Area Under ROC Curve Research Articles

Predicting higher-risk growth patterns in invasive lung adenocarcinoma with multiphase multidetector computed tomography and 18F-fluorodeoxyglucose PET radiomics.

To develop a predictive model for identifying the higher-risk growth pattern of invasive lung adenocarcinoma using multiphase multidetector computed tomography (MDCT) and 18F-fluorodeoxyglucose (FDG) PET radiomics. A total of 203 patients with confirmed invasive lung adenocarcinoma between January 2018 and December 2021 were enrolled and randomly divided into training (n = 143) and testing sets (n = 60). Patients were classified into two groups according to the predominant growth pattern (lower-risk group: lepidic/acinar; higher-risk group: papillary/solid/micropapillary). Preoperative multiphase MDCT and 18F-FDG PET images were evaluated. The Artificial Intelligence Kit software was used to extract radiomic features. Five predictive models [arterial phase, venous phase, and plain scan (AVP), PET, AVP-PET, clinical, and radiomic-clinical (Rad-Clin) combined model] were developed. The models' performance was assessed using receiver-operating characteristic (ROC) curves and compared using the DeLong test. Among the radiomics models (AVP, PET, and AVP-PET), the AVP-PET model [area under ROC curve (AUC) = 0.888] outperformed the PET model (AUC = 0.814; P = 0.015) in predicting the higher-risk growth patterns. The combined Rad-Clin model (AUC = 0.923), which integrates AVP-PET radiomics and five independent clinical predictors (gender, spiculation, long-axis diameter, maximum standardized uptake value, and average standardized uptake value), exhibited superior performance in predicting the higher-risk growth pattern compared with radiomic models (P = 0.043, vs. AVP-PET; P = 0.016, vs. AVP; P = 0.002, vs. PET) or the clinical model alone (constructing based on five clinical predictors; AUC = 0.793; P < 0.001). The combined Rad-Clin model can predict the higher-risk growth patterns of invasive adenocarcinoma (IAC). This approach could help determine individual therapeutic strategies for IAC patients by distinguishing predominant growth patterns with high risk.

Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture

Magnetic resonance imaging remains the gold standard for diagnosing osteoporotic vertebral compression fractures (OVCF), but the use of X-ray imaging, particularly anteroposterior (AP) and lateral views, is prevalent due to its accessibility and cost-effectiveness. We aim to assess whether the performance of AP images-based deep learning is comparable compared to those using lateral images. This retrospective study analyzed X-ray images from two tertiary teaching hospitals, involving 1,507 patients for the training and internal test, and 104 patients for the external test. The EfficientNet-B5-based algorithms were employed to classify OVCF and non-OVCF group. The model was trained with a 1:1 balanced dataset and validated through 5-fold cross validation. Performance outcomes were compared with the area under receiver operating characteristic (AUROC) curve. Out of a total of 1,507 patients, 799 were included in the training dataset and 708 were included in the internal test dataset. The training and internal test datasets were matched 1:1 as OVCF and non-OVCF patients. The DL model showed comparable classifying performance with internal test data (N = 708, AUROC for AP, 0.915; AUROC for lateral, 0.953) and external test data (N = 104, AUROC for AP, 0.982; AUROC for lateral, 0979), respectively. The other performances including F1 score and accuracy were also comparable. Especially, The AUROC of AP and lateral x-ray image-based DL was not significantly different (p for DeLong test = 0.604). The EfficientNet-B5 algorithms using AP X-ray images shows comparable efficacy for classifying OVCF and non-OVCF compared to lateral images.

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis.

Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. This research aims to analyze salivary microRNA expression together with clinical and demographic features with the aim of diagnosing gastric cancer. The study included 19 patients with early-stage gastric cancer and 19 healthy controls. Saliva samples were collected and processed for RNA isolation. Salivary expression of miR-223-3p and miR-21-5p were measured using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curves were generated to evaluate the accuracy of diagnostic models. Machine learning algorithms, multiple logistic regression, and principal component analysis (PCA) were used to assess the predictive power of miRNAs in conjunction with clinical-demographic features. Significant upregulation of miR-223-3p and downregulation of miR-21-5p in saliva were observed in patients with gastric cancer. The area under ROC curve (AUC) values for salivary miR-21-5p, salivary miR-223-3p, and their multiple logistic regression were determined to be 0.723, 0.791, and 0.850, respectively. The AUC for multiple logistic regression model was 0.919. The PCA model led to the highest diagnostic odds ratio (DOR) of 134.33 (sensitivity = 0.785, specificity = 1.00, AUC = 903). Application of machine learning methods, and in particular a random forest algorithm, showed high accuracy in diagnosing patients with gastric cancer (sensitivity = 1.00, specificity = 0.857, AUC = 0.93). The application of validated salivary diagnostics in clinical practice could help facilitate earlier diagnosis of gastric cancer and improve medical outcome. Expression of miR-21 and miR-223-3p in saliva together with clinical and demographic features, appears promising in screening for GC.

Layout Congestion Prediction Based on Regression-ViT

To accelerate the back-end design flow of integrated circuit (IC), numerous studies have made exploratory advancements in machine learning (ML) for electronic design automation (EDA). However, most research works are limited to deep learning (DL) models predominantly based on convolutional neural networks, and the models often suffer from poor generalization due to the scarcity of data. In this study, we propose the Double generative adversarial networks (D-GAN) model to enrich the dataset and propose the Regression Vision Transformer (R-ViT) model to predict layout congestion information. Compared with the baseline model, experimental results show improvements of 3.03% and 2.64% in Receiver Operating Characteristic-Area under Curve (ROC-AUC) and Precision-Recall Curve-Area under Curve (PRC-AUC) respectively. To further enhance the prediction accuracy of the model, an adaptive Huber loss function is designed to optimize the training process, resulting in an improvement of up to 11.03% in ROC-AUC compared with the baseline model. Lastly, extended experiments are conducted to study the effects of parameters and convolutional kernel size on performance, which find a better configuration.

Circulating long noncoding RNA, Zfpm2-As1, and XIST based on medical data analysis are potential plasma biomarkers for gastric cancer diagnosis.

Long noncoding RNAs (lncRNAs) participate in diseases, especially tumorigenesis, including gastric cancer (GC). Although lncRNAs in GC tissues have been extensively studied in previous research, the possible significance of circulating lncRNAs in diagnosing GC is still unknown. The present work investigated lncRNAs ZFPM2-AS1 and XIST with high expression in GC tissues proved as potential plasma biomarkers from 20 early GC cases, 100 GC cases, and 90 normal subjects. The possible correlation between ZFPM2-AS1 and XIST expression levels was analyzed with general characteristics and clinicopathological features. The performance in diagnosis was assessed according to receiver operating characteristic (ROC) analysis. According to the results, XIST and ZFPM2-AS1 expression remarkably increased within GC plasma relative to normal subjects (P< 0.01); besides, lncRNA XIST expression after surgery had a tendency of downregulation compared with preoperative levels (P< 0.05). Moreover, the area under ROC curve (AUC) values were 0.62 for ZFPM2-AS1 and 0.68 for XIST, while the pooled AUC value of CA-724 and two lncRNAs was 0.751. Circulating lncRNAs ZFPM2-AS1 and XIST can serve as the candidate plasma biomarkers used to diagnose GC.

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.

Can resting lung function predict the response of a person living with motor neuron disease to a hypoxic challenge test?

People living with MND (PlwMND) are at risk of altitude-related hypoxia during flight. The Hypoxic Challenge Test (HCT) determines whether in-flight oxygen is required but can be expensive and inaccessible. To assist with travel recommendations, we investigated the relationship between altitude simulation-induced hypoxemia and baseline lung function. Retrospective audit of clinical database of PlwMND who had HCT and lung function. Pearson's correlation assessed relationships between oxygen saturation at altitude (AltSpO2) and lung function. Univariate logistic regression analysis and receiver operator characteristic (ROC) curves determined associations between lung function and HCT pass or fail. Between 2004-2023, 50 PlwMND were identified (median (IQR) diagnosis to HCT = 11.6 (16.9) months, mean ± SD forced vital capacity (FVC) = 2.4 ± 0.9 liters). Ten patients dropped below 85% SpO2 during testing (HCT fail). Baseline SpO2 was associated with AltSpO2 (r = 0.64) and predicted HCT pass or fail (OR 2.0 [95% CI 1.2-3.4], area under ROC curve (AUC) =0.8 [0.6-1.0]), as did FVC (AUC = 0.8 [0.6-0.9]). PlwMND with a FVC > 2.7L or a resting SpO2 > 97% are likely to pass HCT, whereas all those with FVC < 1L and SpO2 < 92% failed. PlwMND with FVC >2.7L or SpO2 >97% are unlikely to require oxygen or ventilatory supports for airline travel. A FVC below 2.7L will require a HCT to confidently determine HCT outcome, with testing still required for FVC <1L or baseline SpO2 <92%, to provide evidence to the airlines for in-flight respiratory support.

