Discriminatory Capacity Research Articles

Overall prognosis of index lung cancer recurrence or of second primary lung cancer in people with non-small cell lung cancer operated with complete resection.

This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: We aim to compare overall survival in people with recurrence and second primary lung cancer (SPLC) after lung cancer surgery. If survival differs between those people categorised as having index lung cancer recurrence and those categorised as having SPLC, it might be possible to identify the definition that has the best discriminatory capacity from the various published definitions of these conditions, so that it can be used in future.

A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.

A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.

Dual Contrastive Learning Network for Graph Clustering.

Graph representation is an important part of graph clustering. Recently, contrastive learning, which maximizes the mutual information between augmented graph views that share the same semantics, has become a popular and powerful paradigm for graph representation. However, in the process of patch contrasting, existing literature tends to learn all features into similar variables, i.e., representation collapse, leading to less discriminative graph representations. To tackle this problem, we propose a novel self-supervised learning method called dual contrastive learning network (DCLN), which aims to reduce the redundant information of learned latent variables in a dual manner. Specifically, the dual curriculum contrastive module (DCCM) is proposed, which approximates the node similarity matrix and feature similarity matrix to a high-order adjacency matrix and an identity matrix, respectively. By doing this, the informative information in high-order neighbors could be well collected and preserved while the irrelevant redundant features among representations could be eliminated, hence improving the discriminative capacity of the graph representation. Moreover, to alleviate the problem of sample imbalance during the contrastive process, we design a curriculum learning strategy, which enables the network to simultaneously learn reliable information from two levels. Extensive experiments on six benchmark datasets have demonstrated the effectiveness and superiority of the proposed algorithm compared with state-of-the-art methods.

Common ground on immune infiltration landscape and diagnostic biomarkers in diabetes-complicated atherosclerosis: an integrated bioinformatics analysis.

Type 2 diabetes mellitus (T2DM) is a major cause of atherosclerosis (AS). However, definitive evidence regarding the common molecular mechanisms underlying these two diseases are lacking. This study aimed to investigate the mechanisms underlying the association between T2DM and AS. The gene expression profiles of T2DM (GSE159984) and AS (GSE100927) were obtained from the Gene Expression Omnibus, after which overlapping differentially expressed gene identification, bioinformatics enrichment analyses, protein-protein interaction network construction, and core genes identification were performed. We confirmed the discriminatory capacity of core genes using receiver operating curve analysis. We further identified transcription factors using TRRUST database to build a transcription factor-mRNA regulatory network. Finally, the immune infiltration and the correlation between core genes and differential infiltrating immune cells were analyzed. A total of 27 overlapping differentially expressed genes were identified under the two-stress conditions. Functional analyses revealed that immune responses and transcriptional regulation may be involved in the potential pathogenesis. After protein-protein interaction network deconstruction, external datasets, and qRT-PCR experimental validation, four core genes (IL1B, C1QA, CCR5, and MSR1) were identified. ROC analysis further showed the reliable value of these core genes. Four common differential infiltrating immune cells (B cells, CD4+ T cells, regulatory T cells, and M2 macrophages) between T2DM and AS datasets were selected based on immune cell infiltration. A significant correlation between core genes and common differential immune cells. Additionally, five transcription factors (RELA, NFκB1, JUN, YY1, and SPI1) regulating the transcription of core genes were mined using upstream gene regulator analysis. In this study, common target genes and co-immune infiltration landscapes were identified between T2DM and AS. The relationship among five transcription factors, four core genes, and four immune cells profiles may be crucial to understanding T2DM complicated with AS pathogenesis and therapeutic direction.

Efficacy of the PEN-FAST score in a French cohort of patients with reported allergy to penicillins.

