Incidence of wrong-site surgery list errors for a 2-year period in a single national health service board

Introduction: Wrong-site/side surgical "never events" continue to cause considerable harm to patients, healthcare professionals, and organizations within the United Kingdom. Incidence has remained static despite the mandatory introduction of surgical checklists. Operating theater list errors have been identified as a regular contributor to these never events. The aims of the study were to identify and to learn from the incidence of wrong-site/side list errors in a single National Health Service board. Methods: The study was conducted in a single National Health Service board serving a population of approximately 300,000. All theater teams systematically recorded errors identified at the morning theater brief or checklist pause as part of a board-wide quality improvement project. Data were reviewed for a 2-year period from May 2013 to April 2015, and all episodes of wrong-site/side list errors were identified for analysis. Results: No episodes of wrong-site/side surgery were recorded for the study period. A total of 86 wrong-site/side list errors were identified in 29,480 cases (0.29%). There was considerable variation in incidence between surgical specialties with ophthalmology recording the largest proportion of errors per number of surgical cases performed (1 in 87 cases) and gynecology recording the smallest proportion (1 in 2671 cases). The commonest errors to occur were "wrong-side" list errors (62/86, 72.1%). Discussion: This is the first study to identify incidence of wrong-site/site list errors in the United Kingdom. Reducing list errors should form part of a wider risk reduction strategy to reduce wrong-site/side never events. Human factors barrier management analysis may help identify the most effective checks and controls to reduce list errors incidence, whereas resilience engineering approaches should help develop understanding of how to best capture and neutralize errors

Post-ureteroscopy infections are linked to pre-operative stent dwell time over two months: outcomes of three European endourology centres

BACKGROUND: The aim of this study is to investigate outcomes of pre-operative stent dwell time on infectious complications following ureteroscopy and stone treatment to identify a time cut-off.MATERIAL AND METHODS: Three tertiary referral centres in Europe retrospectively collected outcomes of ureteroscopy and laser fragmentation (URSL) for all patients with pre-operative indwelling ureteric stents over a period of up to 5 years. Data was collected on patient details, stone demographics, stent dwell time, complications and stone free rate (SFR). Matching for age, sex, operative time, stone size and post-operative stent insertion. To examine for a threshold effect, monthly cut-offs were used to compare post-ureteroscopic febrile UTIs. Binomial logistic regression was used (SPSS v.24) with a significance level set at 0.0036. The risk ratio (RR) with a 95% confidence interval (CI) and the number needed to harm (NNH) are reported.RESULTS: There were 467 patients with a pre-operative stent for analysis. These patients (n = 315) were matched to non-stented controls after excluding 152 patients to achieve adequate matching. There was a significant difference in rates of post-ureteroscopic febrile UTI between stented vs non-stented patients (RR = 2.67, 95% CI: 1.10-6.48, p = 0.03). On adjustment, a dwell time of more than two months was associated with an increased risk of post-ureteroscopic febrile UTI (RR = 3.94, 95% CI: 1.30-12.01, p = 0.02), this increased risk rose with longer dwell time. At stent time longer than four months was associated with a significantly increased risk of post-ureteroscopic febrile UTI (5% vs. 15%, RR = 3.09, 95% CI: 1.56-6.10, p = 0.001), with the number needed to harm at 10.CONCLUSIONS: Overall infectious complication rates from URSL are low. The risk of post-operative UTI after four months of dwell time is nearly tripled compared to less than four months.</p

Routine Urinary Biochemistry Does Not Accurately Predict Stone Type Nor Recurrence in Kidney Stone Formers: A Multi-Centre, Multi-Model, Externally Validated Machine-Learning Study.

OBJECTIVES Urinary biochemistry is used to detect and monitor conditions associated with recurrent kidney stones. There are no predictive machine learning (ML) tools for kidney stone type or recurrence. We therefore aimed to build and validate ML models for these outcomes using age, gender, 24-hour urine biochemistry and stone composition. MATERIALS AND METHODS Data from 3 cohorts were used, Southampton, UK (n=3013), Newcastle, UK (n=5984) and Bern, Switzerland (n=794). Of these 3130 had available 24-hour urine biochemistry measurements (calcium, oxalate, urate, pH, volume), and 1684 had clinical data on kidney stone recurrence. Predictive machine learning models were built for stone type (n=5 models) and recurrence (n=7 models) using the UK data, and externally validated with the Swiss data. Three sets of models were built using complete cases, multiple imputation and oversampling techniques. RESULTS For kidney stone type one model (XGBoost built using oversampled data) was able to effectively discriminate between calcium oxalate, calcium phosphate and urate on both internal and external validation. For stone recurrence, none of the models were able to discriminate between recurrent and non recurrent stone formers. CONCLUSIONS Kidney stone recurrence cannot be accurately predicted using modelling tools built using specific 24-hour urinary biochemistry values alone. A single model was able to differentiate between stone types. Further studies to delineate accurate predictive tools should be undertaken using both known and novel risk factors, including radiomics and genomics. 

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