Perioperative medicine & outcomes
Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator
Authors' conclusion
The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff.
Study context
- Population
- General ward patients with continuous vital signs monitoring by wearable biosensors: postoperative patients and patients with acute exacerbation of COPDtwo observational studies
- Participants
- 491 vs 200
- Comparison
- Postoperative vs COPD
- Postoperativen = 491
- COPDn = 200
- Outcome
- Discrimination of the circadian kernel density estimation (KDE) stability index between physiological stability (last 24 h prior to discharge) and the 8 h before events (SAE or EWS total score ≥ 6, ≥ 8 or ≥ 10)
Methodological detailssecondary analysis of two observational studies, continuous vital signs monitoring, kernel density estimation
- Secondary analysis of two observational studies
- Continuous vital signs monitoring
- Kernel density estimation
Performance
As reported in the abstract
Discrimination (AUROC)
AUROC, stable physiology vs early warning score (EWS) events
0.772-0.993
AUROC, stable physiology vs severe adverse events (SAEs)
0.594-0.611
Further findings
Time of early warning
- EWS events
- 2.8-5.5 h
- SAEs
- 2.5 h
Original publication
Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator. J Clin Monit Comput. Published 2 June 2023. doi:10.
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