ArticleJournal of clinical monitoring and computing2019
Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.
Article in Journal of clinical monitoring and computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supporting clinical reasoning through visual summarization and presentation of patient data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.Scientific reports · 2026Trial
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.Learning health systems · 2023Article
- Supporting Decision-Making About Patient Mobility in the Intensive Care Unit Nurse Work Environment: Work Domain Analysis.JMIR nursing · 2022Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Intelligent Clinical Decision Support.Sensors (Basel, Switzerland) · 2022Article
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study.Frontiers in pediatrics · 2022Article
- Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.PLOS digital health · 2022Article
- Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.International journal of nursing studies advances · 2021Article
- External validation of a novel signature of illness in continuous cardiorespiratory monitoring to detect early respiratory deterioration of ICU patients.Physiological measurement · 2021Article
- Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.Applied clinical informatics · 2021Article
- Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2021Article
- Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study.JMIR biomedical engineering · 2021Article
- Dynamic data in the ED predict requirement for ICU transfer following acute care admission.Journal of clinical monitoring and computing · 2021Article
- Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis.Frontiers in pediatrics · 2021Article
- Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System.Critical care explorations · 2020Article
- Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring.Journal of clinical monitoring and computing · 2020Article
- A narrative review of heart rate and variability in sepsis.Annals of translational medicine · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score.
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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.