Promising biomarker panel to monitor therapeutic efficacy of neoadjuvant chemotherapy in pancreatic cancer patients.

Neoadjuvant chemotherapy (NAC) can provide improved survival outcomes in pancreatic ductal adenocarcinoma (PDAC) patients who respond to treatment, but currently available biomarkers cannot reliably predict NAC response. This study aimed to determine the potential of a previously identified diagnostic and prognostic biomarker panel (i.e. Ca-125, S100A2, S100A4, Mesothelin and Ca19-9) for the monitoring of NAC-response in PDAC patients. This single-centre, retrospective study, utilised serum from NAC treated PDAC patients to determine the levels of biomarkers by Enzyme-Linked Immunosorbent Assay (ELISA). The percentage of the tumour bed occupied by viable carcinoma (PVC) was used to divide patients into good (PVC < 50%) and poor (PVC ≥ 50%) NAC-responders. Statistical analysis was performed to measure the ability of individual biomarkers and a biomarker panel in NAC treatment response and patient survival. Serum specimens from a total of 108 PDAC patients were assessed. Ca-125, Ca19-9 and S100A2 showed a significant positive correlation with PVC. Ca-125 demonstrated a superior ability to monitor NAC treatment response (Area under receiver operating curve (AUC): .6954) compared to the more widely used clinical biomarker, Ca19-9 (AUC: .6291). A panel of Ca-125 and Ca19-9 showed good ability to monitor NAC response in PDAC patients (AUC: .7349). Patients with high levels of both Ca-125 and Ca19-9 were shown to have the poorest overall survival (median overall survival: 17 vs. 30 months). A serum biomarker panel of Ca-125 and Ca19-9 could be used for effective clinical management of PDAC patients undergoing NAC treatment.

Non-invasive lipid panel of MASLD fibrosis transition underscores the role of lipoprotein sulfatides in hepatic immunomodulation.

There exists a pressing need for a non-invasive panel that differentiates mild fibrosis from non-fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). In this work, we applied quantitative lipidomics and sterolomics on sera from the PERSONS cohort with biopsy-based histological assessment of liver pathology. We trained a lasso regression model using quantitative omics data and clinical variables, deriving a combinatorial panel of lipids and clinical indices that differentiates mild fibrosis (>F1, n= 324) from non-fibrosis (F0, n= 195), with an area under receiver operating characteristic curve (AUROC) at 0.775 (95% confidence interval [CI]: 0.735-0.816). Circulating sulfatides (SLs) emerged as central lipids distinctly associated with fibrosis pathogenesis in MASLD. Lipidomics analysis of lipoprotein fractions revealed a redistribution of circulating SLs from high-density lipoproteins (HDLs) onto low-density lipoproteins (LDLs) in MASLD fibrosis. We further verified that patient LDLs with reduced SL content triggered a smaller activation of type II natural killer T lymphocytes, compared with control LDLs. Our results suggest that hepatic crosstalk with systemic immunity mediated by lipoprotein metabolism underlies fibrosis progression at early-stage MASLD.

How do we safely preserve ovaries in patients with cervical adenocarcinoma: risk factors and predictive models.

