Risk Index Research Articles

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Evaluation of serum apolipoprotein concentrations and atherogenic indices in menopausal and premenopausal women in Nkwelle-Ezunaka

Background of study: The hormonal change in menopause affects lipid metabolism which could lead to dyslipidaemia, a risk factor for cardiovascular disease. Aim of study: To evaluate the levels of apolipoprotein A1, apolipoprotein B, Castelli risk index-1, Castelli risk index-11, atherogenic index of plasma and atherogenic coefficient in menopausal and premenopausal women. Materials and Methods: In this cross-sectional study ninety females were selected which consisted of 45 premenopausal, and 45 menopausal women using simple random sampling technique. Levels of apolipoprotein A1 and B were determined spectrophotometrically using enzyme linked immunosorbent assay (ELISA). Atherogenic indices were calculated with their respective formulas. Results: There was no significant difference in the mean levels of apo B (134.83±28.90 vs 130.73±30.55; p>0.05), apo A1, atherogenic index of plasma, Castelli risk index-1, Castelli risk index-11 and atherogenic coefficient in the menopausal women compared with the premenopausal women but the mean values of both groups were outside the normal range of apo B, Castelli risk index-1, atherogenic index of plasma and atherogenic coefficient. Conclusion: Apo B, atherogenic index of plasma, Castelli risk index-1 and atherogenic coefficient are better indicators of cardiovascular risk.

Tracking Smart Beta indices during different market phases: A “smarter” option for passive investors?

Purpose This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to Cap-Weighted (CW) indices. As investors’ strategies differ with market movements, this study analyses how single-factor and multi-factor SB indices perform during different market phases, in relation to CW indices. It also attempts to determine which SB factors are more suitable for investors in these phases. Design/methodology/approach Using various return and risk indicators, this study analyses how SB indices perform vis-à-vis CW indices during bullish, bearish and stagnant phases. The authors also evaluate the upside and downside participation advantage of SB indices and assess their ability to capture upside returns and limit downside risk. The authors attempt to determine the cyclical or defensive nature of SB indices using Average Participation values. Findings This study found that SB indices outperform CW indices during the bearish and stagnant phases. Multi-factor SB indices have lower risk levels in all market phases, providing downside protection to risk-averse investors. Dividend, Low Volatility, Quality and multi-factor SB indices are defensive portfolios offering better payoffs during the down market phases, while Alpha, Beta, Equal Weight and Value SB indices provide higher payoffs during the up-market phases. Originality/value To the best of the authors’ knowledge, this is the first study that examines the performance of single-factor and multi-factor Indian SB indices in different market phases. It determines the suitability of various factors to passive investors during these phases and also identifies whether SB indices are cyclical or defensive.

Pregnancy in women with congenital heart disease : new insights into neonatal risk predictioc

Abstract Background Advances in managing adult congenital heart disease (ACHD) have led to an increased number of women with CHD reaching childbearing age. This demographic shift underscores the need for improved understanding and prediction of complications during pregnancy in this specific ACHD population. Despite progress in maternal cardiac risk assessment, the prediction of neonatal outcomes for ACHD pregnancies remains underdeveloped. Therefore, the aims of this study are (1) to assess neonatal outcomes in a CHD women population, (2) to identify their predictive factors and (3) to propose a new risk score for predicting neonatal complications. Methods This registry study included all women born between 1975 and 1996 diagnosed with ACHD who underwent at least one cardiology consultation for ACHD in Cliniques Universitaires Saint-Luc. A multivariate analysis was performed to identify predictors of neonatal complications and these were incorporated into a new risk index. Its validity was assessed using bootstrap method. This score was then compared with scores adapted from the ZAHARA and CARPREG studies for offspring events prediction. Results Analysis of 491 pregnancies revealed 31.4% of neonatal complications. Four significant predictors of adverse neonatal outcomes were identified: cardiac treatment during pregnancy (OR 14.9, 95%CI [4.6-64.3]), hypertensive disorders of pregnancy (OR 10.4, 95%CI [3.4-39.5]), smoking during pregnancy (OR 10.3, 95% CI [3.0-44.9]), and pre-pregnancy BMI < 18.5 kg/m² (OR 6.2, 95% CI [2.6-16.5]). The risk model demonstrated an AUC of 0.70 (95% CI [0.65-0.75), which remained stable after bootstrap validation. This model significantly outperformed the scores adapted from ZAHARA and CARPREG data. Based on the regression coefficients, a risk score was subsequently developed comprising 5 risk categories. Conclusion One third of ACHD pregnancies are complicated by poor neonatal outcome. These complications are determined by 4 independent factors relating to the cardiac and non-cardiac status of the patients, which have been incorporated into a risk score. Our study is one of the first to propose a predictive risk score of neonatal outcomes in ACHD pregancies, and paves the way for other validation and confirmation studies.Offspring scores: performance comparisonNew risk score for neonatal outcome

