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Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.
- Source :
- BMC Anesthesiology; 9/19/2024, Vol. 24 Issue 1, p1-12, 12p
- Publication Year :
- 2024
-
Abstract
- Background: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation. Methods: Eight hundred forty-six patients with indwelling urinary catheters who were resuscitated in a post-anesthesia care unit (PACU). Trained researchers used the CRBD 4-level assessment method to evaluate the severity of a patient's CRBD. They then inputted 24 predictors into six different machine learning algorithms. The performance of the models was evaluated using metrics like the area under the curve (AUC). Results: The AUCs of the six models ranged from 0.82 to 0.89. Among them, the RF model displayed the highest predictive ability, with an AUC of 0.89 (95%CI: 0.87, 0.91). Additionally, it achieved an accuracy of 0.93 (95%CI: 0.91, 0.95), 0.80 sensitivity, 0.98 specificity, 0.94 positive predictive value (PPV), 0.92 negative predictive value (NPV), 0.87 F1 score, and 0.07 Brier score. The logistic regression (LR) model has achieved good results (AUC:0.87) and converted into a nomogram. Conclusions: The study has successfully developed a machine learning prediction model that exhibits excellent predictive capabilities in identifying patients who may develop moderate-to-severe CRBD after undergoing general anesthesia. Furthermore, the study also presents a nomogram, which serves as a valuable tool for clinical healthcare professionals, enabling them to intervene at an early stage for better patient outcomes. [ABSTRACT FROM AUTHOR]
- Subjects :
- CATHETERIZATION complications
PREDICTIVE tests
PREDICTION models
LOGISTIC regression analysis
SEVERITY of illness index
RESUSCITATION
LONGITUDINAL method
RECOVERY rooms
BLADDER diseases
MATHEMATICAL models
CATHETERS
MACHINE learning
GENERAL anesthesia
THEORY
SENSITIVITY & specificity (Statistics)
DISEASE risk factors
Subjects
Details
- Language :
- English
- ISSN :
- 14712253
- Volume :
- 24
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Anesthesiology
- Publication Type :
- Academic Journal
- Accession number :
- 179738056
- Full Text :
- https://doi.org/10.1186/s12871-024-02720-5