Various clinical decision-making tools for penicillin allergy have been developed to guide delabeling strategies. To evaluate the penicillin allergy PEN-FAST decision score in a retrospective cohort of patients, adults and children, with penicillin-reported allergy. This monocentric retrospective cohort included patients with penicillin-reported allergy. All patients underwent penicillin allergy testing using skin tests and/or drug challenge. The PEN-FAST score sensitivity, specificity, negative (NPV) and positive (PPV) predictive values, and the area under the receiver operating characteristics curve (AUC) were calculated. Two hundred and fourteen patients were included (64 children and 150 adults). Allergy was confirmed in 52 cases (24%). A PEN-FAST score <3 points showed a poor discrimination capacity for the whole population (AUC = 0.66; 95% CI: 0.58-0.75), while it demonstrated a better discrimination capacity in the adults group (AUC = 0.71; 95% CI: 0.63-0.80). The sensitivity to identify penicillin allergy using this cutoff of less than 3 points was 0.67 (95% CI: 0.52-0.80); specificity, 0.58 (95% CI: 0.48-0.68); PPV, 0.43 (95% CI: 0.32-0.55); and NPV, 0.78 (95% CI: 0.68-0.87). Although our data confirm a rather good discrimination value of a PEN-FAST score <3 points, its low negative predictive value (78%) did not advocate for its use as an accurate, simple and cost-effective clinical decision-making tool to effectively reduce the number of penicillin skin tests required before direct oral challenge. Further studies are required to improve the predictive capacity of the PEN-FAST score.

Critical thresholds of long-pressure reactivity index and impact of intracranial pressure monitoring methods in traumatic brain injury

BackgroundModerate-to-severe traumatic brain injury (TBI) has a global mortality rate of about 30%, resulting in acquired life-long disabilities in many survivors. To potentially improve outcomes in this TBI population, the management of secondary injuries, particularly the failure of cerebrovascular reactivity (assessed via the pressure reactivity index; PRx, a correlation between intracranial pressure (ICP) and mean arterial blood pressure (MAP)), has gained interest in the field. However, derivation of PRx requires high-resolution data and expensive technological solutions, as calculations use a short time-window, which has resulted in it being used in only a handful of centers worldwide. As a solution to this, low resolution (longer time-windows) PRx has been suggested, known as Long-PRx or LPRx. Though LPRx has been proposed little is known about the best methodology to derive this measure, with different thresholds and time-windows proposed. Furthermore, the impact of ICP monitoring on cerebrovascular reactivity measures is poorly understood. Hence, this observational study establishes critical thresholds of LPRx associated with long-term functional outcome, comparing different time-windows for calculating LPRx as well as evaluating LPRx determined through external ventricular drains (EVD) vs intraparenchymal pressure device (IPD) ICP monitoring.MethodsThe study included a total of n = 435 TBI patients from the Karolinska University Hospital. Patients were dichotomized into alive vs. dead and favorable vs. unfavorable outcomes based on 1-year Glasgow Outcome Scale (GOS). Pearson’s chi-square values were computed for incrementally increasing LPRx or ICP thresholds against outcome. The thresholds that generated the greatest chi-squared value for each LPRx or ICP parameter had the highest outcome discriminatory capacity. This methodology was also completed for the segmentation of the population based on EVD, IPD, and time of data recorded in hospital stay.ResultsLPRx calculated with 10–120-min windows behaved similarly, with maximal chi-square values ranging at around a LPRx of 0.25–0.35, for both survival and favorable outcome. When investigating the temporal relations of LPRx derived thresholds, the first 4 days appeared to be the most associated with outcomes. The segmentation of the data based on intracranial monitoring found limited differences between EVD and IPD, with similar LPRx values around 0.3.ConclusionOur work suggests that the underlying prognostic factors causing impairment in cerebrovascular reactivity can, to some degree, be detected using lower resolution PRx metrics (similar found thresholding values) with LPRx found clinically using as low as 10 min-by-minute samples of MAP and ICP. Furthermore, EVD derived LPRx with intermittent cerebrospinal fluid draining, seems to present similar outcome capacity as IPD. This low-resolution low sample LPRx method appears to be an adequate substitute for the clinical prognostic value of PRx and may be implemented independent of ICP monitoring method when PRx is not feasible, though further research is warranted.

