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Objective Nociceptive Assessment in Ventilated ICU Patients: A Feasibility Study Using Pupillometry and the Nociceptive Flexion Reflex
Published on: July 4, 2018
Comparison of four pressure ulcer risk assessment tools in critically ill patients
Ampornpan Theeranut1, Suchada Ninbanphot2, Panita Limpawattana3
1Faculty of Nursing and Research and Training Center for Enhancing Quality of Life of Working Age People, Khon Kaen University, Khon Kaen, Thailand.
Insights
The Braden (ALB) scale best predicts pressure ulcers (PUs) in critically ill patients, though all tested tools showed limited validity. Clinical judgment remains essential for optimal outcomes in PU prevention.
Area of Science:
- Critical Care Medicine
- Nursing Research
- Patient Safety
Background:
- Critically ill patients face a higher risk of developing pressure ulcers (PUs).
- Early identification of high-risk patients is crucial for timely interventions and improved outcomes.
- Existing PU risk assessment tools require validation in diverse patient populations.
Purpose of the Study:
- To compare the predictive validity of four pressure ulcer risk assessment tools: the Braden scale, Braden (ALB) scale, CALCULATE, and COMHON index.
- To determine optimal cut-off points for these tools in critically ill patients.
- To evaluate the performance of these tools in a Thai tertiary care hospital ICU setting.
Main Methods:
- Prospective descriptive study conducted in intensive care units (ICUs) in Thailand.
- Recruited 288 critically ill patients from January to April 2019.
- Utilized receiver operating characteristic (ROC) curves to compare tool performance in predicting PUs.
Main Results:
- The incidence of PUs was 11.1% among the study participants.
- The Braden (ALB) scale demonstrated the highest area under the ROC curve (0.74), indicating superior predictive performance.
- The CALCULATE (0.71), Braden (0.67), and COMHON (0.61) indices showed progressively lower predictive accuracy.
Conclusions:
- The Braden (ALB) scale exhibited the best performance in predicting pressure ulcer development among ICU patients.
- The predictive validity of all assessed tools was limited in this Thai patient cohort.
- Clinical judgment should complement risk assessment tools, and improved PU prediction tools are needed.
Background:
Critically ill patients are at a higher risk of developing pressure ulcers (PUs) than non-critically ill patients. Tools that aid in the early identification of those who are most at risk of PUs could help health care providers deliver early interventions and reduce unfavourable outcomes.
Aims:
To compare the validity of four PU risk tools (the Braden scale, the Braden [ALB] scale, the CALCULATE, and the COMHON index) and to demonstrate the optimal cut-off points for each tool in critically ill patients.
Design:
This was a prospective descriptive study.
Method:
This study was conducted in the intensive care units (ICUs) of a tertiary care hospital in Thailand from January to April 2019. Baseline characteristics were collected at admission to the ICUs. Skin assessment was evaluated every 24 hours. PU assessment scores were collected every 72 hours. Receiver operating characteristic curves were used to compare the performance of the tests in predicting PUs.
Results:
A total of 288 patients were recruited. The incidence of PUs was 11.1%. The Braden (ALB) scale performed the best based on the area under the receiver operating characteristic curves (area under curve 0.74), followed by the CALCULATE (area under curve 0.71), the Braden (area under curve 0.67) scale, and the COMHON (area under curve 0.61) index. At the optimal cut-off point, the Braden (ALB) scale (≤13)) and the CALCULATE (≥3) were similar in terms of performance with an area under the curve of 0.69.
Conclusion:
The Braden (ALB) performed the best at predicting PU development in ICU patients.
Relevance To Clinical Practice:
The validity of all four PU risk tools was limited in Thai patients. The scales should thus be used in conjunction with clinical judgement to provide optimal outcomes. The development of better assessment tools for the prediction of PUs is required.
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