The Efficacy and Safety of Laser and Electrosurgical Transurethral Procedures for the Treatment of BPO in High-Risk Patients: A Systematic Review.

Objective: To compare efficacy and safety outcomes of GreenLight, Holmium and Thulium laser  techniques with standard monopolar and bipolar transurethral resection of the prostate (TURP) in high-risk patients with lower urinary tract symptoms (LUTS) secondary to benign prostatic obstruction (BPO). Methods: We conducted a systematic literature review of studies in patients undergoing BPO surgeries who may be considered high-risk for standard TURP, with higher risk defined as follows: large prostates (≥80 mL) and/or taking antithrombotic agents and/or urinary retention and/or age >80 years and/or significant comorbidity.  Outcomes summarised included bleeding complications, re-intervention rates, hospital length of stay, and standard measures of disease and symptom severity for all available timepoints. Results: A total of 276 studies of 32,722 patients reported relevant data. Studies were heterogeneous in methodology, population and outcomes reported. IPSS reduction, Qmax improvement and PVR were similar across all interventions. Mean values at baseline and after 12 months across interventions were 13.2-29 falling to 2.3-10.8 for IPSS, 0-19 mL/s increasing to 7.5-34.1 mL/s for Qmax and 41.4-954 mL falling to 5.1-138.3 mL for PVR. Laser treatments show some advantages compared with monopolar and bipolar TURP for some adverse events and safety parameters such as bleeding complications. Duration of hospital stay, reinterventions and recatheterisations were lower with GreenLight, HoLEP, Thulium lasers, and bipolar enucleation than TURP. Conclusions: Laser therapies are effective and well-tolerated treatment options in high-risk patients with BPO compared with monopolar or bipolar TURP. The advantageous safety profile of laser treatments means that patients with a higher bleeding risk should be offered laser surgery preferentially to mTURP or bTURP.

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case-Control YAU Endourology Study from Nine European Centres.

INTRODUCTION: With the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed. The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a matched cohort. METHODS: A retrospective study was conducted across nine high-volume endourology European centres for all patients who underwent URSL and subsequently needed ICU admission for urosepsis (Group A). This was matched by patients with URSL without urosepsis (Group B). Statistical analysis was performed with 'R statistical software' using the 'randomforests' package. The data were segregated at random into a 70% training set and a 30% test set using the 'sample' command. A random forests ML model was then built with n = 300 trees, with the test set used for internal validation. Diagnostic accuracy statistics were generated using the 'caret' package. RESULTS: A total of 114 patients were included (57 in each group) with a mean age of 60 ± 16 years and a male:female ratio of 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: 63.7-92.8%), 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 risk factors can allow one to create predictive strategies to minimise post-operative morbidity.