ArticleCritical care explorations2020
Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System.
Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.Frontiers in medicine · 2024Review
- Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients.Journal of medical systems · 2023Article
- Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.Learning health systems · 2023Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit.PLOS digital health · 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
- 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
- Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis.Frontiers in pediatrics · 2021Article
- The Quest for System-Theoretical Medicine in the COVID-19 Era.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 can lead to sudden and severe respiratory failure that mandates endotracheal intubation, a procedure much more safely performed under elective rather than emergency conditions. Early warning of rising risk of this event could benefit both patients and healthcare providers by reducing the high risk of emergency intubation. Current illness severity scoring systems, which usually update only when clinicians measure vital signs or laboratory values, are poorly suited for early detection of this kind of rapid clinical deterioration. We propose that continuous predictive analytics monitoring, a new approach to bedside management, is more useful. The principles of this new practice anchor in analysis of continuous bedside monitoring data, training models on diagnosis-specific paths of deterioration using clinician-identified events, and continuous display of trends in risks rather than alerts when arbitrary thresholds are exceeded.
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.