Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer.

BACKGROUND: The goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC. METHODS: We use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment. RESULTS: Our findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.

HEvOD: A database of hurricane evacuation orders in the United States.

Assessing and improving the effectiveness of evacuation orders is critical to improving hurricane emergency response, particularly as the frequency of hurricanes increases in the United States. However, our understanding of causal relationships between evacuation orders and evacuation decision-making is still limited, in large part due to the lack of standardized, high-temporal-resolution data on historical evacuation orders. To overcome this gap, we developed the Hurricane Evacuation Order Database (HEvOD) - a comprehensive database of hurricane evacuation orders issued in the United States between 2014 and 2022. The database features evacuation orders that were systematically retrieved and compiled from a wide range of resources and includes information on order type, announcement time, effective time, and evacuation area. The rich collection of attributes and the resolution of the data in the database will allow researchers to systematically investigate the impact of evacuation orders, as a vital public policy instrument, and can serve as an important resource to identify gaps in current policies, leading to more effective policy design in response to hurricanes.

Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency.

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.