Using item response theory to identify key symptoms of insomnia in a sample of university students with probable eating disorders

PurposeResearchers have theorized that interactions between appetitive and circadian disruptions result in increased eating disorder (ED) symptoms and insomnia. However, it is unclear how specific insomnia symptoms present among people with EDs and if the latent structure of insomnia in this population is similar to that of people with insomnia disorder.MethodsWe conducted a secondary analysis of data collected on ED and insomnia symptoms using a subset of students (N = 547; 79.52% female) with probable EDs at a large Midwestern American university. Item response theory (IRT) for polytomous items was performed to identify item difficulty, discrimination, and information parameters for the Insomnia Severity Index (ISI). IRT parameters were compared to those established in a 2011 study of people diagnosed with insomnia disorder by Morin and colleagues.ResultsClinically significant insomnia symptoms were common among students with ED pathology and symptom endorsement for each ISI item ranged from 40.77 to 86.65%. ISI items assessing insomnia-related impairment and distress showed better discriminative capacities and had higher item information than items assessing sleep behavior alterations (i.e., difficulties falling asleep, difficulties maintaining sleep, waking too early). Item discrimination was largely similar among the ED sample compared to previous IRT analyses in an insomnia disorder sample.ConclusionInsomnia symptoms are common among university students with probable EDs and similar to those reported by people with insomnia disorder. When considering insomnia assessment, items assessing sleep behaviors alone are likely inadequate to provide information about insomnia severity among people with EDs.Level VEvidence obtained from a cross-sectional descriptive study.

Urine Output and Acute Kidney Injury in Neonates/Younger Children: A Prospective Study of Cardiac Surgery Patients with Indwelling Urinary Catheters.

Pediatric acute kidney injury (AKI) is associated with significant morbidity and mortality, yet a precise definition, especially concerning urine output (UO) thresholds, remains unproven. We evaluate UO thresholds for AKI in neonates and children aged 1-24 months with indwelling urinary catheters undergoing cardiac surgery. A six-year prospective cohort study (2018-2023) after cardiac surgery was conducted at a reference center in Brazil. All patients had indwelling urinary catheters up to 48 hours after surgery and at least two serum creatinine (sCr) measurements, including one before surgery. The main objective of this study was to determine the optimal UO thresholds for AKI definition and staging in neonates and younger children compared with the currently used criteria-neonatal and adult Kidney Disease Improving Global Outcomes (KDIGO) definitions. The outcome was a composite of severe AKI (stage 3 AKI diagnosed by the sCr criterion only), kidney replacement therapy, or hospital mortality. The study included 1,024 patients: 253 in the neonatal group and 772 in the younger children group. In both groups, the lowest UO at 24 hours as a continuous variable had good discriminatory capacity for the composite outcome (AUC-ROC 0.75 [95% CI 0.70-0.81] and 0.74 [95% CI 0.68-0.79]). In neonates, the best thresholds were 3.0, 2.0 and 1.0 mL/kg/hour, and in younger children, the thresholds were 1.8, 1.0 and 0.5 mL/kg/hour. These values were used for modified AKI staging for each age group. In neonates, this modified criterion was associated with the best discriminatory capacity (AUC-ROC 0.74 [0.67-0.80] vs. 0.68 [0.61-0.75], P<0.05) and net reclassification improvement (NRI) in comparison with the neonatal KDIGO criteria. In younger children, the modified criteria had good discriminatory capacity but were comparable to the adult KDIGO criteria, and the NRI was near zero. Using indwelling catheters for UO measurements, our study reinforced that the current KDIGO criteria may require adjustments to better serve the neonate population. Additionally, using the UO criteria, we validated the adult KDIGO criteria in children aged 1-24 months.

