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Psoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-related genes (ARGs) in psoriasis have not been systematically elucidated. Therefore, we aim to identify potential biomarkers and subtypes using two algorithmsr. Transcriptome sequencing data of patients with psoriasis were obtained, in which differentially expressed genes were assessed by principal component analysis. A diagnostic model was developed using random forest algorithm and validated by receiver operating characteristic (ROC) curves. Subsequently, we performed consensus clustering to calculate angiogenesis-associated molecular subtypes of psoriasis. Additionally, a correlation analysis was conducted between ARGs and immune cell infiltration. Finally, validation of potential ARG genes was performed by quantitative real-time PCR (qRT-PCR). We identified 29 differentially expressed ARGs, including 13 increased and 16 decreased. Ten ARGs, CXCL8, ANG, EGF, HTATIP2, ANGPTL4, TNFSF12, RHOB, PML, FOXO4, and EMCN were subsequently sifted by the diagnostic model based on a random forest algorithm. Analysis of the ROC curve (area under the curve [AUC] = 1.0) indicated high diagnostic performance in internal validation. The correlation analysis suggested that CXCL8 has a high positive correlation with neutrophil (R =0.8, P < 0.0001) and interleukins pathway (R = 0.79, P < 0.0001). Furthermore, two ARG-mediated subtypes were obtained, indicating potential heterogeneity. Finally, the qRT-PCR demonstrated that the mRNA expression levels of CXCL8 and ANGPTL4 were elevated in psoriasis patients, with a reduced expression of EMCN observed. The current paper indicated potential ARG-related biomarkers of psoriasis, including CXCL8, ANGPTL4, and EMCN, with two molecular subtypes., (© Crown copyright 2024.)

Background: Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) has a high short-term mortality. This study aimed to determine the diagnostic and prognostic role of MER tyrosine kinase (MERTK) in patients with HBV-ACLF., Methods: Transcriptomics analysis evaluated MERTK expression and function during disease progression. The diagnostic and prognostic significance of MERTK for patients with HBV-ACLF were verified by enzyme-linked immunosorbent assay, area under the receiver operating characteristic curve (AUROC) analysis, and immunohistochemistry (IHC) of liver tissues., Results: MERTK mRNA was highly expressed in patients with HBV-ACLF compared to those with liver cirrhosis (LC), chronic hepatitis B (CHB), and normal controls (NC). Elevated MERTK mRNA predicted poor prognosis for HBV-ACLF at 28 and 90 days (AUROC = 0.814 and 0.731, respectively). Functional analysis showed MERTK was significantly associated with toll-like receptor and inflammatory signaling and several key biological processes. External validation with 285 plasma subjects confirmed the high diagnostic accuracy of plasma MERTK for HBV-ACLF (AUROC = 0.859) and potential prognostic value for 28- and 90-day mortality rates (AUROC = 0.673 and 0.644, respectively). Risk stratification analysis indicated higher mortality risk for patients with plasma MERTK level above the cutoff value. Moreover, IHC staining showed increasing MERTK expression from NC, CHB, and LC to HBV-ACLF., Conclusions: MERTK shows promise as a candidate biomarker for early diagnosis and prognosis of HBV-ACLF., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection., Methods: The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods., Results: Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models., Conclusions: This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing., (© Association for Diagnostics & Laboratory Medicine 2024.)

Objectives: PsA is the most prevalent coexisting condition associated with psoriasis. Early-stage PsA patients always present unspecific and subtle clinical manifestations causing delayed diagnosis and leading to unfavourable health outcomes. The application of US enables precise identification of inflammatory changes in musculoskeletal structures. Hence, we constructed US models to aid early diagnosis of PsA., Methods: This was a cross-sectional study carried out in the Department of Dermatology at West China Hospital (October 2018-April 2021). All participants underwent thorough US examinations. Participants were classified into the under 45 group (18 ≤ age ≤ 45 years) and over 45 (age >45 years) group and then randomly grouped into derivation and test cohort (7:3). Univariable logistic regression, least absolute shrinkage and selection operator, and multivariable logistic regression visualized by nomogram were conducted in order. Receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were performed for model verification., Results: A total of 1256 participants were included, with 767 participants in the under 45 group and 489 in the over 45 group. Eleven and 16 independent ultrasonic variables were finally selected to construct the under 45 and over 45 model with the area under the ROC of 0.83 (95% CI 0.78-0.87) and 0.83 (95% CI 0.78-0.88) in derivation cohort, respectively. The DCA and CICA analyses showed good clinical utility of the two models., Conclusion: The implementation of the US models could streamline the diagnostic process for PsA in psoriasis patients, leading to expedited evaluations while maintaining diagnostic accuracy., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: To evaluate different cardiovascular magnetic resonance (CMR) parameters for the differentiation of light chain amyloidosis (AL) and transthyretin-related amyloidosis (ATTR)., Methods and Results: In total, 75 patients, 53 with cardiac amyloidosis {20 patients with AL [66 ± 12 years, 14 males (70%)] and 33 patients with ATTR [78 ± 5 years, 28 males (88%)]} were retrospectively analysed regarding CMR parameters such as T1 and T2 mapping, extracellular volume (ECV), late gadolinium enhancement (LGE) distribution patterns, and myocardial strain, and compared to a control cohort with other causes of left ventricular hypertrophy {LVH; 22 patients [53 ± 16 years, 17 males (85%)]}. One-way ANOVA and receiver operating characteristic analysis were used for statistical analysis. ECV was the single best parameter to differentiate between cardiac amyloidosis and controls [area under the curve (AUC): 0.97, 95% confidence intervals (CI): 0.89-0.99, P < 0.0001, cut-off: >30%]. T2 mapping was the best single parameter to differentiate between AL and ATTR amyloidosis (AL: 63 ± 4 ms, ATTR: 58 ± 2 ms, P < 0.001, AUC: 0.86, 95% CI: 0.74-0.94, cut-off: >61 ms). Subendocardial LGE was predominantly observed in AL patients (10/20 [50%] vs. 5/33 [15%]; P = 0.002). Transmural LGE was predominantly observed in ATTR patients (23/33 [70%] vs. 2/20 [10%]; P < 0.001). The diagnostic performance of T2 mapping to differentiate between AL and ATTR amyloidosis was further increased with the inclusion of LGE patterns [AUC: 0.96, 95% CI: (0.86-0.99); P = 0.05]., Conclusion: ECV differentiates cardiac amyloidosis from other causes of LVH. T2 mapping combined with LGE differentiates AL from ATTR amyloidosis with high accuracy on a patient level., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights., (© The Author(s) 2024. Published by Oxford University Press.)

Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventions. We developed a predictive model integrating genetic, environmental, and clinical factors using data from 7819 IS patients in the Third China National Stroke Registry. Employing an 80:20 split, we randomly divided the dataset into development and internal validation cohorts. The discrimination and calibration performance of models were evaluated using the area under the receiver operating characteristic curves (AUC) for discrimination and Brier score with calibration curve in the internal validation cohort. We conducted genome-wide association studies (GWAS) in the development cohort, identifying rs11109607 (ANKS1B) as the most significant variant associated with IS functional outcome. We employed principal component analysis to reduce dimensionality on the top 100 significant variants identified by the GWAS, incorporating them as genetic factors in the predictive model. We employed a machine learning algorithm capable of identifying nonlinear relationships to establish predictive models for IS patient functional outcome. The optimal model was the XGBoost model, which outperformed the logistic regression model (AUC 0.818 versus 0.756, P < .05) and significantly improved reclassification efficiency. Our study innovatively incorporated genetic, environmental, and clinical factors for predicting the IS functional outcome in East Asian populations, thereby offering novel insights into IS functional outcome., (© The Author(s) 2024. Published by Oxford University Press.)

