Predictors Of Class Research Articles

Assessment of right ventricular myocardial work in adult patients with atrial septal defect before and after percutaneous closure

Abstract Background Although most patients with early repair of an isolated atrial septal defect (ASD) have resolution of right ventricular (RV) dilatation, some studies show persistent RV dysfunction and reduced maximal exercise capacity in long-term follow-up in a considerable subgroup of patients. Purpose Right ventricular myocardial work (RVMW) is a novel method for non-invasive assessment of right ventricular (RV) function using RV pressure-strain loops. We sought to evaluate RV function by RVMW indexes in adult patients with atrial septal defect (ASD) before and six months after transcatheter closure in order to assess their value compared to RV three-dimensional(3D) volumetric indexes and two-dimensional(2D)-Doppler parameters. Methods Fifteen ASD patients before and after percutaneous closure were studied using a commercially available cardiovascular ultrasound system (Vivid E95, GE Vingmed Ultrasound, Norway). 15 healthy age- and sex-matched subjects were selected as controls. RV volumes and ejection fraction (3D-RVEF) were obtained using a software package (4D Auto RVQ). 2D-Doppler parameters of RV function (fractional area change -FAC-, tricuspid annular plane systolic excursion -TAPSE-, myocardial performance index -MPI-) were calculated. RV global longitudinal strain (RVGLS) was evaluated by tracing the RV free wall and interventricular septum. Pulmonary artery systolic pressure (PASP) was estimated by tracing the tricuspid regurgitation velocity-time integral. RV global work index (RVGWI), RV global constructive work (RVGCW), RV global wasted work (RVGWW), and RV global work efficiency (RVGWE) were analysed. Datasets were digitally stored and analyzed offline using GE EchoPAC Version-R6. Peak oxygen consumption(VO2) derived from symptom-limited treadmill tests was obtained. Results Overall, 3D-RVEF, RVGWI and RVGWE were significantly higher than control group in open ASD (p=0.02) and decreased significantly six months after closure (p=0.03). NYHA class improved from 1.9±0.4 before to 1.2±0.5 after closure (p<0.05). In 5 patients (33%) 3D-RVEF was reduced and RV was dilated compared to controls six months after closure (p=0.04 and p=0.02, respectively). RVGWI was significantly correlated with RVGLS (p<0.001), PASP (p=0.002), and TAPSE (p=0.014). By multivariate analysis RVGWE and 3D-RVEF were independent predictors of functional class. ROC analysis showed RVGWE and 3D-RVEF (AUC 0.8917 and 0.8869, respectively) to be more sensitive predictors of unfavorable outcome after defect closure (VO2 ≤16mL/min/kg) compared to FAC, TAPSE, and MPI (AUC 0.7586, 0.7824, and 0.7963, respectively). Conclusions In ASD patients before and after closure, right ventricular myocardial work provides useful insights into the quantitative assessment of RV function. RVMW findings are comparable to RV-3D volumetric parameters as predictors of impaired exercise impairment and more accurate than 2D parameters.

Association between serum BIN1 level and Killip class in patients with acute myocardial infraction

To investigate the correlation of serum levels of bridging integrating factor 1 (BIN1) with acute myocardial infarction (AMI) and Killip class of the patients. We retrospectively collected the data from 94 patients with AMI and 30 healthy individuals for analysis of the correlations of serum BIN1 levels with Killip class, TIMI scores, and neutrophil-to-lymphocyte ratio (NLR). We also assessed the diagnostic value of BIN1 combined with NLR for AMI. Serum BIN1 levels were significantly lower in AMI patients than in the healthy individuals (P=0.032). The AMI patients with Killip class I had significantly lower serum BIN1 levels than the healthy individuals (P=0.008). Serum BIN1 level was an independent predictor of AMI with a predictive value of 0.630 (95% CI: 0.513-0.748) at the optimal cutoff level of 0.341 ng/mL, a specificity of 50%, and a sensitivity of 78.5%. Serum BIN1 level was also an independent predictor for Killip class I group in the AMI patients with a predictive value of 0.672 (95% CI: 0.548-0.797) at the optimal cutoff level of 0.287 ng/mL, a specificity of 74.1%, and a sensitivity of 60%. For AMI diagnosis, the combination of NLR and serum BIN1 level had a predictive value of 0.811 (95% CI: 0.727-0.895) at the optimal cutoff level of 0.548 ng/mL, with a specificity of 92.6% and a sensitivity of 62.2%. There was a positive correlation between serum BIN1 level and TIMI score in AMI patients (r=0.186, P=0.003). BIN1 is correlated with AMI and can be helpful for predicting short-term prognosis of the patients, and BIN1 combined with NLR has a high diagnostic value for AMI.