Genetic characterisation of lymphogranuloma venereum in Spain: a multicentre study

IntroductionLymphogranuloma venereum (LGV) is a sexually transmitted infection caused by Chlamydia trachomatis genotypes L1–L3. A combination of techniques with high discriminatory capacity such as multilocus sequence typing (MLST) and the...

Association Between Different Inflammatory Markers and Generalized Abdominal Obesity: A Cross-Sectional Study.

Inflammation is strongly correlated with obesity. However, very few studies have reported associations between novel inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), C-reactive protein (CRP), and C-reactive protein-to-albumin ratio (CAR), and different obesity types. Therefore, this study aimed to explore the associations of these inflammatory markers with generalized and abdominal obesity. This cross-sectional study included data from 2015 to 2018 obtained from the National Health and Nutrition Examination Survey. Multivariate regression analysis was performed to determine the associations between different inflammatory biomarkers and obesity. The discriminative capacities of the markers for obesity types were depicted using receiver operating characteristic (ROC) curves, with corresponding area under the curve (AUC) metrics quantifying this discrimination. After adjusting for confounding variables, generalized obesity was found to be positively associated with an increased risk of NLR by 35%, SII by 52%, CRP by 941%, and CAR by 925%, compared with the reference groups. In the model, the CRP concentration and CAR demonstrated high AUC values of 0.690 and 0.889, respectively, for the identification of generalized and abdominal obesity (P < 0.05). This study revealed associations between obesity and inflammatory biomarkers, such as the NLR, SII, CRP, and CAR. CRP is the most sensitive marker for generalized obesity, while CAR shows the strongest association with abdominal obesity. These findings suggest that inflammatory biomarkers may be useful for assessing and managing obesity-related health concerns.

Decisional value scores: A new family of metrics for ethical AI-ML

AbstractResearch in ethical AI has made strides in quantitative expression of ethical values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family of metrics called “decisional value scores” (DVS). DVSs are scores assigned to a system based on whether the decisions it makes meet or fail to meet a particular standard (either individually, in total, or as a ratio or average over decisions made). Advantages of DVS include greater discrimination capacity between types of ethically relevant decisions and facilitation of ethical comparisons between decisions and decision-making systems, including across different modalities (for instance: human, machine, or coupled human–machine systems). After clarifying ambiguities in the concept of “decision” itself, including the question of how to individuate the decisions made by a system, we discuss the role and meaning of “decision” in common AI and machine learning approaches such as decision trees, neural networks, SVMs, and unsupervised classifiers. We then show how DVSs may be defined for several ethical values of interest, with an extended discussion of transparency. Finally, we explore how such metrics can be applied to real decision-making systems through two case studies: evaluations of LLMs for transparency; and evaluations of criminal risk assessment tools for utility, rights violations, fairness, and transparency.

External validation and comparison of six cardiovascular risk prediction models in the Prospective Urban Rural Epidemiology (PURE)-Colombia study.

To external validate the SCORE2, AHA/ACC Pooled Cohort Equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity. Validation in individuals aged 40-69 years with at least 10 years follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology prospective cohort study (PURE)-Colombia. For discrimination, the C-statistic, and Receiver Operating Characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used. In 3,802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher overprediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher overprediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI. Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.

Exploring holistic discriminative representation for micro-expression recognition via contrastive learning

Recently, deep learning-based micro-expression recognition (MER) has been remarkably successful in the affective computing and computer vision communities. However, the most challenging issue that hinders the performance of MER is low intensity. Instead of forcefully transforming the input from micro-expressions to exaggerated micro-expressions by a fixed video motion magnification factor, our approach introduces a sophisticated pretext task with an intensity-agnostic strategy to enhance the discriminative capacity of each micro-expression sample holistically through contrastive transfer learning. This strategy enables us to progressively transfer knowledge and leverage the rich facial expression information from macro-expression samples. In addition, we reconsider that the core of the MER task is to refine and incorporate the instance-level and class-level discriminative features from the initial indistinguishable information. As a result, we jointly merge the two views to learn a holistic-level representation. Simultaneously, to ensure a strong association and guidance between the instance-level view and the class-level view, we maintain their consistency through an alignment loss. The results showed that the proposed method could significantly improve the performance of MER on CASME II, SAMM, SMIC, and CAS(ME)3 datasets.

Development and validation of pFIB scores for exclusion of significant liver fibrosis in pediatric MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent pediatric liver disease, yet accurate risk scores for referral of children/adolescents with suspected clinically significant liver fibrosis are currently lacking. Clinical and biochemical variables were collected in a prospective cohort of 327 children and adolescents with severe obesity, in whom liver fibrosis was evaluated by transient elastography. Logistic regression was performed to establish continuous (pFIB-c) and simplified (pFIB-6) diagnostic scores that accurately exclude significant (≥F2) fibrosis. Performance for each was compared to established non-invasive fibrosis scores. These scores were validated in elastography (n=504) and multiple biopsy-proven MASLD (n=261) cohorts. Patient sex, ethnicity, weight z-score, HOMA-IR index, ALT, and presence of hypertension were included in the scores. The pFIB-c and pFIB-6 exhibited good discriminatory capacity (c-statistic of 0.839 and 0.826), outperforming existing indices. Negative predictive values (NPV) were >90% for both scores in the derivation and elastography validation cohorts. Performance in the histological cohorts varied (AUROCs for the pFIB-c between 0.710 and 0.770), as the scores were less accurate when applied to populations in tertiary referral centers characterized by a high prevalence of significant fibrosis and high ALT levels. Analyzing several cohorts totaling approximately 1100 children and adolescents, we developed novel risk scores incorporating readily available clinical variables. In accordance with the aim of excluding pediatric MASLD-associated fibrosis, the scores performed better in non-selected cohorts of children and adolescents living with obesity than in patients referred to tertiary liver units.

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.

This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach. We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC). Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set. Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.

Simple Hip Value: a simple score to evaluate Hip function

BackgroundSeveral self-administered questionnaires are available for assessing hip function in clinical practice (HOOS-12, Oxford-12). These questionnaires can be used to assess and monitor patients with hip pathology. However, they are sometimes difficult to deploy in clinical practice. Recent studies on the shoulder and knee have shown that a single-question assessment provides a relevant evaluation of joint function, and correlates with more complex scores. However, this has not yet been evaluated for the hip. We set out to develop a single-question test that would enable patients to assess their hip function between 0 and 100. Therefore, we did a prospective study aiming to assess: (1) the relevance of the SHV (Simple Hip Value) test by comparing it with the Oxford-12 and HOOS-12 scores, (2) the discriminatory capacity and reproducibility of this test. HypothesisThe hypothesis of this study was that the assessment of hip function by a single-question patient-completed questionnaire (PROM) would be reliable and useful in clinical practice. Patients and methodsWe conducted a prospective study including 74 patients operated on for total hip arthroplasty from February 2020 to April 2021 in a high-volume center and 20 healthy subjects. The SHV questionnaire, as well as HOOS-12 and OXFORD-12 were submitted on average 18 months after surgery, with values scaled to 100 points. A second test containing the 3 questionnaires was resubmitted to 20 patients randomly chosen from hip arthroplasty group to evaluate the reproducibility of the test. To evaluate the discrimination capacity of our test, 20 controls were selected to respond to the three questionnaires. ResultsTest results were median SHV, Oxford-12 and HOOS-12 scores of 90/100 (Interquartile Range [IQR]: 80–95), 88.5 (IQR: 77.1–95.8) and 79.1 (IQR: 65.1–93.2), respectively. The SHV was strongly correlated with the Oxford-12 (Spearman coefficient: 0.63 [IQR: 0.45−0.76]) and the HOOS-12 (Spearman coefficient: 0.66 [IQR: 0.46−0.79]). The SHV differed significantly from the control group (90.0 vs 100.0 in the control group [p < 0.001]) and had excellent reproducibility (Interclass Correlation Coefficient [ICC]: ICC: 0.82 [CI95%, 0.59−0.93]). DiscussionThe SHV test seems to be a reliable, reproducible and discriminating tool for assessing hip function after total hip arthroplasty, thus offering a simplified and practical approach for practitioners. Further testing in different populations would be useful to validate this test. Level of evidenceIII; prospective comparative diagnostic study.

Discriminative capacity of the Spanish version of the Inventory of Depression and Anxiety Symptoms-II (IDAS-II) for detecting DMS-5 specific disorders and poor quality of life in a clinical sample

BackgroundEmotional problems can be evaluated using categorical approaches to guide treatment choices focused on targeting specific disorders, or dimensional approaches to reduce symptom severity. Moreover, recent evidence points out the need to intervene in patients’ quality of life (QoL), which often remains low even after the remission of emotional problems. Thus, assessment instruments are needed to provide information on diagnosis, symptom severity, and QoL. The present study aimed to provide diagnostic and QoL cutoffs for the Inventory of Depression and Anxiety Symptoms-II (IDAS-II).Methods273 patients recruited from mental health services in Huelva (Spain) completed the IDAS-II, Mini International Neuropsychiatric Interview, and Short Form-36 Health Survey. Receiver operating characteristic curve analyses were used to establish cutoff values. Diagnostic, balanced, and screening cutoffs were provided for each IDAS-II scale to detect corresponding diagnoses and poor QoL.ResultsThe specific IDAS-II scales Suicidality, Panic, Social Anxiety, Claustrophobia, and Traumatic Intrusions showed adequate discrimination values for their corresponding diagnoses (suicidal behavior disorder, panic disorder, social anxiety disorder, agoraphobia, and post-traumatic stress disorder, respectively). Both the General Depression and Dysphoria scales showed adequate ability to detect major depressive disorder. The IDAS-II scales showed a higher discrimination ability for Mental Health-related QoL, than for General Health-related QoL.ConclusionsThe diagnostic and QoL cutoffs expand the clinical utility of the IDAS-II in clinical practice and research, making it a comprehensive, detailed, and versatile self-report tool. The IDAS-II allows for the assessment of emotional problems consistent with the dimensional, categorical, transdiagnostic, and QoL approaches.

Patient Reported Outcome Measures in Adults with Fontan Circulatory Failure.

Background: Patient reported outcomes (PROs) are important measures in acquired heart disease but have not been well defined in Adult Congenital Heart Disease (ACHD). Our aim was to explore the discriminatory capacity of PRO survey tools in Fontan circulatory failure (FCF). Methods: Consecutive adults were enrolled from our ambulatory clinics. Inclusion criteria were age ≥18 years, a Fontan circulation or a hemodynamically insignificant shunt lesion, and sufficient cognitive/language abilities to complete PROs. A comprehensive package of PRO measures, designed to assess perceived health-related quality of life (HRQOL) was administered (including the Kansas City Cardiomyopathy Questionnaire [KCCQ-12], EuroQol-5-dimension [EQ5D], Short Form Health Status Survey [SF-12], self-reported New York Heart Association [NYHA] Functional Class, and Specific Activity Scale [SAS]). Results: We compared 54 Fontan patients (35 ± 10 years) to 25 simple shunt lesion patients (34 ± 11 years). The KCCQ-12 score was lower in Fontan versus shunt lesion patients (87 [IQR 79, 95] versus 100 [IQR 97, 100], p-value < 0.001). The FCF subgroup was associated with lower KCCQ-12 scores as compared with the non-FCF subgroup (82 [IQR 56, 89] versus 93 [IQR 81, 98], p-value = 0.002). Although the KCCQ-12 had the best discriminatory capacity for determination of FCF of all PRO tools studied (c-statistic 0.75 [CI 0.62, 0.88]), superior FCF discrimination was achieved when the KCCQ-12 was combined with all PRO tools (c-statistic 0.82 [CI 0.71, 0.93]). Conclusions: The KCCQ-12 questionnaire demonstrated good discriminatory capacity for the identification of FCF, which was further improved through the addition of complementary PRO tools. Further research will establish the value of PRO tools to guide management strategies in ACHD.

