The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Efficient Execution of Continuous Aggregate Queries over Multi-Source Streaming Data

On-line decision making often involves query processing over time-varying data which arrives in the form of data streams from distributed locations. Examples of time-varying data include financial information such as stock prices and currency exchange rates, real-time traffic, weather information and data from process control applications. In such environments, typically a decision is made whenever some function of the current value of a set of data items satisfies a threshold criterion. For example, when the traffic entering a highway exceeds a prespecified limit, some flow control measure is initiated; when the value of a stock portfolio goes below a comfort level, an investor might decide to rethink his portfolio management strategy. In this paper we present data dissemination and query processing techniques where such queries access data from multiple sources. Key challenges in supporting such Continuous Aggregate Queries with Thresholds lie in minimizing network and source overheads, without the loss of fidelity in the responses provided to users. Using real world data we demonstrate the superior performance of our techniques when compared to alternatives based on periodic independent polling of the sources

Efficient Execution of Continuous Threshold Queries over Dynamic Web Data

On-line decision making often involves processing significant amount of time-varying data. Examples of timevarying data available on the Web include financial information such as stock prices and currency exchange rates, real-time traffic, weather information and data from process control applications. In such environments, typically a decision is made whenever some function of the current value of a set of data items satisfies a threshold criterion

India's First Robotic Eye for Time-domain Astrophysics: The GROWTH-India Telescope

We present the design and performance of the GROWTH-India telescope, a 0.7 m robotic telescope dedicated to time-domain astronomy. The telescope is equipped with a 4k back-illuminated camera that gives a 0.degrees 82 field of view and a sensitivity of m (g ') similar to 20.5 in 5 minute exposures. Custom software handles observatory operations: attaining high on-sky observing efficiencies (greater than or similar to 80%) and allowing rapid response to targets of opportunity. The data processing pipelines are capable of performing point-spread function photometry as well as image subtraction for transient searches. We also present an overview of the GROWTH-India telescope's contributions to the studies of gamma-ray bursts, the electromagnetic counterparts to gravitational wave sources, supernovae, novae, and solar system objects