We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions., (© The Author(s) 2024. Published by Oxford University Press.)

Objective: To assess chemokine receptor CXCR4 expression in lung parenchyma and on peripheral immune cells in systemic sclerosis-related interstitial lung disease (SSc-ILD) patients., Methods: SSc-ILD patients underwent 68Ga- CPCR4 Trifluoroacetate positron emission tomography (PET) scan, SUVmean in different lung regions and architecturally abnormal areas, and receiver operating characteristic (ROC) curves were analyzed. CXCR4 expression on peripheral immune cells using flow cytometer was studied and correlated with the different lung regions. In addition, subset analysis of CXCR4 expression by clinical subset (early, progressive, stable), ILD pattern and anti-Scl-70 positivity were done., Results: On PET, SSc-ILD patients showed higher median SUVmean uptake of CXCR4 in the whole lung (0.56; p< 0.0001), different lung regions and architecturally abnormal areas than controls. Highest area under curve (AUC) were observed in dorsobasal regions (AUC-0.91; p< 0.0001) and reticular with architecturally distorted areas (AUC-0.95; p< 0.0001). Progressive subset had higher whole lung median SUVmean (0.73) than early (0.49; p< 0.0001) and stable (0.45; p< 0.0001) subsets, and AUC than early and stable subsets. Usual interstitial pneumonia pattern ILD showed higher CXCR4 uptake compared with non-specific interstitial pneumonia (p= 0.0032). Additionally, a trend for higher uptake was noted in anti-Scl70 positive patients as compared with anti-Scl70 negative ones. SSc-ILD patients had higher CD4+CXCR4+T cells (p= 0.0003), and CD8+CXCR4+T cells (p= 0.0013), and showed moderate to strong association on correlation with the lung parenchymal regions., Conclusion: In SSc-ILD, CXCR4 expression is upregulated in both lung parenchyma and peripheral T cells, significantly in progressive and UIP subsets. CXCR4 expression is a potential tool for activity assessment and prognostication., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The immunopathological mechanisms underlying neurosyphilis remain incompletely elucidated, and the diagnosis of neurosyphilis presents challenges., Methods: We used an antibody microarray to detect 640 proteins in cerebrospinal fluid (CSF) samples collected from 6 patients with non-neurosyphilis and 10 with neurosyphilis. The levels of CSF CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9 in 46 patients with non-neurosyphilis, 51 with untreated neurosyphilis, and 31 posttreatment for neurosyphilis were quantified using enzyme-linked immunosorbent assay. The associations between the levels of these proteins and clinical parameters in neurosyphilis were evaluated using Spearman analysis, and the diagnostic performance of these proteins in neurosyphilis was assessed using receiver operating characteristic curve., Results: A total of 102 differentially expressed proteins between neurosyphilis and non-neurosyphilis were identified. The levels of significantly elevated neutrophil-associated proteins (CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9) in neurosyphilis positively correlated with white blood cell counts, rapid plasma regain (RPR) titer, and protein concentration in CSF. The combination of CSF CXCL8, MMP9, and LCN2 yielded an area under the curve of 0.92 for diagnosing neurosyphilis, surpassing that of CSF RPR., Conclusions: CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9 could be associated with central nervous system damage of neurosyphilis. The combination of CSF CXCL8, MMP9, and LCN2 is a promising biomarker for diagnosing neurosyphilis., Competing Interests: Potential conflicts of interests. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel risk score that is independent of subjective clinical judgment and can be used early, at the time of blood culture positivity., Methods: We conducted a retrospective big data analysis from territory-wide electronic data and included hospitalized patients with SAB between 2009 and 2019. We applied a random forest risk scoring model to select variables from an array of parameters, according to the statistical importance in predicting SA-IE outcome. The data were divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROCs) were determined., Results: We identified 15 741 SAB patients, among them 658 (4.18%) had SA-IE. The AUCROC was 0.74 (95%CI 0.70-0.76), with a negative predictive value of 0.980 (95%CI 0.977-0.983). The four most discriminatory features were age, history of infective endocarditis, valvular heart disease, and community onset., Conclusions: We developed a novel risk score with performance comparable with existing scores, which can be used at the time of SAB and prior to subjective clinical judgment., Competing Interests: Potential conflicts of interest . All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592-0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.)

Objective: Cancer is a well-known risk factor for venous thromboembolism. The D-dimer level is used to predict venous thromboembolism; however, reports on an appropriate D-dimer cut-off value in Japanese patients with advanced lung cancer are lacking. Therefore, this study aimed to calculate the D-dimer cut-off value for venous thromboembolism at the time of lung cancer diagnosis., Methods: The Rising-venous thromboembolism/NEJ037 study was a multicenter, prospective observational study. Patients with lung cancer who were contraindicated for radical resection or radiation were enrolled and followed up for 2 years. In the present study (jRCT no. 061180025), a receiver operating characteristic curve for D-dimer levels was created using the dataset of the Rising-venous thromboembolism/NEJ037 study., Results: The Rising-venous thromboembolism/NEJ037 study included a total of 1008 patients, of whom 976, whose D-dimer levels had been measured at the time of cancer diagnosis, were included in the present study. At the time of lung cancer diagnosis, 62 (6.3%) and 914 (93.7%) patients presented with and without venous thromboembolism, respectively. The D-dimer values ranged from 0.1 to 180.1 μg/ml and from 0.1 to 257.2 μg/ml in patients with and without venous thromboembolism, respectively. The receiver operating characteristic curve was discriminative with a cut-off value of 3.3 μg/ml and an area under the curve of 0.794 (sensitivity, 0.742; specificity, 0.782; 95% confidence interval, 0.725-0.863)., Conclusions: This is the first study to calculate the D-dimer cut-off value in Japanese patients with advanced lung cancer. Patients with D-dimer levels ≥3.3 μg/ml at the time of initial diagnosis may have coexisting venous thromboembolism., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Background: Pancreatic cancer (PC) is usually detected in the advanced stages. Liquid biopsy has become a revolutionary strategy for cancer diagnosis and prognosis prediction. This study aims to investigate the diagnostic and prognostic value of circulating exosomal glypican-1 (GPC-1) in PC., Methods: We systematically searched relevant studies. For diagnostic accuracy, pooled sensitivity and specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated. Regarding prognostic value, hazard ratios (HRs) and 95% CIs for overall survival (OS) were summarized by using a random-effects model., Results: We found 8 studies that examined the diagnostic value of circulating exosomal GPC-1 in PC, and 3 studies that investigated its prognostic value. Pooled sensitivity and specificity were 0.88 (95% CI, 0.65-0.97) and 0.86 (95% CI, 0.72-0.94). The AUC was 0.93 (95% CI, 0.90-0.95). Prognostic analysis showed that higher levels of circulating exosomal GPC-1 were associated with poorer OS in PC patients, and the combined HR for OS was 4.59 (random-effects model, 95% CI = 1.17-18.03, P = .022). The results of both studies were robust and neither had publication bias., Conclusion: Circulating exosomal GPC-1 may be used as a diagnostic and prognostic biomarker for PC. However, this result needs to be validated by further research using a larger sample size., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background & Aims: Inflammatory bowel disease (IBD), involving both Crohn's disease (CD) and ulcerative colitis (UC), represents a chronic, immune-mediated inflammatory disease due to an uncontrolled, ongoing inflammatory response to intestinal bacteria in those with genetic susceptibility. MicroRNA (miRNA) extrusion from relevant remote organs or tissues is reflected in the expression of miRNAs in serum and plasma. Both UC and CD patients had higher blood levels of expressed miR-199a. Long noncoding RNA (lncRNA) ANRIL is a proinflammatory gene that mediates nuclear factor κB to play a role in inflammatory diseases, such as IBD. The aim of the current study is to investigate the potential role of both miR-199a and ANRIL in diagnosing IBD in adult patients., Methods: Sixty-seven IBD patients diagnosed clinically, radiologically, endoscopically, and histologically were included in this prospective cohort study. Participants were classified into 3 groups: the UC group (n = 35), the CD group (n = 32), and the control group (n = 30). Demographics, history taking, laboratory characteristics, and treatments were recorded. Tumor necrosis factor α, miR-199a, and ANRIL were measured., Results: The findings suggested that miR-199a and ANRIL might be associated with the occurrence or progression of IBD because both genes were substantially expressed in the peripheral blood of patients with this condition. Receiver-operating characteristic curve analysis indicated that the detection of miR-199a and ANRIL had a predictive sensitivity of 62.9% and 88.6% and a specificity of 70.7% and 96.7% for the occurrence of UC cases, respectively, and a predictive sensitivity of 72.4% and 46.9% and a specificity of 96.7% and 34.7% for the occurrence of CD cases, respectively., Conclusions: Both miR-199a and ANRIL are abundant in the sera of IBD adult Egyptian patients (UC and CD). Both can represent a noninvasive marker for early disease diagnosis., (© The Author(s) 2024. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Hip fractures in older people result in increased mortality., Objective: We developed and validated an accurate and simple prognostic scoring system for hip fractures that can be used preoperatively., Design: Retrospective study., Setting: Multicenter., Participants: Patients aged ≥65 years with hip fractures who underwent surgery between 2011 and 2021 were enrolled., Methods: The significant factors were determined with logistic regression analysis, and a scoring system was developed. The patients were classified into three groups, and a log-rank test was performed to evaluate 1-year survival rates. The model was internally and externally validated using the 5-fold cross-validation and data from another hospital, respectively., Results: We included 1026 patients. The analysis revealed eight significant prognostic factors: sex, body mass index, history of chronic heart failure and malignancy, activities of daily living (ADLs) before injury, hemoglobin and the prognostic nutritional index (PNI) at injury, and the American Society of Anesthesiologists Physical Status. The area under the receiver operating characteristic curve (AUC) after internal validation was 0.853. The external validation data consisted of 110 patients. The AUC of the model for the validation data was 0.905, showing outstanding discrimination. Sensitivity and specificity were 88.7% vs. 100% and 93.3% vs. 95.2% for the development and validation data, respectively., Conclusions: We developed and validated an accurate and simple prognostic scoring system for hip fractures using only preoperative factors. Our findings highlight PNI as an important predictor of prognosis in hip fracture patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout., Methods: A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients., Results: Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%., Conclusions: The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Von Willebrand factor (VWF) antigen plays a role in vascular inflammation and thrombosis, both of which are important in the pathogenesis of ANCA-associated vasculitis (AAV). Previous work found that VWF correlates with disease activity in childhood-onset primary CNS vasculitis. We sought to determine the relationship between VWF and disease activity over time in children with AAV., Methods: AAV patients with more than one VWF level measured were included in this retrospective study, and the relationships between active vasculitis, VWF and other disease measures were analysed. Generalized estimating equations analysis was used to account for repeated VWF measurements within a patient. Repeated measures correlation was used to determine associations of paired laboratory observations. Diagnostic performance was evaluated using receiver operating curve analysis., Results: A total of 732 total VWF measurements were collected in 33 AAV patients. VWF antigen levels were higher during active disease [median 2.03 IU/ml, interquartile range (IQR) 1.35, 2.55] compared with inactive disease (median 1.18 IU/ml, IQR 0.94, 1.53). VWF antigen was the only variable that was significantly associated with active disease (odds ratio 3.01, P < 0.001, 95% CI 2.3, 3.93). The effect of VWF did not show a substantial difference between the disease subtypes. There was a moderate positive correlation between VWF antigen and disease activity, with an acceptable sensitivity and specificity rates., Conclusion: Increased VWF antigen levels correlate with active vasculitis in this paediatric-onset AAV cohort and may be used as an additional biomarker in childhood AAV., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks., Methods: We propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes., Results: Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt., Conclusion: LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: Although several studies have shown that the right ventricular to pulmonary artery (RV-PA) coupling, assessed by the ratio between tricuspid annular plane systolic excursion and systolic pulmonary artery pressure (TAPSE/sPAP) using echocardiography, is strongly associated with cardiovascular events, its prognostic value is not established in acute coronary syndrome (ACS). We aimed to assess the in-hospital prognostic value of TAPSE/sPAP among patients hospitalized for ACS in a retrospective analysis from the prospective ADDICT-ICCU study., Methods and Results: A total of 481 consecutive patients hospitalized in intensive cardiac care unit [mean age 65 ± 13 years, 73% of male, 46% ST-elevation myocardial infarction (STEMI)] for ACS [either STEMI or non-STEMI (NSTEMI)] with TAPSE/sPAP available were included in this prospective French multicentric study (39 centres). The primary outcome was in-hospital major adverse cardiovascular events (MACEs) defined as all-cause death, resuscitated cardiac arrest, or cardiogenic shock and occurred in 33 (7%) patients. Receiver operating characteristic curve analysis identified 0.55 mm/mmHg as the best TAPSE/sPAP cut-off to predict in-hospital MACEs. TAPSE/sPAP <0.55 was associated with in-hospital MACEs, even after adjustment with comorbidities [odds ratio (OR): 19.1, 95% confidence interval (CI) 7.78-54.8], clinical severity including left ventricular ejection fraction (OR: 14.4, 95% CI 5.70-41.7), and propensity-matched population analysis (OR: 22.8, 95% CI 7.83-97.2, all P < 0.001). After adjustment, TAPSE/sPAP <0.55 showed the best improvement in model discrimination and reclassification above traditional prognosticators (C-statistic improvement: 0.16; global χ2 improvement: 52.8; likelihood ratio test P < 0.001) with similar results for both STEMI and NSTEMI subgroups., Conclusion: A low RV-PA coupling defined as TAPSE/sPAP ratio <0.55 was independently associated with in-hospital MACEs and provided incremental prognostic value over traditional prognosticators in patients hospitalized for ACS., Trial Registration: ClinicalTrials.gov Identifier: NCT05063097., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Clinicians need a tool to gauge patients' ability to understand health conditions and treatment options. The Short-form Test of Functional Health Literacy in Adults (S-TOFHLA) is the gold standard for this, but its length is prohibitive for use in clinical settings. This study seeks to validate a novel three-item question set for predicting health literacy., Methods: This cross-sectional study utilized an in-person questionnaire alongside the S-TOFHLA. The sample included 2027 English- and Spanish-speaking adults (≥18 years) recruited from primary care practices serving a low-income eastern Pennsylvania community. Most patients (57.7%) identified as Hispanic. Diagnostic accuracy of each question and aggregated scores were assessed against the validated survey by calculating the area under the receiver operating characteristic (AUROC) curve., Results: Questions in the 'Problems Learning' and 'Help Reading' domains (AUROC 0.66 for each) performed better than the 'Confident Forms' question (AUROC 0.64). Summing all three scores resulted in an even higher AUROC curve (0.71). Cronbach's alpha of the combined items was 0.696., Conclusions: Study results suggest that any of the three questions are viable options for screening health literacy levels of diverse patients in primary care clinical settings. However, they perform better as a summed score than when used individually., (© The Author(s) 2024. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: to establish a proof-of-concept and ascertain the reliability of an abbreviated 30-second (30s) phonemic fluency measure as a cognitive screening tool in older adults., Methods: in all, 201 English-speaking individuals with normal cognition (NC; n = 119) or cognitive impairment (CI; mild CI or dementia; n = 82) were administered a standard 60s phonemic fluency task (FAS/CFL) with discrete 30s intervals denoted., Results: for all letters, 30s trial scores significantly predicted 60s scores for the same letter, R2 = .7-.9, F(1, 200) = 850-915, p < .001. As with 60s total scores, 30s cumulative scores (for all three trials) were significantly different between NC and CI groups (p < .001). Receiver operating characteristic analyses showed that 30s total scores distinguished NC and CI groups as effectively (AUC = .675) as 60s total scores (AUC = .658)., Conclusions: these findings support the utility and reliability of a short-form phonemic fluency paradigm, as 30s performance reliably predicted 60s/trial totals and was equally accurate in distinguishing impaired/non-impaired groups., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Tuberculous meningitis (TBM) is difficult to diagnose. We investigated whether a 3-gene host response signature in blood can distinguish TBM from other brain infections., Methods: The expression of 3 genes (dual specificity phosphatase 3 [DUSP3], guanylate-binding protein [GBP5], krupple-like factor 2 [KLF2]) was analyzed by RNA sequencing of archived whole blood from 4 cohorts of Vietnamese adults: 281 with TBM, 279 with pulmonary tuberculosis, 50 with other brain infections, and 30 healthy controls. Tuberculosis scores (combined 3-gene expression) were calculated following published methodology and discriminatory performance compared using area under a receiver operator characteristic curve (AUC)., Results: GBP5 was upregulated in TBM compared to other brain infections (P < .001), with no difference in DUSP3 and KLF2 expression. The diagnostic performance of GBP5 alone (AUC, 0.74; 95% confidence interval [CI], .67-.81) was slightly better than the 3-gene tuberculosis score (AUC, 0.66; 95% CI, .58-.73) in TBM. Both GBP5 expression and tuberculosis score were higher in participants with human immunodeficiency virus (HIV; P < .001), with good diagnostic performance of GBP5 alone (AUC, 0.86; 95% CI, .80-.93)., Conclusions: The 3-gene host signature in whole blood has the ability to discriminate TBM from other brain infections, including in individuals with HIV. Validation in large prospective diagnostic study is now required., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objective: The aim of this study was to investigate the association of fasting C-peptide and glucagon with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2DM)., Methods: A comprehensive evaluation was conducted on 797 patients with T2DM to assess the various risk factors affecting DPN. The subjects were categorized into short duration and long duration group according to the duration of diabetes with a threshold of 10 years. Logistic regression analysis was employed to examine the association between DPN and islet function, as well as other parameters. Receiver operating characteristic curve analysis was performed to evaluate the predictive capability of glucagon., Results: The fasting C-peptide levels were significantly lower in the DPN patients with short duration of diabetes, but lost significance in the long duration group. Conversely, a decreased level of glucagon was only observed in DPN patients with long duration of diabetes. For the group with long duration of diabetes, glucagon was the sole risk factor associated with DPN. The receiver operating characteristic curve analysis revealed that glucagon in the long duration group exhibited a moderate area under the curve of 0.706., Conclusions: The serum glucagon levels in T2DM patients with DPN exhibited bidirectional changes based on the duration of diabetes. Decreased glucagon was associated with DPN in T2DM patients with long duration of diabetes., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Host-response biomarkers to differentiate bacterial from viral etiology in children with respiratory infections have shown high accuracies, but are understudied in Mycoplasma pneumoniae (Mp) infections., Methods: We compared BV scores (0-34 indicating viral etiology, and 66-100 indicating bacterial etiology), tumor necrosis factor-related apoptosis-inducing ligand (TRAIL; pg/mL), interferon-γ inducible protein 10 (IP-10; pg/mL), and C-reactive protein (CRP; mg/L) serum levels between Mp-positive (Mp+) and Mp-negative (Mp-) community-acquired pneumonia (CAP) patients. We performed receiver operating characteristic (ROC) curve analyses for clinical features and biomarkers., Results: Of 80 CAP patients (median age, 6.3 years; 57.5% male), 26 had Mp+CAP. In Mp+CAP patients, compared to Mp-CAP patients, BV scores were lower (14.0 [3.0-27.8] vs 54.0 [12.0-84.8]; P = .0008), TRAIL levels were higher (86.5 [67.4-123.0] vs 65.5 [42.5-103.9]; P = .025), CRP levels were lower (12.9 [4.0-22.3] vs 36.7 [13.0-132.8]; P = .0019), and IP-10 levels were comparable (366.0 [150.2-603.8] vs 331.0 [154.3-878.8]; P = .73) (all median [interquartile range]). ROC analyses yielded a comparable discriminatory accuracy for the combination of age, fever duration, and duration of respiratory symptoms, with either procalcitonin or BV score (area under the ROC curve, 0.87 vs 0.86; P = .94)., Conclusions: Children with Mp+CAP have atypically low, viral levels of the BV score, underscoring the complementary role of microbiological testing., Competing Interests: Potential conflicts of interest . All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Aims: Right phrenic nerve (RPN) injury is a disabling but uncommon complication of atrial fibrillation (AF) radiofrequency ablation. Pace-mapping is widely used to infer RPN's course, for limiting the risk of palsy by avoiding ablation at capture sites. However, information is lacking regarding the distance between the endocardial sites of capture and the actual anatomic RPN location. We aimed at determining the distance between endocardial sites of capture and anatomic CT location of the RPN, depending on the capture threshold., Methods and Results: In consecutive patients undergoing AF radiofrequency ablation, we defined the course of the RPN on the electroanatomical map with high-output pacing at up to 50 mA/2 ms, and assessed RPN capture threshold (RPN-t). The true anatomic course of the RPN was delineated and segmented using CT scan, then merged with the electroanatomical map. The distance between pacing sites and the RPN was assessed. In 45 patients, 1033 pacing sites were analysed. Distances from pacing sites to RPN ranged from 7.5 ± 3.0 mm (min 1) when RPN-t was ≤10 mA to 19.2 ± 6.5 mm (min 9.4) in cases of non-capture at 50 mA. A distance to the phrenic nerve > 10 mm was predicted by RPN-t with a ROC curve area of 0.846 [0.821-0.870] (P < 0.001), with Se = 80.8% and Sp = 77.5% if RPN-t > 20 mA, Se = 68.0% and Sp = 91.6% if RPN-t > 30 mA, and Se = 42.4% and Sp = 97.6% if non-capture at 50 mA., Conclusion: These data emphasize the utility of high-output pace-mapping of the RPN. Non-capture at 50 mA/2 ms demonstrated very high specificity for predicting a distance to the RPN > 10 mm, ensuring safe radiofrequency delivery., Competing Interests: Conflict of interest: F.M.: consultant for Biosense-Webster and Abbott., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

We evaluated the diagnostic performance of the β-d-glucan (BDG) test (Beijing Gold Mountain River Tech) in diagnosing invasive fungal disease (IFD) and its variations among patients with different risks. Patients ≥18 years old who underwent a serum BDG test (positive cutoff value >80 pg/ml) from April 2017 through May 2018 were collected consecutively. Patients were classified into three groups: group 1, patients with host factors as defined by the prior 2008 European Organization for Research and Treatment (EORTC) criteria; group 2, those with extended host factors in 2020 EORTC criteria; and group 3, those without any risk factor mentioned in the criteria. IFD was defined by 2020 EORTC criteria, but BDG was not considered. Diagnostic performance of the serum BDG test was measured by the area under the curve (AUC) of the receiver-operating characteristic curve. Among 469 patients, 15.4% (72/469) were diagnosed with IFD (48/191 [25.1%], 14/144 [9.7%], and 10/134 [7.5%] in groups 1, 2, and 3, respectively). The BDG assay showed fair performance (AUC 0.748 [95% CI: 0.688-0.810]). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 77.8%, 60.7%, 26.4%, and 93.8%, respectively. PPV was higher in group 1, and NPV was higher in group 3. Additionally, diagnostic odds ratios were 6.73, 2.88, and 5.92 in groups 1, 2, and 3. Immunosuppressant use, non-IFD/Candida colonization, and central venous catheter were associated with false positivity. Clinicians should cautiously interpret the BDG assay, considering the various diagnostic performances depending on the different levels of risk., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.)