Heterogeneity of pain-fatigue-sleep disturbance symptom clusters in lung cancer patients after chemotherapy: a latent profile analysis.

Pain-fatigue-sleep disturbance symptom cluster (PFS) was common in patients with lung cancer and seriously affected the life quality of patients. However, the heterogeneity and subgroups of PFS were unclear in lung cancer patients after chemotherapy. This study was conducted to identify distinct subgroups of PFS in patients with lung cancer after chemotherapy, and explore the differences and risk factors of PFS subgroups. Lung cancer patients after chemotherapy were recruited. Data were collected using the Chinese version of the Brief Pain Inventory, the Cancer Fatigue Scale, and the Pittsburgh Sleep Quality Index. The latent profile analysis (LPA) was used to identify the subgroups of PFS. Univariate analysis was used to identify the differences among all subgroups. Logistic regression and restricted cubic splines were used to investigate predictors of the PFS subgroups. Based on LPA, 512 participants were divided into four subgroups (Class 1: low pain-fatigue-sleep disturbance; Class 2: moderate pain-moderate fatigue-low sleep disturbance, Class 3: low pain-high fatigue-high sleep disturbance, and Class 4: high pain-fatigue-sleep disturbance). The univariate analysis showed that gender, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, leukocyte, neutrophils, platelet, C-reactive protein, stress, anxiety, depression, and social support were associated with PFS. The logistic regression analysis revealed that patients in Class 2 and Class 3 were more likely to experience great stress than those in Class 1. Additionally, compared to Class 1, females, lower BMI, stress, anxiety, and depression were independent predictors of Class 4. This study successfully identified subgroups of PFS in patients with lung cancer after chemotherapy. Based on the results of this study, medical workers can identify patients with high risks for PFS and conduct more targeted interventions to improve symptom management.

Skeletal and respiratory muscle weakness contributes to decreased exercise capacity in hypertrophic cardiomyopathy

Abstract Background and Aims Although decreased exercise capacity (due to dyspnea or angina-like symptoms) constitutes the fundamental characteristics of hypertrophic cardiomyopathy (HCM), the pathophysiology of these symptoms remains only partly understood. We investigated clinical and functional determinants of decreased exercise capacity in a broad range of HCM patients irrespective of primary disease aetiology (either genetic disorder or e.g. infiltrative phenocopies in the course of cardiac amyloidosis), overt heart failure (HF) and the presence of haemodynamically significant left ventricular outflow tract obstruction (LVOTO). Further, we deterimined the contribution of skeletal and respiratory muscle weakness to decresed exercise capacity. Methods We analyzed clinical data and functional assessments (skeletal and respiratory muscle strength, physical fitness) in relation to symptomatology and sub-maximal exercise capacity in 82 clinically stable patients with HCM (females: 32 %; age: 62±16 years; left ventricular ejection fraction [LVEF] 59±7%; intraventricular septum wall thickness [IVS] 19±4 mm; median plasma N-terminal pro-B-type natriuretic peptide [NT-proBNP]: 818 pg/mL [lower and upper quartile: 208-1917]; New York Heart Association [NYHA] class 0/I/II/III: 38/18/33/11 % [0 indicates no HF]; significant LVOTO: 29%). Results HCM patients with more severe symptomatology (NYHA class II-III vs 0-I) were older, had more frequently significant LVOTO and greater comorbidity burden, and had higher NT-proBNP and worse estimated renal function (all p≤0.03). Gender distribution, percentage of beta-blocker therapy and implanted device, body mass index, major echocardiography parameters (including LVEF, left ventricular end-diastolic diameter and IVS), high-sensitive (hs) cardiac troponin type I and hs-C-reactive protein were comparable in these 2 groups of subjects (all p>0.05). In multivariable linear regression models more advanced age, higher circulating natriuretic peptides (NT-proBNP) and previous coronary revascularization were independent predictors of more advanced NYHA class (standardized β-coefficient=0.30, p<0.01; β=0.27, p<0.01; and β=0.22, p<0.05; respectively). Similarly, more advanced age, female sex and higher NT-proBNP were independent correlates of shorter distance in 6-minute walking test (6MWT) (β=-0.46, p<0.001; β=-0.26, p<0.001; and β=-0.36, p<0.001; respectively). All analyzed muscle parameters (including respiratory muscle strength parameters) and objective measures of physical fitness (Functional Fitness Test for older adults) remained independently associated with 6MWT distance (all p<0.001) when adjusted for aforementioned clinical and laboratory correlates of 6MWT. Conclusions Muscle weakness and low physical fitness are related to decreased exercise capacity in HCM and may be potential therapeutic targets to alleviate disease symptoms also in non-obstructive HCM phenotype.

