High-risk Individuals Research Articles

Which is better for predicting and discriminating cardiovascular events: a traditional risk score, a calcium score or a genetic risk score?

Abstract Background The most effective approach to prevent coronary artery disease (CAD) in the general population remains to quantify the individual risk of CAD followed by controlling risk factors. There has been a lot of curiosity to investigate several scores individually for improving risk prediction. However, whether one would be better than the other in the same population is still unclear. Aim To evaluate the performance of the three individual scores, European SCORE2, Coronary artery calcium score (CACS) and a Genetic risk score (GRS), in predicting and discriminating cardiovascular (CV) events in an asymptomatic Portuguese population. Methods Prospective, observational population-based study, including 1002 asymptomatic subjects (mean age 53.1±6.8 years, 73.8% male) selected from a normal Portuguese population, without apparent CAD and diabetes at baseline. Data were recorded at the end of an extended follow-up (average 6.5±4.9 years). The European SCORE2, a computed tomography CACS, and a GRS were created to evaluate CV events’ predictive and discriminative ability through Receiver Operating Characteristics (ROC) analysis and Harrell’s C-statistics. Results ROC curve analysis showed 0.723 for SCORE2, 0.815 for CACS and 0.649 for GRS. Harrell’s C-statistics were 0.731 in the SCORE2, 0.785 for CACS and 0.684 for GRS. Conclusion The CAC score was the best individual score to predict and discriminate CV events in our asymptomatic population. Its capacity to identify high-risk individuals and those asymptomatic with subclinical atherosclerosis may improve CV risk management strategies to prevent future cardiovascular events. This result can be explained because CAC score, more than a risk marker, represents the existence of disease in the vessel wall.

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P < 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P < 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)

Discrimination and calibration are insensitive metrics for comparing the performance of cardiovascular risk prediction models

Abstract Background/Introduction Accurate cardiovascular disease (CVD) risk prediction models can facilitate the identification and cost-effective treatment of high-risk individuals. Discrimination and calibration are the most commonly recommended metrics for evaluating model performance. However, both are population-level measures, and it remains unclear whether these metrics are sufficiently sensitive to assess the performance of models designed for use with individual patients. Purpose Using a real-world dataset, we aimed to compare CVD risk estimates for example individuals (individual-level measures) and discrimination and calibration metrics (population-level measures) from two CVD risk prediction models; one with "age" as the only predictor and one with multiple predictors. Methods We constructed a cohort of almost all New Zealanders without CVD or heart failure, alive and aged 30–79 years in 2014, with follow-up linkage to hospitalisations and mortality until 2018 (N = 2,098,359). We derived two sets of sex-specific Cox regression models to predict 5-year CVD risk. The age-only models included "age" as the only predictor, and the full models used nine pre-defined routinely available CVD risk predictors with interaction terms. We calculated and compared individuals’ 5-year CVD risk from the age-only and full models and assessed model performance using Harrell’s C-statistics and calibration plots. Results Risk estimates for individuals using the two models showed substantial differences. A 65-year-old woman had predicted 5-year risks of 4.9% with the age-only model but 16.1% with the full model; a 74-year-old man had predicted risks of 15.2% and 9.2%, respectively, with age-only and full models (Figure 1). However, the models had similar discrimination and calibration (Figure 2). Age-only models had good discrimination with C-statistics of 0.773 (95% CI: 0.771–0.775) in women and 0.740 (0.738–0.742) in men, and calibrated well in both sexes. The full models had slightly better discrimination (C-statistics: 0.814 [0.812–0.816] in women and 0.769 [0.767–0.771] in men), and very similar calibration to the age-only models in both sexes. Conclusions Age-only models showed good discrimination and were well calibrated, but predicted very different risks in individual case studies compared to models with multiple predictors. This study calls into question the use of discrimination and calibration metrics as the main measures for comparing the performance of different CVD risk prediction models. We recommend that examples of predicted risk for individuals and an assessment of the magnitude and prevalence of cardiovascular predictors should also be considered when comparing the performance of prognostic models.