To study and predict the risk of ovarian metastasis (OM) in patients with cervical adenocarcinoma (ADC). Patients with ADC who received surgical treatment from January 2015 to December 2022 in the Obstetrics and Gynecology Hospital of Fudan University were included in the study. Patients were further divided into OP (ovaries were preserved in surgery) and BSO (bilateral salpingo-oophorectomy) groups. For the patients who accepted BSO, 60% of the patients were randomly grouped into a training cohort, and predictive prognostic models were constructed with 10-fold cross-validation through random forest, LASSO, stepwise, and optimum subset analysis. The model with the highest area under receiver operator curve (AUC) was screened out in the testing cohort. The nomogram and its calibration curve presented the possibility of OM in future patients. The prognoses between the different populations were compared using Kaplan-Meier analysis. All data processing was carried out in R 4.2.0 software. A total of 934 patients were enrolled in our cohort; 266 patients had their ovaries preserved and 668 patients had BSO according to the previous criteria reported The clinical safety with these criteria was secured, while the 5-year overall survival had no significant difference between the BSO and OP groups (p = 0.069), which suggested that the current criteria could be extended and are more precise. Four predictive models for ovarian metastasis by machine learning were constructed in our study, and the random forest model that obtained the highest AUC in both training and testing sets (0.971 for training and 0.962 for testing set) was taken as the best model. The optimal cut-off point of the ROC curve (specificity 99.5% and 90% sensitivity) was utilized to stratify the patients into high- and low-risk OM. Further comparing the survival curves of the high and low-OM risk groups, it was found that both DFS and OS were significantly prolonged in the low-risk group (p < 0.01). On the basis of this random forest model, a nomogram was used to calculate the OM risk, and the results were validated with calibration. The predictive model was further applied to the whole cohort (934 patients), and we identified the OM low-risk population (n = 822) and the patients with high risk who should be recommended for BSO (n = 112). No significant difference was found in the 5-year survival between the low-risk group with our model and the patients who already had ovaries preserved according to the previous criteria (n = 266). The predictive model constructed in our study could identify the low-risk population of OM in patients with ADC, which remarkably extended the number with the previous criteria, for whom we could potentially preserve ovaries to help improve their life quality.

Machine learning for prediction of incident heart failure and heart failure hospitalisation: multi-cohort development, validation and association with clinical phenotypes

Abstract Background Delayed heart failure (HF) diagnosis is associated with adverse outcomes and costs of hospitalisation.(1,2) Whilst previous tools have been developed to predict incident HF,(3) risk of HF incidence may correlate poorly with risk of HF hospitalisation, which may be more important in targeting diagnostics. Purpose To develop and validate a novel decision support tool for prediction of incident HF and HF hospitalisation, and investigate association of score with HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). Methods We developed the models (FIND-HF) in UK primary care health record data from individuals aged ≥40 years without known HF (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and supervised machine learning models for prediction of 1- and 5- year HF and HF hospitalisation risk in the testing dataset with internal bootstrap validation. Models were externally validated for HF hospitalisation in data from a Taiwanese university hospital. Association of FIND-HF score with HF phenotypes of HFpEF and HFrEF was assessed in two prospective cohorts, the MATCH registry of patients diagnosed with HFrEF and the FIND-AF cohort of individuals at higher predicted risk of atrial fibrillation who underwent echocardiography. Results Of 565 284 UK individuals (mean age 68.9 (SD 12.2) years, 49.2% women), incidence of HF and HF hospitalisation was 1.1% and 0.5% at 1-year, and 6.6% and 3.4% at 5-years, respectively. Prediction performance was superior for machine learning algorithms compared with logistic regression, with best performance for XGBoost (area under receiver operating characteristic curve (AUROC) at 1 and 5-years: 0.755 and 0.723 for incident HF, and 0.738 and 0.711 for incident HF hospitalization). On external validation in 106,026 individuals in Taiwan (mean age 64.7 (SD 10.6) years, 52.8 % women), HF hospitalisation incidence was 0.71%, and 2.46% at 1- and 5- years, respectively, and discrimination performance was excellent (AUROC at 1- and 5-years: 0.834 and 0.843). When the FIND-HF score was applied to individuals in the MATCH cohort (n = 133, mean age 69.0 (SD 11.7) years, 38.3% women) and FIND-AF cohort (n = 82, mean age 71.6 (SD 7.5) years, 50.0% women), the distribution of score to clinical phenotypes demonstrated a binomial distribution (Figure 1), and likelihood of HFpEF diagnosis correlated with the numeric score (Figure 2). Conclusions The machine learning FIND-HF decision support tool can accurately identify individuals at risk of incident HF and HF hospitalisation, to enable new approaches for early detection and prevention. The variation of score amongst high risk individuals with different HF clinical phenotypes emphasizes the distinct pathophysiological pathways but shared adverse outcome profile across the HF syndrome.Figure 1Figure 2

