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Background High temperatures in urban areas caused by global climate change and urban heat island intensification have led to an increase in the number people experiencing heat related illness, the most serious of which is heatstroke. To help prevent heatstroke, an accurate model should be developed that will predict dangerous conditions so that people can take preventive actions. Method The goal of this study was to compare three methods for predicting heatstroke risk: multiple regression analysis (MR), generalized additive model (GAM), and time-stratified case-crossover analysis (TC). Susceptibility to heatstroke is likely to be dependent on year-wise trends and is sensitive to training data, but most previous models have only tested a limited amount of training data. In this study we investigated the optimal number of years to use as training data. By comparing the errors of each method, the error influencing factors in the training data was identified. Results The TC errors were the smallest (p<0.005) and much less sensitive to the training data than others. The MR and GAM errors were significantly larger when the number of extremely hot days differed between the training and test data (p<0.01, p<0.05). All three methods tended to increase in accuracy as more past data years were added to the training data, but to decrease in accuracy after a certain point. The optimal accuracy was obtained by using data from three or four years. Conclusions As a result, a highly accurate risk model that was robust to training data was developed using the odds ratios produced by TC with low sensitivity to training data, something that has not been possible with previous models. This modeling approach is universally applicable and can be used to make urban areas safer in future.

The number of patients with heat illness transported by ambulance has been gradually increasing due to global warming. In intense heat waves, it is crucial to accurately estimate the number of cases with heat illness for management of medical resources. Ambient temperature is an essential factor with respect to the number of patients with heat illness, although thermophysiological response is a more relevant factor with respect to causing symptoms. In this study, we computed daily maximum core temperature increase and daily total amount of sweating in a test subject using a large-scale, integrated computational method considering the time course of actual ambient conditions as input. The correlation between the number of transported people and their thermophysiological temperature is evaluated in addition to conventional ambient temperature. With the exception of one prefecture, which features a different Köppen climate classification, the number of transported people in the remaining prefectures, with a Köppen climate classification of Cfa, are well estimated using either ambient temperature or computed core temperature increase and daily amount of sweating. For estimation using ambient temperature, an additional two parameters were needed to obtain comparable accuracy. Even using ambient temperature, the number of transported people can be estimated if the parameters are carefully chosen. This finding is practically useful for the management of ambulance allocation on hot days as well as public enlightenment.

Objective: Accurately determining the clot position is highly important for immediate recanalization when endovascular mechanical thrombectomy is performed using a stent retriever and aspiration catheter. We describe a new method that facilitates the precise identification of the clot position called pull the trigger sign (PTS).Case Presentation: Selective angiography was performed through a 0.027-inch microcatheter that penetrated the clot into the distal lumen. Although the contrast media highlighted the occluded artery, it often stagnated in the distal artery. It was washed away at a certain point when a stent clot retriever was deployed over the potential clot site. We hypothesized that this point represented the exact position of the clot's proximal end and used in vitro analyses to assess this hypothesis. Briefly, a circulationenabled silicone vascular model in which colored water was used to simulate stagnation beyond a fake clot was developed and utilized to investigate PTS six times. The rate of identifying PTS in the vascular model was 100%. As hypothesized, stagnant fluid was washed away when the deployed stent reached the clot's proximal position. The clinical efficacy of PTS was also confirmed. Conclusion:PTS was useful in revealing the precise position of clot's proximal end, which enabled safer contact aspiration when using an aspiration catheter. Thus, PTS led to a higher success rate and faster recanalization in the first attempt than conventional methods.