Socioeconomic deprivation and atrial fibrillation risk: an observational and gene-environment interaction analysis

Abstract Background The association between socioeconomic deprivation and incident atrial fibrillation (AF) is debated, along with limited evidence on how genetic predisposition to AF interacts with socioeconomic deprivation and AF risk. Purpose To evaluate the association and gene-environmental interaction between socioeconomic deprivation and the risk of incident AF. Methods From the UK Biobank prospective cohort, we selected participants who were free from previous AF. Participants with missing values and those who did not pass the genetic quality control were excluded. As an indicator of socioeconomic deprivation, we utilised the indices of multiple deprivation (IMD), which is calculated from scores of multiple socioeconomic factors, including crime, education, employment, health, housing, income, and living environment. Participants were divided into 3 groups by IMD; the top 20% as high IMD (most deprived), the bottom 20% as low IMD, and 20-80% as mid IMD. Using a polygenic risk score (PRS) for AF generated using a Bayesian approach applied to external genome-wide association study data, the interaction between genetic risk and socioeconomic deprivation was assessed. The primary endpoint was incident AF during a median of 13.7 years of follow-up. Results A total of 377,386 (aged 56.5±8.1 years, 45.2% male) were analysed. The high IMD group (n=85,108) was younger and had a higher proportion of comorbidities than the mid or low-IMD groups. An increase in IMD as a continuous variable was significantly associated with an increased risk of incident AF. The high IMD group showed a significantly increased risk of AF (multivariable-adjusted hazard ratio [aHR]=1.12, 95% confidence interval=1.07-1.17, P<0.001) compared with the low IMD group (incidence rate 7.7 vs. 4.8/1,000 person-year). All components of IMD except housing score significantly increased the risk of incident AF (aHR ranged from 1.07 to 1.15). Within each IMD group, higher AF PRS groups were significantly associated with an increased risk of incident AF with the low AF PRS group as a reference, showing aHRs of 1.50 for mid-PRS and 2.57 for high-PRS in the low IMD group, 1.60 and 2.65 in the mid-IMD group, and 1.55 and 2.33 in the high IMD group, respectively (all P<0.001). The impact of socioeconomic deprivation was attenuated in participants with high AF PRS (P for interaction=0.042), mainly driven by the interaction effect of crime (P for interaction [Pi]=0.005), employment deprivation (Pi=0.005), and health deprivation scores (Pi=0.043). Conclusion Socioeconomically deprived individuals had a significantly higher risk of AF, and the effect was partially modified by genetic predisposition to AF. A personalised approach to applying AF screening for high-risk individuals may be more efficacious. Legal measures and policies to enhance public health by alleviating socioeconomic inequalities are needed to mitigate AF risk and improve cardiovascular health outcomes.

The effect of the LPA gene on aortic valve calcification in a Portuguese population

Abstract Introduction Aortic valve calcium (AVC) deposition is one of the mechanisms behind aortic sclerosis and stenosis. There is a known association between LPA gene rs3798220 T>C polymorphism and the development of severe aortic stenosis. However, it is still unknown if it has been a driving factor since the early stages of the disease. Objective Investigate, in our population, the association between the LPA gene variant rs3798220 T>C and aortic valve calcification. Methods AVC was measured in 451 consecutive individuals from the prospective arm of the GENEMACOR study with a mean age of 63.4±9.7 years, 58.8% male. AVC score was performed by cardiac computed tomography and reported as Agatston units. Results were separated into two groups (Group A: AVC=0; Group B: AVC>0). Lp(a) levels were analyzed in the different groups. Each individual was genotyped for LPA rs3798220 T>C. This variant has a minor allele frequency (MAF)<2%; hence, the risk homozygous CC is a rare genotype, and we used the heterozygous CT in our analysis. The bivariate and multivariate logistic regression analysis evaluated the presence of AVC adjusting to traditional risk factors (TRFs) and the LPA heterozygous genotype (CT). Results LP(a) levels were higher in the CT allele versus the homozygous TT, but this difference did not reach a statistical significance. After multivariate analysis, the heterozygous LPA rs3798220 T>C polymorphism remained an independent risk factor for the presence of aortic valve calcification (OR=12.14, p=0.002) together with age (OR=1.13, p<0.0001), arterial hypertension (AHT) (OR=1.96, p=0.006) and alcohol consumption (OR=2.46, p=0.016). Conclusion The LPA gene variant rs3798220 T>C is associated with the presence of aortic valve calcification in a Portuguese population. In such an age-dependent disease as aortic stenosis, the inversion in the age pyramid will see a dramatic increase in the prevalence of aortic stenosis. The early identification of high-risk individuals might enable prompt traditional risk factors control and disease prevention. Larger population studies may help to determine if this difference is driven by Lp(a) levels.

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention. Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m2) and 30 adults without DR (age of 41.8±14.7 years, HbA1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m2) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance. Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.

Exome sequencing in over 63,000 patients in TIMI trials uncovers pathogenic cardiomyopathy variant carriers with high risk for heart failure and cardiovascular death

Abstract Background Rare genetic variants predisposing to cardiomyopathy (CMP) confer increased risk for adverse events. The prevalence of CMP variants in contemporary cardiovascular (CV) trials and their association with prevalent heart failure (HF) and incident HF hospitalizations (HHF) is not established. Purpose We aimed to identify carriers of rare pathogenic/likely pathogenic (PLP) variants for dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) in well-phenotyped clinical trial cohorts and to evaluate their prognostic significance. Methods Rare variants were identified from high-quality exome sequencing data from 5 large clinical trials (ENGAGE AF-TIMI 48, SAVOR-TIMI 53, PEGASUS-TIMI 54, DECLARE-TIMI 58, FOURIER (TIMI 59)). Putatively pathogenic variants for cardiomyopathy were identified using phenotype- and gene-specific curation of protein-truncating variants and high-confidence variant classifications from ClinVar. We conducted logistic regression for prevalent disease and Cox proportional hazard models for incident HHF, both adjusted by age, sex, trial, and first 10 principal components and using those without CMP variants as the reference. Sensitivity analysis by history of HF at baseline was performed for prospective outcomes. Results In 63,700 participants, we identified 866 CMP variant carriers of which 518 identified as DCM carriers (age 65±9 yrs, male 66%), 194 HCM carriers (65.5±9 yrs, male 69%), and 125 ARVC carriers (66±9 yrs, male 77%). Out of the 518 DCM variant carriers, 402 had a PLP variant in TTN. DCM variant carriers had higher odds of history of HF (Odds ratio [OR] 1.94, 1.57-2.39, p<0.0001) whereas this was not observed for HCM variant carriers (OR 1.18, 0.85-1.65, p=0.33) or ARVC variant carriers (OR 1.23, 0.80-1.90, p=0.35). Over a mean follow-up of 2.5 years, the rate of HHF was significantly higher for DCM variant carriers (Hazard ratio [HR] 1.58, 1.14-2.20, p=0.006), HCM variant carriers (HR 2.78, 1.87-4.13, p<0.001), and ARVC variant carriers (HR 1.89, 1.02-3.53, p=0.04). These associations were, if anything, stronger in patients without HF at baseline (HR 2.06, 1.30-3.25) than in patients with HF at baseline (HR 1.48, 1.12-1.95). DCM variant carriers also had a higher rate of CV death (HR 1.50, 1.05-2.12, p=0.02), which was not observed for HCM (HR 1.10, 0.57-2.11, p=0.78) or ARVC (HR 0.35, 0.09-1.41, p=0.14) variant carriers. Conclusion In 5 contemporary CV trials, CMP variant carriers have an increased rate of HHF, while DCM variant carriers also have a higher rate of CV death. These findings motivate better identification of these high-risk individuals and the incorporation of genetic testing in CV trials.

Thyroid Function in the Time of COVID-19: A Systematic Review of Disease Progression and Vaccination Effect

Objectives: This systematic review sought to address three key questions: (1) what differences in abnormal thyroid function test results are observed between COVID-19 patients and healthy individuals? (2) How does the severity of COVID-19 infection influence the development of thyroid dysfunction? (3) What impact do COVID-19 vaccines have on thyroid function and autoimmune processes? Methods: A literature search was conducted in PubMed, Web of Science, and Scopus from December 2019 to April 2023 to identify studies on thyroid dysfunction in COVID-19 patients without pre-existing thyroid conditions. The search focused on observational and case-control studies. Results: The literature search yielded 329 reports, from which duplicates and unrelated publications were excluded. Ultimately, 21 studies met the inclusion criteria and were selected for review. A second literature search yielded 605 reports, from which 5 studies were selected for inclusion in the systematic review. Conclusions: The findings suggest that SARS-CoV-2 infection can induce transient and reversible thyroid dysfunction, possibly through direct viral effects on the thyroid gland or via indirect immune-mediated mechanisms. Clinicians should be mindful of the potential, albeit rare, thyroid-related adverse effects of COVID-19 vaccines and monitor thyroid function, particularly in high-risk individuals.

Pre-diagnostic trajectory of pediatric hemophagocytic lymphohistiocytosis: observations from hematological and hepatic parameters.

Understanding the early features and characteristics of hemophagocytic lymphohistiocytosis (HLH) is essential for identifying high-risk individuals and also providing valuable pathological insights. This study aims to investigate the characteristics and trends of blood and hepatic parameters before an HLH diagnosis was established. Longitudinal hematological and hepatic test results from pediatric patients with HLH and an age- and sex-matched control group were analyzed. According to the length of time between hospital admission and the establishment of the HLH diagnosis, the HLH cases were divided into early-onset (≤ 7 days) and late-onset (> 7days) groups. Among the 229 pediatric HLH patients, the length of time between hospital admission and the establishment of an HLH diagnosis ranged from 0 to 41 days (median = 4 days). Over 80% of pediatric HLH patients presented abnormal laboratory results for aspartate aminotransferase (AST), triglycerides, lactate dehydrogenase (LDH), and hemoglobin at admission. The abnormal rates in the initial platelet count, neutrophil count, and fibrinogen tests were 67.3%, 48.3%, and 52.2%, respectively. The initial test results for AST, alanine aminotransferase (ALT), LDH, serum sodium, and albumin showed AUCs > 80% for discriminating early-onset HLH. For the discrimination of late-onset HLH, the performance of initial test results was poor. To conclude, abnormalities in AST, triglycerides, LDH, and hemoglobin are early presentations of pediatric HLH; platelet, neutrophil, and fibrinogen levels may become abnormal at a relatively late stage of the HLH disease trajectory; and the initial test results for AST, ALT, LDH, serum sodium, and albumin can be used to identify suspected early-onset HLH.

Parenthood and poverty risk within couples: individual and household poverty risks by gender and education in four European countries

Abstract This study examines how the presence of children is associated with poverty risk within different-sex couples across welfare state regimes, using no- and full-income pooling scenarios. It focuses on whether partners can achieve an adequate living standard without relying on family ties, and how the costs of children shape poverty risk within these scenarios. Using cross-sectional EU-SILC data (2016–2019) on 30,150 coresidential couples from Austria, France, Spain, and Sweden, I use linear probability models (LPM) to estimate household and individual poverty risks among partners with/out children, by education, gender and country. The results indicate that poverty risks vary by gender, income pooling scenario and country. While partners bear household poverty risks together, there is a pronounced gender gap in individual poverty risk across countries. Men, regardless of their education and fatherhood status, have a relatively low individual poverty risk, with little difference between income pooling scenarios. Conversely, women, especially low-educated mothers, have a higher individual than household poverty risk. Observed cross-country variations highlight that the relationship between motherhood and poverty risk is context-specific. Although education is often promoted as a shield against poverty, in some countries, such as Austria, highly educated mothers also face a high individual poverty risk.

An equation for estimating low-density lipoprotein-triglyceride content and its use for cardiovascular disease risk stratification.

The triglyceride (TG) content of low-density lipoprotein (LDL-TG) has been shown to be more predictive of atherosclerotic cardiovascular disease (ASCVD) events than the cholesterol content of LDL (LDL-C). The goal of our study was to develop an equation for estimating LDL-TG (eLDL-TG) based on the standard lipid panel and to compare it to estimated LDL-C as an ASCVD risk biomarker. Using least-square regression analysis, the following eLDL-TG equation was developed: . LDL-TG was measured by the β-quantification (BQ) reference method (N = 40,202). LDL-C was calculated by the Sampson-NIH equation. The association of LDL-C and eLDL-TG with ASCVD risk markers was performed in the National Heart and Nutrition Examination Survey (NHANES) (N = 37,053) and with ASCVD events in a primary prevention cohort from the UK Biobank (UKB) (N = 429,367) and the Atherosclerosis Risk in Communities (ARIC) study (N = 14,632). eLDL-TG showed better ASCVD risk stratification of UKB participants than LDL-C (Wilcoxon Chi-Square: 2,099.6 vs. 418.7, respectively). Receiving-operating characteristics analysis revealed that eLDL-TG had a stronger association with ASCVD events than LDL-C (AUC: 0.596 vs. 0.542, respectively) and other conventional lipid markers. Similar findings were found in ARIC. Discordance analysis in UKB showed that the group with low LDL-C/high eLDL-TG had a similar risk as the high LDL-C/high eLDL-TG group. Furthermore, these same two groups with the highest eLDL-TG levels and the highest ASCVD event rate also had higher mean levels of systolic blood pressure, Body Mass Index, hemoglobin A1C, and C-reactive protein than the two lower eLDL-TG groups. Using eLDL-TG > 44.6 mg/dl (80th percentile) as a cut-point leads to a hazard ratio of 1.32 (95% CI, 1.29-1.36) for ASCVD events, which remained significant after adjustment for LDL-C and apoB. Furthemore, using eLDL-TG as a risk-enhancer test leads to reclassification of 50% more high-risk individuals than current lipid-enhancer test rules. Like LDL-C, LDL-TG can also be calculated from the results of the standard lipid panel. Compared to estimated LDL-C, eLDL-TG was a better risk marker for primary prevention and hence could improve initial ASCVD risk stratification.

Comprehensive assessment of the association between tumor-infiltrating immune cells and the prognosis of renal cell carcinoma.

According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC. To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy. The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of P < 0.05 was regarded as statistically significant. Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve (P = 1E-05). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) via the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342-1.791; P < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC. TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.

Health perceptions of adverse weather in older adults in England: analysis of 2019/20 survey data.

Risks to older adults (OA) (aged 65+ years) associated with hot and cold weather in the UK are well-documented. The study aim is to explore OA perception of health risks from high and low temperatures, health-protective measures undertaken, and implications for public health messaging. In 2019/20, Ipsos MORI conducted face-to-face surveys with OA in England (n = 461 cold weather survey, n = 452 hot weather survey). Participants reported temperature-related symptoms, risk perceptions for different groups, and behaviours during hot and cold weather. Analysis involved binomial logistic regression models to assess potential factors (demographics, vulnerability, behaviours) associated with older adults' health risk perception in hot and cold weather. Less than half of OA in both surveys agreed that hot or cold weather posed a risk to their health. OA with higher education, annual income >£25000 or home ownership were less likely to perceive their health at risk during cold weather and regional differences in hot weather were identified. OA who recognized those the same age or living alone as at an increased risk were more likely to perceive their own health as at risk. OA were more likely to self-identify health risks when reporting those aged 65 yrs+ to be at an increased risk in cold weather. Various temperature-related protective behaviours were associated with older adults' risk perception in hot and cold weather. These findings provide evidence for public health agencies to target high risk individuals, and modify temperature-related public health messaging to protect OA.

Adding Biomarker Change Information to the Kidney Failure Risk Equation Improves Predictive Ability for Dialysis Dependency in eGFR &amp;lt;30 mL/min/1.73m2

Abstract Background Although the Kidney Failure Risk Equation (KFRE), a well-known predictive model for predicting dialysis dependency, is useful, it remains unclear whether the addition of biomarker changes to the KFRE model in patients with an estimated glomerular filtration rate (eGFR) &amp;lt; 30 mL/min/1.73 m2 will improve its predictive value. Methods We retrospectively identified adults with eGFR &amp;lt; 30 mL/min/1.73 m2 without dialysis dependency, and available health checkup data for 2 successive years using a large Japanese claims database (DeSC, Tokyo, Japan). We dichotomized the entire population into 50% training set and the other half as validation set. To assess the incremental value in the predictive ability for dialysis dependency by the addition of changes in eGFR and proteinuria, we calculated the difference in the C-statistics and net reclassification index (NRI). Results We identified 4,499 individuals and observed 422 individuals (incidence of 45.2 per 1000 person-years) who developed dialysis dependency during the observation period (9,343 person-years). Adding biomarker changes to the KFRE model improved C-statistics from 0.862 to 0.921, with an improvement of 0.060 (95% CI of 0.043–0.076, P &amp;lt;0.001). The corresponding NRI was 0.773 (95% CI: 0.637– 0.908), with an NRI for events of 0.544 (95% CI of 0.415–0.672) and NRI for non-events of 0.229 (95% CI of 0.186–0.272). Conclusions The KFRE model was improved by incorporating yearly changes in its components. The added information may help clinicians identify high-risk individuals and improve their care.

