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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Dai, Suwan Ren, Yingchun Chen, Lingyan Wu, Min Wang, Rong Zhou, Qinghe |
| 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. |
| Related Links | https://bmcanesthesiol.biomedcentral.com/counter/pdf/10.1186/s12871-024-02720-5.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712253 |
| DOI | 10.1186/s12871-024-02720-5 |
| Journal | BMC Anesthesiology |
| Issue Number | 1 |
| Volume Number | 24 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-09-19 |
| Access Restriction | Open |
| Subject Keyword | Anesthesiology Internal Medicine Emergency Medicine Intensive Critical Care Medicine Catheter-related bladder discomfort General anesthesia Machine learning Nomogram Prediction model |
| Content Type | Text |
| Resource Type | Article |
| Subject | Anesthesiology and Pain Medicine |
| Journal Impact Factor | 2.3/2023 |
| 5-Year Journal Impact Factor | 2.6/2023 |
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