Machine Learning Prediction of Stress-Induced D-dimer Reactivity in Male Physicians with and without Burnout.

Endothelin-1 Release and Stimulation of the Inflammatory Cascade: Is Acute Coronary Syndrome Triggered by Watching Spectator Sports?

BackgroundExtensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; however, its ability to predict events or stratify risks is less known. Individuals with mental illness and ACE exposure have been shown to visit emergency departments (ED) more often than those in the general population. This study thus examined the ability of the ACEs checklist to predict ED visits within the subsequent year among children and adolescents presenting to mental health clinics with pre-existing mental health issues.MethodsThe study analyzed linked data (n = 6100) from two databases provided by Alberta Health Services (AHS). The Regional Access and Intake System (RAIS 2016–2018) database provided data on the predictors (ACE items, age, sex, residence, mental health program type, and primary diagnosis) regarding children and adolescents (aged 0–17 years) accessing addiction and mental health services within Calgary Zone, and the National Ambulatory Care Reporting System (NACRS 2016–2019) database provided data on ED visits. A 25% random sample of the data was reserved for validation purposes. Two Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models, each employing a different method to tune the shrinkage parameter lambda (namely cross-validated and adaptive) and performing 10-fold cross-validation for a set of 100 lambdas in each model were examined.ResultsThe adaptive LASSO model had a slightly better fit in the validation dataset than the cross-validated model; however, it still demonstrated poor discrimination (AUC 0.60, sensitivity 37.8%, PPV 49.6%) and poor calibration (over-triaged in low-risk and under-triaged in high-risk subgroups). The model’s poor performance was evident from an out-of-sample deviance ratio of − 0.044.ConclusionThe ACEs checklist did not perform well in predicting ED visits among children and adolescents with existing mental health concerns. The diverse causes of ED visits may have hindered accurate predictions, requiring more advanced statistical procedures. Future studies exploring other machine learning approaches and including a more extensive set of childhood adversities and other important predictors may produce better predictions. Furthermore, despite highly significant associations being observed, ACEs may not be deterministic in predicting health-related events at the individual level, such as general ED use.

Adverse Childhood Experiences and Blood Pressure in Women in the United States: A Systematic Review.

Background Masked uncontrolled hypertension (MUCH) is defined as normal office blood pressure (BP) but elevated out-of-office BP in patients on antihypertensive treatment. MUCH is associated with similar cardiovascular risk as sustained hypertension and is clinically challenging since this often remains undetected without out-of-office BP measurements. Purpose To study the prevalence of MUCH following an acute coronary syndrome and to utilise machine learning methods to develop prediction models for identifying MUCH. Methods Ambulatory 24-h BP measurement (ABPM) was performed in 100 patients attending the cardiology outpatient clinic at a Swedish university hospital following an acute coronary syndrome, as part of a study which screened for co-morbidities. From the SWEDEHEART registry, 106 variables during the hospital care period and subsequent follow-up visit (after 6-10 weeks) were pre-processed including filtering on 5 or more missing values and zero or near-zero variance. Variable importance for the prediction of MUCH (office BP < 140/90 mm Hg at ABPM start but mean 24-h BP ≥ 130/80 mm Hg) was assessed using the Boruta and least absolute shrinkage and selection operator (LASSO) machine learning algorithms. Subsequently, logistic regression, LASSO and random forest models using different variable subsets were evaluated by receiver operating characteristic area under the curve (AUC) in repeated cross-validation. Results Age was 62.0±8.4 years, 74% male, 54% had NSTEMI and 46% STEMI. The follow-up visit and ABPM were performed at median 7 and 11 weeks, respectively, after hospital discharge. Among 90 patients with complete data and ABPM recordings, 31 had mean 24-h BP above target levels, of which 18 were identified with MUCH. Patients with MUCH had lower eGFR (68±11 vs 76±12 ml/min/1.73m², P=0.009), and more often a history of hypertension (89 vs 53%, P=0.005) and diabetes mellitus (44 vs 11%, P=0.003). In total, 65 variables where eligible for machine learning after filtering. Boruta and LASSO identified pulse pressure at the follow-up visit, serum creatinine, diabetes mellitus and history of hypertension as important predictors. Random forest, logistic regression and LASSO showed mean AUC 0.826, 0.822, and 822, respectively, in cross validation using these predictors. Conclusions In this small trial, one in five had MUCH at follow-up after an acute coronary syndrome, which highlights the importance of out-of-office BP measurements. The easily accessible measures of pulse pressure at the follow-up visit, serum creatinine, diabetes mellitus and history of hypertension were identified as important predictors of MUCH. A simple prediction model may be used as a clinical decision support tool after appropriate external validation.

BackgroundEarly identification of individuals at high risk for autism spectrum disorder (ASD) is crucial for optimizing intervention strategies and improving outcomes. This study aims to develop a risk prediction model integrating biopsychosocial factors through a systematic review with multicenter validation.MethodsA comprehensive search was conducted across PubMed, Cochrane Library, and Embase for articles on biopsychosocial ASD risk factors during 2010–2023. Two reviewers independently extracted data. Meta-regression analysis of 37 systematic reviews/meta-analyses identified 18 potential risk factors by Stata 16.0. Four core variables were included in the prediction model, while 14 were excluded due to low-quality evidence or insufficient data after screening. Multivariate logistic regression with least absolute shrinkage and selection operator (LASSO) variable selection derived model weights. External validation was performed in a Chinese cohort (n=1,175) from two tertiary hospitals. Model discrimination was assessed via receiver operating characteristic (ROC) curves and clinical utility by decision curve analysis (DCA).ResultsAnalysis of 37 systematic reviews identified four independent predictors of ASD risk: adverse childhood experiences (ACEs) [odds ratio (OR) =2.11; 95% confidence interval (CI): 1.61–2.77], preterm birth (OR =3.3; 95% CI: 1.24–7.60), antidepressant exposure during pregnancy (OR =1.17; 95% CI: 1.08–1.21), and perinatal antibiotic exposure (OR =1.52; 95% CI: 1.09–2.12). The risk model formula was: 0.82 × (ACEs) + 1.19 × (preterm birth) + 0.42 × (antidepressant exposure) + 0.21 × (perinatal antibiotic exposure). External validation showed excellent discrimination [area under the curve (AUC) =0.78; 95% CI: 0.75–0.81]. DCA confirmed significantly higher net clinical benefit compared to universal intervention strategies.ConclusionsThis study developed a risk prediction model integrating biopsychosocial factors, providing an evidence-based tool for early identification of individuals at high risk for ASD.

In contrast to heavy alcohol consumption, which is harmful, light to moderate drinking has been linked to reduced cardiovascular morbidity and mortality. Effects on lipid status or clotting do not fully explain these benefits. Exaggerated cardiovascular responses to mental stress are detrimental to cardiovascular health. We hypothesized that habitual alcohol consumption might reduce these responses, with potential benefits. Advanced magnetic resonance techniques were used to accurately measure cardiovascular responses to an acute mental stressor (Montreal Imaging Stress Task) in 88 healthy adults (∼1:1 male:female). Salivary cortisol and task performance measures were used to assess endocrine and cognitive responses. Habitual alcohol consumption and confounding factors were assessed by questionnaire. Alcohol consumption was inversely related to responses of heart rate (HR) (r = −0.31, p = 0.01), cardiac output (CO) (r = −0.32, p = 0.01), vascular resistance (r = 0.25, p = 0.04) and mean blood pressure (r = −0.31, p = 0.01) provoked by stress, but not to stroke volume (SV), or arterial compliance changes. However, high alcohol consumers had greater cortisol stress responses, compared to moderate consumers (3.5 versus 0.7 nmol/L, p = 0.04). Cognitive measures did not differ. Findings were not explained by variations in age, sex, social class, ethnicity, physical activity, adrenocortical activity, adiposity, smoking, menstrual phase and chronic stress. Habitual alcohol consumption is associated with reduced cardiac responsiveness during mental stress, which has been linked to lower risk of hypertension and vascular disease. Consistent with established evidence, our findings suggest a mechanism by which moderate alcohol consumption might reduce cardiovascular disease, but not high consumption, where effects such as greater cortisol stress responses may negate any benefits.

Association of osteoporotic fracture with smoking, alcohol consumption, tea consumption and exercise among Chinese nonagenarians/centenarians.

Background: This study identifies optimal radiomic machine-learning classifiers to differentiate glioblastomas (GBM) from solitary brain metastases (MET), the most common neoplasms in adults, preoperatively. Methods: Four hundred and twelve patients with solitary brain tumors (242 with GBM and 170 with solitary brain MET) were divided into training (n =227) and validation (n =185) sets. Extraction of radiomic features from preoperative magnetic resonance images of each patient was accomplished with PyRadiomics software. Twelve feature selection methods and seven classification methods were evaluated to construct optimal radiomic machine-learning classifiers in the training set that were subsequently evaluated in a validation set. The role of the classifiers in differential diagnosis was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). Findings:In the training set, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the validation set, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy, followed by Adaboost (ADa) LASSO (AUC, 0.89), Multi-Layer Perceptron (MLP) LASSO (AUC, 0.87), and random forest (RF) LASSO (AUC, 0.87). Furthermore, the clinical performance of these radiomic machine-learning classifiers were superior to neuroradiologists in accuracy, sensitivity, and specificity. Interpretation: By employing radiomic machine-learning technology, optimal machine-learning classifiers were identified for differentiating GBM from solitary brain MET preoperatively, which could significantly augment therapeutic strategies. Funding Statement: We acknowledge financial support from the National Natural Science Foundation of China (No. 81601452), and Key laboratory of functional and clinical translational medicine, Fujian province university(JNYLC1808) Declaration of Interests: The author declares no competing interest. Ethics Approval Statement: This study was approved by the ethics committee of Beijing Tiantan Hospital.

Introduction. Adults who report Adverse Childhood Experiences (ACEs) are more likely to engage in health-risk behaviors. While previous research has explored the associations between ACEs and health-risk behaviors in the general population, studies focusing on cancer survivors are lacking. We examined the association between cannabis use, alcohol consumption, and smoking by race and ACEs among cancer survivors. Methods. Using cross-sectional data from the 2020 Behavioral Risk Factor Surveillance System, we analyzed 7,896 cancer survivors aged ≥18. We categorized the main exposure, ACEs, based on severity into four categories: zero, one, two to three, and ≥four, derived from questions assessing exposure to eight types of ACEs before the age of 18. The outcome measures were cannabis use (yes or no) and cancer risk behaviors: smoking status (current/former smoker or never smoker), binge alcohol use (yes or no). Binge drinking was defined as males consuming five or more drinks on one occasion and females consuming four or more drinks on one occasion. Weighted multivariable logistic regression models examined association between ACE and the outcomes; cannabis use, binge alcohol use and smoking adjusting for sociodemographic factors. Results. In this sample of cancer survivors, 44.1%, 22.7%, 20.2%, and 13.0% reported having experienced zero, one, two to three, and ≥four ACEs, respectively. Personal history of cannabis use was reported by 6.0% of respondents, current/former smokers by 43.4%, and binge alcohol use by 6.2%. In the adjusted models, cancer survivors with one ACE (aOR: 1.55, 95% CI: 0.92–2.60), two to three ACEs (aOR: 2.56, 95% CI: 1.57–4.27), or ≥four ACEs (aOR: 4.10, 95% CI: 2.54–6.64) had higher odds of cannabis use compared to those with zero ACEs. Similarly, cancer survivors with one ACE (aOR: 1.53, 95% CI: 0.99–2.39), two to three ACEs (aOR: 2.18, 95% CI: 1.36–3.48), or ≥four ACEs (aOR: 2.08, 95% CI: 1.25–3.48) had higher odds of binge drinking compared to those with zero ACEs. Finally, cancer survivors with one ACE (aOR: 1.40, 95% CI: 1.12–1.74), two to three ACEs (aOR: 1.94, 95% CI: 1.54–2.44), or ≥four ACEs (aOR: 3.06, 95% CI: 2.29–4.08) had higher odds being current/former smoker compared to those with zero ACEs. In terms of racial differences, the adjusted models indicated that Hispanic cancer survivors had higher odds of cannabis use (aOR: 2.49, 95% CI: 1.13–5.48) compared to Whites, but lower odds of smoking (aOR: 0.47, 95% CI: 0.28–0.80) compared to Whites. Conclusion. ACEs are strong predictors of adult cannabis use and cancer risk behaviors such as binge drinking and smoking. Therefore, individuals with a history of ACEs may represent an important target group for preventive interventions aimed at reducing these risks. Citation Format: Oluwole A. Babatunde, Melanie S. Jefferson, Swann Adams, Chanita Hughes Halbert, Nosayaba Osazuwa-Peters, Eric Adjei Boakye. Disparities in cancer risk behaviors, cannabis use and adverse childhood experiences among cancer survivors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B015.

Background: Studies have demonstrated that exposure to nature decreases acute stress responses, and that adverse childhood experiences (ACEs) are linked to physiological hyperreactivity to acute psychosocial stressors. Nature exposure may therefore have potential to act as a protective factor against the heightened stress responses that are associated with high ACEs. We investigated whether ACEs, and other measures of stress vulnerability, modified the effects of virtual nature exposure on physiological responses to an acute psychosocial stressor, and subsequent recovery in contrasting environmental conditions, among 95 healthy adult males.Methods: Baseline assessments included the ACE questionnaire, the Miller-Smith Lifestyle Assessment Inventory (stress vulnerability) and immunological hyperactivity; measured via immune cell interleukin-6 production after lipopolysaccharides (LPS) exposure. We then conducted an experiment in which participants experienced an acute stressor (Trier Social Stress Test), followed by randomly assigned exposure to one of three virtual environments (natural park, natural desert, or control [office]) for 10-min, followed by two 20-min recovery periods. Physiological stress indicators, including systolic and diastolic blood pressure (SBP & DBP), pulse, mean arterial pressure (MAP) and salivary cortisol, were measured repeatedly throughout the experimental sessions.Results: We found that high-ACE, stress-vulnerable and LPS-hyperactive participants exhibited physiological hyperreactivity to the stressor. During the recovery periods, high-ACE and LPS-hyperactive participants in the natural park condition experienced greater drops in blood pressure vs. low-ACE and non-LPS-hyperactive participants. Additionally, high-ACE participants in the desert condition showed greater decreases in DBP vs. low-ACE participants.Conclusions: High-ACE participants responded differently to both the acute stressor and the virtual natural environments than low-ACE participants. This indicates that assessments of ACEs may be a critical consideration when examining the ways in which the stress-buffering effects of nature exposure may vary across individuals and subpopulations, and opens up a new set of questions about why this may be the case.

Predictors of suicidal ideation in social anxiety disorder – evidence for the validity of the Interpersonal Theory of Suicide

ObjectivePhysicians face documented challenges to their mental and physical well-being, particularly in the forms of occupational burnout and cardiovascular disease. This study examined the previously under-researched intersection of early life stressors, prolonged occupational stress, and cardiovascular health in physicians. MethodsParticipants were 60 practicing male physicians, 30 with clinical burnout, defined by the Maslach Burnout Inventory, and 30 non-burnout controls. They completed the Adverse Childhood Experiences (ACE) Questionnaire asking about abuse, neglect and household dysfunctions before the age of 18, and the Perceived Stress Scale to rate thoughts and feelings about stress in the past month. Endothelium-independent (adenosine challenge) coronary flow reserve (CFR) and endothelium-dependent CFR (cold pressor test) were assessed by positron emission tomography-computed tomography. The segment stenosis score was determined by coronary computed tomography angiography. ResultsTwenty-six (43%) participants reported at least one ACE and five (8%) reported ≥4 ACEs. A higher ACEs sum score was associated with lower endothelium-independent CFR (r partial (rp) = −0.347, p = .01) and endothelium-dependent CFR (rp = −0.278, p = .04), adjusting for age, body mass index, perceived stress and segment stenosis score. In exploratory analyses, participants with ≥4 ACEs had lower endothelium-independent CFR (rp = −0.419, p = .001) and endothelium-dependent CFR (rp = −0.278, p = .04), than those with <4 ACEs. Endothelium-dependent CFR was higher in physicians with burnout than in controls (rp = 0.277, p = .04). No significant interaction emerged between burnout and ACEs for CFR. ConclusionThe findings suggest an independent association between ACEs and CFR in male physicians and emphasize the nuanced relationship between early life stressors, professional stress, and cardiovascular health.

Spontaneous coronary artery dissection (SCAD) is the acute development of a false lumen within the coronary artery wall by the spontaneous formation of an intramural hematoma which may compromise coronary (blood) flow by compression of the true lumen. Psychological factors have been implicated in its pathophysiology, but a synthesis of available data has not been previously undertaken. A literature search was conducted with the terms coronary artery dissection or spontaneous coronary artery dissection AND the terms psychological stress, anxiety, or depression. Initial studies in the field reported that psychological stress, anxiety, or depression was associated with SCAD and that acute stress may have a role in producing the SCAD. Recent studies with control groups of either acute coronary syndromes or acute myocardial infarction have produced discordant results. A meta-analysis of these studies, in this review, using a fixed effects model, showed that there was no significant association between SCAD and either moderate-to-high psychological stress or moderate-to-severe depression. However, one study reported that patients with SCAD were two-fold more likely to have experienced an emotional precipitant in the 24 h prior to the event. Assessment of patients with SCAD found long-term psychological consequences, and in some cases similar to posttraumatic stress disorder. In conclusion, chronic psychological stress, anxiety, or depression is not associated with the development of SCAD, however acute emotional stress may be a factor precipitating SCAD in some patients. Further research is necessary to examine the biological basis for SCAD and how acute stress might play a role in its pathogenesis.

IntroductionDecades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.MethodsUsing the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models—random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor—over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable’s importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.ResultsWith the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.DiscussionOur models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

The effects of N-methyl-D-aspartate (NMDA) and luteinizing hormone releasing hormone (LH-RH) on luteinizing hormone (LH) secretion were examined in ovariectomized estrogen-primed rats under nonstressed and acutely stressed conditions. The basal LH levels were significantly elevated 15 min after the onset of acute immobilization stress, but were not altered in emotionally stressed or nonstressed rats. Intravenous injections of 10 and 40 mg/kg NMDA significantly elevated serum LH levels by 161 and 212%, respectively, from baseline within 10 min in nonstressed animals. However, the NMDA-induced LH release was significantly reduced when tested 30 min after the onset of acute immobilization stress. Acute emotional stress, which did not affect the baseline LH, also suppressed the LH release response to NMDA, suggesting that the reduced LH responses to NMDA in stressed animals was not due to the elevated baseline level. Pituitary LH release responses to LH-RH were not affected by acute immobilization. We conclude from these results: (1) acute immobilization stress exerts both stimulatory and inhibitory effects on LH release, while acute emotional stress has only an inhibitory effect in estrogen-primed ovariectomized rats; (2) this inhibition occurs at the suprapituitary level, and (3) it involves a suppression of the responsiveness of the hypothalamic LH-RH neuronal system to the excitatory amino acid input.