ArticleSensors (Basel, Switzerland)2022
Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities.Frontiers in medical technology · 2024Pooled it
- Wearable Electronic Monitoring of Vital Signs in Hospitalised Adults: A Nursing Focused Scoping Review of Clinical, Economic and Implementation Outcomes.Journal of advanced nursing · 2026Article
- Agreement and Performance of Intermittent and Continuous Vital Signs in Critically Ill Children.Hospital pediatrics · 2026Article
- Continuous Temperature Monitoring via Wearable Devices for Fever or Infection Management in Acute Care Hospitals: Scoping Review of Clinical Implementation and Data Analytics.JMIR mHealth and uHealth · 2026Article
- Continuous remote monitoring of postoperative vital signs with the Biobeat patch: A multicenter prospective observational study.PloS one · 2026Observational
- Machine Learning for Sensor Analytics: A Comprehensive Review and Benchmark of Boosting Algorithms in Healthcare, Environmental, and Energy Applications.Sensors (Basel, Switzerland) · 2025Review
- User Acceptance of a Home Robotic Assistant for Individuals With Physical Disabilities: Explorative Qualitative Study.JMIR rehabilitation and assistive technologies · 2025Article
- A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system.Scientific reports · 2024Article
- Article
- Digitisation of emergency medicine: opportunities, examples and issues for consideration.Singapore medical journal · 2024Article
- Clinical value of CVP+VIVC in predicting fluid resuscitation in patients with septic shock.African health sciences · 2023Observational
- Data integration using information and communication technology for emergency medical services and systems.Clinical and experimental emergency medicine · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.
Indexed as
Identifiers
What Socratic 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 Socratic graph.