Cardiac magnetic resonance in MINOCA patients: a non-tertiary hospital experience

Abstract Background Cardiac magnetic resonance (CMR) is a key tool in the diagnostic process of myocardial infarction with non-obstructive coronary arteries (MINOCA), impacting patient management and prognosis. Its use in non-tertiary hospitals is often constrained mainly by accessibility. Thus, the aim of this study was thoroughly evaluate CMR application in this context to optimize the process. Methods 136 MINOCA patients who were admitted to our hospital between 2016 and January 2024 were analyzed. Each MINOCA patient was reviewed by experts to align with the diagnosis criteria outlined in the 2020 ESC Guidelines and the American Heart Association position paper, which recommended CMR for MINOCA patient’s evaluation. To assess the factors associated with normal-CMR, a logistic regression analysis was performed. In a first step, some factors were detected, showing a trend in the association. In a second step, a backward elimination procedure was used, excluding factors with p&amp;gt;0.1. Results CMR was performed in 65 MINOCA patients (48% of the cohort), with a mean age of 59±15 years old, 35 of them were males (54% of total patients). During the follow-up, there were 4 deaths (6%) and 4 readmissions (6%). Out of the total 65 patients, the CMR diagnosis was myocardial infarction (MI) in 14 (21.5%) and myocarditis in 16 (24.6%), and was normal in 35 (53.9%). Therefore, CMR provided diagnostic information in 46% of the cases. It was noted that a normal CMR was associated with female gender, (OR 2.7, 95% CI 0.96-7.34, p=0.057), hypertension (OR 2.25, 95% CI 0.81-6.22, p=0.11), absence of segmental abnormalities in echocardiography (OR 0.14, 95% CI 0.035-0566, p=0.06) and lower high-sensitivity Troponin T (OR 0.79, 95% CI: 0.66-0.95, p=0.001). In the second step, only segmental abnormalities (OR 0.2, 95%CI 0.04-1.09, p=0.063) and troponin (OR 0.31, 95%CI 0.10-0.99, p=0.048) remained included. Additionally, the Youden Index was determined to obtain the threshold for troponin, resulting a value of 606 nG/mL, area under ROC curve (AUC) = 0.784 (figure). Conclusions - CMR was performed in 48% of the MINOCA patients, reflecting the limitation for its use in routine clinical practice. Furthermore, it was useful in 46% of those patients. - The main factors associated with normal CMR were absence of segmental echocardiography abnormalities and reduced troponin levels. - A troponin cutoff point of 606 ng/mL identified patients with a normal CMR (AUC=0.784).Figure 1.CMR in MINOCA patientsFigure 2.Area under ROC curve = 0.784

Association of type-D personality and left-ventricular remodelling in patients treated with primary percutaneous intervention after ST-segment elevation myocardial infarction

BackgroundType-D personality is an established predisposing factor for various diseases. Type-D traits have been shown to pose a 26% increased risk of coronary artery disease after controlling for other confounding factors. Significant associations have been reported between type-D personality traits and dyslipidaemia, impaired endothelial function, coronary heart disease (CAD), acute myocardial infarction, and other adverse cardiovascular events.ObjectiveTo assess the association between type-D personality and left-ventricular adverse remodelling in patients treated with percutaneous coronary intervention following index ST-segment elevation myocardial infarction.MethodsAll patients hospitalized and treated with percutaneous coronary intervention (PCI) after their index ST-segment elevation myocardial infarction (STEMI) between 1 January 2022 to 31 December 2023 were prospectively enrolled. Type-D personality traits in the study population were determined at baseline using type-D Scale-14 (DS14) instrument, whereas any positive change in left ventricular end diastolic volume (LVEDV) ≥ 20% at follow up period of 12-months from baseline was defined as left-ventricular adverse remodelling (LVAR). Univariate and multivariate analysis was done to establish the independent predictors of LVAR. The area under receiver-operating characteristic curve (AUROC) was employed to assess the sensitivity and specificity of the identified independent predictors.ResultsA total of 124 patients were enrolled in the study. The mean age of the study population was 67 ± 10 years and the overall incidence of LVAR was found to be 25%. Multivariate regression analysis revealed that type-D personality is a significant independent predictor of LVAR \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(OR:2.51;95\\%CI:1.16-5.77;p=0.03)$$\\end{document} apart from the already established independent predictors Killip Class\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:(OR:7.30;95\\%CI:3.03-17.55;p<0.0001)$$\\end{document}, baseline Global Longitudinal strain (GLS)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:(OR:2.69;95\\%CI:1.19-6.61;p=0.01)$$\\end{document}, and 3-vessel CAD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:(OR:2.27;95CI:1.10-5.33;p=0.04)$$\\end{document}. In ROC curve analysis type-D personality as an independent predictor of LVAR achieved a sensitivity of 41.4% and a specificity of 87.1%, p < 0.02.ConclusionType-D personality trait is a significant independent predictor of LVAR in patients treated with PCI after their index-STEMI.