Association of Preoperative Copeptin Levels with Risk of All-Cause Mortality in a Prospective Cohort of Adult Cardiac Surgery Patients.

We aimed to investigate the association of preoperative copeptin, a new cardiovascular biomarker, with short- and long-term mortality in a cohort of adult patients undergoing cardiac surgery, including its potential as a prognostic marker for clinical outcome. Preoperative blood samples of the Bern Perioperative Biobank, a prospective cohort of adults undergoing cardiac surgery during 2019, were analyzed. The primary and secondary outcome measures were 30-day and 1-year all-cause mortality. Optimal copeptin thresholds were calculated with the Youden Index. Associations of copeptin levels with the two outcomes were examined with multivariable logistic regression models; their discriminatory capacity was assessed with the area under the receiver operating characteristic (AUROC). A total of 519 patients (78.4% male, median age 67 y (IQR: 60-73 y)) were included, with a median preoperative copeptin level of 7.6 pmol/L (IQR: 4.7-13.2 pmol/L). We identified an optimal threshold of 15.9 pmol/l (95%-CI: 7.7 to 46.5 pmol/L) for 30-day mortality and 15.9 pmol/L (95%-CI: 9.0 to 21.3 pmol/L) for 1-year all-cause mortality. Regression models featured an AUROC of 0.79 (95%-CI: 0.56 to 0.95) for adjusted log-transformed preoperative copeptin for 30-day mortality and an AUROC of 0.76 (95%-CI: 0.64 to 0.88) for 1-year mortality. In patients undergoing cardiac surgery, the baseline levels of copeptin emerged as a strong marker for 1-year all-cause death. Preoperative copeptin levels might possibly identify patients at risk for a complicated, long-term postoperative course, and therefore requiring a more rigorous postoperative observation and follow-up.

Quantitative analysis of aeolian dust incidence and diagnosis using a dynamic Bayesian network model: A case study of estuary area in central Taiwan

The presence of high concentrations of PM10 in aeolian dust within the estuary area of central Taiwan has been a focal point for air quality protection and remediation efforts over the past 15 years. This study explores the quantitative analysis of aeolian dust occurrences concerning various levels of PM10 concentration, aiming to offer early warning information to local communities and government agencies. Employing Dynamic Bayesian network (DBN) models, the research interrelates conditional probability dependencies among air quality and meteorological factors monitored in the downstream area of the Jhuoshuei River, predicting the probability distribution of aeolian dust events. The constructed DBNs demonstrate the inference of probability distributions of meteorological factors' states based on PM10 concentrations, enabling diagnostic analysis of meteorological conditions during high PM10 concentration dust events. The DBN models exhibit robust predictive ability, achieving an overall accuracy of 85.72% in validation and 85.8% in testing, with high discriminatory capacity that the area under the receiver operating characteristic curve (AUC) values of 0.924 and 0.915 during validation and testing, respectively. Sensitivity analysis highlights wind direction, wind speed, relative humidity as significant factors affecting PM10 concentrations. The probability of low PM10 concentrations was notably higher compared to higher concentrations, indicating effective aeolian dust remediation efforts. However, attention is warranted toward the 10% incidence of aeolian dust events deemed unhealthy or hazardous to human health. Diagnosis of meteorological conditions during high PM10 concentration events suggests low temperature, high wind speeds, and dry conditions, particularly with northeastern or southwestern winds, as conducive factors. This study underscores the physical relationship between meteorological factors and PM10 concentrations, employing DBNs to consider temporal relationships and uncertainties in data, aligning with human health impact standards proposed by the Ministry of Environment. Future research should incorporate soil and land surface conditions to provide a more comprehensive understanding of aeolian dust dynamics and mitigation strategies.