Adherence to Mediterranean diet and cardiometabolic risk among mountain communities in European Alps

Abstract Background The Mediterranean diet has emerged as a model promoting a better health status, particularly for cardiovascular diseases prevention. Adherence to the Mediterranean diet (AMD) may vary depending on geographical location and sociocultural factors. Mountains and rural communities may experience lower adherence, also depending on food provision-related services availability. Our cross-sectional study aims to investigate the association between AMD and cardiometabolic risk in a cohort of mountain dwellers. As a secondary aim, we will test if services availability may influence health status via AMD. Methods In spring/summer 2023, healthy volunteers from 5 small villages in Italian Alps were enrolled. Dietary habits were investigated by Medi-Lite standardized questionnaire. The waist-to-height ratio (WHtR) was calculated by dividing the waist circumference by height, then used as an anthropometric index for central adiposity and related cardiometabolic risk. Services availability was assessed by network analysis implemented in QGIS. A multilevel logistic regression model, with center as random intercept and adjusted for age, sex, BMI, education and physical activity was used to test the association between AMD and cardiometabolic risk. Ethical approval was provided by University of Turin. Results To date, 142 subjects (59.2% females, 49.9±14.0 y.o.) have been enrolled. Preliminary analyses showed that, as compared to the least adherent group, subjects reporting the highest AMD level (3rd tertile of Medi-Lite scoring) were strongly protected from cardiometabolic risk (OR: 0.07, 95% C.I.: 0.01, 0.67; p = 0.021). A similar trend was observed for the 2nd tertile. Conclusions Our preliminary findings suggest the beneficial role of the Mediterranean diet also in remotely located populations. We plan to investigate how service availability may impact AMD and, indirectly, cardiometabolic health to reinforce the importance of equity in services distribution for Public Health Key messages • Independently of age, sex, BMI, education and physical activity, adherence to the Mediterranean diet can promote cardiometabolic health among rural and mountain communities. • Mediterranean eating pattern can positively influence central adiposity and should be promoted to ensure more effective prevention strategies in mountain dwellers.

Vascular adaptations following an eight-week sprint interval training intervention in healthy men.

Sprint interval training (SIT) has been shown to improve many indices of cardiovascular risk. The effect of SIT on emerging indicators of cardiovascular health, including arterial stiffness and carotid intima media thickness, remains unclear. Thus, the purpose of this study was to assess changes in augmentation index at 75 beats per minute (AIx@75), pulse wave velocity (cfPWV), and carotid intima media thickness (CIMT) at three time points of an 8-week SIT intervention. Eighteen sedentary men (age: 24.7±5.1 years, BMI: 26.7±5.8 kg/m2) participated in the study. Subjects trained 3 times a week for 8 weeks. Training consisted of 3-6 consecutive 30-second bouts of maximal intensity cycling, with 4.5 minutes of active recovery between bouts. Baseline, 4-week, and 8-week vascular assessments were performed. Training effects were analyzed using repeated measures ANOVA. Pearson correlations were used to determine the relationship between baseline values and the change scores (baseline to 8 weeks) of each vascular measure. AIx@75 (BL: -3.6±10.9%, 4W: -5.6±8.3%, 8W: -3.2±9.5%), cfPWV (BL: 5.6±1.0 m/s, 4W: 5.8±0.9 m/s, 8W: 5.6±0.7 m/s), and CIMT (BL: 0.51±0.08 mm, 4W: 0.52±0.08 mm, 8W: 0.51±0.08 mm) did not significantly change (all P>0.05). Baseline cfPWV and AIx@75 were negatively correlated to their change from baseline to 8 weeks (P<0.05). These findings demonstrate that 8 weeks of SIT is an insufficient stimulus to reduce cfPWV, AIx@75, or CIMT in a group of young, healthy men. Baseline arterial stiffness may modulate vascular adaptations to SIT.