Background: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important for genetic counseling of couples at risk. The aim of this study was to develop and validate a novel machine learning model on a multicenter data set, covering a wide spectrum of hemoglobinopathies based on routine complete blood count (CBC) testing., Methods: Hemoglobinopathy test results from 10 322 adults were extracted retrospectively from 8 Dutch laboratories. eXtreme Gradient Boosting (XGB) and logistic regression models were developed to differentiate negative from positive hemoglobinopathy cases, using 7 routine CBC parameters. External validation was conducted on a data set from an independent Dutch laboratory, with an additional external validation on a Spanish data set (n = 2629) specifically for differentiating thalassemia from iron deficiency anemia (IDA)., Results: The XGB and logistic regression models achieved an area under the receiver operating characteristic (AUROC) of 0.88 and 0.84, respectively, in distinguishing negative from positive hemoglobinopathy cases in the independent external validation set. Subclass analysis showed that the XGB model reached an AUROC of 0.97 for β-thalassemia, 0.98 for α0-thalassemia, 0.95 for homozygous α+-thalassemia, 0.78 for heterozygous α+-thalassemia, and 0.94 for the structural hemoglobin variants Hemoglobin C, Hemoglobin D, Hemoglobin E. Both models attained AUROCs of 0.95 in differentiating IDA from thalassemia., Conclusions: Both the XGB and logistic regression model demonstrate high accuracy in predicting a broad range of hemoglobinopathies and are effective in differentiating hemoglobinopathies from IDA. Integration of these models into the laboratory information system facilitates automated hemoglobinopathy detection using routine CBC parameters., (© Association for Diagnostics & Laboratory Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective: To develop the optimal US scanning protocol for the diagnosis of calcium pyrophosphate crystal deposition (CPPD) disease., Methods: In this cross-sectional study, consecutive patients with a crystal-proven diagnosis of CPPD disease, and age-, sex-matched disease controls with a negative synovial fluid analysis were prospectively enrolled in two Italian Institutions. Four rheumatologists, blinded to patients' clinical details, performed US examinations using a standardized scanning protocol including 20 joints (shoulders, elbows, wrists, metacarpophalangeal joints from second to fifth fingers, hips, knees, ankles). CPPD was identified as presence/absence, according to the OMERACT definitions. Reduced US scanning protocols were developed by selecting the most informative joints to be imaged by US using the LASSO technique. Patients were randomly divided into training and validation sets. Their diagnostic accuracy was tested comparing the area under the receiver operating characteristic curves., Results: The number of participants enrolled was 204: 102 with CPPD disease and 102 disease controls [age, mean (s.d.): 71.3 (12.0) vs 71.1 (13.5) years; female: 62.8% vs 57.8%]. The median number of joints with US evidence of CPPD was 5 [interquartile range (IQR): 4-7] and 0 (IQR: 0-1) in patients with CPPD disease and controls, respectively (P < 0.01). The detection of CPPD in ≥2 joints using a reduced scanning protocol (bilateral assessment of knees, wrists and hips) showed a sensitivity of 96.7% (95% CI: 82.8, 99.9) and a specificity of 100 (95% CI: 88.8, 100.0) for the diagnosis of CPPD disease and had good feasibility [mean (s.d.): 12.5 (5.3) min]., Conclusion: Bilateral US assessment of knees, wrists and hips had excellent accuracy and good feasibility for the diagnosis of CPPD disease., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Objective: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission., Materials and Methods: We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing., Results: In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day., Discussion: Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain., Conclusion: Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: Tricuspid annular plane systolic excursion over systolic pulmonary artery pressure (TAPSE/sPAP) assessed by echocardiography appears to be a good non-invasive approach for right ventricular to pulmonary artery coupling assessment. We aimed to assess the in-hospital prognostic value of TAPSE/sPAP among patients hospitalized for acute heart failure (AHF)., Methods and Results: In total, 333 consecutive patients (mean age 68 ± 14 years, 70% of male, mean left ventricular ejection fraction 44 ± 16%) were hospitalized for AHF across 39 French cardiology departments, with TAPSE/sPAP measured by echocardiography within the first 24 h of hospitalization were included in this prospective study. The primary outcome was in-hospital major adverse cardiovascular events (MACEs) defined as all-cause death, resuscitated cardiac arrest or cardiogenic shock and occurred in 50 (15%) patients. Using receiver operating characteristic curve analysis, the best TAPSE/sPAP threshold for in-hospital MACEs was 0.40 mm/mmHg. TAPSE/sPAP < 0.40 mm/mmHg was independently associated with in-hospital MACEs, even after adjustment with comorbidities [odds ratio (OR): 3.75, 95% CI (1.87-7.93), P < 0.001], clinical severity [OR: 2.80, 95% CI (1.36-5.95), P = 0.006]. Using a 1:1 propensity-matched population, TAPSE/sPAP ratio < 0.40 was associated with a higher rate of in-hospital MACEs [OR: 2.98, 95% CI (1.53-6.12), P = 0.002]. After adjustment, TAPSE/sPAP < 0.40 showed the best improvement in model discrimination and reclassification above traditional prognostic factors (C-statistic improvement: 0.05; χ2 improvement: 14.4; likelihood-ratio test P < 0.001). These results were consistent in an external validation cohort of 133 patients., Conclusion: TAPSE/sPAP < 0.40 mm/mmHg assessed by an early echocardiography during an AHF episode is independently associated with in-hospital MACEs suggesting enhanced close monitoring and strengthened heart failure-specific care in these patients., Trial Registration: ClinicalTrials.gov Identifier: NCT05063097., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Soluble inflammatory factors in the cerebrospinal fluid (CSF) of patients with neurosyphilis have been investigated with low-throughput technology. This study aimed to illustrate the characteristics of soluble factor profiles in CSF of patients with neurosyphilis., Methods: We measured the concentrations of 45 cytokines, chemokines, and growth factors in CSF from 112 untreated syphilis cases, including latent syphilis (LS), asymptomatic neurosyphilis (ANS), meningeal neurosyphilis (MNS), meningovascular neurosyphilis (MVNS), paralytic dementia (PD), and ocular syphilis (OS)., Results: Thirty-three differentially expressed soluble factors (DeSFs) were categorized into 3 clusters. DeSF scores of clusters 1 and 2 (DeSFS1 and DeSFS2) were positively correlated with elevated neopterin and neurofilament light subunit (NF-L) concentration, respectively. DeSF scores of cluster 3 were positively correlated with white blood cells, protein, NF-L, and neopterin. Patients with LS, ANS, and OS exhibited an overall lower abundance of DeSFs. Patients with PD exhibited significantly increased levels of clusters 1 and 3, and the highest total DeSF score, whereas patients with MNS and MVNS showed enhanced levels of cluster 2. Receiver operating characteristic analysis revealed that DeSFS1 effectively discriminated PD, and DeSFS2 discriminated MNS/MVNS with high accuracy., Conclusions: Patients with neurosyphilis at different stages have distinctive patterns of soluble factors in CSF, which are correlated with immune status and neuronal damage., Competing Interests: Potential conflicts of interest . The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The microRNAs (miRNAs) play crucial roles in several biological processes. It is essential for a deeper insight into their functions and mechanisms by detecting their subcellular localizations. The traditional methods for determining miRNAs subcellular localizations are expensive. The computational methods are alternative ways to quickly predict miRNAs subcellular localizations. Although several computational methods have been proposed in this regard, the