Abstract
Inadequate assessment of the agitation associated with clinical outcomes has an adverse impact on a patient’s wellbeing including under or oversedation. Earlier research found that the majority of nurses under-estimate more severe pain and over-estimate mild pain.
Empirical distributions of the nurses’ ratings of a patient’s agitation levels and the administered dose of a sedative are often positively skewed so that their joint distributions are non-elliptical. Therefore, the high nurses’ ratings of a patient’s agitation levels may not correspond to the cases with large doses of sedative.
Copulas measure nonlinear dependencies capturing the dependence between skewed distributions. Therefore, we propose to use a copula-based dependence measure between the nurses’ rating of patients’ agitation level and the automated sedation dose to identify the patient-specific thresholds that separate the regions of mild and severe agitation. Delineating the regions with different agitation intensities allows us to establish the regions where nurses are more likely to over or under-estimate the patient’s agitation levels.
This study uses agitation-sedation profiles of two patients collected at Christchurch Hospital, Christchurch School of Medicine and Health Sciences, NZ. Classification of patients into poor and good trackers based on Wavelet Probability Band. The best-fitting copula shows that the dependency structure between the nurses’ rating of a patient’s agitation level and the administered dose of sedative for both patients has an upper tail. Specifically, the value of the tail threshold is lower and the average magnitude of the bias in the nurses’ rating of a patient’s agitation level is smaller for a good tracker compared with a poor tracker.
Establishing the presence of tail dependence and patient-specific thresholds for areas with different agitation intensities has significant implications for the effective administration of sedatives. Better management of agitation-sedation states will allow clinicians to improve the efficacy of care and reduce healthcare costs.
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Tursunalieva, A., Hudson, I., Chase, G.: Copula modelling of agitation-sedation rating of ICU patients: towards monitoring and alerting tools. Under review for Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019), Canberra, ACT (2019)
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Tursunalieva, A., Hudson, I., Chase, G. (2019). Copula Modelling of Nurses’ Agitation-Sedation Rating of ICU Patients. In: Nguyen, H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science, vol 1150. Springer, Singapore. https://doi.org/10.1007/978-981-15-1960-4_11
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