Quick Sequential Organ Failure Assessment Score Is a Better Predictor of Septic Shock After Percutaneous Nephrolithotomy: A Secondary Analysis of Two Multicenter Prospective Trials.

Introduction: Recent retrospective literature suggests that the quick sequential organ failure assessment (qSOFA) scoring tool is a potentially superior tool over use of the systemic inflammatory response syndrome (SIRS) criteria to predict septic shock after percutaneous nephrolithotomy (PCNL) surgery. Here we examine use of qSOFA and SIRS to predict septic shock within data series collected prospectively on PCNL patients as part of a greater study of infectious complications. Materials and Methods: We performed a secondary analysis of two prospective multicenter studies including PCNL patients across nine institutions. Clinical signs informing SIRS and qSOFA scores were collected no later than postoperative day 1. The primary outcome was sensitivity and specificity of SIRS and qSOFA (high-risk score of greater-or-equal to two points) in predicting admission to the intensive care unit (ICU) for vasopressor support. Results: A total of 218 cases at 9 institutions were analyzed. One patient required vasopressor support in the ICU. The sensitivity/specificity was 100%/72.4% (McNemar's test p < 0.001) for SIRS and was 100%/90.8% (McNemar's test p < 0.001) for qSOFA. Conclusion: Although positive predictive value for both qSOFA and SIRS in prediction of post-PCNL septic shock is low, prospectively collected data demonstrate use of qSOFA may offer greater specificity than SIRS criteria when predicting post-PCNL septic shock.

Design and validation of a low-cost, high-fidelity model for robotic pyeloplasty simulation training.

INTRODUCTION/BACKGROUND: Owing to restrictions in operative experiences, urology residents can no longer solely rely on 'hands-on' operative time to master their surgical skills by the end of residency. Simulation training could help residents master basic surgical skills and steps of a procedure to maximize time in the operative room. However, simulators can be expensive or tedious to set up, limiting the availability to residents and training programs. OBJECTIVE: The authors sought to develop and validate an inexpensive, high-fidelity training model for robotic pyeloplasty. STUDY DESIGN: Pyeloplasty models were created using Dragon Skin® FX-Pro tissue-mimicking silicone cast over 3-dimensional molds. Urology faculty and trainees completed a demographic questionnaire. The participants viewed a brief instructional video and then independently performed robotic dismembered pyeloplasty on the model. Acceptability and content validity were evaluated via post-task evaluation of the model. Construct validity was evaluated by comparing procedure completion time, the Global Evaluative Assessment of Robotic Skills (GEARS) score, blinded subjective physical evaluation of repair quality (1-10 scale), and flow rate between experts and novices. RESULTS: In total, 5 urology faculty, 6 fellows, and 14 residents participated. The median robotic console experience among faculty, fellows, and residents was 8 years (interquartile range [IQR] = 6-11), 3.5 years (IQR = 2-4 years), and 0 years (IQR = 0-0.5 years), respectively. The median procedure completion time was 29 min (IQR = 26-40 min), and the median flow rate was 1.11 mL/s (IQR = 0-1.34 mL/s). All faculty had flow rates >1.25 mL/s and procedure times <30 min compared with 2 of 6 fellows and none of the residents (P < 0.001). All faculty, half of the fellows, and none of the residents achieved a GEARS score ≥20, with a median resident score of 12.5 (IQR = 8-13) (P < 0.001). For repair quality, all faculty scored ≥9 (out of 10), all fellows scored ≥8, and the median score among residents was 6 (IQR = 2-6) (P < 0.001). The material cost was $1.32/model, and the average production time was 0.12 person-hours/model. DISCUSSION AND CONCLUSION: This low-cost pyeloplasty model exhibits acceptability and content validity. Construct validity is supported by significant correlation between participant expertise and simulator performance across multiple assessment domains. The model has excellent potential to be used as a training tool in urology and allows for repetitive practice of pyeloplasty skills before live cases.