Frequency is not enough: Characterizing heterogenous patterns of cannabis use intensity among reservation-area American Indian youth

BackgroundReservation-area American Indian (AI) youth use cannabis at significantly higher rates than their national counterparts. This discrepancy is concerning, as cannabis use—particularly heavy use—can negatively impact adolescents’ health. Studies primarily use frequency to classify cannabis use intensity; however, frequency alone may not fully capture heterogenous patterns of use. This study aimed to classify AI adolescents’ cannabis use based on multiple intensity indicators, and to investigate interclass differences in problematic characteristics and outcomes of use. MethodsParticipants were 799 reservation-area AI youth (7–12th grade) reporting 12-month cannabis use. Latent Class Analysis (LCA) was used to distinguish cannabis use intensity patterns based on frequency, typical intoxication levels and duration. Auxiliary tests using R3STEP and BCH 3-step procedures were used to assess class predictors (age, initiation age, sex) and interclass differences in simultaneous drug use, stress-motivated use, problems quitting and cannabis-related consequences. ResultsFour classes emerged: Light Use (LU; 19 %), Occasional Intoxication (OI; 32 %), Mid-frequency Use(MU; 28 %), and Heavy Use (HU; 21 %). Age and initiation age correlated with membership odds in a heavier use class. Interclass differences in problematic characteristics and outcomes occurred between all classes, particularly for stress-motivated use and cannabis-related consequences—with HU reporting the most problematic characteristics and negative outcomes. ConclusionThese findings suggest that accounting for multiple dimensions of usage intensity may be important in studies examining cannabis use and related problems among AI adolescents. Tailoring intervention programming to address complex cannabis use patterns, with particular focus on stress-coping skills and harm reduction, can ensure AI youth most at risk for cannabis problems gain maximal benefit from prevention efforts.

Latent profiles and predictors of barriers to care in Swiss children and adolescents with rare diseases.

Children and adolescents with rare diseases face significant barriers when accessing healthcare. We aimed to assess and predict these barriers and investigate associations with health-related quality of life (HRQoL). We conducted a cross-sectional survey of Swiss parents (N = 189) of children with rare diseases including the Barriers to Care Questionnaire (BCQ), containing six barriers and the Pediatric Quality of Life Inventory (PedsQL). Latent profile analysis (LPA) was used to uncover distinct classes, which were compared using chi-square tests and Mann-Whitney U tests. Relevant medical and sociodemographic class predictors were identified using Elastic Net regression, followed by regression analysis to investigate their role in predicting barriers to care and examine the effects of these classes on HRQoL. Two distinct groups were identified, a higher barriers class (59%) and a lower barriers class (41%). In the higher barriers class, participants showed elevated scores across all subscales and specifically on pragmatics and expectations. More barriers to care were linked to a nonstable disease course (OR = 2.27, p = .002) and a diagnosis after the age of 3months (OR = 2.17, p = .006). Individuals in the higher barriers class exhibited more psychological comorbidities (p = .044), congenital malformations/deformations/chromosomal abnormalities (p=.042), and medical misdiagnoses (p = .006). Children in the higher barriers class had significantly lower PedsQL scores compared to the lower barriers class (p<.05). This study highlights the need for comprehensive assessment of barriers to pediatric care in rare diseases, offering potential entry points for targeted interventions.

