ArticleMedical & biological engineering & computing2013
Modelling physiological deterioration in post-operative patient vital-sign data.
Article in Medical & biological engineering & computing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Article
- Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator.Journal of clinical monitoring and computing · 2023Article
- Missing data imputation techniques for wireless continuous vital signs monitoring.Journal of clinical monitoring and computing · 2023Article
- Day-to-day progression of vital-sign circadian rhythms in the intensive care unit.Critical care (London, England) · 2021Article
- Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.Sensors (Basel, Switzerland) · 2020Article
- Trajectories of vital signs in patients with COVID-19.Resuscitation · 2020Observational
- A review of big data applications of physiological signal data.Biophysical reviews · 2019Review
- A review of recent advances in data analytics for post-operative patient deterioration detection.Journal of clinical monitoring and computing · 2018Review
- Evaluating performance of early warning indices to predict physiological instabilities.Journal of biomedical informatics · 2017Article
- Health Informatics via Machine Learning for the Clinical Management of Patients.Yearbook of medical informatics · 2015Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients' vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a "normal" recovery was constructed using a kernel density estimate, and tested with "abnormal" data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from "normal" patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen "abnormal" data, suggesting that such techniques may be used to provide early warning of adverse physiological events.
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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.