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Selecting an evaluation metric is fundamental to model development, but uncertainty remains about when certain metrics are preferable and why. This paper introduces the concept of resolving power to describe the ability of an evaluation metric to distinguish between binary classifiers of similar quality. This ability depends on two attributes: 1. The metric's response to improvements in classifier quality (its signal), and 2. The metric's sampling variability (its noise). The paper defines resolving power generically as a metric's sampling uncertainty scaled by its signal. A simulation study compares the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC) in a variety of contexts. It finds that the AUROC generally has greater resolving power, but that the AUPRC is better when searching among high-quality classifiers applied to low prevalence outcomes. The paper also proposes an empirical method to estimate resolving power that can be applied to any dataset and any initial classification model. The AUROC is useful for developing the resolving power concept, but it has been criticized for being misleading. Newer metrics developed to address its interpretative issues can be easily incorporated into the resolving power framework. The best metrics for model search will be both interpretable and high in resolving power. Sometimes these objectives will conflict and how to address this tension remains an open question. [ABSTRACT FROM AUTHOR]

BACKGROUND: Perfusion index (PI) and CRIB (Clinical risk index of babies) Score II both are minimally invasive tools for prediction of mortality and morbidity in sick neonates. This study aims to know the predictive values of both PI and CRIB Score II in assessment of severity of illness in preterm neonates of 28 to 32 weeks and their correlation. METHODS: PI and CRIB II Score of 125 sick preterm neonates admitted in NICU were assessed within 12 hours of admission. Severity noted in form of outcome. Correlation between PI and CRIB Score II were observed. RESULTS: Receiver operating characteristic (ROC) curve were plotted for PI and CIRB Score II with outcome (discharged vs expired). Area under curve for perfusion index was 0.776 at 95% confidence interval. Optimum cutoff point based on ROC curve was 1.65 with sensitivity of 84%., specificity of 37.2% with positive predictive value of 70% and negative predictive value of 57%. Area under curve for CRIB Score II was 0.622 (p value < 0.028), optimum cut off point based on ROC curve for CRIB II score was 3.5 with sensitivity 79.10% of specificity of 40% positive predictive value is 43.03% and negative predictive value of 76.9%. Spearman's correlation coefficient between PI and CRIB II score was –0.272 and p value is 0.002. CONCLUSION: This study concluded that perfusion index (p < 0.001) is better for predicting the severity compared to CRIB II score (p < 0.028). A weak negative correlation exists between PI and CRIB II score (r = –0.272). [ABSTRACT FROM AUTHOR]

Objective To study the correlation between achilles tendon rupture (ATR) and hyperuricemia, also verify the known risk factors for ATR. Methods A retrospective review of 488 subjects was performed (182 with Achilles tendon rupture, 306 controls with ankle sprains). Demographic variables and risk factors for rupture were tabulated and compared. The baseline data and related indicators were compared, and the risk factors of ATR were analyzed by constructing a binary logistic regression model. Results Univariate logistic analysis showed that BMI, smoking, and hyperuricemia were risk factors for the development of ATR (OR=1.65, 95%CI 1.13–2.42, P=0.01; OR=1.47, 95%CI 1.00–2.24, P<0.05; OR=2.85, 95%CI 1.84–4.42, P<0.01). Multifactorial analysis showed that BMI≥25 kg/m2 , smoking, and hyperuricemia were independent risk factors for the development of ATR (OR=1.66, 95%CI 1.11–2.49, P=0.01; OR=2.15, 95%CI 1.28–3.60, P<0.01; OR=3.06, 95%CI 1.92–4.89, P<0.01). Among the blood biochemical indicators, total cholesterol (TC) and uric acid (UA) were independent risk factors for the occurrence of ATR (OR=1.54, 95% CI 1.12–2.12, P=0.01; OR=1.01, 95% CI 1.01–1.01, P<0.01). Conclusion Our study confrmed that, as in previous results, higher BMI, smoking, and total cholesterol are risk factors for ATR, Hyperuricemia may contribute to the development of ATR, and adjunctive tests for TC and UA in the blood biochemistry may be helpful in predicting the risk of ATR. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. Methods: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein–protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. Results: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. Conclusion: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression. [ABSTRACT FROM AUTHOR]

Abstract Background Acute kidney injury (AKI) is a major complication following cardiac surgery. We explored the clinical utility of iron metabolism indexes for identification of patients at risk for AKI after cardiac surgery. Methods This prospective observational study included patients who underwent cardiac surgery between March 2023 and June 2023. Iron metabolism indexes were measured upon admission to the intensive care unit. Multivariable logistic regression analyses were performed to explore the relationship between iron metabolism indexes and cardiac surgery-associated AKI (CSA-AKI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of iron, APACHE II score and the combination of the two indicators. Restricted cubic splines (RCS) was used to further confirm the linear relationship between iron and CSA-AKI. Results Among the 112 recruited patients, 38 (33.9%) were diagnosed with AKI. Multivariable logistic regression analysis indicated that APACHE II score (odds ratio [OR], 1.208; 95% confidence interval [CI], 1.003–1.455, P = 0.036) and iron (OR 1.069; 95% CI 1.009–1.133, P = 0.036) could be used as independent risk factors to predict CSA-AKI. ROC curve analysis showed that iron (area under curve [AUC] = 0.669, 95% CI 0.572–0.757), APACHE II score (AUC = 0.655, 95% CI 0.557–0.744) and iron and APACHE II score combination (AUC = 0.726, 95% CI 0.632–0.807) were predictive indicators for CSA-AKI. RCS further confirmed the linear relationship between iron and CSA-AKI. Conclusions Elevated iron levels were independently associated with higher risk of CSA-AKI, and there was a linear relationship between iron and CSA-AKI.

Objective: This study aims to investigate the predictive value of interleukin‐6 (IL‐6) and lymphocyte subsets for death in children with influenza‐associated encephalopathy (IAE). Patients: This study included 76 children with IAE who were divided into a death group and a survival group. The differences in the levels of IL‐6 and lymphocyte subsets between the two groups were analyzed, and the predictive value of these two parameters was compared by receiver operating characteristic (ROC) curve analysis. Results: The level of IL‐6 and the percentage of natural killer (NK) cells in the death group were higher than those in the survival group (p < 0.05). The percentage of CD4+ T cells and CD4+/CD8+ levels in the death group were lower than those in the survival group. ROC curves were used for analysis, and the area under the curves (AUCs) of IL‐6, the percentage of CD4+ T cells, the percentage of CD4+/CD8+, and the percentage of NK cells were 0.812, 0.810, 0.740, and 0.706, respectively. The AUC of the combination of these four metrics was 0.870. There was a little difference in the efficacy of the four clinical indicators, and the predictive efficacy of the combined test was higher than that of the single test. Conclusion: The IL‐6 concentration, percentage of CD4+ T cells, percentage of NK cells, and CD4+/CD8+ have predictive value for death in children with IAE, and the combination of these four metrics has improved the predictive value. [ABSTRACT FROM AUTHOR]

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility. [ABSTRACT FROM AUTHOR]

In the cognitive process of establishing a diagnosis, the performance of a diagnostician can be characterized in terms of sensitivity and specificity. The aims of the present study are to analyze in quantitative terms how cognitive bias affects the performance of a diagnostician, and how a diagnostician's biased decision making is further influenced by personal cost-benefit considerations. The test matrices of two sequential diagnostic tests are manipulated according to the rules of linear algebra, using multiplication of the second with the first test matrix to calculate their joint test characteristics. The decision tree and receiver operating characteristic (ROC) of a biased and unbiased diagnostician are used to calculate which combination of test characteristics maximizes the expected utility value. Biased diagnosticians cannot establish a diagnosis beyond their own limited or distorted level of understanding. An unbiased and a biased diagnostician alike adjust their choice of test characteristics according to their different cost-benefit estimation of the various test outcomes. From the perspective of an unbiased diagnostician, the choices made by a biased diagnostician appear to invert reality. However, the same appearance of inverted reality is perceived by the biased diagnostician, judging the choices made by the unbiased diagnostician. As a general principle, human testers cannot test beyond their own level of understanding. They only see what they know. As they base their judgment on preconceived notions about the utilities associated with different test outcomes, human testers also tend to only know what they want to know. [ABSTRACT FROM AUTHOR]

This study aims to investigate the association of Chinese visceral adiposity index (CVAI) with incident hyperuricemia (HUA). We included 5186 adults aged ≥45 years from China Health and Retirement Longitudinal Study. Modified Poisson regression model was used to estimate the relative risks (RRs) of incident HUA associated with baseline CVAI, and logistic model was used to estimate the odds ratios (ORs) of HUA for CVAI change. Restricted cubic splines analysis was adopted to model the dose–response associations. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the predictive value of CVAI. During 4-year follow-up, a total of 510 (9.8%) HUA cases were identified. The RRs (95%CIs) of incident HUA were 3.75 (2.85–4.93) for quartile 4 versus quartile 1 and 1.56 (1.45–1.69) for per-standard deviation increase in baseline CVAI. For the analyses of CVAI change, compared with stable group, participants in decreased group had 34% lower risk (OR 0.66, 95%CI 0.49–0.87) and those in increased group had 35% (1.35, 1.03–1.78) higher risk of HUA. Linear associations of baseline CVAI and its change with HUA were observed (P nonlinear >0.05). Besides, the AUC value for HUA was 0.654 (0.629–0.679), which was higher than other five obesity indices. Our study found linear associations between baseline CVAI and its change and risk of HUA. CVAI had the best predictive performance in predicting incident HUA. These findings suggest CVAI as a reliable obesity index to identify individuals with higher HUA risk. • This is the first cohort study to explore the associations of CVAI and its change with risk of HUA. • Linear associations between baseline CVAI and its change and risk of HUA was found. • CAVI is the best predictor of HUA compared with other five obesity indices. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionsThis study aims to evaluate the diagnostic significance of serum uric acid (UA), glutathione (GSH), and amyloid-β1-42 (Aβ1-42) levels in relation to disease progression and cognitive impairment in patients with Parkinson’s disease (PD).A total of 209 PD patients with disease duration ranging from 4.0 to 6.8 years were enrolled. Based on the Hoehn-Yahr staging system, patients were classified into Early (n = 67), Medium-term (n = 70), and Advanced (n = 72) stages. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), dividing the cohort into CD (cognitive dysfunction, n = 94) and NO-CD (no cognitive dysfunction, n = 115) groups. Serum UA, GSH, and Aβ1-42 levels were analyzed for correlations with clinical data. Independent risk factors and diagnostic value were determined through multivariable logistic regression models and receiver operating characteristic curve analysis.Serum UA and GSH levels progressively declined with advancing disease stage, while Aβ1-42 increased. Compared to the NO-CD group, the CD group showed lower serum UA and GSH levels, and higher Aβ1-42 levels. Serum UA and GSH were inversely correlated with disease duration, levodopa equivalent daily dose, and Unified Parkinson’s Disease Rating Scale scores, while Aβ1-42 showed positive correlations. UA (p = 0.006), GSH (p < 0.001), and Aβ1-42 (p = 0.040) were independent predictors of disease stage. Similarly, UA (p = 0.003), GSH (p < 0.001), and Aβ1-42 (p < 0.001) were independent predictors of cognitive dysfunction. The combined assessment of these markers demonstrated a higher area under the curve (AUC) than individual markers for disease and cognitive decline identification.Serum UA, GSH, and Aβ1-42 are independent predictors of disease progression and cognitive decline in PD patients. Their combined use offers enhanced diagnostic accuracy for disease staging and cognitive impairment in PD. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cardiopulmonary exercise testing (CPET) is a unique diagnostic tool that assesses the functional capacity of the heart, lungs, and peripheral oxidative system in an integrated manner. However, the clinical utility of CPET for evaluating interstitial lung disease (ILD) remains uncertain. The objective of this study was to determine the predictive value of CPET for mortality in subjects with ILD. METHODS: We prospectively enrolled subjects with ILD who underwent CPET at a tertiary medical center in Taiwan and followed up their survival status for 12 months. Mortality prediction was based on comparing CPET parameters between subjects who survived and those who died. We further analyzed CPET parameters that showed significant differences using receiver operating characteristic curves to identify their optimal cutoff values. RESULTS: A total of 106 newly diagnosed subjects with ILD underwent CPET, and the 1-y mortality rate was 7.5%. Six CPET variables were found to be significant predictors of mortality: peak oxygen consumption, oxygen pulse, end-tidal partial pressure of carbon dioxide, heart rate recovery 1 min after CPET, minute ventilation to carbon dioxide output slope, and functional aerobic impairment. We calculated a summed score by adding the number of CPET variables that exceeded their cutoff values. Subjects with a summed score of 6 had a 1-y survival rate of only 25%, whereas subjects with scores of 0-5 had a survival rate of 98%. CONCLUSIONS: In conclusion, the summed score represents a useful tool for screening patients with ILD who can undergo a CPET to determine their prognosis. [ABSTRACT FROM AUTHOR]

Natural hazards, such as flood, landslide, and erosion, are the reality of human life. spatial prediction of these hazards and their effectiveness factors are extremely important. The main goal of this study was to prepare multi-hazard probability mapping (flood, landslide, and gully erosion) of the Gorganrood Watershed. In addition, different machine learning models such as Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Multivariate Adaptive Regression Spilines (MARS) were applied. First, a flood, landslide, and gully erosion inventory map was produced using GPS in the field surveys and Google Earth. Factors affecting the hazards were identified, and GIS maps were prepared. The MARS model (AUC = 99.1%) provided the highest predictive performance for flood, landslide, and gully erosion hazards. However, for flood and landslide, the RF model exposed excellent and good performance, respectively. According to the variable importance analysis, drainage density (89.4%), digital elevation model (30.5%), and rainfall (41.7%) were consistently highly ranked variables for flood, landslide, and gully erosion, respectively. Multi-hazard maps can be a valuable tool for the conservation of natural resources and the environment, as well as for sustainable land use planning in multi-hazard-prone areas. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is a major complication following cardiac surgery. We explored the clinical utility of iron metabolism indexes for identification of patients at risk for AKI after cardiac surgery. Methods: This prospective observational study included patients who underwent cardiac surgery between March 2023 and June 2023. Iron metabolism indexes were measured upon admission to the intensive care unit. Multivariable logistic regression analyses were performed to explore the relationship between iron metabolism indexes and cardiac surgery-associated AKI (CSA-AKI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of iron, APACHE II score and the combination of the two indicators. Restricted cubic splines (RCS) was used to further confirm the linear relationship between iron and CSA-AKI. Results: Among the 112 recruited patients, 38 (33.9%) were diagnosed with AKI. Multivariable logistic regression analysis indicated that APACHE II score (odds ratio [OR], 1.208; 95% confidence interval [CI], 1.003–1.455, P = 0.036) and iron (OR 1.069; 95% CI 1.009–1.133, P = 0.036) could be used as independent risk factors to predict CSA-AKI. ROC curve analysis showed that iron (area under curve [AUC] = 0.669, 95% CI 0.572–0.757), APACHE II score (AUC = 0.655, 95% CI 0.557–0.744) and iron and APACHE II score combination (AUC = 0.726, 95% CI 0.632–0.807) were predictive indicators for CSA-AKI. RCS further confirmed the linear relationship between iron and CSA-AKI. Conclusions: Elevated iron levels were independently associated with higher risk of CSA-AKI, and there was a linear relationship between iron and CSA-AKI. [ABSTRACT FROM AUTHOR]

Background: Multiple clinical studies have demonstrated the numerous advantages of urine methylation test over cytology for monitoring patients with non-muscle invasive bladder cancer (NMIBC) following surgery. This research aims to provide a systematic review and meta-analysis to evaluate the efficacy and limits of urine methylation test in the clinical management of NMIBC. Methods: This research was carried out by conducting a comprehensive search of clinical trials comparing cytology and urine methylation test for NMIBC followup using databases such as PubMed, Embase, Web of Science, and the Cochrane Library up until May 2023, including references from relevant articles. The study is registered on PROSPERO with ID CRD42023398969. Result: This study comprised six studies with a total of 1676 patients. The analysis revealed that the AUC of urine methylation test had a greater AUC than that of the cytology examination (0.89 vs 0.71). In post-operative follow-up of patients with NMIBC, the urine methylation test demonstrated a significant sensitivity (0.69 vs 0.52), but with lower specificity (0.87 vs 0.93) than cytology examination. Conclusion: The urine methylation test and cytology examination have both shown strong diagnostic performance in screening for NMIBC patients. However, urine methylation test outperforms cytology examination in terms of accuracy and sensitivity. Systematic review registration: PROSPERO, identifier CRD42023398969. [ABSTRACT FROM AUTHOR]

Introduction Floods cause financial losses and countless lives in the world every year. Identifying flood-prone areas is one of the basic steps in flood management. In the past, careful observation and note-taking of the mechanism of occurrence and the natural course of the cause and effect of the effective factors that led to the final occurrence of the phenomenon in a chain manner helped to understand the pattern and process of the occurrence to some extent. Because the processes of creating floods are numerous and affected by various factors and the flood phenomenon is multidimensional and dynamic, all-natural, human, and organizational-management factors affect the occurrence, intensity, extent, and continuity of floods. So far, many efforts have been made to use data-mining models and artificial intelligence in the spatial prediction of floods. The models try to better and more accurately estimate the distribution of the flood phenomenon by examining the relationships between each flood event (dependent factor) and the set of underlying and stimulating factors (independent factors) and fitting them to educational evidence. Since the applicability of the flexible discriminant analysis model has not been fully investigated in the field of flood susceptibility prediction, this research quantitatively evaluated its performance using real-world flood data. Materials and Methods Based on the availability of periodic history of flood events, the Zarrineh-Rood Watershed of Kurdistan Province, Iran, was chosen as the study area. It was tried to select the factors based on different criteria such as familiarity with the process of flood inundation, ease of data preparation, having the most spatial variability at the regional level (not uniform), and containing the most information for the model should be selected to separate areas with different levels of flood susceptibility. Thirteen diverse geo-environmental factors including elevation, aspect, slope percent, land use, drainage density, lithology, plan curvature, profile curvature, mean annual precipitation, soil texture, stream power index, distance from the stream, and topographic wetness index were used as independent variables. The maps of elevation, aspect, slope percent, plan and profile curvatures, stream power index, and topographic wetness index were produced using a digital elevation model. Hydrological layers including distance from the stream and drainage density were produced using the stream network layer. The location of the flooding events was also collected as the dependent variable. The spatial data of flooding were randomly divided into two groups of training and validation with a ratio of 70:30. After running the model (i.e., Flexible Discriminant Analysis) based on the training group, the flood susceptibility map was produced. The validation of the model results was conducted using the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS) metrics. Results and Discussion The results indicated that the FDA model with the value of AUROC= 0.96 and TSS= 0.86 efficiently and accurately produced the flood susceptibility map. The flexible nature of the model in the selection of regression equations, as well as the possibility of weighting, and determining the priority of the evidence of presence over the evidence of absence, are among the special capabilities of the FDA model, which many machine learning models lack. Using probability distribution estimation algorithms in the model is very important and can not only extract the hidden spatial pattern of occurrence from a set of data but also help to predict flood-prone areas in datascarce Watersheds. Based on the results, about 14% (62 thousand ha) of the study area was categorized in the high and very high flood susceptibility zones, which include the northern, northwestern, and southeastern areas. Spatial analysis of the flood susceptibility map showed that in total 25897 ha (18.12%) of agricultural lands, 343 ha (50.91%) of garden lands, and 2126 ha (39.93%) of residential areas located in high and very high susceptible zones. Considering the successfulness of the FDA model in goodness-of-fit and validation phases, the flood susceptibility map can be used as a basis for planning flood control and management measures. Conclusion The findings of this study proved that the flexible discriminant analysis model provides the possibility of processing diverse and big geo-environmental data to predict the flood susceptibility of Watersheds and it had a high efficiency in this context. There is a lot of spatial correspondence between the vegetation status map and the flood susceptibility map; in such a way that the places that had a high flood susceptibility degree, their upstream areas were generally destroyed in terms of vegetation. The results of this research showed that a significant area of the Zarineh-Rood Watershed had a high and very high flood potential, which was characterized by the interaction of low slope and flat areas, formations and soils with low penetration and dense drainage network, and more importantly, flood-prone areas located in the northern, northwestern and southeastern parts of the Watershed. The situation of the flood probability of the Zarineh-Rood Watershed has been determined and managers and decision-makers must put the critical areas in the priority of flood management programs. More flood-driver factors are suggested to be used as predictor variables in flood susceptibility modeling in future studies. On the other hand, it is very important to determine the role of predictor variables in the flood susceptibility degree at the Watershed scale, which can be investigated in future research. [ABSTRACT FROM AUTHOR]

Proliferative diabetic retinopathy (PDR) is a vision-threatening complication of diabetes mellitus (DM). Anterior chamber (AC) flare and intraocular cytokines are potent biomarkers reflecting the intraocular immune status in PDR. This study aimed to elucidate the complex interrelationship between AC flare and intraocular cytokines in PDR eyes. A retrospective observational study was conducted on 19 PDR eyes of 19 patients with type 2 DM, and on 19 eyes of 19 patients with idiopathic macular hole or epiretinal membrane as controls. AC flare was measured before pars plana vitrectomy (PPV). Aqueous humor (AH) and vitreous fluid (VF) samples were collected at the time of PPV, and the quantities of 27 cytokines in both intraocular fluids were analyzed. In the PDR and control groups, Spearman's rank correlation analysis revealed a positive correlation between AC flare and IL-8 level in both AH and VF. Additionally, IL-8 levels in AH correlated positively with IL-8 levels in VF. In the PDR group, receiver operating characteristic curve analysis identified IL-8 level in AH as a significant predictor for both diabetic macular edema (DME) and vitreous hemorrhage (VH) complications. The cut-off values of IL-8 were established at ≥26.6 pg/mL for DME and ≥7.96 pg/mL for VH. Given the positive correlation between AC flare and AH IL-8 level, the present findings suggest that AC flare value may potentially be a non-invasive biomarker for predicting DME. [ABSTRACT FROM AUTHOR]

Few researchers have compared the effectiveness of traditional and novel obesity indicators in predicting stroke incidence. We aimed to evaluate the associations between six obesity indices and stroke risk, and to further identify the optimal indicator. A total of 14,539 individuals from the Rural Chinese Cohort Study were included in the analyses. We used the Cox proportional hazards regression models to evaluate the association between six obesity indices (including body mass index [BMI], waist circumference [WC], conicity index [C-index], lipid accumulation product [LAP], visceral adiposity index [VAI], and Chinese visceral adiposity index [CVAI]) and stroke risk. Receiver operating characteristic curves were employed to compare their predictive ability on stroke risk. During a median follow-up period of 11.13 years, a total of 1257 cases of stroke occurred. In the multiple-adjusted Cox regression model, WC, BMI, C-index, and CVAI were positively associated with ischemic stroke (P < 0.01) rather than hemorrhagic stroke risk. Dose-response analyses showed a linear correlation of WC, BMI, C-index, and LAP (P overall <0.05, and P nonlinear >0.05), but a non-linear correlation of CVAI (P overall <0.05, and P nonlinear <0.05) with the risk of ischemic stroke. CVAI demonstrates the highest areas under the curves (AUC: 0.661, 95% CI: 0.653–0.668), indicating a superior predictive ability for ischemic stroke occurrence compared to other five indices (P < 0.001). WC, BMI, C-index, LAP, and CVAI were all positively related to the risk of ischemic stroke, among which CVAI exhibited stronger predictive ability for ischemic stroke. • WC, BMI, C-index, LAP and CVAI were positively associated with stroke risk. • Elevated obesity indices were associated with ischemic but not hemorrhagic stroke risk. • As the CVAI increased, the risk of stroke followed a "reverse L" shape. • CVAI was more effective in predicting ischemic stroke than other indices. [ABSTRACT FROM AUTHOR]

PURPOSE: To compare the evaluation metrics for deep learning methods that were developed using imbalanced imaging data in osteoarthritis studies. MATERIALS AND METHODS: This retrospective study utilized 2996 sagittal intermediate-weighted fat-suppressed knee MRIs with MRI Osteoarthritis Knee Score readings from 2467 participants in the Osteoarthritis Initiative study. We obtained probabilities of the presence of bone marrow lesions (BMLs) from MRIs in the testing dataset at the sub-region (15 sub-regions), compartment, and whole-knee levels based on the trained deep learning models. We compared different evaluation metrics (e.g., receiver operating characteristic (ROC) and precision-recall (PR) curves) in the testing dataset with various class ratios (presence of BMLs vs. absence of BMLs) at these three data levels to assess the models performance. RESULTS: In a subregion with an extremely high imbalance ratio, the model achieved a ROC-AUC of 0.84, a PR-AUC of 0.10, a sensitivity of 0, and a specificity of 1. CONCLUSION: The commonly used ROC curve is not sufficiently informative, especially in the case of imbalanced data. We provide the following practical suggestions based on our data analysis: 1) ROC-AUC is recommended for balanced data, 2) PR-AUC should be used for moderately imbalanced data (i.e., when the proportion of the minor class is above 5% and less than 50%), and 3) for severely imbalanced data (i.e., when the proportion of the minor class is below 5%), it is not practical to apply a deep learning model, even with the application of techniques addressing imbalanced data issues.

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

Tropical cyclones, including surge inundation, are a common event in the coastal regions of Bangladesh. The surge washes out the area within a very short period and remains in flooded condition for several days. Spatial analysis to understand the surge susceptibility level can assist the cyclone management system. Surge susceptibility analysis could be one of the most essential parts of disaster risk reduction through which cyclone vulnerability can be minimized. A Geographic Information Systems-based analytical hierarchy process (AHP) multi-criteria analysis and bivariate frequency ratio (FR) techniques were conducted to understand the surge susceptibility level of a cyclone-prone area on the Bangladesh coast. A total of 10 criteria were considered influential to surge flooding, i.e. Topographic Wetness Index, elevation, wind velocity, slope, distance from sea and rivers, drainage density, Land Use and Land Cover, Normalized Difference Vegetation Index, precipitation, and soil types. The final surge susceptibility maps were categorized into five classes, i.e. very low, low, moderate, high, and very high. Conferring to these susceptibility classes, policymakers can make decisions for future land use management and disaster risk reduction activities. According to this research, AHP showed better precision (Receiver Operating Characteristic) than FR for surge susceptibility prediction on the Bangladesh coast.

Abstract Background Deep venous thrombosis (DVT) after spinal surgery has recently attracted increasing attention. Patients with spinal metastases who undergo decompression with fixation are at a high risk of developing DVT. D-dimer levels indicate the risk of DVT, and the purpose of our study was to investigate D-dimer levels as a predictor of DVT perioperatively. Methods We prospectively evaluated 100 patients with spinal metastases. D-dimer tests were performed twice: once before surgery and one day postoperatively. DVT was diagnosed by duplex ultrasonographic assessment of both lower extremities. Pulmonary embolisms (PEs) were diagnosed using multidetector computed tomography and pulmonary angiography. Perioperative serum D-dimer levels were compared between the DVT (+) and DVT (-) groups. The cutoff value of the D-dimer level was calculated using receiver operating characteristic analysis. Results Preoperative and postoperative DVT prevalences were 8.0% (8/100) and 6.6% (6/91), respectively, and none of the patients developed PE. Before surgery, there was no significant differences in D-dimer levels between the pre-DVT (+) and pre-DVT (-) groups. After surgery, the D-dimer level one-day postoperatively for the post-DVT (+) group (17.6 ± 11.8 mg/L) was significantly higher than that of the post-DVT (-) group (5.0 ± 4.7 mg/L). The cutoff value of the postoperative D-dimer level was 9.51(mg/L), and the sensitivity and specificity for the optimum threshold were 83.3% and 89.4%, respectively. Conclusions Our findings suggest that preoperative D-dimer level may not be a predictor of DVT. Preoperative ultrasound examinations should be routinely performed in patients with spinal metastases. Postoperative D-dimer levels greater than 9.51(mg/L) are a predictive factor for the early diagnosis of DVT after spine surgery. Trial registration Our study was registered on Chinese Clinical Trial Registry (No.ChiCTR2000029737). Registered 11 February 2020 - Retrospectively registered, https://www.chictr.org.cn/index.aspx.

Abstract Purpose To identify which non-invasive infection indicators could better predict post-cervical cerclage (CC) infections, and on which days after CC infection indicators should be closely monitored. Methods The retrospective, single-center study included 619 single-pregnancy patients from January 2021 to December 2022. Patients were categorized into infected and uninfected groups based on physicians’ judgments of post-CC infections. Registered information included patient characteristics, cervical insufficiency history, gestational age at CC, surgical method (McDonald/Shirodkar), purpose of CC, mid-pregnancy miscarriage/preterm birth, infection history or risk factors, and infection indices on days 1, 3, 5, and 7 after CC. Propensity score matching (PSM) was applied to reduce patient characteristic bias. Statistical analysis of C-reactive protein (CRP), white blood cell (WBC), neutrophil count (NEU), percentage of neutrophil count (NEU_P), interleukin-6 (IL-6), and procalcitonin (PCT) in the infected group compared with the uninfected group was performed using chi-square tests and t-tests. Receiver operating characteristic (ROC) curves were used to further assess the diagnostic value of CRP, PCT, and CRP-PCT in combination. Results Among the 619 included patients, 206 patients were matched using PSM and subsequently assessed. PCT values on day 1 and day 3 after CC exhibited significant differences between the two groups in two statistical ways (P

Purpose: This study aimed to compare the discriminative properties (discriminative effect, sensitivity, specificity, and cutoff values) of four commonly used balance measures for nonfallers, fallers, and multiple fallers among Turkish community-dwelling older adults. Methods: Three hundred fifty-one community-dwelling older adults (122 fallers and 229 nonfallers) were evaluated with the timed up and go test, functional reach test, one-leg stance test, and Berg Balance Scale (BBS). Results: Timed up and go test and functional reach test were not sensitive in detecting group differences between fallers and nonfallers, and BBS and one-leg stance test had significant but limited discriminative power with cutoff values of 53.5 points and 7.50 s, respectively. In addition, timed up and go test, functional reach test, and one-leg stance test had significant but limited discriminative power, and BBS had acceptable discriminative power for older adults who fell multiple times. Conclusions: These findings suggest that BBS is the most suitable tool for assessing the fall risk of Turkish community-dwelling older adults. [ABSTRACT FROM AUTHOR]

Background: Deep venous thrombosis (DVT) after spinal surgery has recently attracted increasing attention. Patients with spinal metastases who undergo decompression with fixation are at a high risk of developing DVT. D-dimer levels indicate the risk of DVT, and the purpose of our study was to investigate D-dimer levels as a predictor of DVT perioperatively. Methods: We prospectively evaluated 100 patients with spinal metastases. D-dimer tests were performed twice: once before surgery and one day postoperatively. DVT was diagnosed by duplex ultrasonographic assessment of both lower extremities. Pulmonary embolisms (PEs) were diagnosed using multidetector computed tomography and pulmonary angiography. Perioperative serum D-dimer levels were compared between the DVT (+) and DVT (-) groups. The cutoff value of the D-dimer level was calculated using receiver operating characteristic analysis. Results: Preoperative and postoperative DVT prevalences were 8.0% (8/100) and 6.6% (6/91), respectively, and none of the patients developed PE. Before surgery, there was no significant differences in D-dimer levels between the pre-DVT (+) and pre-DVT (-) groups. After surgery, the D-dimer level one-day postoperatively for the post-DVT (+) group (17.6 ± 11.8 mg/L) was significantly higher than that of the post-DVT (-) group (5.0 ± 4.7 mg/L). The cutoff value of the postoperative D-dimer level was 9.51(mg/L), and the sensitivity and specificity for the optimum threshold were 83.3% and 89.4%, respectively. Conclusions: Our findings suggest that preoperative D-dimer level may not be a predictor of DVT. Preoperative ultrasound examinations should be routinely performed in patients with spinal metastases. Postoperative D-dimer levels greater than 9.51(mg/L) are a predictive factor for the early diagnosis of DVT after spine surgery. Trial registration: Our study was registered on Chinese Clinical Trial Registry (No.ChiCTR2000029737). Registered 11 February 2020 - Retrospectively registered, https://www.chictr.org.cn/index.aspx. [ABSTRACT FROM AUTHOR]

Time is an essential element in the field of survival analysis. Time to an event is essential in adjudicating diagnostic utility and determining the effectiveness of treatment. However, time‐to‐event outcomes—in a sense—are complicated to deal with, since they involve both a binary component, which is the outcome itself (death or recurrence) and a continuous component, which is the time for the occurrence of the outcome. In such scenarios, simpler metrics such as sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are insufficient to establish the utility of a diagnostic marker. Given the above constraint, this review discusses established ways in which sensitivity and specificity can be determined and are therefore sufficient in establishing utility in the context of time‐dependent outcomes. This review also discusses how studies investigating the sensitivity and specificity of a diagnostic marker in the context of time‐dependent outcomes can be improved through the use of existing user‐friendly statistical software. Level of Evidence: 5 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes. Methods: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve. Results: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases). Conclusion: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD. [ABSTRACT FROM AUTHOR]

Purpose: To identify which non-invasive infection indicators could better predict post-cervical cerclage (CC) infections, and on which days after CC infection indicators should be closely monitored. Methods: The retrospective, single-center study included 619 single-pregnancy patients from January 2021 to December 2022. Patients were categorized into infected and uninfected groups based on physicians' judgments of post-CC infections. Registered information included patient characteristics, cervical insufficiency history, gestational age at CC, surgical method (McDonald/Shirodkar), purpose of CC, mid-pregnancy miscarriage/preterm birth, infection history or risk factors, and infection indices on days 1, 3, 5, and 7 after CC. Propensity score matching (PSM) was applied to reduce patient characteristic bias. Statistical analysis of C-reactive protein (CRP), white blood cell (WBC), neutrophil count (NEU), percentage of neutrophil count (NEU_P), interleukin-6 (IL-6), and procalcitonin (PCT) in the infected group compared with the uninfected group was performed using chi-square tests and t-tests. Receiver operating characteristic (ROC) curves were used to further assess the diagnostic value of CRP, PCT, and CRP-PCT in combination. Results: Among the 619 included patients, 206 patients were matched using PSM and subsequently assessed. PCT values on day 1 and day 3 after CC exhibited significant differences between the two groups in two statistical ways (P < 0.01, P < 0.05). The CRP levels on day 1 were significantly higher in the infected group compared to the uninfected group in two statistical ways (P < 0.05). On day 3, the mean CRP value was significantly elevated in the infected group compared to the uninfected group (P < 0.05). Analyses of IL-6, WBC, NEU, and NEU_P did not yield clinically significant results. The area under the ROC curves for CRP, PCT, and CRP-PCT on day 1 and day 3 were all below 0.7. In the preventive CC group, the AUC values of CRP and CRP-PCT obtained on d1 were found to be higher than 0.7, indicating moderate diagnostic accuracy. Conclusion: For women after CC surgery, especially of preventive aim, increased serum CRP and PCT levels from post-CC day 1 to day 3 may signal a potential postoperative infection, warranting close monitoring. [ABSTRACT FROM AUTHOR]

Introduction: Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are non-invasive nuclear medicine techniques that can identify areas of abnormal myocardial perfusion. We assessed the prevalence of cardiovascular risk factors in patients with suspected coronary artery disease (CAD) undergoing SPECT or PET stress myocardial perfusion imaging (MPI). Based on significant risk factors associated with an abnormal MPI, we developed a nomogram for each cohort as a pretest that would be helpful in decision-making for clinicians. Methods: A total of 6,854 patients with suspected CAD who underwent stress myocardial perfusion imaging by SPECT or PET/CT was studied. As part of the baseline examination, clinical teams collected information on traditional cardiovascular risk factors: age, gender, body mass index, angina, dyspnea, diabetes, hypertension, hyperlipidemia, family history of CAD, and smoking. Results: The prevalence of cardiovascular risk factors was different in the two cohorts of patients undergoing SPECT (n = 4,397) or PET (n = 2,457) myocardial perfusion imaging. A statistical significance was observed in both cohorts for age, gender, and diabetes. At multivariable analysis, only age and male gender were significant covariates in both cohorts. The risk of abnormal myocardial perfusion imaging related to age was greater in patients undergoing PET (odds ratio 4% vs. 1% per year). In contrast, male gender odds ratio was slightly higher for SPECT compared to PET (2.52 vs. 2.06). In the SPECT cohort, smoking increased the risk of abnormal perfusion of 24%. Among patients undergoing PET, diabetes and hypertension increased the risk of abnormal perfusion by 63% and 37%, respectively. For each cohort, we obtained a nomogram by significant risk factors at multivariable logistic regression. The area under the receiver operating characteristic curve associated with the nomogram was 0.67 for SPECT and 0.73 for the PET model. Conclusions: Patients with suspected CAD belonging to two different cohorts undergoing SPECT or PET stress myocardial perfusion imaging can have different cardiovascular risk factors associated with a higher risk of an abnormal MPI study. As crude variables, age, gender, and diabetes were significant for both cohorts. Net of the effect of other covariates, age and gender were the only risk factors in common between the two cohorts. Furthermore, smoking and type of stress test were significant for the SPECT cohort, where as diabetes and hypertension were significant for the PET cohort. Nomograms obtained by significant risk factors for the two cohorts can be used by clinicians to evaluate the risk of an abnormal study. [ABSTRACT FROM AUTHOR]

Aim: The current study aimed at examining the ability of the Weiss Functional Impairment Rating Scale‐Parent Report (WFIRS‐P) to discriminate between children with ADHD and controls in functional impairment and identifying optimal cutoff scores for the WFIRS‐P subscales and total scale. Methods: Parents of 51 children with ADHD (90.2% male; grades 1–6) and 51 gender/grade matched controls (90.2% male; grades 1–6) completed the WFIRS‐P. Receiver operating characteristic (ROC) curve analysis was used to examine the ability of the WFIRS‐P to differentiate children with ADHD from controls and to determine optimal cutoff scores of the WFIRS‐P. Results: Area under the curve (AUC) was 0.98 for the WFIRS‐P total scale, indicating excellent ability to differentiate children with ADHD from controls. The score of 0.45 with 0.88 for sensitivity and 0.96 for specificity was determined as the optimal cutoff score for the total scale of the WFIRS‐P. AUC was 0.73 to 0.97 for the WFIRS‐P subscales, suggesting good to excellent ability for discriminating between children with ADHD and controls. Among the subscales, the family subscale score of 0.42 with 0.92 for sensitivity and 0.96 for specificity showed the highest discriminating power. The self‐concept and life skills subscales had low sensitivity, suggesting Iranian mothers do not identify problems with self‐concept or difficulty with life skills as particularly problematic in ADHD. Conclusions: The WFIRS‐P is a sensitive and specific measure of the functional impairment associated with ADHD in Iranian children. Our sample was predominantly male, limiting the generalizability of results to females. [ABSTRACT FROM AUTHOR]

Purpose: Dysthyroid optic neuropathy (DON) leads to vision loss. This study aimed to investigate a new method that can directly evaluate the change in muscle cone inner volume (MCIV) and distinguish DON orbits from non-DONs. Materials and methods: This study included 54 patients (108 orbits) who were diagnosed with thyroid eye disease and treated at the Beijing Tongren Hospital between December 2019 and September 2021. The extraocular muscle volume (EOMV), orbital fat volume (OFV), and bony orbit volume (BOV) of the patients were measured using three-dimensional reconstruction. MCIV was measured using artificially defined boundaries. The associations between these volumes and clinical indicators were studied, and the diagnostic efficacy of these volumes for DON was described using receiver operating characteristic (ROC) curves. Results: The ROC curve showed that the area under the curve of MCIV/BOV (%) combined with EOMV/BOV (%) reached 0.862 (p < 0.001), with a sensitivity of 85.7% and a specificity of 76.1%. Conclusion: The combination of MCIV/BOV (%) and EOMV/BOV (%) is a good indicator for the diagnosis of DON, which aids in the early detection and intervention of DON. [ABSTRACT FROM AUTHOR]

Abstract Background Certain studies have indicated a link between obstructive sleep apnea and insulin resistance in specific populations. To gain more clarity, extensive research involving a broad sample of the overall population is essential. The primary objective of this study was to investigate this correlation by utilizing data from the National Health and Nutrition Examination Survey database. Methods The analysis incorporated data from the National Health and Nutrition Examination Survey database spanning the time periods from 2005 to 2008 and from 2015 to 2018, with a focus on American adults aged 18 years and older after applying weight adjustments. Key variables such as obstructive sleep apnea, triglyceride glucose index, and various confounding factors were considered. A generalized linear logistic regression model was used to investigate the association between obstructive sleep apnea and the triglyceride glucose index, with additional exploration of the consistency of the results through hierarchical analysis and other techniques. Results The study included participants aged between 18 and 90 years, with an average age of 46.75 years. Among the total sample, 50.76% were male. The triglyceride glucose index demonstrated a diagnostic capability for obstructive sleep apnea, with an AUC of 0.701 (95% CI: 0.6619–0.688). According to the fully adjusted model, individuals in the fourth quartile of the triglyceride glucose index showed an increased likelihood of having obstructive sleep apnea compared to those in the first quartile (OR: 1.45; 95% CI: 1.02–2.06; P

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two-parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain-hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude-longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

Background: Certain studies have indicated a link between obstructive sleep apnea and insulin resistance in specific populations. To gain more clarity, extensive research involving a broad sample of the overall population is essential. The primary objective of this study was to investigate this correlation by utilizing data from the National Health and Nutrition Examination Survey database. Methods: The analysis incorporated data from the National Health and Nutrition Examination Survey database spanning the time periods from 2005 to 2008 and from 2015 to 2018, with a focus on American adults aged 18 years and older after applying weight adjustments. Key variables such as obstructive sleep apnea, triglyceride glucose index, and various confounding factors were considered. A generalized linear logistic regression model was used to investigate the association between obstructive sleep apnea and the triglyceride glucose index, with additional exploration of the consistency of the results through hierarchical analysis and other techniques. Results: The study included participants aged between 18 and 90 years, with an average age of 46.75 years. Among the total sample, 50.76% were male. The triglyceride glucose index demonstrated a diagnostic capability for obstructive sleep apnea, with an AUC of 0.701 (95% CI: 0.6619–0.688). According to the fully adjusted model, individuals in the fourth quartile of the triglyceride glucose index showed an increased likelihood of having obstructive sleep apnea compared to those in the first quartile (OR: 1.45; 95% CI: 1.02–2.06; P < 0.05). Subgroup analysis indicated that male sex (OR: 2.09; 95% CI: 1.76–2.45; P < 0.05), younger age (OR: 2.83; 95% CI: 2.02–3.96; P < 0.05), white ethnicity (OR: 2.29; 95% CI: 1.93–2.73; P < 0.05), and obesity (OR: 1.54; 95% CI: 1.28–1.85; P < 0.05) were correlated with an elevated risk of OSA. Conclusions: This study demonstrated a strong association between an elevated TG index and OSA. Additionally, the triglyceride glucose index could serve as an independent predictor of obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

Background: Chinese visceral adiposity index (CVAI) is a reliable visceral obesity index, but the association between CVAI and risk of cardiovascular disease (CVD) remains unclear. We explored the associations of CVAI with incident CVD, heart disease, and stroke and compared the predictive power of CVAI with other obesity indices based on a national cohort study. Methods: The present study included 7,439 participants aged ≥45 years from China Health and Retirement Longitudinal Study (CHARLS). Cox regression models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic splines analyses were adopted to model the dose--response associations. Receiver operator characteristic (ROC) analyses were used to compare the predictive ability of different obesity indices (CVAI, visceral adiposity index [VAI], a body shape index [ABSI], conicity index [CI], waist circumference [WC], and body mass index [BMI]). Results: During 7 years' follow-up, 1,326 incident CVD, 1,032 incident heart disease, and 399 stroke cases were identified. The HRs (95% CI) of CVD, heart disease, and stroke were 1.50 (1.25-1.79), 1.29 (1.05-1.57), and 2.45 (1.74-3.45) for quartile 4 versus quartile 1 in CVAI. Linear associations of CVAI with CVD, heart disease, and stroke were observed (Pnonlinear >0.05) and per-standard deviation (SD) increase was associated with 17% (HR 1.17, 1.10-1.24), 12% (1.12, 1.04-1.20), and 31% (1.31, 1.18-1.46) increased risk, respectively. Per-SD increase in CVAI conferred higher risk in participants aged<60 years than those aged ≥60 years (P interaction<0.05). ROC analyses showed that CVAI had higher predictive value than other obesity indices (P<0.05). Conclusions: CVAI was linearly associated with risk of CVD, heart disease, and stroke and had best performance for predicting incident CVD. Our findings indicate CVAI as a reliable and applicable obesity index to identify higher risk of CVD. [ABSTRACT FROM AUTHOR]

The Hopkins Symptom Checklist-10 (HSCL-10) is a self-report inventory of anxiety and depression symptoms that may assist clinicians in screening for clinical conditions among patients with substance use disorder (SUD). We examined the HSCL-10 as a screening tool for anxiety and depressive disorders within a general population of SUD inpatients. We used data from a cohort study of 611 SUD inpatients. Receiver operating characteristic (ROC) analyses were conducted, with and without covariates, to evaluate the potential of the HSCL-10 as a screening tool. This was explored using any anxiety disorder, especially posttraumatic stress disorder (PTSD), and any mood disorder, especially major depressive disorders, as the outcome criteria. Candidate covariates included gender, age, education, polydrug use and treatment center. Results: The HSCL-10 had a moderate ability to identify caseness (i.e. having or not having a clinical diagnosis) according to each outcome criterion, with the area under the ROC curve (AUC) varying from 0.64 to 0.66. Adding relevant covariates markedly enhanced the instrument's ability to identify those who met the criteria for any anxiety disorder (AUC = 0.77), especially PTSD (AUC = 0.82). In a real-world clinical setting, the HSCL-10 has fair-to-good clinical utility for identifying SUD inpatients who have comorbid clinical symptoms of anxiety disorders or PTSD, when combined with common background variables. The HSCL-10, a brief self-report screening tool, may serve as an efficient proxy for comprehensive interviews used in research and for clinical anxiety symptom screening among patients with SUD. [ABSTRACT FROM AUTHOR]

The aim was to explore the value of neutrophil-lymphocyte ratio (NLR) as a biomarker for predicting the prognosis or diagnosis in adrenocortical carcinoma (ACC). We identified 262 patients with adrenal gland disease who underwent operation at our institution. NLR carry a differential property was evaluated with ROC curve to distinguish the ACC and non-ACC groups. The cut-off value of NLR was estimated as 2.65 according to the Youden index. With this value, sensitivity was found as 67.5%, specificity was 83.8% and AUC was 0.749 (p < 0.001). In ACC, the higher NLR group was not shown significantly poorer overall survival than the lower NLR group (p > 0.05). Background: The aim was to explore the value of neutrophil-lymphocyte ratio (NLR) as a biomarker for predicting the prognosis or diagnosis in adrenocortical carcinoma (ACC). Methods: We identified 262 patients with adrenal gland disease who underwent operation at our institution between 2013 and 2018. According to postoperative pathology, patients were divided into 2 groups: ACC and non-ACC groups. The neutrophil and lymphocyte count of patients were recorded. Within the intergroup comparison, data obtained from ACC and non-ACC groups were evaluated using ANOVA test. The cut-off values of NLR for the prognosis in ACC were determined according to 3 methods. Results: The NLR values of ACC and non-ACC groups were 5.36 ± 5.30 and (1.73 ± 0.26) ~ (2.56 ± 1.35), respectively (P < .001). NLR carry a differential property was evaluated with ROC curve to distinguish the above 2 groups. The cut-off value of NLR was estimated as 2.65 according to the Youden index. With this value, sensitivity was found as 67.5%, specificity was 83.8% and AUC was 0.749 (P < .001, confidence interval = 0.638-0.860). In ACC, the higher NLR group was not shown significantly poorer overall survival than the lower NLR group (NLR =2.65 vs. NLR < 2.65, NLR =5 vs. NLR < 5, NLR =5.36 vs. NLR < 5.36) (P > .05). Conclusion: According to the data in this study, it can be said that adrenocortical tumors are likely to be malignant by 67.5% if the NLR value is greater than 2.65. When we use the NLR to predict the prognosis of ACC, there is not statistically significant. [ABSTRACT FROM AUTHOR]

The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However, the corresponding literature under a three-class setting is limited. In our study, we provide parametric and nonparametric methods that allow investigators to optimally combine biomarkers that seek to discriminate between three classes by minimizing the Euclidean distance from the receiver operating characteristic surface to the perfection corner. Using this Euclidean distance as the objective function allows for estimation of the optimal combination coefficients along with the optimal cutoff values for the combined score. An advantage of the proposed methods is that they can accommodate biomarker data from all three groups simultaneously, as opposed to a pairwise analysis such as the one implied by the three-class Youden index. We illustrate that the derived true classification rates exhibit narrower confidence intervals than those derived from the Youden-based approach under a parametric, flexible parametric, and nonparametric kernel-based framework. We evaluate our approaches through extensive simulations and apply them to real data sets that refer to liver cancer patients. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the effectiveness of tract-specific diffusion tensor imaging (DTI) metrics in identifying the responsible segments for neurological dysfunction in cervical spondylotic myelopathy (CSM). Methods: The study encompassed nineteen participants diagnosed with CSM, including 10 males and 9 females. Additionally, a control group consisting of ten healthy caregivers (5 males and 5 females) were recruited with no symptoms and no compressions on magnetic resonance imaging (MRI). All participants underwent a comprehensive physical examination, MRI assessment, and DTI examination conducted by a senior chief physician. Several parameters were collected from the MR images, including the aspect ratio (defined as the anteroposterior diameter / the transverse diameter of the corresponding segment's spinal cord), transverse ratio (defined as the transverse diameter of the corresponding segment's spinal cord / the transverse diameter of the spinal cord at C2/3), and T2 high signal of the spinal cord. Furthermore, quantitative DTI metrics, such as axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA), were calculated using automatic region-of-interest (ROI) analysis for both whole spinal cord column and dorsal column. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of the aspect ratio, transverse ratio, and DTI parameters. The area under the curve (AUC), sensitivity, and specificity were calculated. Intraoperative spinal cord electrophysiological examination was performed as the objective measure of spinal cord function during surgery. Results: As determined by electrophysiological examination, neurological dysfunction was found in 2 patients due to C3/4 compression, in 10 patients due to C4/5 compression, in 6 patients due to C5/6 compression, and in 1 patient due to C6/7 compression. The modified Japanese Orthopedic Association scale (mJOA) was 12.71 ± 1.55 in the CSM group, with 4.87 ± 0.72 for sensory nerve function and 5.05 ± 1.35 for motor nerve function. For the control group, none of the volunteers had neurological dysfunction. T2 high signal was found at the most stenotic segment in 13 patients of the CSM group. Considering all the cervical segments, the aspect ratio (AUC = 0.823, P = 0.001, Sensitivity = 68.42%, Specificity = 82.47%) was more capable of determining the responsible segment than transverse ratio (AUC = 0.661, P = 0.027, Sensitivity = 68.42%, Specificity = 67.01%). AD, MD, and RD were significantly higher while FA was significantly lower in the responsible segment than in the irresponsible segment (P < 0.05). The AUC of DTI-Dorsal column parameters (AD, MD, RD, FA) was larger than the corresponding parameters of the DTI (Whole spinal cord). AD of DTI-Dorsal Column possessed the greatest efficacy (AUC = 0.823, sensitivity = 84.21%, specificity = 77.32%) to determine the responsible segment, larger than AD of DTI-Whole spinal cord (AUC = 0.822, P = 0.001, Sensitivity = 89.47%, Specificity = 77.32%), aspect ratio (AUC = 0.823, P = 0.001, Sensitivity = 68.42%, Specificity = 82.47%) and transverse ratio (AUC = 0.661, P = 0.027, Sensitivity = 68.42%, Specificity = 67.01%). Subgroup analysis revealed that the diagnostic efficacy of DTI and MRI parameters was influenced by cervical spine segment. Conclusions: When considering all cervical segments, AD from the DTI-Dorsal Column exhibited the most significant potential in identifying responsible segments. This potential was found to be superior to that of DTI-Whole spinal cord, aspect ratio, the most stenotic segment, T2 high signals, transverse ratio, motor nerve dysfunction, and sensory nerve dysfunction. The diagnostic effectiveness of both DTI and MRI parameters was notably influenced by the specific cervical spine segment. [ABSTRACT FROM AUTHOR]