Accuracy assessment of the geomorphon approach to detect ecological sites in the Dry Chaco region of Argentina

Ecological sites are a classification of local-land types based on differences in important environmental factors including soil properties, slope and landscape position within a geomorphologic and climatic zone. The concept is pivotal to conduct adaptative management in arid rangelands because it defines homogeneous areas over which specific soil-vegetation-disturbance dynamic occurs. Geomorphon is a computationally efficient method that uses Digital Elevation Models (DEMs) for the classification of landforms at multiple scales. The objective of this study was to evaluate the accuracy of the geomorphon approach to map ecological sites in a 11,259-ha study area within the Dry Chaco region of Argentina, utilizing local topographic positions along soil catena as a predictor of broader ecological sites classes. We used two DEM pixel resolutions (12.5 m and 30 m) and optimized the flatness threshold (t) and search radius (L) geomorphon parameters. As “ground truth” we used legacy data of 62 soil profiles descriptions. The geomorphon elements detected were reclassified into the three main ecological sites of the study area: highlands, midlands and lowlands. We used the overall disagreement (D) as the main metric for evaluating the accuracy of ecological site classifications. We found that: i) the lowest t value (0.05°) captured subtle topographical changes and thus more effectively reflected the soil-landscape relationship, and ii) larger L values paired with the lower pixel resolution (30 m) diminished the impact of minor landforms, improving the accuracy of ecological site detections. We determined that the geomorphon model with the pixel resolution of 30 m, t-value of 0.05°, and L-value of 12 produced the ecological site classification map with the highest accuracy, achieving a moderate-high accuracy (D of 33.9 %). Our study suggests that the geomorphon approach shows potential for consistent, reproducible and easily updatable ecological site mapping over larger areas.

Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network

This study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and construct an interpretable prediction model to assess the risk of unfavorable CPP evolution patterns. TBI patients with CPP records were identified from the Medical Information Mart for Intensive Care (MIMIC)-IV 2.1, eICU Collaborative Research Database (eICU-CRD) 2.0, and HiRID dataset 1.1.1. The research process consisted of 2 stages. First, group-based trajectory modeling (GBTM) was used to identify different CPP trajectories. Second, different artificial neural network (ANN) algorithms were used to predict the trajectories of CPP. A total of 331 eligible patients' records from MIMIC-IV 2.1 and eICU-CRD 2.0 were used for trajectory analysis and model development. Additionally, 310 patients' data from HiRID were used for external validation. The GBTM identified 5 CPP trajectory groups, group 1 and group 5 were merged into class 1 based on unfavorable in-hospital mortality. The best 6 predictors were invasive systolic blood pressure coefficient of variation, venous blood chloride ion concentration, PaCO2, prothrombin time, CPP coefficient of variation, and mean CPP. Compared with other algorithms, Scaled Conjugate Gradient performed relatively better in identifying class1. This study identified 2 CPP trajectory groups associated with elevated risk and 3 with reduced risk. PaCO2 might be a strong predictor for the unfavorable CPP class. The ANN model achieved the primary goal of risk stratification, which is conducive to early intervention and individualized treatment.

Predicting the trajectory of non-suicidal self-injury among adolescents.

Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of post-discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment. The study included adolescents (N = 612; females n = 435; 71.1%) aged 13-19-years-old (M = 15.6, SD = 1.4) undergoing inpatient treatment. Youth were administered clinical interviews and symptom questionnaires at intake (baseline) and before termination. NSSI frequency was assessed at 1-, 3-, and 6-month follow-ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI across follow-ups. Three classes were identified: Low Stable (n = 83), Moderate Fluctuating (n = 260), and High Persistent (n = 269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past-week suicidal ideation (SI) severity, lifetime average and worst-point SI intensity, and NSSI in the past 30 days (bs = 0.75-2.33). Only worst-point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class (b = -8.82); no predictors of the Moderate Fluctuating class emerged. This study found a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post-discharge. Accordingly, this may help identify youth that may benefit from additional monitoring and support post-hospitalization.