The relationship between coronary flow reserve and the TyG index in patients with gestational diabetes mellitus

Abstract Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder characterized by glucose intolerance during pregnancy. The triglyceride glucose (TyG) index, a marker of insulin resistance, and coronary flow reserve (CFR), a measure of coronary microvascular function, are emerging as potential indicators of cardiovascular risk. This study aims to investigate the association between CFR and the TyG index in GDM patients. This cross-sectional study of 87 GDM patients and 36 healthy controls was conducted. The participants underwent clinical assessments, blood tests, and echocardiographic evaluations. The TyG index was calculated as ln(triglycerides × fasting glucose/2). CFR was measured using Doppler echocardiography during rest and hyperemia induced by dipyridamole. The study included 87 individuals in the GDM group and 36 individuals in the control group. There was no significant difference in age between the two groups (34.1 ± 5.3 years for GDM vs. 33.1 ± 4.9 years for the control, p = 0.364). The TyG index was significantly higher in the GDM group compared to the controls (p &amp;lt; 0.001) and CFR was lower in the GDM group (p &amp;lt; 0.001) as shown in Figure 1a and 1b. A negative and significant correlation between the TyG index and CFR was observed, which was demonstrated in Figure 2 (r = −0.624, p &amp;lt; 0.001). Linear regression revealed the TyG index as an independent predictor of reduced CFR. The study findings reveal a significant association between the TyG index and CFR in GDM patients, suggesting their potential role in assessing cardiovascular risk.TyG index and CFR of the groupsTyG and CFR correlation analysis

Low physical fitness indicates future injury, mental health, menstrual cycle disruptions and burnout in female emergency service personnel and healthcare providers

Musculoskeletal injury (MSKi), depression, anxiety and burnout place a considerable burden on emergency services personnel and healthcare providers (HCP). Physical fitness is related to both mental and physical health in these populations but females in these are hugely underrepresented in this literature. As female representation in first-responder and HCP roles increases, the need for female specific research is needed. This study examines physical fitness as a short-term indicator of future reproductive health, MSKi, and mental health for females employed as first-responders or HCP. Thirteen first-responders and 29 HCP completed an initial health and demographics questionnaire, a comprehensive physical assessment (e.g., bone mineral density, muscular strength, muscular endurance, muscular power, flexibility, and aerobic capacity), and a health questionnaire 6-7 months after the physical testing. We found that i) bone mineral density, relative upper body strength, and lowerbody power were related to sustaining future MSKi, ii) better lower body endurance and flexibility was related to future menstrual cycle disruptions, and iii) low bone mineral density was related future self-reported burnout and Patient Health Questionnaire score &gt;10. Physical fitness characteristics can be helpful indicators of future MSKi risk, menstrual cycle disruptions, and mental health status in females employed in arduous occupations.

Investigating the influence of possible liver fibrosis on mortality in patients with coronary artery disease - a post-hoc ISCHEMIA trial analysis

Abstract Background There is a plethora of studies demonstrating that comorbidities (e.g., diabetes and renal dysfunction) directly influence outcomes in patients undergoing PCI or CABG for coronary artery disease (CAD). Yet, liver dysfunction is not really included in current risk assessment beyond the presence of liver cirrhosis. Retrospective single center evidence now suggests that the presence of liver fibrosis may be an indicator of elevated risk. Using the FIB-4 score, where increasing values correlate with increaseing degrees of histological liver fibrosis, liver fibrosis could already be detected with all required laboratory values (AST, ALT, platelets) within normal range. In addition, the study suggests a beneficial effect of off-pump CABG when FIB-4 is elevated. This finding contradicts current randomized evidence, negating any difference between off and on-pump CABG. Since randomized trials always suffer from strong selection of low risk patients, it is conceivable that trial patients may primarily have low FIB-4 scores. Purpose To address the distribution of FIB-4 scores in a selected trial population with CAD (from the ISCHEMIA trial) and relate the degree of liver fibrosis reflected by the score to mortality. Methods The FIB-4 score was calculated for all patients. According to the standard classification, a value of greater than 1.2 reflects a 90% chance of the presence of histological liver fibrosis. The imapct of FIB-4 on late all-cause mortality was assessed as a continuous and categorical variable using maximally selected rank statistics determined FIB-4 cutoff. Cox regression model was peformed including the adjustment for the main CAD risk factors. Results Among 3735 included patients, 58.8% had a FIB-4 value above 1.2. Patients with higher FIB-4 score (cutoff= 1.64) were slighly older, presented higher prevalence of male sex, prior PCI and CABG, and prior history of atrial fibrillation and cerebrovascular disease. FIB-4 score was directly associated with higher risk of mortality when addressed as a continouos variable and as a categorical variable (Fig. 1). This relationship was evident in the overall population and in all subsets (PCI, CABG and no revascularization; Fig. 2). Conclusions The use of Fib-4 to determine the degree of liver fibrosis may be powerful to predict mortality in CAD populations which may not show any evident clinical signs of liver dysfunction. The Fib-4 distribution in this trial population, considered as selected and low risk, warrants investigating randomized trial populations having compared on- and off-pump CABG.Figure 1.Mortality: overall population.Figure 2.Mortality: by therapy subsets.

Gut microbiota predict retinopathy in patients with diabetes: A longitudinal cohort study

The gut microbiota has emerged as an independent risk factor for diabetes and its complications. This research aimed to delve into the intricate relationship between the gut microbiome and diabetic retinopathy (DR) through a dual approach of cross-sectional and prospective cohort studies. In our cross-sectional study cross-sectional investigation involving ninety-nine individuals with diabetes, distinct microbial signatures associated with DR were identified. Specifically, gut microbiome profiling revealed decreased levels of Butyricicoccus and Ruminococcus torques group, alongside upregulated methanogenesis pathways among DR patients. These individuals concurrently exhibited lower concentrations of short-chain fatty acids in their plasma. Leveraging machine learning models, including random forest classifiers, we constructed a panel of microbial genera and genes that robustly differentiated DR cases. Importantly, these genera also demonstrated significant correlations with dietary patterns and the molecular profiles of peripheral blood mononuclear cells. Building upon these findings, our prospective cohort study followed 62 diabetes patients over a 2-year period to assess the predictive value of these microbial markers. The results underlined the panel’s efficacy in predicting DR incidence. By stratifying patients based on the predictive genera and metabolites identified in the cross-sectional phase, we established significant associations between reduced levels of Butyricicoccus, plasma acetate, and increased susceptibility to DR. This investigation not only deepens our understanding of how gut microbiota influences DR but also underscores the potential of microbial markers as early indicators of disease risk. These insights hold promise for developing targeted interventions aimed at mitigating the impact of diabetic complications.Key points• Microbial signatures are differed in diabetic patients with and without retinopathy• DR-related taxa are linked to dietary habits and transcriptomic profiles• Lower abundances of Butyricicoccus and acetate were prospectively associated with DR

Long-term time-to-ventricular arrhythmic event prediction using a deep learning based model on the electrocardiogram signal

Abstract Background Ventricular arrhythmias (VA) risk stratification tools in individuals without known cardiovascular disease are still insufficient. Previous studies have used convolutional neural networks (CNN) to classify VA by the end of an established follow-up period using the electrocardiogram (ECG), showing a moderate performance (area under the curve [AUC] of 0.610). We hypothesised that the appearance of the VA substrate on the ECG signal depends on the proximity of the ECG acquisition to the VA event. Thus, the performance could improve if the CNN was trained to predict time-to-VA event instead, and if the presence of censored data was considered. Purpose We developed and tested a CNN to predict time-to-VA event in middle-aged individuals without known cardiovascular disease, and we compared its performance with that from ECG indices, such as QRS duration, QT interval and T-wave morphology variations (TMV), as well as sex and age. Methods A total of 69,283 individuals without known cardiovascular disease from the UK Biobank were used as the training set. We applied 10-fold cross validation to train a CNN, where the input was a 15-second ECG at rest (lead I), and the output was ‘occurrence of VA’, ‘censored’, or ‘survival’, up to 1, 2, 3, 4, 6, 8, 10 and 12 years after the ECG acquisition. We used 15-second ECG (lead I) from an independent cohort of 17,320 individuals without cardiovascular disease also from UK Biobank as a test set, where we set a follow-up of 10 years. The above ECG indices were derived from the median heartbeat from the ECG using in-house algorithms. We derived optimal cut-off thresholds from the training test, and we evaluated the AUC, sensitivity, specificity and Cox regression hazard ratios (HR) in the test set. Results In the test set, 9,206 individuals (53%) reached the 10-year follow-up, during which 60 (0.7%) had a VA event. The AUC, sensitivity and specificity values of the CNN were 0.715, 0.881 and 0.400, respectively. These values were 0.592, 0.686 and 0.383 for the QRS duration, 0.569, 0.156 and 0.717 for the QT interval and 0.552, 0.712 and 0.350 for TMV. The combination of sex and age alone showed performance values of 0.760, 0.694 and 0.683, respectively. Survival analyses showed a HR of 3.883 (P = 3.0 x 10-7) for individuals with a CNN prediction greater than the threshold derived in the training set after adjusting for age and gender. Conclusions A CNN that is trained to predict time-to-VA event and accounts for censored data outperforms previous classification proposed models and the predictive value of individual ECG indices of risk. Although the predictive value of the CNN does not show a better long-term VA predictive value than sex and age alone, it provides a significant contribution independently from these two risk factors. Our findings support the use of the ECG to improve long-term VA risk stratification, which might be particularly useful in age- and sex-stratified analyses.

Clinical impact of combined assessment of the CHADS2 score and geriatric nutritional risk index on clinical outcomes in patients with lower extremity artery disease without atrial fibrillation

Abstract Background The CHADS2 score is a well-known risk score for ischemic stroke in atrial fibrillation (AF) patients. Recently, it has been reported that the CHADS2 score was associated with an increased risk of new-onset lower extremity artery disease (LEAD) in patients without AF, the association between the CHADS2 score and clinical outcomes in patients with LEAD undergoing endovascular treatment (EVT) without AF remains unclear. Furthermore, malnutrition assessed by the geriatric nutritional risk index (GNRI) has been reported to be predictive of clinical outcomes in patients with LEAD. However, there are limited studies regarding the association of combined assessment of the GNRI and CHADS2 score and clinical outcomes in patients with LEAD without AF. Purpose The purpose of this study was to investigate the association between the combined assessment of GNRI and CHADS2 score, and mortality of LEAD patients undergoing EVT without AF. Methods This retrospective study investigated 236 consecutive LEAD patients who underwent EVT without AF. CHADS2 scores were calculated by assigning one point for heart failure, hypertension, age ≥75 years, and diabetes; and assigning 2 points for a prior history of stroke or transient ischemic attack. The GNRI on admission was calculated as follows: [14.89 × albumin (g/dL)] + [41.7 × (body weight/ideal body weight)], and the CHADS2 score and GNRI were calculated for each patient. We scored GNRI by assigning 2 points to low GNRI (GNRI &amp;lt;82) and 1 point to intermediate GNRI (GNRI =82 to &amp;lt;92). We then calculated the CHADS2 plus GNRI score for each patient and then patients were divided into the high CHADS2 plus GNRI scoregroup (CHADS2 plus GNRI score&amp;gt; 2, n = 123) and the low CHADS2plus GNRI score group (CHADS2 plus GNRI score≤ 2, n = 113) according to the median CHADS2 plus GNRI score. We investigated the associations between all-cause mortality in patients with LEAD who underwent EVT and the CHADS2 plus GNRI score. Results The mean age was 71.9±10.4 years, and 66.9% were men. During the median follow-up of 752 (283–1472) days, 50 patients died. Kaplan-Meier curves revealed that the cumulative incidence of all-cause death was significantly higher in the high CHADS2 plus GNRI score group than in the low CHADS2 plus GNRI score group (log-rank p = 0.003). Furthermore, even in the multivariate analysis,after adjusting for other risk factors including hemodialysis and clinical frailty scale, CHADS2 plus GNRI score was independently associated with all-cause death (per one score increase, hazard ratio: 1.26, 95% confidence interval (CI) 1.01-1.56, P=0.038). Conclusions In LEAD patients who underwent EVT without AF, a higher CHADS2 plus GNRI score was associated with an increased risk of all-cause death. A Combined CHADS2 plus GNRI score assessment may be a useful prognostic marker in patients with LEAD without AF.

Circulating secretoneurin concentrations predict 90-day mortality in patients with cardiogenic shock

Abstract Background Secretoneurin (SN) concentrations have been found to provide strong prognostic information in critically ill patients. Accordingly, in this study we hypothesized that SN would provide prognostic information in patients with cardiogenic shock. Objectives To evaluate the utility of SN concentrations to predict 90-day mortality in two multicenter, prospective clinical trials including patients with cardiogenic shock. Methods We measured SN concentrations with a CE-marked ELISA in patients with cardiogenic shock of all etiologies (Cohort #1, n=168) and in patients with post-myocardial infarction cardiogenic shock (Cohort #2, n=45). Inclusion criteria in both cohorts focused on systolic blood pressure &amp;lt;90 mmHg and evidence of tissue hypoperfusion, including increased lactate concentrations. We used Cohort #1 for primary analysis and Cohort #2 for validation. We assessed association between SN concentrations and mortality by Kaplan-Meier curves and by univariate and multivariable Cox proportional hazard regression analysis. Results Median age was 67 and 69 years (Cohort #1 and #2) and the patients were predominantly male (74% and 67%, respectively). Median SN concentration on study inclusion was 53 pmol/L in Cohort #1 and 50 pmol/L in Cohort #2, and SN concentrations decreased from study inclusion to 72 hours (p=0.03 for trend). 90-day mortality was 42% in Cohort #1 and patients with high SN concentrations had worse outcome (Figure A). Adjusting for variables that were associated with 90-day mortality in univariate analysis and not part of the inclusion selection, i.e., age, sex, comorbidities, blood pressure, estimated glomerular filtration rate, treatment, and N-terminal pro-B-type natriuretic peptide concentrations, SN concentrations on study inclusion were still associated with 90-day mortality: hazard ratio per 100 pmol/L increment 1.97 (1.29-3.01), p=0.002. 90-day mortality was 45% in Cohort #2 and SN concentrations were predictive of 90-day mortality also in Cohort #2 (Figure B). Conclusion SN concentrations provide incremental prognostic information to established risk indices in the early phase of cardiogenic shock.A. Cohort #1 with SN tertilesB. Cohort #2 divided by SN median

Usefulness of preoperative NT-proBNP for prediction of acute heart failure, perioperative myocardial infarction/injury and mortality after non-cardiac surgery

Abstract Background Predicting cardiac complications after non-cardiac surgery is challenging. Purpose to determine the predictive accuracy of N-Terminal Pro–B-Type Natriuretic Peptide (NT-proBNP) and, to assess the utility of existing NT-proBNP cutoffs of the 2021 ESC Guidelines for the diagnosis of acute and chronic heart failure for prediction of postoperative acute heart failure (pAHF), perioperative myocardial infarction/injury (PMI) and mortality following non-cardiac surgery. Methods we prospectively determined preoperative NT-proBNP concentrations in consecutive patients at high cardiac risk undergoing inpatient non-cardiac surgery. Uni- and multivariable regression models were constructed to test the association between NT-proBNP concentrations and the outcomes (pAHF, PMI, 30-day and one-year mortality), using NT-proBNP as a continuous variable. We further stratified NT-proBNP into 5 categories according to guidelines (&amp;lt;125 pg/ml, 125 to 299pg/ml, 300 to 900 pg/ml, 901 to 1800pg/ml and &amp;gt;1800 pg/ml). Receiver operating characteristic curves were constructed to determine the predictive value of NT-proBNP. Results Among the 2,334 cases included, pAHF occurred in 83 (3.6%), PMI in 381 (17%), 45 (2.5%) patients died within 30 days and 179 (10%) within one year. After adjusting for confounders, NT-proBNP concentrations were independent predictors of pAHF (adjusted hazard ratio [aHR] 1.8 [95%CI, 1.5-2.1], p&amp;lt;0.001), PMI (aHR 1.3 [95%CI, 1.2-1.4],p&amp;lt;0.001) and 30 day/one year mortality (aHR 1.6[95%CI, 1.3-2.0], p&amp;lt;0.001)/aHR 1.5 [95%CI 1.4-1.7], p&amp;lt;0.001). By quintiles, higher the NT-proBNP levels were strongly associated with adverse outcome (Figure). Accuracy of preoperative NT-proBNP for prediction of pAHF was excellent (area under the curve [AUC] 0.825) and better than accuracy of the Revised Cardiac Risk Index score (AUC 0.692, p&amp;lt;0.001), whereas accuracy of NT-proBNP for prediction of PMI (AUC 0.654) and mortality (AUC 0.787) was only moderate. Conclusion NT-proBNP concentrations are independent predictors of pAHF, PMI and short and long-term mortality following non-cardiac surgery. Stratifying NT-proBNP concentrations using current recommended cut-offs could help to further determine the magnitude of the risk, mainly of pAHF.

An artificial intelligence-enabled electrocardiogram model outperforms the revised cardiac risk index score for predicting major adverse cardiovascular events in patients undergoing noncardiac surgery

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

Necrotizing fasciitis: treatment concepts & clinical outcomes – an institutional experience

BackgroundA severe infection of the skin and soft tissues, Necrotizing Fasciitis (NF), spreads quickly along the deep fascia. This study aimed to characterize the clinicopathological features, analyze the implicated bacteria’s antibiotic sensitivity, evaluate surgical management, and assess the diagnostic accuracy of the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score in Necrotizing Soft Tissue Infection (NST).MethodsThis single-center prospective observational study was conducted in the Department of General Surgery, Kasturba Medical College, Manipal, with 171 proven cases of NSTI between 2019 and 2021. Clinico-demographic data and laboratory investigation values were collected at two-time points (at admission and 72 h after admission). Imaging data, LRINEC score, culture results, and antibiotic sensitivity were recorded. Appropriate descriptive and analytical statistics were used for the statistical analysis.ResultsOf the 171 patients, 150 were male (87.7%). The mean age was 57.6 ± 13.1 years. The presenting features in all the cases were pain, swelling, and fever. Diabetes mellitus (DM) is the most common comorbidity. The lower extremities were the most commonly affected sites. Streptococcus pyogenes showed significant growth in 25.41% of the samples. Ceftriaxone sensitivity was seen in 41/141. A score of ≥ 8 was obtained in 118/171 (69%) patients, suggesting a higher severity and significant risk for NSTI. The Area Under the Curve of Receiver Operating characteristic Curve (ROC) for establishing diagnostic accuracy for LRINEC was 0.694. Mortality was significantly higher in the patients with higher LRINEC scores and elevated procalcitonin. The mortality rate was higher in patients who underwent surgery within 12 h.ConclusionNecrotizing fasciitis is a soft tissue infection with a high mortality rate. The clinical features and determinants of mortality in patients with NF are highlighted in this study. At the outset, a high index of suspicion was critical. Using prognostic evaluation techniques in daily clinical practice will assist medical professionals in providing adequate on-time care and significantly lowering mortality. The AUC for LRINEC score, although significant, is low. LRINEC score is not to be used to determine whether surgical intervention should be expedited or anticipated. Its role is to aid in prognosticating the outcome of the individual patient. Our study concludes that early extensive surgical debridement remains the single most crucial intervention in patients diagnosed with necrotizing fasciitis (NF), regardless of disease severity and the LRINEC score.

Links between adolescent binge drinking and midlife alcohol use behaviors by age, sex, and race/ethnicity.

