weight infants.Pediatric research · 2026Article
- Cardiovascular and Autonomic Phenotypes Reveal Distinct Mechanisms of Sepsis Decompensation via Deep Learning.Research square · 2026Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Equipping mathematical models for hospital dynamics using information theory.NPJ digital medicine · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Machine Learning Models for the Early Real-Time Prediction of Deterioration in Intensive Care Units-A Novel Approach to the Early Identification of High-Risk Patients.Journal of clinical medicine · 2025Article
- Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.Computational and structural biotechnology journal · 2025Article
- Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure.Scientific reports · 2024Article
- New Hemodynamic Parameters in Peri-Operative and Critical Care-Challenges in Translation.Sensors (Basel, Switzerland) · 2023Review
- Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.Pediatric research · 2023Review
- Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.Learning health systems · 2023Article
- Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis.Physiological reports · 2022Article
- Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data.Anesthesiology · 2022Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Engaging clinicians early during the development of a graphical user display of an intelligent alerting system at the bedside.International journal of medical informatics · 2022Article
- Development and validation of a deep-learning-based pediatric early warning system: A single-center study.Biomedical journal · 2022Observational
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectivesPatients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness.
designWe report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation).
settingUniversity of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. PATIENTS: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar.
conclusionsSubacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.
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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.