Preparatory study for carbon sequestration modelling of agroforestry systems in Hungary: The assessment of the yield class distribution of windbreaks

The escalating carbon dioxide emissions leading to global climate change are acknowledged as a paramount environmental challenge in the twenty-first century. The significance of land use systems in stabilising carbon dioxide levels and enhancing carbon sink potential has gained noteworthy attention from both the scientific and political communities. The Intergovernmental Panel on Climate Change emphasises that agroforestry systems present vital prospects for synergising climate change adaptation and mitigation efforts, offering substantial technical mitigation potential. Windbreaks are well-known agroforestry systems in Hungary and form an important part of agricultural landscapes. The improved agroforestry subsidy system in our country makes it relevant to model the carbon sequestration potential of windbreaks. In the framework of the ForestLab project we plan to develop a carbon sequestration model specific for Hungarian agroforestry systems. In this study, as a preparatory step of the model development, we assessed the yield class distribution of Hungarian windbreaks by tree species group and identified variables that had significant effect on yield class based on the data of the National Forestry Database. Our results show that among the examined effects the most important predictor of the yield class of windbreaks was the tree species group, followed by the thickness of the productive soil layer and the hydrology of the site.

Patterns of cognitive and social-emotional parental stimulation practices among Ghanaian kindergarteners

Across geographically diverse samples, engagement in stimulating activities with young children is considered an important element of parenting that promotes cognitive, behavioral, and social-emotional development. Indicators of stimulation activities are often summed, based on the assumption that more stimulation is better and that different stimulating activities are equivalent. Instead, this study takes a household-centered analytic approach, applying latent class analysis to detect patterns of parental stimulation practices across six activities. We then examine predictors of the resultant latent classes and their implications for children's school readiness among a sample of Ghanaian kindergarteners (N = 2,117; Mage = 5.2 years; 51% male). Results support a 4-class solution that represents (i) Highly Stimulating; (ii) Social-emotionally Stimulating; (iii) Playful; and (iv) Playful and Academically Stimulating households. Multiple child and family characteristics differentially predict latent class membership, although class membership did not differentially predict children's literacy, numeracy, executive function, or social-emotional skills. Findings suggest that all combinations of parental stimulation are equivalently promotive of children's school-related skills and help reify a strengths-based perspective. The results deepen our understanding of the different configurations of parental stimulation and suggest that a tailored approach to parenting programs may be beneficial. Programs can build on activities that parents already do (e.g., social-emotional) to encourage increased stimulation in other areas (e.g., academic).

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies

BackgroundRNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins.ResultsWe aimed to investigate the impact of data preprocessing steps—focusing on normalization, batch effect correction, and data scaling—through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer.ConclusionBy using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Evaluation of the Co-Go-Me angle as a predictor in Class II patients treated with Herbst appliance and skeletal anchorage: a retrospective cohort study.

A condylion-gonion-menton (Co-Go-Me) angle threshold of 125.5° has been introduced as a predictive parameter of cephalometric mandibular response in the orthopedic treatment of growing Class II patients with functional appliances, despite some contradictions in the literature. Considering the lack of studies evaluating the role of skeletal anchorage, this study aims to reassess the threshold of 125.5° in the Co-Go-Me angle as a useful predictor in growing skeletal Class II patients treated with acrylic splint Herbst appliance and two mini-screws in the lower arch (STM2). Thirty-five consecutively treated patients (20 males, 15 females; mean age, 11.37 years) with mandibular retrusion were classified into two groups according to their Co-Go-Me baseline values (Group 1, <125.5°; Group 2, >125.5°). The STM2 protocol involved the use of the MTH Herbst appliance with an acrylic splint in the lower arch and two interradicular mini-screws as anchorage reinforcement. Cephalometric analysis was performed by the same operator for each patient at baseline (T0) and at the end of the Herbst phase (T1). The effects of time and group on the variables were assessed by a repeated-measures analysis of variance. The primary research outcome was the difference between the groups in terms of mandibular responsiveness to treatment referred to as the relative difference (T1-T0) in Co_Gn. The mean duration of the treatment was 9.5 months. No statistically significant differences between groups were detected at baseline, except from the expected SN/GoMe° (p < 0.001) and Co-Go mm (p = 0.028). No statistically significant changes between groups, which were caused by the treatment, were found considering the mandibular sagittal and vertical skeletal parameters. Similarly, no statistically significant differences were found in the dental changes between the high-angle and low-angle patients, apart from the upper molar sagittal position (p = 0.013). The 125.5° threshold in the Co-Go-Me value was not a reliable predictive parameter for the mandibular response in growing patients treated with the MTH Herbst appliance and lower skeletal anchorage. Due to its effective control in the sagittal and vertical planes, the STM2 technique might be an appropriate protocol to use in treating skeletal Class II patients, regardless of the growth pattern.

