Identification Of Factors Research Articles

Echocardiographic variables may improve baseline risk prediction of cancer therapy related cardiac dysfunction

Abstract Background Baseline prediction of cancer therapy related cardiac dysfunction (CTRCD) in clinical practice is difficult, despite many known cancer and cardiovascular risk factors. Risk scores exist, including the HFA-ICOS score which is recommended by the 2022 European Society of Cardiology (ESC) Cardio-Oncology guidelines. Validation of such risk scores is required though, and identification of alternative risk factors that may improve risk prediction is needed. Purpose To assess the accuracy of the HFA-ICOS score for predicting CTRCD, and identify new risk factors with a focus on baseline echocardiography. Methods Echocardiograms and medical records were assessed for a cohort of breast cancer patients receiving trastuzumab or an anthracycline, who underwent serial echocardiographic monitoring. CTRCD was defined according to the 2022 ESC Cardio-Oncology guidelines. Baseline risk factors and echocardiography were compared between CTRCD and non-CTRCD groups, and the accuracy of the HFA-ICOS score in predicting CTRCD assessed. Results Data from 258 patients were analysed with CTRCD occurring in 23 (9%). 135 were low risk, 85 moderate-risk, 31 high-risk and 8 very high-risk according to the HFA-ICOS score with a trend of increasing rates of CTRCD with each risk category: 5%, 12%, 10%, 25%, respectively. The sensitivity and specificity of a combined moderate/high/very high-risk group was 70% and 54% (AUC 0.618, 95% CI 0.5-0.73). Only three individual baseline variables were associated with CTRCD in this cohort; ejection fraction 50-54%, (18% vs 2%, p=0.0027), borderline/abnormal average global longitudinal strain <-18, (43% vs 19%, p=0.04) and combination anthracycline plus trastuzumab, (45% vs 22%, p=0.0392). Left atrial volume index >34mL/m² was also higher in the CTRCD group (38% vs 23%) although not statistically significant. A simplified risk score using only these variables yielded a sensitivity and specificity of 74% and 53%, respectively (AUC 0.633, 95% CI 0.52-0.75). Conclusion Baseline prediction of CTRCD remains difficult in clinical practice. Although HFA-ICOS risk categories correlate with CTRCD diagnosis, the overall sensitivity is only moderate. Echocardiographic variables may further enhance risk prediction to enable development of targeted monitoring strategies.HFA-ICOS risk score categoriesROC curves

Independent risk factors of ejection fraction deterioration post discharge in hospitalized heart failure patients with mildly reduced ejection fraction

Abstract Background The identification of independent risk factors for left ventricular ejection fraction (LVEF) deterioration is of clinical importance in patients diagnosed heart failure with mildly reduced ejection fraction (HFmrEF). This study aimed to determine the independent risk factors of LVEF deterioration post discharge among hospitalized HFmrEF patients. Methods This retrospective study analyzed data from 807 HFmrEF patients admitted to our hospital between January 2015 and August 2020. LVEF values were assessed by echocardiography between 6 months and 1 year after discharge. Patients were followed up to a mean of 33 months. The primary endpoint was disease progression expressed as heart failure with reduced ejection fraction (HFrEF) between 6 months and 1 year post discharge. The secondary endpoint was all-cause death. Multivariate logistic regression analysis was conducted to identify independent risk factors associated with LVEF deterioration. Results There were 225 patients progressed to HFrEF between 6 months and 1 year post discharge. All-cause death was significantly higher in HFrEF patients (43%) compared to patients with stable LVEF (unchanged or improved, 24%, P<0.001). Multivariate logistic regression analysis showed that the independent risk factors associated with worsening LVEF in patients with HFmrEF were: female (OR=1.71, 95% CI 1.14 to 2.57, P=0.009), higher NT-proBNP (≥2312 pg/ml, OR=1.71, 95% CI 1.14 to 2.57, P=0.010), higher uric acid (≥318 µmol/L, OR=11.63, 95% CI 7.89 to 17.15, P<0.001), and larger end-diastolic left ventricular dimension (OR=1.05, 95% CI 1.02 to 1.08, P<0.001). Conclusion Results of this study might help risk stratification to identify hospitalized HFmrEF patients with increased risk of progression to HFrEF post discharge. Patients with above clinical features should undergo more intensively monitoring and enhance care adherence to HF guideline medications. Future studies are needed to validate if tailored guideline adherence management strategies for post-discharged HFmrEF patients based on personalized risk stratification could mitigate LVEF progression and improve survival of these patients.