incomplete representations of miRNAs in these methods left the room for improvement. In this study, a novel computational method for predicting miRNA subcellular localizations, named PMiSLocMF, was developed. As lots of miRNAs have multiple subcellular localizations, this method was a multi-label classifier. Several properties of miRNA, such as miRNA sequences, miRNA functional similarity, miRNA-disease, miRNA-drug, and miRNA-mRNA associations were adopted for generating informative miRNA features. To this end, powerful algorithms [node2vec and graph attention auto-encoder (GATE)] and one newly designed scheme were adopted to process above properties, producing five feature types. All features were poured into self-attention and fully connected layers to make predictions. The cross-validation results indicated the high performance of PMiSLocMF with accuracy higher than 0.83, average area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) exceeding 0.90 and 0.77, respectively. Such performance was better than all previous methods based on the same dataset. Further tests proved that using all feature types can improve the performance of PMiSLocMF, and GATE and self-attention layer can help enhance the performance. Finally, we deeply analyzed the influence of miRNA associations with diseases, drugs, and mRNAs on PMiSLocMF. The dataset and codes are available at https://github.com/Gu20201017/PMiSLocMF., (© The Author(s) 2024. Published by Oxford University Press.)

Purpose: To explore the relationship between liver fat content (LFC) and nonalcoholic fatty liver disease (NAFLD) and determine the new threshold of LFC to diagnose NAFLD., Methods: The data from questionnaire survey, general physical examination, laboratory examination, and image examination were collected. Multivariate regression analysis, receiver operating characteristic curve analysis, smooth curve fitting, and threshold effect analysis were performed using the R software to investigate the relationship between LFC and NAFLD and to identify the new threshold of LFC to diagnose NAFLD., Results: The prevalence of NAFLD was 30.42%, with a significantly higher prevalence in men than in women. Regression analyses demonstrated that LFC odds ratio [95% confidence interval (CI)] was 1.28 (95% CI: 1.24-1.31) in fully-adjust model. Analysis of the LFC quartile, with Q1 as a reference, revealed that the odds ratios of NAFLD were 1.47 (95% CI: 1.08-1.99), 2.29 (95% CI: 1.72-3.06), and 10.02 (95% CI: 7.45-13.47) for Q2, Q3, and Q4 groups, respectively. Smooth curve fitting and threshold effect analysis displayed a nonlinear relationship between LFC and NAFLD, and the threshold was 4.5%. The receiver operating characteristic curve indicated that when LFC was 4.5%, the area under curve (95% CI) was 0.80 (0.79-0.82), and the sensitivity and specificity of LFC in diagnosing NAFLD were 0.64% and 0.82%, respectively., Conclusion: The relationship between LFC and NAFLD was sigmoidal, with an inflection point of 4.5%., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed., Objective: To develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM., Methods: Feature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The performance of the multiple logistic regression model was quantified by sensitivity, specificity, and percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments., Results: We studied 2000 patients with DM (mean age 58.85 ± 17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95% CI, 0.82-0.86), while DCA showed a higher clinical utility of the model, ranging from 30% to 84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74; AUC: 0.81 [95% CI, 0.76-0.87]), whereas unsupervised clustering identified high-risk patients., Conclusion: A ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

Objective: To assess the diagnostic value of urinary transferrin (Tf) in early diabetic nephropathy (DN) to propose a more sensitive and noninvasive biomarker for screening and monitoring DN in clinical practice., Methods: We searched 3 databases from their inception to May 2023, to identify studies investigating the diagnostic value of Tf in patients with DN. Meta-DiSc software, version 1.4, and Stata software, version 15.1 (StataCorp) were used to conduct a meta-analysis and evaluate the diagnostic accuracy of urine Tf levels for DN., Results: The meta-analysis included 6 relevant studies investigating the diagnostic value of Tf level for DN. Urinary Tf as a diagnostic marker demonstrated a combined sensitivity of 0.82 (95% CI, 0.71-0.89) and specificity of 0.88 (0.84-0.92). the positive diagnostic likelihood ratio was 7.07 (4.57-10.93), the negative diagnostic likelihood ratio was 0.20 (0.12-0.35), and the diagnostic odds ratio was 34.49 (13.61-87.44). Also, the area under the receiver operating characteristic curve was 0.92 (0.89-0.94), indicating that urinary Tf has a decent discriminative ability in diagnosing DN., Conclusion: Tf level is a valuable biological marker for early diagnosis and monitoring of DN in clinical practice. It has statistically significant predictive value for patients in the early phases of DN., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: The noninvasive serum markers pepsinogen I (PGI), pepsinogen II (PGII), gastrin-17 (G17), and PGI:PGII ratio (PGR) have recently been proposed as a new tool for predicting various gastric pathologies., Methods: A total of 83 gastritis patients confirmed by gastroscopy were enrolled, with 78 undergoing concurrent colonoscopies. The control group included 99 healthy subjects. Enzyme-linked immunosorbent assay was used to detect PGI, PGII, G17, and PGR. The performance of serological analysis for detecting gastritis pathology was evaluated using receiver operating characteristic (ROC) curves., Results: The G17 and PGII levels increased significantly (P < .001), whereas PGR levels decreased (P = .001) in the gastritis group. The ROC analysis revealed that PGR had a sensitivity and specificity of 70.83% and 86.67%, respectively, in predicting Helicobacter pylori-infected gastritis and a sensitivity and specificity of 88% and 65.52%, respectively, in predicting active gastritis. The G17 levels were significantly elevated in gastritis patients undergoing concurrent colonoscopies (P < .05)., Conclusion: Pepsinogen I:pepsinogen II ratio was found to be a useful predictor of active gastritis and H pylori-infected gastritis. Furthermore, G17 was found to be closely related to pathological conditions found by colonoscopy and may provide recommendations for whether gastritis patients should undergo a concurrent colonoscopy., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Subjective cognitive decline (SCD), i.e. self/other-reported concerns on one's cognitive functioning without objective evidence of significant decline, is an indicator of dementia risk. There is little consensus on reliability and validity of the available SCD measures. Therefore, introducing a novel and psychometrically sound measure of SCD is timely., Objective: The psychometric properties of a new SCD measure, the McCusker Subjective Cognitive Impairment Inventory-Self-Report (McSCI-S), are reported., Methods: Through review of previously published measures as well as our clinical and research data on people with SCD, we developed a 46-item self-report questionnaire to assess concerns on six cognitive domains, namely, memory, language, orientation, attention and concentration, visuoconstruction abilities and executive function. The McSCI-S was examined in a cohort of 526 participants using factor analysis, item response theory analysis and receiver operating characteristic (ROC) curve., Results: A unidimensional model provided acceptable fit (CFI = 0.94, TLI = 0.94, RMSEA [90% CI] = 0.052 [.049, 0.055], WRMR = 1.45). The McSCI-S internal consistency was excellent (.96). A cut-off score of ≥24 is proposed to identify participants with SCDs. Higher McSCI-S scores were associated with poorer general cognition, episodic verbal memory, executive function and greater memory complaints and depressive scores (P < .001), controlling for age, sex and education., Conclusions: Excellent reliability and construct validity suggest the McSCI-S estimates SCDs with acceptable accuracy while capturing self-reported concerns for