Utilizing Attention-Enhanced Deep Neural Networks for Large-Scale Preliminary Diabetes Screening in Population Health Data

Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an Attention-based Feature Weighting Layer combined with deep neural network layers to achieve precise diabetes prediction. In this study, we utilized the Diabetes-NHANES dataset and the Pima Indians Diabetes dataset. To handle significant missing values and outliers, group median imputation was applied. Oversampling techniques were used to balance the diabetes and non-diabetes groups. The data were processed through an Attention-based Feature Weighting Layer for feature extraction, producing a feature matrix. This matrix was subjected to Hadamard product operations with the raw data to obtain weighted data, which were subsequently input into deep neural network layers for training. The parameters were fine-tuned and the L2 regularization and dropout layers were added to enhance the generalization performance of the model. The model’s reliability was thoroughly assessed through various metrics, including the accuracy, precision, recall, F1 score, mean squared error (MSE), and R2 score, as well as the ROC and AUC curves. The proposed model achieved a prediction accuracy of 98.4% in the Pima Indians Diabetes dataset. When the test dataset was expanded to the large-scale Diabetes-NHANES dataset, which contains 52,390 samples, the test precision of the model improved further to 99.82%, with an AUC of 0.9995. A comparative analysis was conducted using multiple models, including logistic regression with L1 regularization, support vector machine (SVM), random forest, K-nearest neighbors (KNNs), AdaBoost, XGBoost, and the latest semi-supervised XGBoost. The feature extraction method using attention mechanisms was compared with the classical feature selection methods, Lasso and Ridge. The experiments were performed on the same dataset, and the conclusion was that the Attention-based Ensemble Deep Neural Network (AEDNN) outperformed all the aforementioned methods. These results indicate that the model not only performs well on smaller datasets but also fully leverages its advantages on larger datasets, demonstrating strong generalization ability and robustness. The proposed model can effectively assist clinicians in the early screening of diabetes patients. This is particularly beneficial for the preliminary screening of high-risk individuals in large-scale, extensive healthcare datasets, followed by detailed examination and diagnosis. Compared to the existing methods, our AEDNN model showed an overall performance improvement of 1.75%.

Background predation risk induces anxiety-like behaviour and predator neophobia in zebrafish

Prey face a major challenge in balancing predator avoidance with other essential activities. In environments with high risk, prey may exhibit neophobia (fear of novelty) due to the increased likelihood of novel stimuli being dangerous. The zebrafish, Danio rerio, is an established model organism for many scientific studies. Although spatial and object neophobia in zebrafish have received previous attention, little is known about the role of background risk in inducing neophobia in zebrafish. Here, we present two experiments using zebrafish to explore whether background predation risk can induce anxiety-like behaviour in a novel environment and neophobic responses when exposed to a novel odour. Over five days, we repeatedly exposed zebrafish to either high background risk in the form of chemical alarm cues (i.e., injured conspecific cues that indicate a predator attack) or a low-risk water control stimulus. In Experiment 1, when tested in a novel tank, zebrafish exposed to high predation risk displayed anxiety-like responses (reduced activity and increased bottom time spent) compared to their low-risk counterparts. Moreover, high-risk individuals showed reduced intra-session habituation to the novel tank compared to low-risk individuals. In Experiment 2, high-risk individuals exhibited fear responses toward a novel odour, unlike low-risk individuals. These results reveal that short-term repeated exposures to high risk can induce anxiety-like behaviour and predator odour neophobia in zebrafish.

Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram.

Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction. This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and random forest) were then used for analysis to find risk features using a random 70/30% split for discovery and validation cohorts. Five-year SCD occurrence rate was 3.3% (n = 287). Regardless of the used ML algorithm, the most significant risk ECG risk features detected by the CIE included known risk features such as QRS duration and factors associated with QRS duration, heart rate-corrected QT time (QTc), and the presence of premature ventricular contractions (PVCs). Risk score formed by using most significant CIE features associated with the risk of SCD despite adjusting for any clinical risk factor (including left ventricular ejection fraction). Sensitivity of CIE data to correctly identify patients with high risk of SCD (over 10% 5-year risk of SCD) was usually low, but specificity and negative prediction value reached up to 96.9% and 97.3% when selecting only the most significant features identified by logistic regression modeling (p-value threshold <0.01 for accepting features in the model). Overall, CIE data showed a modest overall performance for identifying high risk individuals with area under the receiver operating characteristic curve values ranging between 0.652 and 0.693 (highest for extreme gradient boosting and lowest for logistic regression). This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.

Postpartum depression, and post-traumatic stress disorder and resultant risk factors among teenage mothers: a community-based study

The prevalence of postpartum depression in teenage mothers is higher than that in adult mothers. The present study aimed to investigate the prevalence of depression, distress, and post-traumatic stress disorders (PTSD) following childbirth and its resultant risk factors among teenage mothers. Material and Methods. This cross-sectional study was carried out on 167 teenage women aged 11–19 years, 8-week after giving birth, in Tabriz, Iran. The data were collected using by socio-demographic characteristics questionnaire, Edinburgh Postnatal Depression Scale, Posttraumatic Stress Disorder Checklist and Postpartum Distress Measure scale. Results. The study results revealed that 12.6% and 7.2% of the participants had postpartum depression and PTSD, respectively. The results showed that the variables of the number of children, dissatisfaction with marriage, and history of preconception depression were among the risk factors associated with postpartum depression. The results indicated that unwanted pregnancy, lack of spousal support, insufficient income, and infant restlessness variables were predictors of PTSD. Conclusion. Based on our findings, it is crucial to identify teenage mothers with predisposing factors for postpartum depression and PTSD. Providing family planning counseling to reduce unwanted pregnancies, training fathers to support their spouses, and ensuring timely referral of high-risk individuals are essential interventions to address the mental health challenges faced by teenage mothers.

Risk-contamination and Dilution: Advancing the RNR Model in Prison Settings

The Risk-Needs-Responsivity (RNR) model posits separating lower from higher-risk individuals to avoid “risk contamination.” In a prison setting, policies and restrictions lead to mixing different populations within a housing unit. While this may pose a threat, it may allow higher-risk individuals to gradually promote through security levels, reducing risk. We term this “risk dilution.” The current study compares the infraction behavior of individuals that are over-classified (housed higher than their risk) and those under-classified (housed lower than their risk). Using mixed-effect models with a large ( N = 52,000) sample of incarcerated men, findings demonstrate support for both contamination and dilution. Policy implications identify the impact of these two processes on overcrowded prisons and the consolidation of housing units post decarceration.

Adverse events associated with carbamazepine: a pharmacovigilance study using the FDA Adverse Event Reporting System

ABSTRACT Introduction Carbamazepine (CBZ) is a commonly used antiseizures medications (ASM), but its adverse drug reactions (ADRs) can impact the successful management of epilepsy. There are currently limited systematic studies on ADRs related to CBZ, necessitating further investigation. Areas covered Using the FDA Adverse Event Reporting System (FAERS) database , we extracted reports where CBZ was the primary suspect, conducting subgroup analyses stratified by sex and age. Four risk signal detection methods ROR, PRR, BCPNN, and EGBM were employed to systematically analyze the ADRs associated with CBZ. Expert opinion In the epilepsy population, ADRs related to CBZ often involve examinations, hereditary disorders, and infections. Subgroup analysis showed differences in ADR signals among male, female, elderly, and young patients. Human Herpesvirus 6 Infection and Dermatitis Exfoliative were consistent CBZ-induced ADRs, unaffected by age or sex. The study also identified previously overlooked ADRs such as bone metabolism abnormalities, ocular toxicity, and ototoxicity. Many ADRs linked to CBZ remain underreported. It is crucial to enhance monitoring and information dissemination about CBZ use in epileptic patients. Adjusting medication regimens for high-risk individuals, potentially incorporating acupuncture, traditional Chinese medicine, or alternative anti-seizure medications, should be considered when necessary.