Key result
A multivariate model of normality using kernel density estimates of vital-sign data successfully identified physiological deterioration in post-operative patients prior to ICU readmission or death.
Observational (n=171)
No
A multivariate model of normality using continuous vital-sign data can identify physiological deterioration in post-operative patients prior to ICU readmission or death.
May not yet support routine clinical adoption of multivariate vital-sign models; leaves open need for prospective outcome trials.
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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Pimentel et al. (2013) conducted an observational in Post-operative upper-gastrointestinal surgery (n=171). Multivariate novelty detection model (kernel density estimate) for vital signs vs. Normal recovery trajectory was evaluated on Identification of patient deterioration (ICU readmission or death) using novelty scores. A multivariate model of normality using kernel density estimates of vital-sign data successfully identified physiological deterioration in post-operative patients prior to ICU readmission or death.
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