A Strategy for Predicting the Performance of Supervised and Unsupervised Tabular Data Classifiers

AbstractMachine Learning algorithms that perform classification are increasingly been adopted in Information and Communication Technology (ICT) systems and infrastructures due to their capability to profile their expected behavior and detect anomalies due to ongoing errors or intrusions. Deploying a classifier for a given system requires conducting comparison and sensitivity analyses that are time-consuming, require domain expertise, and may even not achieve satisfactory classification performance, resulting in a waste of money and time for practitioners and stakeholders. This paper predicts the expected performance of classifiers without needing to select, craft, exercise, or compare them, requiring minimal expertise and machinery. Should classification performance be predicted worse than expectations, the users could focus on improving data quality and monitoring systems instead of wasting time in exercising classifiers, saving key time and money. The prediction strategy uses scores of feature rankers, which are processed by regressors to predict metrics such as Matthews Correlation Coefficient (MCC) and Area Under ROC-Curve (AUC) for quantifying classification performance. We validate our prediction strategy through a massive experimental analysis using up to 12 feature rankers that process features from 23 public datasets, creating additional variants in the process and exercising supervised and unsupervised classifiers. Our findings show that it is possible to predict the value of performance metrics for supervised or unsupervised classifiers with a mean average error (MAE) of residuals lower than 0.1 for many classification tasks. The predictors are publicly available in a Python library whose usage is straightforward and does not require domain-specific skill or expertise.

Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data.

Patients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system. Patients visiting the ED or hospitalized with HF between 2002-2016 in Alberta, Canada were included. Outcomes of interest were 30-day and 1-year HF-related ED visits, HF hospital readmission or all-cause mortality. We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. The area under receiver operating characteristic curve (AUC-ROC) was used to assess model performance. We included 50,630 patients with 93,552 HF ED visits/hospitalizations. At 30-day follow-up in the holdout validation cohort, the AUC-ROC for the combined endpoint of HF ED visit, HF hospital readmission or death for the Catboost and logistic regression models was 74.16 (73.18-75.11) versus 62.25 (61.25-63.18), respectively. At 1-year follow-up corresponding values were 76.80 (76.1-77.47) versus 69.52 (68.77-70.26), respectively. AUC-ROC values for the endpoint of all-cause death alone at 30-days and 1-year follow-up were 83.21 (81.83-84.41) versus 69.53 (67.98-71.18), and 85.73 (85.14-86.29) versus 69.40 (68.57-70.26), for the CatBoost and logistic regression models, respectively. ML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.

A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer.

The objective of the present study was to build and validate a radio-clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast-enhanced computed tomography within one month before surgery. The focal area was mapped by ITK-SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio-clinical model was constructed by employing multi-variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio-clinical model, consisting of rad-score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio-clinical model. In conclusion, the radio-clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.

Platelet-to-Lymphocyte Ratio as Coagulopathy Predictor in COVID-19 Patients at Margono Soekarjo Hospital

Coronavirus Disease 2019 (COVID-19) is a disease possibly attacking various organs and systems of the body including the coagulation system causing coagulopathy. Various laboratory biomarkers have been developed to detect coagulopathy. This research aimed to determine the correlation and ability of Platelet-to-Lymphocyte Ratio (PLR) in predicting coagulopathy when compared to D-dimer. A cross-sectional research was conducted on 1580 data of COVID-19 patients at Margono Soekarjo Hospital. The research data were then analyzed using Spearman’s correlation test to figure out the correlation between PLR and D-dimer. This research was also intended to find the PLR Cut-Off Value (COV), Odds Ratio (OR), Area Under Receiver Operating Characteristic (AUROC), and diagnostic value. Platelet-to-lymphocyte ratio value had a significant correlation with D-dimer levels (r=0.260, p=0.000). The COV of PLR was 176.61 with the OR of 2.7 (2.2-3.3 95%CI), AUROC of 0.638, sensitivity of 62%, and specificity of 62%. PLR can be used as a screening biomarker to predict the occurrence of coagulopathy in COVID-19 patients.

