Ho, Meng-Ni
(2018)
Assessment of Patient-Reported Outcome and Sedation-Agitation Score in Critically Ill Patients.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
When evaluating patients’ outcomes, the US health care system has shifted from a “disease control” model to a “patient-centered” model, which takes patients’ feedback into consideration to monitor the interventions and quality of care. Therefore, comparing patients’ feedback and clinicians’ assessments is an important indicator in evaluating interventions, especially of critically ill patients in the intensive care unit (ICU). In the intensive care unit, more than 70% of critically ill patients experience agitation and 40-60% of them are under mismanagement with either inadequate relief of anxiety or over-sedation.
In this project, the main goal was to assess the association between patient-reported outcome (PRO, reported by patients according to pain, sedation, discomfort questions) and patient the Sedation-Agitation Score (SAS, reported by clinicians), to take patients’ feedback into consideration to monitor interventions. The other goal is to establish the best model in predicting SAS score using PRO along with other demographic variables.
Our results show that overall there is not a strong correlation between PRO and median SAS scores. However, patients experienced variations in treatment duration and different numbers of nursing shifts during hospitalization. Treatment plan may vary; thus, SAS scores may vary within each nursing shift. Each patient has his/her own trajectory of SAS scores by shifts; therefore, considering number of shifts is one important factor to build associations between SAS score and PRO score.
In our mixed model analysis, if the model only includes number of shifts during hospitalization and PRO survey score (median level of pain score, median level of discomfort score, median level of sedation score), variables including shift, median pain and median discomfort generate a better association with median SAS score per shift. If demographic variables (age, gender, severity of illness) are included in the model, adding the age variable in the above model generates a better model fit and produces better association with median SAS score per shift compared to other demographic models. In conclusion, the best model to predict patients’ SAS scores will be using number of shifts during hospitalization, pain and discomfort scores from the PRO survey as well as the age variables.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
9 April 2018 |
| Date Type: |
Publication |
| Defense Date: |
26 March 2018 |
| Approval Date: |
9 April 2018 |
| Submission Date: |
9 April 2018 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
79 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Pharmacy > Pharmaceutical Sciences |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
patient-reported outcome, sedation-agitation score, spearman correlation, mixed model analysis |
| Date Deposited: |
09 Apr 2018 14:08 |
| Last Modified: |
09 Apr 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/34196 |
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