various cognitive domains. The psychometric analysis indicated that this measure can be used in cohort studies as well as on individual, clinical settings to assess SCDs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The diagnostic criteria for lateral lymph node metastasis in rectal cancer have not been established. This research aimed to investigate the risk factors for lateral lymph node metastasis and develop machine learning models combining these risk factors to improve the diagnostic performance of standard imaging., Method: This multicentre prospective study included patients who underwent lateral lymph node dissection without preoperative treatment for rectal cancer between 2017 and 2019 in 15 Japanese institutions. First, preoperative clinicopathological factors and magnetic resonance imaging findings were evaluated using multivariable analyses for their correlation with lateral lymph node metastasis. Next, machine learning diagnostic models for lateral lymph node metastasis were developed combining these risk factors. The models were tested in a training set and in an internal validation cohort and their diagnostic performance was tested using receiver operating characteristic curve analyses., Results: Of 212 rectal cancers, 122 patients were selected, including 232 lateral pelvic sides, 30 sides of which had pathological lateral lymph node metastasis. Multivariable analysis revealed that poorly differentiated/mucinous adenocarcinoma, extramural vascular invasion, tumour deposit and a short-axis diameter of lateral lymph node ≥ 6.0 mm were independent risk factors for lateral lymph node metastasis. Patients were randomly divided into a training cohort (139 sides) and a test cohort (93 sides) and machine learning models were computed on the basis of a combination of significant features (including: histological type, extramural vascular invasion, tumour deposit, short- and long-axis diameter of lateral lymph node, body mass index, serum carcinoembryonic antigen level, cT, cN, cM, irregular border and mixed signal intensity). The top three models with the highest sensitivity in the training cohort were as follows: support vector machine (sensitivity, 1.000; specificity, 0.773), light gradient boosting machine (sensitivity, 0.950; specificity, 0.918) and ensemble learning (sensitivity, 0.950; specificity, 0.917). The diagnostic performances of these models in the test cohort were as follows: support vector machine (sensitivity, 0.750; specificity, 0.667), light gradient boosting machine (sensitivity, 0.500; specificity, 0.852) and ensemble learning (sensitivity, 0.667; specificity, 0.864)., Conclusion: Machine learning models combining multiple risk factors can contribute to improving diagnostic performance of lateral lymph node metastasis., (© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.)

Background: Measurements of aldosterone by mass spectrometry are more accurate and less prone to interferences than immunoassay measurements, and may produce a more accurate aldosterone:renin ratio (ARR) when screening for primary aldosteronism (PA)., Methods: Differences in diagnostic performance of the ARR using mass spectrometry vs immunoassay measurements of aldosterone were examined in 710 patients screened for PA. PA was confirmed in 153 patients and excluded in 451 others. Disease classifications were not achieved in 106 patients. Areas under receiver-operating characteristic curves (AUROC) and other measures were used to compare diagnostic performance., Results: Mass spectrometry-based measurements yielded lower plasma aldosterone concentrations than immunoassay measurements. For the ARR based on immunoassay measurements of aldosterone, AUROCs were slightly lower (P = 0.018) than those using mass spectrometry measurements (0.895 vs 0.906). The cutoff for the ARR to reach a sensitivity of 95% was 30 and 21.5 pmol/mU by respective immunoassay and mass spectrometry-based measurements, which corresponded to specificities of 57% for both. With data restricted to patients with unilateral PA, diagnostic sensitivities of 94% with specificities >81% could be achieved at cutoffs of 68 and 52 pmol/mU for respective immunoassay and mass spectrometry measurements., Conclusions: Mass spectrometry-based measurements of aldosterone for the ARR provide no clear diagnostic advantage over immunoassay-based measurements. Both approaches offer limited diagnostic accuracy for the ARR as a screening test. One solution is to employ the higher cutoffs to triage patients likely to have unilateral PA for further tests and possible adrenalectomy, while using the lower cutoffs to identify others for targeted medical therapy.German Clinical Trials Register ID: DRKS00017084., (© Association for Diagnostics & Laboratory Medicine 2024.)

Aims: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU)., Methods and Results: We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH., Conclusion: The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure., Competing Interests: Conflict of interest: N.S. reports consulting for Abbott Vascular as a member of an advisory board., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims: We sought to investigate the association of left atrial strain with the outcome in a large cohort of patients with at least moderate aortic stenosis (AS)., Methods and Results: We analysed 467 patients (mean age 80.6 ± 8.2 years; 51% men) with at least moderate AS and sinus rhythm. The primary study endpoint was the composite of all-cause mortality and hospitalizations for heart failure. After a median follow-up of 19.2 (inter-quartile range 12.5-24.4) months, 96 events occurred. Using the receiver operator characteristic curve analysis, the cut-off value of peak atrial longitudinal strain (PALS) more strongly associated with outcome was <16% {area under the curve (AUC) 0.70 [95% confidence interval (CI): 0.63-0.78], P < 0.001}. The Kaplan-Meier curves demonstrated a higher rate of events for patients with PALS < 16% (log-rank P < 0.001). On multivariable analysis, PALS [adjusted HR (aHR) 0.95 (95% CI 0.91-0.99), P = 0.017] and age were the only variables independently associated with the combined endpoint. PALS provided incremental prognostic value over left ventricular (LV) global longitudinal strain, LV ejection fraction, and right ventricular function. Subgroup analysis revealed that impaired PALS was also independently associated with outcome in the subgroups of paucisymptomatic patients [aHR 0.98 (95% CI 0.97-0.98), P = 0.048], moderate AS [aHR 0.92, (95% CI 0.86-0.98), P = 0.016], and low-flow AS [aHR 0.90 (95% CI 0.83-0.98), P = 0.020]., Conclusion: In our patients with at least moderate AS, PALS was independently associated with outcome. In asymptomatic patients, PALS could be a potential marker of sub-clinical damage, leading to better risk stratification and, potentially, earlier treatment., Competing Interests: Conflict of interest: D.S. and L.B. are members of the speaker bureaus of GE healthcare and Philips Medical Systems, and received research grants from GE Healthcare, Philips Medical Systems, TomTec Imaging Systems, and ESaOTE SpA. The remaining authors have nothing to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models., Methods: This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit., Results: The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes., Conclusion: By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Aims: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex., Methods and Results: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex., Conclusion: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients., Competing Interests: Conflict of interest: D.S.B. and P.J.S. participated in software royalties for QPS software at Cedars-Sinai Medical Center. P.J.S. has received research grant support from Siemens Medical Systems. D.S.B., S.D., A.J.E., and E.J.M. are consultants for GE Healthcare. S.D. is a consultant for Bracco Diagnostics and has received a grant through her institution from Astellas. M.D.C. has received research grant support from Spectrum Dynamics; and he is a consultant for Sanof and GE Healthcare. D.S.B.’s institution has received grant support from HeartFlow. E.J.M. has served as a consultant for Bracco Inc, and he and his institution