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 postoperative 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.
In a study based on observational vital sign data, models of normality were built for a specific post-operative patient population based on three different metrics calculated from the vital sign distributions REF .
Modelling physiological deterioration in post-operative patient vital-sign data
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing
Computer Science Medicine