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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative
Eveline H J Mestrom1, Tom H G F Bakkes2, Nassim Ourahou1
1Anesthesiology Department, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.
Insights
Predicting unplanned intensive care unit (ICU) admissions is crucial for patient care. Combining pre-operative, intra-operative, and post-operative factors significantly improves the prediction of ICU admissions, aiding in better patient allocation. Keywords: ICU admission prediction, patient allocation, post-operative care.
Area of Science:
- Anesthesiology
- Critical Care Medicine
- Health Informatics
Background:
- Lack of evidence-based criteria for post-anesthesia care unit (PACU) decision-making.
- Need for improved patient allocation to appropriate care levels.
- Potential to reduce unanticipated intensive care unit (ICU) admissions and improve patient outcomes.
Purpose of the Study:
- To assess if including intra- and postoperative factors improves prediction of patient deterioration.
- To evaluate the prediction of unanticipated ICU admissions.
- To inform the development of clinical decision support tools for PACU patient allocation.
Main Methods:
- Retrospective observational cohort study (January 2013 - December 2017).
- Tertiary Dutch hospital setting.
- Exclusion criteria: cardiothoracic, obstetric, day care, and pediatric surgeries, among others.
- Primary outcome: unanticipated ICU admission.
Main Results:
- 0.9% of patients (223) experienced unanticipated ICU admission, with higher mortality (13.9% vs 0.2%).
- Key predictors identified: age, BMI, anesthesia type, preoperative score, diabetes, vasopressor use, erythrocyte administration, surgery duration, PACU stay, heart rate, and oxygen saturation.
- Model achieved an area under the curve of 0.86 for predicting ICU admissions.
Conclusions:
- Combining pre-operative, intra-operative, and early post-operative factors enhances the prediction of unanticipated ICU admissions.
- Electronic medical record data integration is effective for predictive modeling.
- Highlights the necessity for clinical decision support systems in PACU for optimized patient management and allocation.
Background And Objectives:
Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions.
Methods:
A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission.
Results:
An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88).
Conclusions:
The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.
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