have received grant support from Bracco Inc. T.D.R. has received research grant support from GE Healthcare and Advanced Accelerator Applications. A.J.E. reports receiving a speaker's fee from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare—UpToDate, and serving on a scientific advisory board for Canon America Medical Systems USA; his institution has grants/grants pending from Attralus, BridgeBio Pharma, Canon Medical Systems USA, GE Healthcare, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims: Myocardial perfusion imaging (MPI) using [15O]H2O positron emission tomography (PET) is used to guide the selection of patients with angina for invasive angiography and possible revascularization. Our study evaluated (i) whether atrial fibrillation (AF) reduces global hyperaemic myocardial blood flow (MBF) and (ii) whether [15O]H2O PET MPI effectively guides revascularization procedures for patients with ongoing AF., Methods and Results: We prospectively recruited 346 patients with angina and persistent or paroxysmal AF referred for baseline/hyperaemic [15O]H2O PET MPI. The primary outcome was revascularization within 3 months of MPI. In the analyses, patients were divided into four groups based on whether they had ongoing AF or sinus rhythm (SR) and whether they had previously documented coronary artery disease (CAD) or not. Thus, four groups were compared: SR-noCAD, AF-noCAD, SR-CAD, and AF-CAD. Hyperaemic MBF was affected by both ongoing AF and prior CAD [MBF (mL/min/g): 2.82 (SR-noCAD) vs. 2.12 (AF-noCAD) vs. 2.22 (SR-CAD) vs. 1.80 (AF-CAD), two-way analysis of variance P < 0.0001]. In multiple linear regression, ongoing AF was independently associated with reduced hyperaemic MBF. Every 0.1 mL/min/g decrease in hyperaemic MBF was associated with a 23% increase in odds of early revascularization. Receiver operating characteristic (ROC) analysis of vessel-specific hyperaemic MBF to predict early revascularization yielded the following areas under the ROC curve: SR-noCAD: 0.95 (P < 0.0001); AF-noCAD: 0.79 (P < 0.0001); SR-CAD: 0.78 (P < 0.0001); and AF-CAD: 0.88 (P < 0.0001)., Conclusion: Ongoing AF is associated with 19-25% reduced global hyperaemic MBF as measured by [15O]H2O MPI PET. Regardless, vessel-specific hyperaemic MBF still predicts early revascularization in patients with AF., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Aims: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs., Methods: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists., Results: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%., Conclusions: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Lynch syndrome is a hereditary cancer predisposition syndrome caused by germline mutations in DNA mismatch repair genes, which lead to high microsatellite instability and frameshift mutations at coding mononucleotide repeats in the genome. Recurrent frameshift mutations in these regions are thought to play a central role in the increased risk of various cancers, but no biomarkers are currently available for the surveillance of high microsatellite instability-associated cancers., Methods: A frameshift mutation-based biomarker panel was developed and validated by targeted next-generation sequencing of supernatant DNA from cultured high microsatellite instability colorectal cancer cells. This panel supported selection of 122 frameshift mutation targets as potential biomarkers. This biomarker panel was then tested using matched tumor, adjacent normal tissue, and buffy coat samples (53 samples) and blood-derived cell-free DNA (cfDNA) (38 samples) obtained from 45 high microsatellite instability and mismatch repair-deficient patients. We also sequenced cfDNA from 84 healthy participants to assess background noise., Results: Recurrent frameshift mutations at coding mononucleotide repeats were detectable not only in tumors but also in cfDNA from high microsatellite instability and mismatch repair-deficient patients, including a Lynch syndrome carrier, with a varying range of target detection (up to 85.2%), whereas they were virtually undetectable in healthy participants. Receiver operating characteristic curve analysis showed high sensitivity and specificity (area under the curve = 0.94) of the investigated panel., Conclusions: We demonstrated that frameshift mutations can be detected in cfDNA from high microsatellite instability and mismatch repair-deficient patients and asymptomatic carriers. The 122-target frameshift mutation panel described here has promise as a tool for improved surveillance of high microsatellite instability and mismatch repair-deficient patients, with the potential to reduce the frequency of invasive screening methods for this high-cancer-risk cohort., (Published by Oxford University Press 2024.)

Aims: Although impaired left ventricular (LV) global longitudinal strain (GLS) with apical sparing is a feature of cardiac amyloidosis (CA), its diagnostic accuracy has varied across studies. We aimed to determine the ability of apical sparing ratio (ASR) and most common echocardiographic parameters to differentiate patients with confirmed CA from those with clinical and/or echocardiographic suspicion of CA but with this diagnosis ruled out., Methods and Results: We identified 544 patients with confirmed CA and 200 controls (CTRLs) as defined above (CTRL patients). Measurements from transthoracic echocardiograms were performed using artificial intelligence software (Us2.AI, Singapore) and audited by an experienced echocardiographer. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance and optimal cut-offs for the differentiation of CA patients from CTRL patients. Additionally, a group of 174 healthy subjects (healthy CTRL) was included to provide insight on how patients and healthy CTRLs differed echocardiographically. LV GLS was more impaired (-13.9 ± 4.6% vs. -15.9 ± 2.7%, P < 0.0005), and ASR was higher (2.4 ± 1.2 vs. 1.7 ± 0.9, P < 0.0005) in the CA group vs. CTRL patients. Relative wall thickness and ASR were the most accurate parameters for differentiating CA from CTRL patients [area under the curve (AUC): 0.77 and 0.74, respectively]. However, even with the optimal cut-off of 1.67, ASR was only 72% sensitive and 66% specific for CA, indicating the presence of apical sparing in 32% of CTRL patients and even in 6% healthy subjects., Conclusion: Apical sparing did not prove to be a CA-specific biomarker for accurate identification of CA, when compared with clinically similar CTRLs with no CA., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: We aimed to evaluate the psychometric properties and diagnostic accuracy of the 32-item version of the Multilingual Naming Test (MINT) in participants from 2 ethnic groups (European Americans [EA; n = 106] and Hispanic Americans [HA; n = 175]) with 3 diagnostic groups (cognitively normal [CN], n = 94, mild cognitive impairment [MCI], n = 148, and dementia, n = 39)., Method: An Item Response Theory model was used to evaluate items across ethnicity and language groups (Spanish and English), resulting in a 24-item version. We analyzed the MINT discriminant and predictive validity across diagnostic groups., Results: A total of 8 items were differentially difficult between languages in the 32-item version of the MINT. EA scored significantly higher than HA, but the difference was not significant when removing those 8 items (controlling for Education). The Receiver Operating Characteristics showed that the MINT had poor accuracy when identifying CN participants and was acceptable in identifying dementia participants but unacceptable in classifying MCI participants. Finally, we tested the association between MINT scores and magnetic resonance imaging volumetric measures of language-related areas in the temporal and frontal lobes. The 32-item MINT in English and Spanish and the 24-item MINT in Spanish were significantly correlated with the bilateral middle temporal gyrus. The left fusiform gyrus correlated with MINT scores regardless of language and MINT version. We also found differential correlations depending on the language of administration., Conclusions: Our results highlight the importance of analyzing cross-cultural samples when implementing clinical neuropsychological tests such as the MINT., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)