Alcohol use is increasing among adults in midlife (i.e., ages 35-60), but few studies examine specific alcohol use behaviors in this age group. We examined measures of typical drinks, maximum drinks, binge drinking, and high-intensity drinking by age, sex, and race/ethnicity among midlife adults, as well as the prospective association between age 18 binge drinking and midlife behaviors. Data from 5180 respondents participating in the national Monitoring the Future Panel study who were aged 35-60 in 2022 (followed since they were in 12th grade in 1980-2005) were used to estimate past 30-day midlife drinking behaviors (i.e., typical drinks, maximum drinks, binge, and high-intensity drinking) by age group, sex, and race/ethnicity. Associations between age 18 binge drinking status and midlife drinking outcomes were examined, as well as moderation by sociodemographic characteristics. Across ages 35-60, the mean typical number of drinks on drinking days within the past month ranged from 1.4 to 1.8; the mean maximum drinks ranged from 2.3 to 3.2. Past-month binge and high-intensity drinking prevalence ranged from 19.1% to 31.2% and 3.6% to 8.1%, respectively. Estimates of drinking behaviors were generally higher among respondents aged 35-40 (vs. older age groups), males (vs. females), those identifying as White (vs. other racial/ethnic groups), and those who reported age 18 binge drinking (vs. not). Adolescent binge drinking was a stronger predictor of high-intensity drinking among females than males and of typical and maximum drinks among older (age 60) than younger (age 35) respondents. Binge and high-intensity drinking were reported by a meaningful percentage of the US midlife adults. Binge drinking in adolescence was a predictor of subsequent alcohol-related risks. These long-term connections were especially strong among females. Age 18 binge drinking was a stronger predictor of high-intensity drinking at age 60 than earlier in midlife, underscoring that adolescent binge drinking is a key indicator of risk across the lifespan.

Spatiotemporal Distribution of Mercury in Tree Rings and Soils Within Forests Surrounding Coal-Fired Power Plants

The release of mercury (Hg) from coal-fired power plants (CPPs) into local ecosystems poses substantial environmental and health hazards. This study was conducted in Chungcheong-nam-do, South Korea, a region featuring over half of the country’s coal power facilities, to estimate the impacts of CPPs on Hg distribution in forest ecosystems. By analyzing Hg concentrations in pine tree rings and soil at 21 locations around CPPs and comparing them to control sites and industrial zones, we present a nuanced understanding of the effects of CPPs on Hg concentration. The analysis of Hg concentrations in tree rings showed a significant decrease in Hg levels as the distance from the power plants increased, suggesting that CPPs primarily influence Hg distribution in trees within a 25 km radius. In contrast, soil Hg concentrations did not exhibit a clear trend. This may reflect the limitations of this study in accounting for the physicochemical properties of the soil at each sampling site. Nevertheless, the Potential Ecological Risk Index for soil Hg contamination indicated a higher risk rating within a 1 km radius of the CPPs compared to other locations. Hg concentrations in tree rings have shown a steady decline since the 1970s, suggesting the positive effects of air pollution regulations. This also highlights the value of tree core samples as effective tools for monitoring historical Hg pollution. Furthermore, the higher historical concentrations of Hg in tree rings imply that trees may have acted as sinks for atmospheric Hg in the past.

Association between malnutrition and adverse renal outcomes in patients with type 2 diabetes.

Nutritional management is crucial in patients with chronic kidney disease. Therefore, it is important to assess nutritional status and detect malnutrition, especially in patients with diabetes. However, there is currently a lack of evidence regarding the relationship between nutritional indices and renal function in patients with type 2 diabetes. This study investigated whether the geriatric nutritional risk index (GNRI) is related to renal prognosis in type 2 diabetes patients. The study included 946 type 2 diabetes patients enrolled in the Fukushima Cohort Study. The primary endpoint of this study was a renal event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease. All-cause death and new cardiovascular events were also measured as secondary outcomes. The association between GNRI and these endpoints was assessed using Cox regression analysis. The median patient age was 66 years, 57% were men, the median eGFR was 67.9 mL/min/1.73 m2, and the median GNRI was 100.0. Compared to patients in the highest GNRI tertile, patients in the lowest tertile had a significantly increased risk of therenal event (HR 5.15, 95% CI 2.51-10.6) and all-cause death (HR 2.30, 95% CI 1.20-4.42). A significant association was not observed between GNRI levels and cardiovascular events. We observed an association between poor nutritional status, assessed by GNRI, and adverse outcomes in patients with type 2 diabetes. Nutritional status assessment has potential utility as a prognostic tool for individuals with type 2 diabetes.

Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.