232 Onco-functional Outcome After Resection for Eloquent Glioblastoma (GLIOFO): A Propensity-score Matched Analysis of an International, Multicenter, Cohort Study

INTRODUCTION: Minimizing residual tumor volume and preventing functional loss are the primary goal in glioblastoma resections in eloquent areas. However, their combined impact on patient outcomes remains poorly understood. METHODS: Propensity-score matching was used to match OFO 1 (gross-total resection with no functional loss), OFO 2 (no gross-total resection with no functional loss), OFO 3 (gross-total resection with functional loss) and OFO 4 patients (no gross-total resection with functional loss) for the overall cohort and subgroups. Cox proportional-hazard regressions were performed to analyze the association between OFO class and surgical outcomes. Logistic regressions were performed to identify preoperative predictors for OFO class. RESULTS: Between 2010 and 2020, 858 patients were included as the overall unmatched cohort. After matching, the cohort comprised of 512 patients: 256 received OFO 1 and 256 received non-OFO 1. OFO 1 resulted in fewer postoperative neurological deficits at 6 weeks (10.2% vs. 25.8%, p &lt; 0.001), 3 months (12.7% vs. 9.9%, p &lt; 0.001), and 6 months (21.0% vs. 35.1%, p = 0.0010); lower rates of KPS deterioration at 3 months (14.2% vs. 52.4%, p &lt; 0.001), and 6 months (26.5% vs. 52.9%, p &lt; 0.001); higher rate of receipt of adjuvant therapy (HR 3.91, p &lt; 0.001), longer overall survival (median 21.0 vs. 13.0 months, p &lt; 0.001), and longer progression-free survival (median 10.0 vs. 7.5 months, p &lt; 0.001). Patients who underwent awake craniotomy more often received OFO 1 (43.0% vs. 26.9%, p &lt; 0.001; OR 1.92, p = 0.0070). CONCLUSIONS: OFO 1 is beneficial in patients with eloquent glioblastoma to improve outcomes - irrespective of age, preoperative NIHSS, or KPS - in terms of survival, functional outcomes, and receipt of adjuvant therapy. Awake craniotomy was significantly associated with achieving this.

Trajectories and predictive factors of weight recovery in patients with anorexia nervosa completing treatment. Alatent class mixed model approach.

Treatment of anorexia nervosa (AN) sometimes requires hospitalisation, which is often lengthy, with little ability to predict individual trajectory. Depicting specific profiles of treatment response and their clinical predictors could be beneficial to tailor inpatient management. The aim of this research was to identify clusters of weight recovery during inpatient treatment, and their clinical predictors. A sample of 181 inpatients who completed a treatment programme for AN was included in a retrospective study. A latent class mixed model approach was used to identify distinct weight-gain trajectories. Clinical variables were introduced in a multinomial logistic regression model as predictors of the different classes. A four-class quadratic model was retained, able to correctly classify 63.7% of the cohort. It encompassed a late-rising, flattening, moderate trajectory of body mass index (BMI) increase (class 1), a late-rising, steady, high trajectory (class 2), an early-rising, flattening, high trajectory (class 3) and an early-rising, steady, high trajectory (class 4). Significant predictors of belonging to a class were baseline BMI (all classes), illness duration (class 2), and benzodiazepine prescription (class 3). Predicting different kinetics of weight recovery based on routinely collected clinical indicators could improve clinician awareness and patient engagement by enabling shared expectations of treatment response.

