Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers.

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

Management of large kidney stones in the geriatric population.

PURPOSE: The aim of this review is to highlight the unique factors that predispose geriatric patients to nephrolithiasis and to compare the utility and efficacy of surgical techniques in this specific patient population. METHODS: PubMed and EMBASE databases were reviewed, and studies were organized according to surgical treatments. RESULTS: Few prospective studies exist comparing kidney stone removal in the elderly to younger cohorts. In addition, various age cut-offs were used to determine who was considered elderly. Most studies which analyzed Percutaneous Nephrolithotomy (PCNL) found a slightly higher rate of minor complications but comparable stone free rate and operative time. For ureteroscopy (URS) and extracorporeal shockwave lithotripsy (ESWL), there were minimal complications observed and no difference in clinical success in the elderly. All surgical techniques were presumed to be safe in the elderly and most found no difference in stone-free rates. CONCLUSIONS: Unique attributes of the geriatric population contribute to stone formation and must be considered when determining appropriate management modalities. This review provides an overview of the utility and efficacy of PCNL, URS and ESWL in the elderly, as well as a porposed algorithm for management in this population.

Financial Toxicity of Nephrolithiasis: The First Assessment of the Economic Stresses of Kidney Stone Treatment.

OBJECTIVE: To investigate the financial toxicity (FT) related to kidney stone treatment. METHODS: We performed a cross-sectional cohort study with multi-institutional in-person and online cohorts of stone formers.  Participants were surveyed using the validated COST tool (COmprehensive Score for financial Toxicity). The maximum score is 44 and lower scores indicate increased FT. "Moderate FT" was defined by COST scores between 25 and 14 points and "severe FT" for scores <14. Descriptive statistics, X2 tests, T tests, Spearman correlation, and logistic regression were performed using SPSS v28. RESULTS: Two hundred and forty-one participants were surveyed, including 126 in-person participants and 115 online. A total of 60% of participants reported at least moderate FT (COST score <26) and 26% reported severe FT (COST score <14). Patients who reported moderate to severe FT were younger than those with low FT by a median difference of 8 years (95%CI = 4, 12). There was a significant correlation between out-of-pocket expense and COST scores, such that as out-of-pocket expenses increased, COST scores decreased, (Spearman's rho =-0.406, P = <.001). Participants with moderate to severe FT tended to miss more workdays (P = .002), and their caretakers tended to miss more workdays (P = .007) due to their stone disease. CONCLUSION: Most participants reported moderate to severe FT. As prior studies have shown that patients with "moderate FT" employ cost-coping strategies (i.e., medication rationing) and those with "severe FT" have worse health outcomes, urologists need to be sensitive to the financial burdens of treatment experienced by such patients undergoing kidney stone treatment.

Evaluating the association between food insecurity and risk of nephrolithiasis: an analysis of the National Health and Nutrition Examination Survey.

PURPOSE: This study aimed to investigate the relationship between self-reported food security and kidney stone formation. METHODS: Data were collected from the National Health and Nutrition Examination Survey (NHANES), a database representative of the United States population. Food security status was assessed using the US Household Food Security Survey Module: Six-Item Short Form. Characteristics of patients were compared using the Chi-square test and the student t-test. Multivariate logistic regression was performed using a multi-model approach. RESULTS: We analyzed 6,800 NHANES survey respondents. 37.2% of respondents were categorized as having "low food security" (scores 2-4) and 24.0% having "very low food security" (scores 5-6). 8.4% of respondents had a history of kidney stones. We found that people with very low food security had a 42% increased likelihood of developing kidney stones compared to those with high or marginal food security, after controlling for race, age, and comorbidities (OR 1.42; 95% CI 1.01-1.99). Between the different food security groups, no significant differences were observed in age, race/ethnicity, body mass index, gout history, osteoporosis history, or coronary artery disease history. Lower food security was associated with slightly younger age (< 1 year difference, p = 0.001), higher poverty-income ratio (p = 0.001), and many comorbidities, including kidney stones (p = 0.007). CONCLUSION: Our study provides evidence for an association between food access and the risk of kidney stone disease. Given these findings, food insecurity should be investigated as a modifiable risk factor for the development of kidney stone disease.

Evaluation of MJO Predictive Skill in Multiphysics and Multimodel Global Ensembles.

Monthlong hindcasts of the Madden-Julian oscillation (MJO) from the atmospheric Flow-following Icosahedral Model coupled with an icosahedral-grid version of the Hybrid Coordinate Ocean Model (FIM-iHYCOM), and from the coupled Climate Forecast System, version 2 (CFSv2), are evaluated over the 12-yr period 1999-2010. Two sets of FIM-iHYCOM hindcasts are run to test the impact of using Grell-Freitas (FIM-CGF) versus simplified Arakawa-Schubert (FIM-SAS) deep convection parameterizations. Each hindcast set consists of four time-lagged ensemble members initialized weekly every 6 h from 1200 UTC Tuesday to 0600 UTC Wednesday. The ensemble means of FIM-CGF, FIM-SAS, and CFSv2 produce skillful forecasts of a variant of the Real-time Multivariate MJO (RMM) index out to 19, 17, and 17 days, respectively; this is consistent with FIM-CGF having the lowest root-mean-square errors (RMSEs) for zonal winds at both 850 and 200 hPa. FIM-CGF and CFSv2 exhibit similar RMSEs in RMM, and their multimodel ensemble mean extends skillful RMM prediction out to 21 days. Conversely, adding FIM-SAS-with much higher RMSEs-to CFSv2 (as a multimodel ensemble) or FIM-CGF (as a multiphysics ensemble) yields either little benefit, or even a degradation, compared to the better single-model ensemble mean. This suggests that multiphysics/multimodel ensemble mean forecasts may only add value when the individual models possess similar skill and error. An atmosphere-only version of FIM-CGF loses skill after 11 days, highlighting the importance of ocean coupling. Further examination reveals some sensitivity in skill and error metrics to the choice of MJO index.