PREVALÊNCIA E FATORES DE RISCO DA LOMBALGIA EM OPERADORES DE CALL CENTER

This study investigates the prevalence and risk factors of low back pain among call center operators through an integrative literature review. The central problem is the identification of the main factors that contribute to the development of low back pain in this professional group and the impact of these factors on workers’ health. The objectives are to understand the relationship between working conditions and the incidence of low back pain, analyzing the risk factors and their consequences on the quality of life of call center operators. To achieve these objectives, an integrative review of the scientific literature was carried out. Data collection in the integrative review included searches in the Lilacs, Scielo, Pubmed, Medline and Pedro databases, considering studies published in the last 4 years, both in Portuguese and English. The results reveal a series of risk factors associated with low back pain, such as inadequate postures, long periods of sitting and lack of regular breaks, which significantly contribute to the high prevalence of this condition among call center operators. It is concluded that the adoption of ergonomic measures and preventive practices is crucial to reduce the incidence of low back pain and improve the quality of life of these workers.

Does depression with current suicidal ideation lead to treatment-resistant depression? Two large naturalistic cohorts of outpatients with depression and current suicidal ideation

As treatment-resistant depression (TRD) is linked to suicidal behaviors and suicidal risk is a predictor of TRD, depression with current suicidal ideation (SI) may lead to TRD. Early identification of TRD risk factors in patients with depression and current SI is crucial. The aims of our study were: i) to identify risk factors for depression non-remission and TRD in patients with depression and current SI; ii) to assess if SI at baseline mediated the relationship between depression severity at baseline and depression remission at week 6. We analyzed data from two large, prospective, naturalistic French cohorts of adult outpatients with depression (DSM-IV criteria) followed for 6 weeks after starting or changing antidepressants (LUEUR and GENESE). Sociodemographic and clinical characteristics, along with early symptom improvement, were compared between patients with and without current SI using logistic regression models (univariate and multivariate). Patients with antidepressant change or initiation were analyzed separately. Those without depression remission at week 6 after an antidepressant change were considered TRD cases. In patients with antidepressant change, the major predictor of non-remission was poorer early improvement (at week 2) of anxiety. For patients with treatment initiation, SI at baseline mediated the relation between depression severity at baseline and depression remission. Depression severity at baseline alone did not explain depression remission. Clinicians should systematically target with specific pharmacological and non-pharmacological treatments anxiety and SI and assess their changes in the short term to increase the chance of depression remission in depressed patients with current SI.

Machine learning identification of risk factors for ischaemic stroke in patients with heart failure with reduced ejection fraction but without atrial fibrillation: A report from WARCEF trial

Abstract Background The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging and risk stratification based clinical factors alone has limited discrimination. Our aim was to evaluate the performance of machine learning (ML) to identify risk factors for ischaemic stroke in such HFrEF patients. Methods We performed a post-hoc analysis of the WARCEF trial, only including patients without prior history of AF, and censoring those who developed incident AF during follow-up. We evaluated the performance of 9 ML models to identify risk factors for incident stroke, using metrics including area under the curve (AUC), precision, specificity, and decision curve analysis. The importance of each feature in constructing the model was quantified using SHapley Additive exPlanations (SHAP) values. Results We included 2,213 patients with HFrEF but without AF (mean age 59±12 years; 79% male). Of these, 74 (3.3%) had an ischaemic stroke during mean follow-up of 3.5±1.8 years. Out of the 29 patient-demographics variables, 12 were selected for the ML training. All ML models demonstrated high AUC values, outperforming the CHA2DS2-VASc score (Figure 1). The logistic regression model exhibited a slight deviation from this trend. The XGBoost model achieved an AUC of 0.86 (95% confidence interval [CI]: 0.72-0.99), while the Random Forest model showed an AUC of 0.85 (95% CI: 0.74-0.96). Similarly, the Support Vector Machine and Light Gradient Boosting Machine models recorded AUCs of 0.85 (95% CI: 0.72-0.97) and 0.83 (95% CI: 0.70-0.96), respectively. Each of these models attained precision and specificity scores of 1.0, indicating high performance. These models consistently provided significant net clinical benefits. Key risk factors for stroke identified across these models were creatinine clearance and blood urea nitrogen. The use of warfarin appears to be protective. Conclusions Machine-learning models can identify risk factors for ischaemic stroke in patients with HFrEF but without AF. Worsening renal failure was predictive of an incident stroke, while warfarin use was protective against stroke (Figure 2).Figure 1Figure 2

Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Abstract Background/Introduction Atrial fibrillation (AF) presents a significant global health burden, affecting millions and associated with elevated risks of mortality, heart failure, and stroke despite current treatment strategies. The current diagnosis of AF relies on episode characteristics, yet its heterogeneity calls for personalized assessment incorporating clinical features, biomarkers, and substrate determination for improved classification and management. Purpose This study aimed to develop a refined classification of AF according to comprehensive risk factors to identify patients at high-risk for poor prognosis and their life expectancy, aiming to facilitate personalized interventions. Methods A total of 7,391 participants aged 40-69 with AF at baseline, from UK Biobank, were recruited in the main analyses. Firstly, LASSO regression with Cox model and factor analysis were adopted for identification and integration of principal risk factors. Second, Consensus clustering analysis was employed to estimate the optimal cluster number and group all 7,391 participants. Difference in the risk of death and major complications, as well as reductions in life expectancy, among clusters were estimated. Replication was done in 2,399 participants with newly diagnosed AF within two years after baseline. Results Risk factors for AF were categorized into seven aspects (metabolic factors, respiratory factors, cardiovascular factors, renal and immunity diseases, mental health, acute illness, age), based on the correlation between them. Five distinct AF clusters were identified according to these seven characteristics: acute illness-related (Cluster 1), mental health-related (Cluster 2), cardiovascular disease-related (Cluster 3), immune-and-renal disease-related (Cluster 4), and respiratory-and-metabolism disease-related AF (Cluster 5). Patients with respiratory-and-metabolism disease-related AF had the highest risk of death, acute myocardial infarction, heart failure, and cerebral ischemic stroke, while those with acute illness-related AF had the lowest corresponding risk. In addition, compared with individuals with acute illness-related AF, those with respiratory-and-metabolism disease-related and mental health-related AF had the top 2 greatest loss of life expectancy. Furthermore, genetic variants for AF had different effect among the five clusters. Replication analysis confirmed the result stability. Conclusion A novel AF classification was developed, which provided insights into varying life expectancy and risks of death and complications among AF subgroups with distinct characteristics. It offers a practical approach for identifying high-risk patients, which may help to tailor precise interventions for AF management.Graphic abstractCluster features

Evaluating trends and insights in cardiovascular risk among the polish population

Abstract Introduction Cardiovascular diseases (CVD) are the leading cause of death in Poland and worldwide. Lifestyle changes and early diagnosis can reverse this unfavorable trend. In the face of escalating global health challenges, the identification of CVD risk factors becomes paramount, especially within national contexts where demographic shifts and lifestyle changes can significantly impact the health profile of the population. Aim of the study. Determining the prevalence of CVD risk factors in the Polish population and analyzing their changes over time. Material and Methods Data of 705,420 Poles aged 35-55 years who were enrolled in CVD disease prevention programme between 2012 and 2021 were analysed. The prevalence of the following CVD risk factors was determined: nicotinism, hypertension, elevated total cholesterol, elevated LDL, reduced HDL, elevated triglycerides, elevated glucose (indicating pre-diabetes), low physical activity, overweight, obesity, SCORE ≥5%. In addition, we assessed how the mean SCORE, BMI, systolic blood pressure, diastolic blood pressure, total cholesterol, LDL cholesterol, LDL cholesterol, triglycerides, glucose levels and waist circumference changed over time. Results Nicotinism affected 17.98%-24% of women (W) and 27.29%-36% of men (M). Hypertension was in 7.04%-8.58% of W and 17.45%-19.62% of M. Elevated total cholesterol were in 55.69%-63.64% of W and 65.91%-72.68% of M. Elevated LDL were in 44.77%-54.2% of W and 58.26%-65.48% of M. Reduced HDL were in 9.84%-11.52% of W and 14.48%-16.57% of M. Elevated triglyceride were in 13.44%-15.37% of W and in 33.81%-36.29% of M. Elevated glucose indicating a pre-diabetic status were in 10.70%-13.16% of W and in 18.91%-22.76% of M. Low physical activity was in 75.96%-82.2% of W and 73.32%-81.67% of M. Overweight conditions were in 30.37%-31.67% of W and 48.61%-49.45% of M. Obesity affected 14.04%-17.12% of W and 20.67%-24.69% of M. SCORE ≥ 5% was in 0.23%-0.38% of W and in 13.08%-18.72% of M. There was a decrease in the mean SCORE value (-0.08; 95%CI: -0.09 - -0.06) from 2012. An increase over time was noted in mean BMI (-0.01; 95%CI: -0.05 - 0.04). There was a slight decrease followed by a notable increase over time in mean systolic (-0.35; 95%CI: -0.47 - -0.24) and diastolic blood pressure (-0.25; 95%CI: -0.32 - -0.18). There was a decrease in mean total cholesterol (-3.29; 95%CI: -4.27 - -2.31) and mean LDL cholesterol (-3.11 95%CI: -4.22 - -2.01). A notable increase was observed in mean glucose values (0.01; 95%CI: 0-0.01). Over time, there was an increase in mean waist circumference value (-0.19; 95%CI: -0.54-0.16). Conclusions The escalation of CVD risk among the Polish population is illustrated, with a marked increase in obesity, sedentary lifestyles and indicators of metabolic syndrome. These trends highlight the urgent need for targeted public health initiatives and for the implementation of comprehensive strategies to mitigate these risks