Application of serum SAA, CRP, PCT, WBC and N% in the diagnosis of neonatal septicemia

To explore the application value of SAA (serum amyloid A), CRP (C reactive protein), PCT (procalcitonin), WBC (white blood cell) and N% (neutrophil %) in the diagnosis of neonatal septicemia. This study was a retrospective study. 173 children with clinically diagnosed septicemia and 66 children with definitely diagnosed septicemia admitted to the Department of Neonatology, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China,from January 2022 to January 2024 were selected as the case group, and 148 children with neonatal jaundice who were hospitalized during the same period were selected as the control group. Fasting venous blood was collected within 24 hours after the children's admission to detect the levels of serum WBC, N%, SAA, CRP and PCT. One-way analysis of variance and Kruskal-Wallis H test were used to compare the general data and inflammatory index levels of the three groups of children. The correlation analysis between SAA and other inflammatory indicators was conducted using Spearman correlation analysis. The receiver operating characteristic (ROC) curve was used to determine the diagnostic efficacy of different inflammatory indicators for patients with definitely diagnosed septicemia and those with clinically diagnosed septicemia, and for those with clinically diagnosed septicemia and those without infection. The results showed that the levels of WBC [(16.88±5.64)×109/L], N% [70.00 (63.00, 75.00)], PCT [2.22 (1.20, 5.55) mg/L], CRP [3.00 (0.50, 10.30) mg/L], SAA [19.70 (10.82, 49.90) mg/L] in the clinically diagnosed septicemia group and WBC [(16.10±7.48)×109/L], N% [73.50 (61.50, 80.93)], PCT [5.35 (0.69, 20.07) mg/L], CRP [15.52 (4.98, 30.50) mg/L], SAA [43.95 (14.00, 175.98) mg/L] in the definitely diagnosed septicemia group were all higher than those in the control group (11.17±3.38)×109/L, 49.81 (36.93, 62.75), 0.20 (0.07, 0.99) mg/L, 0.54 (0.20, 1.40) mg/L, 5.15 (3.60, 8.68) mg/L, and the differences were all statistically significant (all P<0.05). Spearman correlation analysis showed that the level of SAA was positively correlated with WBC, N%, PCT and CRP (rs=0.453, 0.540, 0.343, 0.550, all P<0.05). ROC curve analysis showed that the area under ROC curve(AUC) of SAA for the definitely diagnosed septicemia group and the clinically diagnosed septicemia group was higher than that of other inflammatory indicators, among them, the AUC of SAA for diagnosing the definitely diagnosed neonatal septicemia group was 0.933 (95%CI: 0.809-1.000, P<0.05), with a sensitivity of 92.90% and a specificity of 99.30%. The AUC of SAA for diagnosing the clinically diagnosed septicemia group was 0.861 (95%CI: 0.818-0.904, P<0.05), with a sensitivity of 83.20% and a specificity of 81.80%. In conclusion, compared with CRP, PCT, WBC and N%, SAA has higher sensitivity and specificity for distinguishing neonatal septicemia (including definitely diagnosed septicemia and clinically diagnosed septicemia), and has certain auxiliary diagnostic value for neonatal septicemia.

Development and validation of a nomogram for predicting pulmonary complications in elderly patients undergoing thoracic surgery

BackgroundPostoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population.MethodsA total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA).ResultsA total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO2) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719–0.762), and it was 0.703 for the validation cohort (95% CI: 0.657–0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort.ConclusionThis study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions.Graphical

GCNPMDA: Human microbe-disease association prediction by hierarchical graph convolutional network with layer attention

Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe–microbe similarities, and disease–disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe–microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease–disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model’s predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts.