Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study.

Background: Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach. Aims: Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence-based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM). Methods: Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern-based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5%, 5-15%, 15-50%, 50-85%, > 85%). Ischemia was defined as summed difference score (SDS) ≥ 2. Results: Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA's sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15-85%) range by 29% (ACC 2012)-51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category. Conclusion: The MPA model enhanced ischemia testing according to the PPPM framework:The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing ("predictive").It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs ("preventive").Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm ("personalised"). Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00341-5.

Growth mindset: An umbrella for protecting socially stressed adolescents' life satisfaction.

Research suggests that growth mindset shows positive effects on adolescents' academic achievement, especially in overcoming academic-related setbacks. It remains unclear, however, how growth mindset functions in the presence of social stress, a risk factor for adolescent mental health. In the present study, we explored how growth mindset of thoughts-emotions-behaviors predicted dual indicators of adolescents' mental health (life satisfaction and emotional problems), and if and how growth mindset interacted with peer and family stress to predict mental health. A total of 791 adolescents (Mage = 16.32 years, SD = 1.1, range 14-18; 60.8% female; 9th-12th grades; African American 34.5%, White 31.4%, Asian 13.2%, Hispanic 11.6%, biracial or multiracial 8.2%, others 1%, and missing 1%) participated via self-report surveys. A structural equation modeling approach was adopted to simultaneously model both the main and interaction effects. Results showed one significant interaction effect-between growth mindset and peer stress-on predicting life satisfaction (ß = 0.13) and a significant main effect of growth mindset on predicting emotional problems (ß = -0.35). The main effects of family stress on both outcomes (ß = -0.22, life satisfaction; ß = 0.18, emotional problems) were significant in the expected directions. Thus, growth mindset is a contributing factor to better mental health (higher life satisfaction, fewer emotional problems) and a buffering factor that mitigates the negative impact of peer stress on life satisfaction. These findings enhance the understanding of growth mindset, which can be integrated into school psychologists' work to assess or promote adolescent mental health. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Yoga Instructor Beliefs Scales Epistemic (YIBS-E) and Pedagogic (YIBS-P): Development and Initial Assessment of the Factor Structure.

Yoga is a multidimensional and heterogeneous mind-body practice led by a therapist or teacher (e.g., yoga instructor). Although they constitute an integral part of delivery, content, and curriculum, factors that influence yoga instructors' choices have yet to be explored. Using a mixed methods sequential design for development of an instrument that identifies measurable epistemic (YIBS-E) and pedagogic (YIBS-P) beliefs, the Yoga Instructor Beliefs Scale (YIBS) reports validity evidence from four distinct phases. Phase I presents qualitative findings from literature-informed semistructured interviews to give a comprehensive construct model of yoga instructor beliefs from diverse styles/ schools/lineages of yoga (nine content-specific clusters). In Phase II, focus group panels of experts evaluated construct novelty and importance of themes, resulting in a pool of potential questionnaire items. Phase III employed cognitive interviews to assess the perceived meaning and clarity of using the preliminary YIBS items. Phase IV included exploratory factor analysis and correlational analyses using 204 yoga instructor responses, suggesting a 44-item instrument with distinct epistemic (Experiential, Energetic, Systems-Based, Affectual, Mindful, and Physical) and pedagogic (Curricular Integration, Student Awareness, Accessibility, and Differentiated Instruction) factors (YIBS- E α = 0.90, YIBS-P α = 0.85). Measurable belief constructs can inform research on individual yoga instructor differences that may influence curriculum content choices and delivery. The purpose of this instrument is to enable research linking instructor beliefs to the presence of various components of a yoga program and to contextualize defining qualities of yoga programs. Long-term use of this instrument should enable in-depth analyses such as mediation or moderation of yoga instructor beliefs on intervention components/content or outcomes.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk.

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

Procalcitonin concentration in the emergency department predicts 30-day mortality in COVID-19 better than the lymphocyte count, the neutrophil-tolymphocyte ratio, or the C-reactive protein level. / Procalcitonina es superior a recuento linfocitario, índice neutrófilo/linfocito y proteína C reactiva para la predicción de mortalidad a 30 días de pacientes con COVID-19 en el servicio de urgencias.

OBJECTIVES: Although many demographic and clinical predictors of mortality have been studied in relation to COVID-19, little has been reported about the prognostic utility of inflammatory biomarkers. MATERIAL AND METHODS: Retrospective cohort study. All patients with laboratory-confirmed COVID-19 treated in a hospital emergency department were included consecutively if baseline measurements of the following biomarkers were on record: lymphocyte counts, neutrophil-to-lymphocyte ratio NRL, and C-reactive protein (CRP) and procalcitonin (PCT) levels. We analyzed associations between the biomarkers and all-cause 30-day mortality using Cox regression models and dose-response curves. RESULTS: We included 896 patients, 151 (17%) of whom died within 30 days. The median (interquartile range) age was 63 (51-78) years, and 494 (55%) were men. NLR, CRP and PCT levels at ED presentation were higher, while lymphocyte counts were lower, in patients who died compared to those who survived (P .001). The areas under the receiver operating characteristic curves revealed the PCT concentration (0.79; 95% CI, 0.75-0.83) to be a better predictor of 30-day mortality than the lymphocyte count (0.70; 95% CI, 0.65-0.74; P .001), the NLR (0.74; 95% CI, 0.69-0.78; P = .03), or the CRP level (0.72; 95% CI, 0.68-0.76; P .001). The proposed PCT concentration decision points for use in emergency department case management were 0.06 ng/L (negative) and 0.72 ng/L (positive). These cutoffs helped classify risk in 357 patients (40%). Multivariable analysis demonstrated that the PCT concentration had the strongest association with mortality. CONCLUSION: PCT concentration in the emergency department predicts all-cause 30-day mortality in patients with COVID-19 better than other inflammatory biomarkers.

OBJETIVO: Existen múltiples variables demográficas y clínicas predictivas de mortalidad en pacientes con COVID-19. Sin embargo, hay menos información sobre el valor pronóstico de los biomarcadores inflamatorios. METODO: Estudio de cohorte retrospectivo. Se incluyeron de forma consecutiva todos los pacientes con COVID-19, confirmado por laboratorio, atendidos en un servicio de urgencias hospitalario (SUH) y con valor basal de los siguientes biomarcadores: recuento linfocitario, índice neutrófilo/linfocito (INL), proteína C reactiva (PCR) y procalcitonina (PCT). La relación entre los biomarcadores y la mortalidad total a 30 días se analizó mediante una regresión de Cox y gráficos de dosis-respuesta. RESULTADOS: Se incluyeron 896 pacientes, 151 (17%) fallecieron en los primeros 30 días. La mediana de edad fue de 63 años (51-78) y 494 (55%) eran hombres. El valor de INL, PCR y PCT fue mayor, mientras que el recuento linfocitario fue menor, en los pacientes que fallecieron respecto a los que sobrevivieron (p 0,001). La PCT fue superior al recuento linfocitario, INL y PCR en la predicción de mortalidad a 30 días (ABC 0,79 [IC 95%: 0,75-0,83] vs 0,70 [IC 95%: 0,65-0,74], p 0,001; 0,74 [IC 95%: 0,69-0,78], p = 0,03; y 0,72 [IC 95%: 0,68-0,76], p 0,001). Los puntos de decisión de PCT propuestos, 0,06 ng/l para exclusión y 0,72 ng/l para inclusión de muerte a 30 días, podrían facilitar la toma de decisiones en urgencias. Hubo 357 pacientes (40%) con valores de PCT en estas categorías. El análisis multivariable mostró una mayor asociación con la mortalidad para PCT que en los otros biomarcadores estudiados. CONCLUSIONES: PCT es el biomarcador con mejor capacidad para predecir mortalidad a 30 días por cualquier causa en pacientes con COVID-19 valorados en un SUH.

Analysis of standards of quality for outcomes in acute heart failure patients directly discharged home from emergency departments and their relationship with the emergency department direct discharge rate.

OBJECTIVE: Experts recommended that direct discharge without hospitalization (DDWH) for emergency departments (EDs) able to observe acute heart failure (AHF) patients should be >40%, and these discharged patients should fulfil the following outcome standards: 30-day all-cause mortality <2% (outcome A); 7-day ED revisit due to AHF < 10% (outcome B); and 30-day ED revisit/hospitalization due to AHF < 20% (outcome C). We investigated these outcomes in a nationwide cohort and their relationship with the ED DDWH percentage. METHODS: We analyzed the EAHFE registry (includes about 15% of Spanish EDs), calculated DDWH percentage of each ED, and A/B/C outcomes of DDWH patients, overall and in each individual ED. Relationship between ED DDWH and outcomes was assessed by linear and quadratic regression models, non-weighted and weighted by DDWH patients provided by each ED. RESULTS: Among 17,420 patients, 4488 had DDWH (25.8%, median ED stay = 0 days, IQR = 0-1). Only 12.9% EDs achieved DDWH > 40%. Considering DDWH patients altogether, outcomes A/C were above the recommended standards (4.3%/29.4%), while outcome B was nearly met (B = 10.1%). When analyzing individual EDs, 58.1% of them achieved the outcome B standard, while outcomes A/C standards were barely achieved (19.3%/9.7%). We observed clinically relevant linear/quadratic relationships between higher DDWH and worse outcomes B (weighted R2 = 0.184/0.322) and C (weighted R2 = 0.430/0.624), but not with outcome A (weighted R2 = 0.002/0.022). CONCLUSIONS: The EDs of this nationwide cohort do not fulfil the standards for AHF patients with DDWH. High DDWH rates negatively impact ED revisit or hospitalization but not mortality. This may represent an opportunity for improvement in better selecting patients for early ED discharge and in ensuring early follow-up after ED discharge.

Predicting progression in nondiabetic kidney disease: the importance of cardiorenal interactions.

The interplay between the heart and the kidneys has received widespread attention in recent years. A novel five-class definition of cardiorenal syndromes has been proposed. The ability of two markers of cardiac dysfunction to predict progression of primary kidney disease, described by Dieplinger and his co-workers, highlights the prognostic importance of the chronic cardiorenal (types 2 and 4) syndromes.