Abstract P326: Patient Predictors of Antihypertensive Medications Prescription Classes Revealed by the All of Us Research Project (AOURP) Registry

Hypertension (HTN) treatment guidelines recommend five general classes (ACEi, ARB, CCB, diuretics, beta-blockers) of medications with minimal guidance regarding specific classes for different types of patients. Understanding heterogeneity in HTN treatment and demographic and clinical traits of individuals receiving different medication classes may provide insights into outcome disparities and enable the development of more tailored treatment recommendations. Using EHR data from the national All of Us Research Project (AOURP) registry, we examined patient predictors of first-line HTN medication class usage. From the AOURP we selected: 1) patients aged 18+ and diagnosed with new-onset HTN between 2010 and 2021 which is determined by having no HTN medication prescription prior to diagnosis date; 2) patients not pregnant within a year before and after the HTN diagnosis. We restricted our analyses to individuals receiving routine care within the EHR database as defined by having at least one in-person visit in the year prior to the first HTN diagnosis and prescribed with a single class of HTN medications within a year of their HTN diagnosis. We performed multivariate regression analysis on the outcome of whether a certain class of first-line medication was prescribed another class. Our covariates include age, sex, race/ethnicity, comorbidities (diabetes, CVD, CKD), and household income. All reported odds ratios below have a p-value of less than 0.05. Among 5,521 AOURP participants, ACEi was the most prescribed class (29%), followed by CCB (23%), Diuretic (22%), Beta-blocker (17%), and ARB (9%). We found ACEi were more likely to prescribed for females (OR: 0.74) while diuretics were less likely to be prescribed for males (OR: 1.45). We found CCB (OR: 2.73) and diuretics (OR: 1.54) were more likely to be prescribed for Blacks than their White counterparts. We also found that ACEi (OR: 2.75) were more likely prescribed for diabetics. In terms of household income, we found that ARB (OR: 1.67) were prescribed more frequently for those in higher income brackets (&lt;$25,000 vs $100,000+) compared to other classes of medication. This corresponds to the fact that ARB are relatively newer and more expensive class of HTN medications. Our findings show that although there are levels of clinical equipoise in terms of prescribed medication classes among the general population, evidence-based guidance on personalized antihypertension treatments may be needed.

Multiple trauma exposure and psychopathology in Syrian refugees living in Turkey: A latent class analysis

BackgroundAfter the Syrian civil war, millions of Syrian refugees migrated to neighboring countries, with the majority settling in Turkey. The prevalence of mental disorders is notably higher among refugee populations. However, prior research on Syrian refugees have mostly used variable-centred approaches which have known limitations. Thus, the present study sought to examine the occurrence and co-occurrence of traumatic experiences among Syrian refugees living in Turkey using latent class analysis (LCA). Additionally, this study aimed to identify the associations between latent class membership and trauma-related psychopathology. MethodsParticipants included 593 Syrian refugees living in Turkey. LCA was used to identify latent classes of trauma exposure. Demographic predictors of the latent classes were examined and associations with PTSD, CPTSD, depression and anxiety were also explored. ResultsThree latent classes were identified based on trauma exposure; a “multiple traumas” class, “war and human suffering” class, and “low exposure, combat-exposed” class. Odds of PTSD, CPTSD, depression, or anxiety diagnosis did not differ significantly between classes, with the exception of the “multiple traumas” class who were more likely to meet the criteria for anxiety diagnosis compared to the “low exposure” class. LimitationsHistoric psychiatric symptoms or post-migration stressors could not be accounted for in this study, which may explain the lack of significant differences in most mental health outcomes by trauma exposure class. DiscussionThe latent classes identified in this study correspond with previous research regarding trauma in Syrian refugees. However, this study largely failed to find significant differences in mental health diagnoses between classes. Future research should consider the effect of post-migration stressors in refugee populations, which may play a crucial role in mental health outcomes.

CMR Predictors of Favorable Outcome in Myocarditis: A Single-Center Experience.

