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. 2024 Sep 19;24(1):334.
doi: 10.1186/s12871-024-02720-5.

Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study

Suwan Dai  1 , Yingchun Ren  2 , Lingyan Chen  1 , Min Wu  1 , Rong Wang  3 , Qinghe Zhou  4
Affiliations

Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study

Suwan Dai et al. BMC Anesthesiol. .

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.

Keywords: Catheter-related bladder discomfort; General anesthesia; Machine learning; Nomogram; Prediction model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
ROC curve of six machine learning models in the training cohort and validation cohort
Fig. 2
Fig. 2
Feature importance ranking based on Random Forest (RF) in the training cohort
Fig. 3
Fig. 3
Nomogram for estimating moderate-to-severe CRBD
See this image and copyright information in PMC

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