Machine Learning techniques for predicting inpatient probability and LOS from EU Injury Database

Abstract Background Unlike statistical inference Machine Learning (ML) makes repeatable predictions without prior assumptions about the underlying relationships among variables. The Full Data Set (FDS) of the EU-IDB (European Injury DataBase) consent to explore risk of hospitalization due to injuries using many predictors. The Length Of Stay of patients in hospital (LOS) is a relevant proxy of complexity of treatment and resources consumption. Methods The IDB-FDS provides more than 3.800.000 ED records, in years 2008-19 for 19 Countries. LASSO (Least Absolute Shrinkage and Selection Operator) cross-validated linearized regression technique (linear for LOS) was used for variable selection and parameter regularization. Inpatients are those admitted or transferred to hospital. Days of hospitalization were used for LOS. A cross-validated Generalized Linear Model was performed on 5 folds randomly sampled assigning 80% of records to training and 20% to testing samples. Results The strongest predictors of hospital admission risk, selected by the model were in order of importance: EUROCOST-39 diagnoses categories, Age Group, Intent, Mechanism Of Injury, Activity When Injured, Transport Injury Event, Sex Of Patient, Place Of Occurrence. EUROCOST-39 represents 61,9% of explained variability and age group 19,4%. The strongest predictors of LOS were substantially the same: EUROCOST-39 86,2% of explained variability and age group 8,6%. Conclusions The main part of variability in the ML model is explained by diagnoses reclassified according to a disability standardization method. For instance, in the maximum training sample risk of hospitalization ranges from odd 0,76% for hand/fingers sprain up to 154,02% for brain concussion. In the median sample LOS ranges from 0,45 days for strain of hand/fingers up to 11,65 for multi-trauma. A combination of more disabling injury, older age and mechanism of injury (i.e. threat to breathing) increases enormously the risk of hospitalization and LOS. Key messages • Machine Learning techniques applied on EU-IDB can provide identification of relevant risk factors of hospital admission from injuries for targeting preventive measures and organizing health care. • Artificial Intelligence consents to analyse big amount of data as dimension or analytical detail of information for identifying patterns and predictors of specific indicators for injuries or diseases.

Characteristics associated with prevalent atrial fibrillation and risk profile for future atrial fibrillation among those in sinus rhythm in an elderly population in the ELSA Brasil Study

Abstract Background Atrial fibrillation (AF) is an arrhythmia that can lead to limiting symptoms and increases the risk of stroke and heart failure (HF). Purpose To evaluate the association of clinical, electrocardiographic, and echocardiographic parameters with prevalent AF or flutter (AFF), and the risk profile for future AFF according to Charge-AF and EHR scores in an elderly population. Methods The ELSA-Brasil is a prospective cohort study designed to investigate cardiovascular disease and diabetes in 15,105 men and women, civil servants from universities or research institutions in six cities of Brazil. We included all participants in ELSA-Brasil aged 60 and over whose diagnosis of AFF could be assessed and had echocardiography performed at the study's baseline. From the initial sample with participants ≥ 60 years (n = 3,263), 400 participants were excluded due to missing data about AFF, and 775 who did not have echocardiographic studies. Results There were 88 (4.2%) participants with AFF at baseline among the 2,088 (65 ± 4.1 years; 53% women) included in this analysis. Those with AFF were older (66.8 ± 4.4 vs 65 ± 4.0 y-o) and had a higher prevalence of HF (16% vs 3.1%), previous myocardial infarction (14.8% vs 3.8%), left bundle branch block (LBBB: 4.6% vs 0,9%), prolonged QT interval (7.9% vs 2.0%), supraventricular extrasystoles (4.6% vs 1.2%), and sinus bradycardia (11.3% vs 2.9%). AFF individuals also presented larger left atrial volume index - LA (33.3 mL/m2 versus 27 mL/m2), left ventricular end-diastolic diameter (4,63 ± 0,6 cm versus 4,47 ± 0,4 cm) and lower left ventricular ejection fraction (LVEF: 63.6 ± 9.1% vs 67.4 ± 6.4%) that non-AFF (all p-values <0.05). In multivariable analysis, prevalent HF, LBBB, larger LA, and lower LVEF were independently associated with the presence of AFF. Among those in sinus rhythm at study baseline, both risk models showed that most individuals in our sample were at low risk. In the Charge-AF, 63% of individuals were identified as presenting low risk (<2.5%), and 12% of the sample was categorized as high risk, with a risk of five-year AFF greater than 5%. Similarly, in the EHR, 67% of participants were classified as low risk, and 13% were considered high risk. Conclusion We found that AFF was present in 4.2% of the subset of older participants in a cohort composed of free-living individuals in Brazil. The presence of AFF was associated with HF history, altered cardiac electrical activity, LA dilation, and worse LV systolic function. Among those with sinus rhythm, 12-13% were considered at high risk of developing AFF (> 5% in five years) based on two clinical scores. Clinical surveillance and monitoring of electrocardiography and echocardiography parameters may help the early identification of risk factors for AFF.

Prevecardio: a single-visit population screening model for detection and prevention of 10-year cardiovascular risk

Abstract Introduction Cardiovascular diseases (CVDs) are a major contributor to global mortality. The early detection of cardiovascular risk factors, coupled with timely intervention strategies, is essential for CVD prevention. PreveCardio was developed as an innovative, single-visit population screening model designed to facilitate the early identification and management of cardiovascular risk factors within a population of 1 million in a European capital city region totaling 6 million inhabitants. Purpose This study aimed to ascertain the prevalence of cardiovascular risk factors within the general population and to evaluate the effectiveness of the PreveCardio model in estimating 10-year cardiovascular risks and promoting preventive interventions. Methods In this cross-sectional prevalence study, individuals aged 50 to 75 from a health network were randomly selected and invited to participate in a comprehensive health check. The 30-minute screening consisted of lifestyle evaluations, anthropometric measurements, and blood tests for lipid profiles and glycated haemoglobin. The methodology was designed to provide participants with immediate feedback on their results, cardiovascular health and practical guidance for risk management. Results From the 8,492 individuals solicited, 3,546 took part in the screening: 56,83% were female, mean age 61,17 ± 7,33 y.o. Picture 1 shows the population's percentages with various modifiable cardiovascular risk factors, including 20% with active smoking, 30% with sedentarism, 16% with risky alcohol consumption, 49% with non-adherence to the Mediterranean diet, 40% with overweight, and 30% with obesity. The screening revealed new diagnoses of LDL-hypercholesterolemia in 47% participants, high levels of blood pressure in hypertension range in 49%, prediabetes in 30% and diabetes in 2% (Picture 2). After use SCORE2 and SCORE-OP risk prediction algorithms, PreveCardio is estimated to potentially prevent between 39 to 202 fatal and non-fatal cardiovascular events over 10 years. Predictive modelling based on PreveCardio data projected that expanding the screening across the capital region could prevent at least 26,260 cardiovascular events over 10 years. Conclusion(s) PreveCardio has established itself as a viable and effective model for the early detection of cardiovascular risk factors in a general population. It holds significant promise for public health impact by potentially reducing the incidence of CVD through comprehensive screening and proactive intervention.

Risk factors of all-cause mortality in heart failure with mildly reduced ejection fraction patients with different post-discharge left ventricular ejection fraction variation patterns

Abstract Background The identification of risk factors for all-cause mortality in patients diagnosed with heart failure with mildly reduced ejection fraction (HFmrEF) and exhibiting varying post-discharge left ventricular ejection fraction (LVEF) patterns remains incomplete. This study aimed to determine the independent risk factors of all-cause mortality among HFmrEF patients with different LVEF variation patterns post-discharge. Methods This retrospective study analyzed data from 807 HFmrEF patients admitted to our hospital between January 2015 and August 2020. Patients were categorized into three groups based on LVEF variation patterns within 6 months to one year post-discharge: Group A (transitioning to heart failure with reduced ejection fraction [HFrEF], n=225), Group B (remaining as HFmrEF, n=335), and Group C (reverting to heart failure with preserved ejection fraction [HFpEF], n=227). Patients were followed up for a mean of 33 months, with all-cause mortality as the primary endpoint. Multivariate Cox regression analysis was conducted to identify independent risk factors for all-cause mortality in each group. Results The mortality rates for Groups A, B, and C were 43.11%, 23.94%, and 24.67%, respectively (P<0.001). Independent risk factors for all-cause mortality in Group A included age (HR=1.04, P=0.007), obesity (HR=1.76, P=0.040), higher creatinine (>100 µmol/L, HR=1.83, P=0.029), higher E/e’ (HR=1.06, P=0.001), absence of PCI (HR=0.51, P=0.019), and non-use of ACEI/ARB (HR=0.039, P=0.006). In Group B, independent risk factors for all-cause mortality were age (HR=1.06, P<0.001), higher NT-proBNP (>2505 pg/ml, HR=2.50, P=0.002), and absence of PCI (HR=0.040, P=0.005). Group C exhibited age (HR=1.04, P=0.002), pleural effusion (HR=2.38, P=0.007), higher creatinine (>100 µmol/L, HR=2.84, P<0.001), and non-use of ACEI/ARB (HR=0.42, P=0.021) as significant independent risk factors associated with increased all-cause mortality risk. Conclusion This study highlights distinct risk factors associated with all-cause mortality in HFmrEF patients exhibiting different post-discharge LVEF variation patterns. Age emerged as a consistent risk factor across all groups, underscoring the importance of age-related considerations in managing HFmrEF patients. Additionally, factors such as obesity, renal function, echocardiographic parameters, and medication adherence played varying roles in influencing mortality risk depending on the evolution of LVEF following discharge. These findings emphasize the need for personalized risk stratification and tailored management strategies for HFmrEF patients to improve outcomes and reduce mortality rates.

Evaluation of Inherent Damping Feature of Beam Structure Based on Complex Modulus

Structural damping, which has been widely used to quantify the energy dissipation of engineering structures, plays an important role in dynamic analysis during the design phase and the assessment of existing structures. However, the development of an accurate damping model remains an open question due to the unclear mechanism. In this study, structural damping was identified based on loss factors for beam structures, which were used for presenting the damping behavior during vibrations. The equation of motion for the bending deformation of a Timoshenko beam was derived, accounting for the material damping. To identify the loss factor of the beam from the response signals at a limited number of measurement points, an inverse algorithm based on wave coefficient estimation was developed, considering the numerical stability for data interpretation. Based on the proposed loss factor identification method, several beam models with different shapes of cross-sections were investigated, focusing on the feasibility of the method of varying cross-section cases. Comparing the spectral element model with the loss factor and the finite element model with Rayleigh damping, it was confirmed that the loss factor can better describe the actual energy dissipation behavior of the structure. It provides a more accurate theoretical model for estimating structural damping in practical engineering.

Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm

ABSTRACTMachine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of developing heart disease. However, most ML algorithms require more accurate data in order to build an accurate prediction model and do not tolerate missing values. Handling missing risk factors is critical during dataset preprocessing and becomes more difficult when the risk factors are completely missing. Removing this completely missing feature may result in the loss of critical information, but there are no readily available imputation methods, which presents a significant challenge. To overcome this difficulty, in this study, we take an attempt to impute using statistical multiple linear regression and Huber regression (HR) methods using four blended datasets (Statlog, Cleveland, Hungarian, and Switzerland) sourced from the UCI ML repository. The entire dataset comprises 14 attributes, including one target variable; however, in the Switzerland dataset, one feature value (“serum cholesterol”) is entirely missing. Missing “serum cholesterol” is recognized as a predisposing factor including “chest pain,” “supreme heartbeat rate,” “type of defect,” “exercise induced ST stress related to rest,” and “exercise generated angina” in the proposed imputation methods. We also proposed applying the majority voting ensemble technique in an individual's and integrated dataset using ML algorithms as part of the risk factor identification strategy. The results show that our proposed stacked algorithm for the combined dataset with the ensemble features significantly improved accuracy by 93.47%, and an AUC score of 94.50% demonstrated more accurate and early prediction than the previous research and also provided the model's diversity, resilience, generalization, and adaptability to varied datasets.

Pathology-Based Animal Cancer Registry of Abruzzo and Molise Regions (Central Italy): A Ten-Year Retrospective Study (2014–2023)

Pets have a crucial role in cancer research. Specifically, dogs and cats share the same environment as their owners and thus may serve as sentinels of naturally occurring tumors that are linked to the exposure to environmental hazards. Quantitative comparison of tumor types may reveal unusual cancer frequencies, providing directions for research and generation of hypotheses of cancer causation in a specific area and identification of risk factors. The aim of this study was to describe the data collected by the pathology-based animal cancer registry, managed by Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise (IZSAM), during 10 years of activity (2014–2023) and to assess its potential epidemiological relevance. Frequencies of tumor topography and morphology in dogs and cats were described, analyzed and compared. Proportional morbidity ratios (PMRs) were calculated, taking into consideration some potential risk factors such as species, breed, sex, diet and living environment. The database comprises 5311 tumors (n = 4719 in dogs and n = 592 in cats), with a higher prevalence in females (67.3% in dogs and 61.2% in cats). The mean age at the first diagnosis of tumors was similar between sexes and slightly lower in dogs compared to cats. PMRs highlighted certain risk and “protective” factors for the development of tumors in specific topography. The risk of developing tumors of the blood and hematopoietic system (PMR = 0.44; 95% CI: 0.21–0.94), skin and subcutaneous tissues (PMR = 0.70; 95% CI: 0.61–0.80), oral cavity and pharynx (PMR = 0.60; 95% CI: 0.24–0.89), urinary organs (PMR = 0.33; 95% CI: 0.11–0.99) and bones, joints and cartilage (PMR = 0.72; 95% CI: 0.22–0.98) was lower in non-neutered male dogs than in neutered male dogs. Non-spayed female dogs had a greater risk of developing tumors of the mammary gland (PMR = 1.75; 95% CI: 1.57–1.96), female sexual organs (PMR = 2.12; 95% CI: 1.01–4.36) and respiratory system (PMR = 2.25; 95% CI: 1.55–6.74) but less risk for cutaneous and subcutaneous tissue tumors (PMR = 0.44; 95% CI: 0.38–0.51) and blood/hematopoietic system tumors (PMR = 0.47; 95% CI: 0.26–0.85) compared to spayed female dogs. Compared with mixed breed, purebred dogs had a significantly greater risk of developing mammary gland tumors (PMR = 1.36; 95% CI: 1.20–1.54) and lower risk for respiratory (PMR = 0.15; 95% CI: 0.07–0.32), gastrointestinal (PMR = 0.63; 95% CI: 0.34–0.94) and oral (PMR = 0.59; 95% CI: 0.36–0.96) neoplasia. Non-neutered male cats had a lower risk of developing skin and subcutaneous tumors (PMR = 0.68; 95% CI: 0.50–0.92) compared with neutered cats. The risk of developing skin and subcutaneous tissues tumors was higher for dogs and cats that lived mostly outdoor (PMR dogs = 1.21; 95% CI: 1.10–1.33; PMR cats = 1.18; 95% CI: 1.08–1.47), while dogs that live mainly indoor had a greater risk to develop mammary gland tumors (PMR = 0.78; 95% CI: 0.68–0.89). Results described herein highlight the fundamental role of animal cancer registration initiatives. These efforts would contribute to the possibility of conducting multicentric collaborative studies to deepen the knowledge of the epidemiology of tumors in dogs and cats from a comparative perspective, thus fulfilling the One Health approach.

Identification of influential environmental factors in wheat crop yield based on feature selection techniques

Identification of influential environmental factors in wheat crop yield based on feature selection techniques

Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR

Team errors are identified as a major contributor to incidents in safety-critical systems like nuclear power plants (NPPs). Safety in NPPs is more dependent on team performance than individual, and reducing team errors requires identifying key causative factors and chains. Current studies primarily use qualitative analysis whereas lacking quantitative methods. This study addressed this by constructing a complex network model of team error based on semantic features and coding of the Semantic Web and determining criteria for selecting key causative factors by three node failure strategies. The top 5 team error key PSFs and top 5 high-risk causative chains were identified in the results. Finally, a set of control strategies was proposed further. This research can guide identifying key causative factors which could be a good starting point to scrutinize the team performance and establish control strategies for team error in NPPs.

To use or not to use behavioural science evidence in designing health promotion interventions: Identification of targets for capacity building.

The behavioural sciences provide useful evidence to design effective health promotion interventions, but evidence is infrequently integrated in practice. This study examined associations between theoretical domains framework (TDF) constructs and public health practitioners' use of behavioural science evidence to plan public health actions. Using a cross-sectional design, a convenience sample of 160 practitioners were recruited from public health agencies across Canada. Respondents completed an online questionnaire assessing TDF constructs and the use of behavioural science theory and approaches (i.e., evidence) in their practice. Logistic regression analyses allowed for identification of factors associated with evidence use and intentions. All analyses were adjusted for sex, years of experience, and type of public health agency. Greater skills (ORadj = 4.1, 95%CI 1.3, 13.5) and stronger intentions/aligned goals (ORadj = 9.2, 95%CI 2.3, 36.1) were associated with greater use of behavioural science evidence to plan public health actions. Greater perceived capacity to overcome widespread absence of use of behavioural science evidence in their organization (ORadj = 7.2, 95%CI 1.7, 30.3) was also associated with greater use. More knowledge (ORadj = 8.6, 95%CI 1.9, 39.1) and stronger beliefs about consequences (ORadj = 4.0, 95%CI 1.1, 14.7) were significantly associated with stronger intentions/aligned goals. Findings show that more knowledge, positive attitudes, and stronger perceived competence are associated with greater likelihood of using behavioural science evidence to plan interventions. The use of behavioural science evidence will also require strengthening the norm pertaining to this professional practice in public health organizations.

Analysis of Biological Factors Associated with Preeclampsia in Pregnant Women

Preeclampsia is a serious complication during pregnancy that poses significant health risks to both mother and fetus, potentially leading to death. Early identification of biological factors associated with preeclampsia is crucial for effective prevention. This study aims to analyze the biological factors influencing preeclampsia incidence among pregnant women. A quantitative design with a case-control approach was used, focusing on pregnant women with a history of preeclampsia in the Curug Community Health Center, Karawang Regency. The sample included 10 respondents. The results revealed a significant relationship between parity and preeclampsia incidence (p-value 0.031), BMI (p-value 0.018), diet (p-value 0.041), and history of complications (p-value 0.001). However, no significant association was found between lifestyle activities and preeclampsia (p-value 0.114). These findings suggest that factors like parity, BMI, diet, and complications history are significantly linked to preeclampsia incidence. These results provide a basis for targeted interventions in pregnant women's care. Future studies should expand the sample size and consider additional variables such as employment status, family support, and stress levels for more comprehensive insights into preeclampsia risk factors.

Revealing Ecotourism Potential of Streams and Waterfalls: Identification of Influential Factors in a Case Study from Thua Thien Hue Province, Vietnam

Ecotourism is recognized as a fundamental aspect of sustainable tourism development. This study aimed to identify the factors influencing ecotourism in Thua Thien Hue province, Vietnam, specifically focusing on streams and waterfalls, using an ecotourism potential model. This model utilizes quantitative approaches, incorporating questionnaires and spatial information, including remote sensing data. The model is employed to determine the factors affecting ecotourism potential in the 31 locations of streams and waterfalls in Thua Thien Hue province. The model considers ecotourism potential as the dependent variable, while the independent variables are greenness using the normalized difference vegetation index (NDVI), connectivity, accessibility, and infrastructure, e.g., restaurants and hotels. The results revealed that greenness has the most significant positive impact on ecotourism potential around streams and waterfalls in Thua Thien Hue province, followed by accessibility, restaurants, and hotels, while connectivity has a negative impact on ecotourism potential in these areas. This analysis underscores the importance of prioritizing the conservation of natural environments and ecosystems surrounding streams and waterfalls. Furthermore, this study utilizes geospatial methodologies to identify and enhance our understanding of the key factors influencing ecotourism.

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.