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Purpose: To describe the retinal contour in optical coherence tomography (OCT) images, and report the relationship between retinal contour and axial length., Methods: Retinal contour was defined by the path of the retinal pigment epithelial (RPE) line in macular and extra-macular OCTs of 70 eyes of 70 participants recruited from ophthalmology clinics in South Australia. The shape of this contour was described by the best-fit curvature (K), and Fourier analysis of the difference between K and the RPE. The Fourier transformation was summarised by total difference (sumdiff), maximum single frequency difference (MaxE), and root mean square difference (rmse) between each B scan residual and the average normal. All-of-eye and regional median and interquartile range (IQR) shape features were correlated to axial length., Results: Retinal shape irregularity measured by Fourier transformation correlated with axial length: all-of-eye median and IQR sumdiff (ρ = 0.66 and ρ = 0.60 respectively), median and IQR rmse (ρ = 0.67 and ρ = 0.48), median MaxE (ρ = 0.61), and IQR K (ρ = 0.61) all correlated with axial length. Correlation with axial length was also seen in these parameters for 11 of 17 regions. Retinal irregularity was greatest at the macula and in inferior regions., Conclusion: Retinal OCT shape becomes increasingly irregular as axial length increases. The range of curvature correlates with axial length, while median curvature does not., Competing Interests: The authors have declared that no competing interests exist.

Total hip replacements must be robust to patient variability for long-term success in the population. The challenge during the design process is evaluating an implant in a diverse population but the computational cost of simulating a population of subject-specific finite element (FE) models is not practical. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models to approximate the variability in output metrics. Comparing with the median and distribution of the 95 th percentile composite peak micromotion (CPM) and polar gap in the full cohort (CPM median: 136 μm, interquartile range [IQR]: 74-230 μm) (Polar Gap median: 467 μm, IQR: 434-548 μm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169 μm, IQR: 78-236 μm) (Polar Gap median: 469 μm, IQR: 448-537 μm). Convex hull sampling (41 subjects) more closely captured the output metrics (CPM median: 99 μm, IQR: 70-191 μm) (Polar Gap median: 456 μm, IQR: 418-533 μm) but required over three times the number of subjects. Volume reduction of the convex hull captured the extremes of variability with subsets of 5 to 20 subjects, while the largest minimum-distance strategy captured the variability toward the middle of the cohort. These strategies can estimate the level of variability in FE model output metrics with a low computational cost when evaluating implants during the design process., (© 2018 John Wiley & Sons, Ltd.)

IntroductionMass gatherings are common in Australia. The interplay of variables, including crowd density and behavior, weather, and the consumption of alcohol and other drugs, can pose a unique set of challenges to attendees' well-being. On-site health services are available at most mass gatherings and reduce the strain on community health facilities. In order to efficiently plan and manage these services, it is important to be able to predict the number and type of presenting problems at mass gatherings.ProblemThere is a lack of reliable tools to predict patient presentations at mass gatherings. While a number of factors have been identified as having an influence on attendees' health, the exact contribution of these variables to patient load is poorly understood. Furthermore, predicting patient load at mass gatherings is an inherently nonlinear problem, due to the nonlinear relationships previously observed between patient presentations and many event characteristics., Methods: Data were collected at 216 Australian mass gatherings and included event type, crowd demographics, and weather. Nonlinear models were constructed using regression trees. The full data set was used to construct each model and the model was then used to predict the response variable for each event. Nine-fold cross validation was used to estimate the error that may be expected when applying the model in practice., Results: The mean training errors for total patient presentations were very high; however, the distribution of errors per event was highly skewed, with small errors for the majority of events and a few large errors for a small number of events with a high number of presentations. The error was five or less for 40% of events and 15 or less for 85% of events. The median error was 6.9 presentations per event., Conclusion: This study built on previous research by undertaking nonlinear modeling, which provides a more realistic representation of the interactions between event variables. The developed models were less useful for predicting patient presentation numbers for very large events; however, they were generally useful for more typical, smaller scale community events. Further research is required to confirm this conclusion and develop models suitable for very large international events.Arbon P, Bottema M, Zeitz K, Lund A, Turris S, Anikeeva O, Steenkamp M. Nonlinear modelling for predicting patient presentation rates for mass gatherings. Prehosp Disaster Med. 2018;33(4):362-367.

IntroductionMass gatherings are complex events that present a unique set of challenges to attendees' health and well-being. There are numerous factors that influence the number and type of injuries and illnesses that occur at these events, including weather, event and venue type, and crowd demographics and behavior.ProblemWhile the impact of some factors, such as weather conditions and the availability of alcohol, on patient presentations at mass gatherings have been described previously, the influence of many other variables, including crowd demographics, crowd behavior, and event type, is poorly understood. Furthermore, a large number of studies reporting on the influence of these variables on patient presentations are based on anecdotal evidence at a single mass-gathering event., Methods: Data were collected by trained fieldworkers at 15 mass gatherings in South Australia and included event characteristics, crowd demographics, and weather. De-identified patient records were obtained from on-site health care providers. Data analysis included the calculation of patient proportions in each variable category, as well as the total number of patient presentations per event and the patient presentation rate (PPR)., Results: The total number of expected attendees at the 15 mass gatherings was 303,500, of which 146 presented to on-site health care services. The majority of patient presentations occurred at events with a mean temperature between 20°C and 25°C. The PPR was more than double at events with a predominantly male crowd compared to events with a more equal sex distribution. Almost 90.0% of patient presentations occurred at events where alcohol was available., Conclusion: The results of the study suggest that several weather, crowd, and event variables influence the type and number of patient presentations observed at mass-gathering events. Given that the study sample size did not allow for these interactions to be quantified, further research is warranted to investigate the relationships between alcohol availability, crowd demographics, crowd mobility, venue design, and injuries and illnesses.Anikeeva O, Arbon P, Zeitz K, Bottema M, Lund A, Turris S, Steenkamp M. Patient presentation trends at 15 mass-gathering events in South Australia. Prehosp Disaster Med. 2018;33(4):368-374.

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.