ArticleAnnals of the American Thoracic Society2017
Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms.
Article in Annals of the American Thoracic Society, 2017. 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.
- Vital signs as biomarkers of early clinical deterioration in pediatric emergency departments: physiology, interpretation, and innovations: a narrative review.International journal of emergency medicine · 2026Review
- Construction of a predictive early warning model based on machine learning neural network for prognosis of patients with traumatic brain injury.Frontiers in surgery · 2026Article
- Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.Scientific reports · 2025Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- A Reinforcement Learning Model for Optimal Treatment Strategies in Intensive Care: Assessment of the Role of Cardiorespiratory Features.IEEE open journal of engineering in medicine and biology · 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
- Artificial Intelligence in Critical Care Medicine.Critical care (London, England) · 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
- Intelligent Clinical Decision Support.Sensors (Basel, Switzerland) · 2022Article
- Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms.Sensors (Basel, Switzerland) · 2022Review
- Decision support by machine learning systems for acute management of severely injured patients: A systematic review.Frontiers in surgery · 2022Review
- Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.NPJ digital medicine · 2021Article
- Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- 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
- Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection.Critical care explorations · 2019Article
- Automated continuous noninvasive ward monitoring: future directions and challenges.Critical care (London, England) · 2019Article
- Predicting adverse hemodynamic events in critically ill patients.Current opinion in critical care · 2018Review
- Evaluating performance of early warning indices to predict physiological instabilities.Journal of biomedical informatics · 2017Article
- A call to alarms: Current state and future directions in the battle against alarm fatigue.Journal of electrocardiologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
rationaleCardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored.
objectivesTo describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients.
methodsUsing a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after step-down unit admission, and again during the 4 hours immediately before the CRI event, between cases (ever had a CRI) and control subjects (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk. MEASUREMENTS AND MAIN
resultsEstimated risk was significantly higher for cases (918) than control subjects (1,053; P ≤ 0.001) during the initial 4-hour stable periods. Among cases, the aggregated nonpersonalized risk trend increased 2 hours before the CRI, whereas the personalized risk trend became significantly different from control subjects 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for nonpersonalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset).
conclusionsInsights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI, taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful in the dynamic allocation of technological and clinical personnel resources in acute care hospitals.
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