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In their meta-analysis, Bhojani et al

In their meta-analysis, Bhojani et al. each group) with a mean age of 60 16 years and a male:female ratio of 1 1:1.19. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst overall it also discriminated between the two groups predicting both those with and without sepsis. Our model accuracy was 81.3% (95%, CI: 63.7C92.8%), sensitivity = 0.80, specificity = 0.82 and area under the curve = 0.89. Predictive values most commonly accounting for nodal points in the trees were a large proximal stone location, long stent time, large stone size and long operative time. Conclusion: Urosepsis after endourological procedures remains one of the main reasons for ICU admission. Risk factors for urosepsis are reasonably accurately predicted by our innovative ML model. Focusing on these CD4 risk factors can allow one to create predictive strategies to minimise post-operative morbidity. = 300 trees, with the test set used for internal validation. A random forests model generates a set number (i.e., 300 in this case) of TX1-85-1 random decision trees, which are then aggregated to form the single model. Diagnostic accuracy statistics (sensitivity, specificity and area under the curve) for model performance were generated using the caret package. Graphs were generated using ggplot2, and these include a receiver operator curve (ROC) for the model, along with a mean decrease gini plot (demonstrates variables ranked according to how frequently they are represented in the random trees prior to aggregationmore important variables will be represented more frequently). Explanatory graphs with individual predictions are presented following generation with the lime (local interpretable model agnostic explanations) package. The model was deployed as a shiny application using the shiny package. 3. Results A total of 114 patients were included (57 in each group) with a mean age of 60 years (16) with a male:female ratio of 1 1:1.19 in both groups (Table 1). Table 1 Patient characteristics of both Groups A and B. = 57= 57(%)26 (45.6%)26 (45.6%)Diabetes, (%)15 (26.3%)12 (21.1%)Immunosuppression/modulation, (%)3 (5.3%)1 (1.8%)Neurological disorder, (%)1 (1.8%)1 (1.8%)Previous urinary tract reconstruction, (%)1 (1.8%)0Abnormal upper tract anatomy, (%)1 (1.8%)5 (8.8%)History of recurrent UTI, (%)14 (24.6%)3 (5.3%)Emergency admission30 (52.6%)9 (15.8%)Presence of pre-operative stent, (%)33 (57.9%)26 (45.6%)Mean stent dwell time (days) SD52 6330 60Number of stones13131220133313420510Mean largest stone diameter (mm) SD10 58 4Location, (%)15 (26.3%)15 (26.3%)Mean TX1-85-1 operative time (mins) SD58 3143 23Post-operative stent insertion, (%)36 (46.2%)42 (53.8%)Stone free, (%)34 (48.6%)51 (89.5%) Open in a separate window The numbers of patients in Groups A and B with DM (= 15, 26.3% and = 12, 21.1%), immunocompromise (= 3 and 1), neurological disorder (= 1 and 1), previous urinary tract reconstruction (= 1 and 0) and abnormal upper tract anatomy (= 1 and 5) were as shown. There were 14 (24.6%) TX1-85-1 and 3 (5.3%) patients with a history of UTI for Groups A and B, respectively. Indwelling stent dwell time for Groups A and B were 52 63 days and 30 60 days for 33 and 26 patients, respectively. In each group, 31 patients (54.3%) had a single stone; the remaining (45.6%) had more than one stone (range: 2C5). The single largest stone sizes in Groups A and B were 10 5 mm and 8 4 mm, respectively. In both groups, 15 patients (26.3%) had a pre-operative positive urine culture that was treated as per local protocol. The mean operative time was 58 31 min and 43 23 min, and the SFR was 48.6% and 89.5% in Groups A and B, respectively. One patient in Group A (83-year-old female) died from urosepsis. She also had a history of prior recurrent UTIs, was ASA 3 and suffered with Alzheimers dementia. She was not pre-stented, no access sheath was used, and a procedural time of 45 min with a post-operative stent left in situ was noted. She developed multi-resistant contamination and died of septic shock after 2 days. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst, overall, it also discriminated between.

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