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Different types of natural events hit the United States every year. The data of natural hazards from 1900 to 2016 in the US shows that there is an increasing trend in annul natural disaster losses after 1980. Climate change is recognized as one of the factors causing this trend, and predictive analysis of natural losses becomes important in loss prediction and risk prevention as this trend continues. In this paper, we convert natural disaster losses to the year 2016 dollars using yearly average Consumers Price Index (CPI), and conduct several tests to verify that the CPI adjusted amounts of loss from individual natural disasters are independent and identically distributed. Based on these test results, we use various model selection quantities to find the best model for the natural loss severity among three composite distributions, namely Exponential-Pareto, Inverse Gamma-Pareto, and Lognormal-Pareto. These composite distributions model piecewise small losses with high frequency and large losses with low frequency. Remarkably, we make the first attempt to derive analytical Bayesian estimate of the Lognormal-Pareto distribution based on the selected priors, and show that the Lognormal-Pareto distribution outperforms the other two composite distributions in modeling natural disaster losses. Important risk measures for natural disasters are thereafter derived and discussed. [ABSTRACT FROM AUTHOR]

Abstract: Heterogeneous echogenicity and micro-nodulations of diffuse thyroid disease on ultrasonography (US) might influence the diagnostic performance of pre-operative US staging, especially the detection of multi-focality. This study was designed to determine whether heterogeneous echogenicity of the thyroid parenchyma influences the diagnostic performance of US in the detection of multi-focality in papillary thyroid carcinoma. Between December 2010 and April 2011, 811 patients underwent pre-operative staging US for papillary thyroid carcinoma and surgery. Twelve radiologists performed the pre-operative US for T and N staging. Underlying parenchymal echogenicity and unilateral and bilateral multi-focality of the thyroid nodules were also evaluated. Patients were divided into two groups on the basis of the underlying echogenicity of the thyroid gland. To evaluate the diagnostic accuracy of US with respect to underlying echogenicity, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were calculated and compared between the two groups. Among the 811 patients included, US revealed underlying heterogeneous echogenicity of the thyroid parenchyma in 204 (25.2%) and underlying homogeneous echogenicity of the thyroid parenchyma in 607 (74.8%). There were no significant differences between the two groups in the diagnostic performance of pre-operative staging US in predicting unilateral multi-focality and bilaterality. Underlying heterogeneous echogenicity in a thyroid gland with Hashimoto's thyroiditis does not significantly influence the detection of multi-focality in papillary thyroid cancer on pre-operative US staging. [Copyright &y& Elsevier]

Objectives: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA., Methods: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM., Results: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists., Conclusions: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered., Key Points: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Patients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study tested the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass operations., Methods: This was a prospective cohort study of patients with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard model was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known CV risk factors, and the incremental value of the addition of clinical chemistry, lipid assessment, and a panel of 11 inflammatory parameters was investigated using the C statistic, the integrated discrimination improvement index, and Akaike information criterion., Results: The study monitored 225 patients for a median of 893 days (interquartile range, 539-1315 days). In this study, 50 patients (22.22%) died during the follow-up period. By life-table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years, respectively, was 90.5% ± 1.9%, 83.4% ± 2.5%, 77.5% ± 3.1%, 71.0% ± 3.8%, and 65.3% ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant coronary artery disease, and were more likely to present with critical limb ischemia as their indication for bypass surgery (P < .05). After adjustment for the above, clinical chemistry and inflammatory parameters significant (hazard ratio [95% confidence interval]) for all-cause mortality were albumin (0.43 [0.26-0.71]; P = .001), estimated glomerular filtration rate (0.98 [0.97-0.99]; P = .023), high-sensitivity C-reactive protein (hsCRP; 3.21 [1.21-8.55]; P = .019), and soluble vascular cell adhesion molecule (1.74 [1.04-2.91]; P = .034). Of the inflammatory molecules investigated, hsCRP proved most robust and representative of the integrated inflammatory response. Albumin, eGFR, and hsCRP improved the C statistic and integrated discrimination improvement index beyond that of the clinical model and produced a final C statistic of 0.82., Conclusions: A risk prediction model including traditional risk factors and parameters of inflammation, renal function, and nutrition had excellent discriminatory ability in predicting all-cause mortality in patients with clinically advanced PAD undergoing bypass surgery., (Copyright © 2012 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.)