Background: Cardiovascular magnetic resonance (CMR) has emerged as the most accurate, non-invasive method to support the diagnosis of clinically suspected myocarditis and as a risk-stratification tool in patients with cardiomyopathies. We aim to assess the diagnostic and prognostic role of CMR at diagnosis in patients with myocarditis. Methods: We enrolled consecutive single-center patients with 2013 ESC consensus-based endomyocardial biopsy (EMB)-proven or clinically suspected myocarditis undergoing CMR at diagnosis. The pre-specified outcome was defined as NYHA class > I and echocardiographic left ventricular ejection fraction (LVEF) < 50% at follow-up. Results: We included 207 patients (74% male, median age 36 years; 25% EMB-proven). CMR showed the highest sensitivity in myocarditis with infarct-like presentation. Patients with EMB-proven myocarditis were more likely to have diffuse LGE and right ventricular LGE (p < 0.001), which was also more common among patients with arrhythmic presentation (p = 0.001). The outcome was met in 17 patients at any follow-up time point, more commonly in those with larger biventricular volumes (p < 0.001), CMR-based diagnosis of dilated cardiomyopathy (p < 0.001), and ischemic LGE (p = 0.005). Higher biventricular systolic function (p < 0.001) and greater LGE extent (p = 0.033) at diagnosis had a protective effect. Conclusions: In our single-center cohort of rigorously defined myocarditis patients, higher biventricular systolic function and greater LGE extent on CMR at diagnosis identified patients with better functional class and higher left ventricular ejection fraction at follow-up. Conversely, larger biventricular volumes, CMR-based DCM features, and the presence of an ischemic LGE pattern at diagnosis were predictors of worse functional class and LV systolic dysfunction at follow-up. Larger prospective studies are warranted to extend our findings to multi-center cohorts.

Insomnia is more likely to persist than remit after a time of stress and uncertainty: a longitudinal cohort study examining trajectories and predictors of insomnia symptoms.

The study aimed to characterize insomnia symptom trajectories over 12 months during a time of stress and uncertainty, the coronavirus disease 2019 (COVID-19) pandemic. It also aimed to investigate sleep and psychological predictors of persistent insomnia symptoms. This longitudinal cohort study comprised 2069 participants with and without insomnia symptoms during the first year of the pandemic. Participants completed online surveys investigating sleep, insomnia, and mental health at four timepoints over 12 months (April 2020-May 2021). Additional trait-level cognitive/psychological questionnaires were administered at 3 months only. Six distinct classes of insomnia symptoms emerged: (1) severe persistent insomnia symptoms (21.65%), (2) moderate persistent insomnia symptoms (32.62%), (3) persistent good sleep (32.82%), (4) severe insomnia symptoms at baseline but remitting over time (2.27%), (5) moderate insomnia symptoms at baseline but remitting over time (7.78%), and (6) good sleep at baseline but deteriorating into insomnia symptoms over time (2.85%). Persistent insomnia trajectories were predicted by high levels of sleep reactivity, sleep effort, pre-sleep cognitive arousal, and depressive symptoms at baseline. A combination of high sleep reactivity and sleep effort reduced the odds of insomnia remitting. Higher sleep reactivity also predicted the deterioration of good sleep into insomnia symptoms over 12 months. Lastly, intolerance of uncertainty emerged as the only trait-level cognitive/psychological predictor of insomnia trajectory classes. Insomnia was more likely to persist than remit over the first year of the COVID-19 pandemic. Addressing sleep reactivity and sleep effort appears critical for reducing insomnia persistence rates after times of stress and uncertainty.

Latent class of depressive symptoms of and its determinants: A cross-sectional study among Iranian University students.

According to the report of the World Health Organization, mental disorders are one of the 10 most important causes of disability in the world. This study was conducted with the aim of determining the number and frequency of latent classes of depression and its determinants in Isfahan university of medical students. A total of 1408 medical students from Isfahan University of Medical Sciences, Iran, were enrolled in the study in 2017. The symptoms and severity of depression were assessed using the standard Hospital Anxiety and Depression scale questionnaire. Latent class analysis was applied to seven symptoms of depression, all of which had four levels. Latent class subgroups were compared using the Chi-square test and analysis of variance test. The regression model was used to check the relationship between identified classes and related factors. Analyzes were done using SPSS-21 and Mplus7 software. In this study, three latent classes were identified, that is, the group of healthy people, the group of borderline people, and the group of people suspected of depression. The prevalence of identified latent classes among medical students is 0.52, 0.32, and 0.16%, respectively. The regression results showed that compared to the healthy group, the factors affecting depression in the borderline and suspicious group were increasing age, female gender, interest in the field of study, physical activity, history of depression, and history of anxiety. The three classes that were identified based on the students' answers to the depression symptoms questions differed only based on severity. The history of depression and anxiety were the strongest predictors of latent classes of depression.