Faculty Bibliography

INTRODUCTION: Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. METHODS: Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65's, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. RESULTS: A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65's population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. CONCLUSIONS: This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources.

This study aimed to address two queries: firstly, the relationship between two cataract surgical feedback tools for training, one human and one software based, and, secondly, evaluating microscope control during phacoemulsification using the software. Videos of surgeons with varying experience were enrolled and independently scored with the validated PhacoTrack motion capture software and the Objective Structured Assessment of Cataract Surgical Skill (OSACCS) human scoring tool. Microscope centration and path length travelled were also evaluated with the PhacoTrack software. Twenty-two videos correlated PhacoTrack motion capture with OSACCS. The PhacoTrack path length, number of movements, and total procedure time were found to have high levels of Spearman's rank correlation of -0.6792619 (p = 0.001), -0.6652021 (p = 0.002), and -0.771529 (p = 0001), respectively, with OSACCS. Sixty-two videos evaluated microscope camera control. Novice surgeons had their camera off the pupil centre at a far greater mean distance (SD) of 6.9 (3.3) mm, compared with experts of 3.6 (1.6) mm (p << 0.05). The expert surgeons maintained good microscope camera control and limited total pupil path length travelled 2512 (1031) mm compared with novices of 4049 (2709) mm (p << 0.05). Good agreement between human and machine quantified measurements of surgical skill exists. Our results demonstrate that surrogate markers for camera control are predictors of surgical skills.

Complex medical data sometimes requires significant data preprocessing to prepare for analysis. The complexity can lead non-domain experts to apply simple filters of available data or to not use the data at all. The preprocessing choices can also have serious effects on the results of the study if incorrect decision or missteps are made. In this work, we present open-source data filters for an analysis motivated by understanding mortality in the context of sepsis- associated cardiomyopathy in the ICU. We report specific ICU filters and validations through chart review and graphs. These published filters reduce the complexity of using data in analysis by (1) encapsulating the domain expertise and feature engineering applied to the filter, by (2) providing debugged and ready code for use, and by (3) providing sensible validations. We intend these filters to evolve through pull requests and forks and serve as common starting points for specific analyses.

BACKGROUND: Improving access to psychological therapies (IAPT) is a major programme in England to treat common mental health problems, mainly through cognitive behaviour therapy. In 2012, a Pathfinder scheme was launched to develop interventions for people with chronic physical health conditions or medically-unexplained symptoms. AIM: This qualitative component of the evaluation investigated feasibility and acceptability of IAPT provision for people with enduring physical health problems. METHOD: Qualitative interviews were conducted with project leaders in all 14 Pathfinder sites. FINDINGS: Various therapeutic and training interventions were introduced. Most patients received low-intensity, structured therapy, with high-intensity input provided by some Pathfinders for complex cases. Whether the focus was on psychological symptoms or on broader well-being, psychiatric terminology was avoided to improve utilisation. Participants perceived high satisfaction among service-users. Training needs were indicated for IAPT workers in this specialised work. CONCLUSIONS: Cognitive behaviour interventions appeared to be acceptable for people struggling with physical health problems. Robust outcome evidence will be pursued in Phase II.