Objective To take a first step towards assembling population based cohorts of bladder cancer patients with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full text pathology reports. Methods Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence versus absence and depth), grade, presence of muscularis propria, and presence of carcinoma in situ. Our gold standard was based on independent review of reports by two urologists, followed by adjudication. We assessed NLP performance by calculating accuracy, positive predictive value (PPV), and sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 bladder cancer patients. Results When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for presence versus absence of carcinoma in situ. Accuracy for histology, invasion (presence versus absence), grade, and presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with acceptable PPV for lamina propria (0.82) and muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of bladder cancer patients. Conclusions NLP had high accuracy for five of six variables and abstracted data for the vast majority of patients. This now allows for assembly of population based cohorts with longitudinal pathology data.

Almost 15% of patients in whom a stent is placed with a string sustain stent dislodgment and most of these patients will be women. We recommend considering the risks of dislodgment in each patient who undergoes ureteroscopy with stent placement and considering string removal if the surgeon believes that dislodgment could result in adverse events such as severe colic or obstruction.

Study Type – Therapy (case series) Level of Evidence 4 What's known on the subject? and What does the study add? Malfunctions of the robotic surgical system have been reported, and the critical failure rate leading to converting or aborting the case occurs in less than 1% of cases. However, little is known about how global robotic experience with time and the advent of newer robotic surgical systems impact robot malfunctions. In this study, we characterize the changes in type and consequences of robot malfunctions over time and by type of robotic system used (da Vinci or da Vinci S). OBJECTIVES To assess annual rates of robotic system malfunctions and compare the da Vinci S® system (dVS) and da Vinci® surgical system (dV). To assess the types of malfunctions and associated outcomes for robotic cases and determine the extent to which experience and technological improvements impact these. PATIENTS AND METHODS This study is a retrospective review of the US Food and Drug Administration (FDA) MAUDE (Manufacturer and User Facility Device Experience) database, a publicly available, voluntary reporting system (http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmaude/search.cfm). The database was searched using the two phrases ‘da Vinci’ and ‘Intuitive Surgical’ from 2003 to 2009. Malfunctions of the instruments, console, patient‐side cart, camera and cannula were recorded. Data on intraoperative injuries, case delays and conversions were also collected. RESULTS In all, 1914 reports were reviewed (991 dVS and 878 dV, 45 unclassified) with peak years for reports of 2008 for dVS (571) and 2007 for dV (211), P < 0.001. With respect to time, the proportion of console and patient‐side cart malfunctions declined from 2007 onward compared with the proportions prior to 2007 (5.1% vs 9.4% and 6.6% vs 10.9%). Patient injury did not change with year of surgery (0.5–5.4% of malfunctions, P= 0.358), open conversions declined (21.3% of malfunctions before 2007 vs 9.9% from 2007 onward, P < 0.001) and patient deaths increased (0.0013% of cases before 2007 vs 0.0061% of cases from 2007 onward, P < 0.001). With regard to robotic system, console and patient‐side cart malfunctions were more frequent with the dV than the dVS: 82/878 vs 39/991 and 100/878 vs 48/991, P < 0.001. Open conversion was more frequent with dV than dVS (19.3% vs 7.7% of reported malfunctions, P < 0.001), while patient injury was less with dV than dVS (3.5% vs 5.9%, P= 0.021). CONCLUSIONS The dVS decreased console and patient‐side cart errors relative to total malfunctions, which were also influenced by surgical year. Open conversions were reduced by increased robotic experience and newer surgical system. Differences in patient injury may reflect changes in reporting or case complexity.