Area Under The ROC Curve Research Articles

Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis

Abstract Background Transthyretin amyloid cardiomyopathy (ATTR-CM) remains largely under-recognized, under-diagnosed, and under-treated. We hypothesized that the myocardial remodeling seen in ATTR-CM may be detectable through artificial intelligence (AI) applied to 12-lead electrocardiographic (ECG) images. Purpose To develop an AI-ECG algorithm that can detect ATTR-CM from images of 12-lead electrocardiograms. Methods Across 5 hospitals of a large U.S.-based hospital system, we identified patients with ATTR-CM, defined by the presence of a positive bone scintigraphy scan or pharmacotherapy with an approved transthyretin stabilizer between 2015 and the first half of 2023. The development cohort consisted of 1,011 ECGs from 234 patients (age 79 [IQR:70-85], n=176 (17.4%) women), who were age- and sex-matched in a 10:1 ratio to 10,110 ECGs from 10,110 controls (age 79 [IQR:70-85] years, n=1,800 [17.7%] female). A convolutional neural network (CNN) pre-trained using a bio-contrastive pretext on ECGs before 2015 was fine-tuned for ATTR-CM using 5-fold cross-validation and subsequently tested in an independent set of cases (139 ECGs in 47 patients; age 80 [75-86] years, n=44 (31.7% women)) and matched controls (1390 ECGs and patients) from the second half of 2023. Results The AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for discriminating ATTR-CM in the cross-validation set was 0.90 [95%CI: 0.89-0.91], and in the leave-out, temporally distinct dataset was 0.915 [95%CI: 0.89-0.94] (A), with a sensitivity of 0.85 [95%CI: 0.79-0.91] and specificity 0.80 [95%CI 0.78-0.82]. Representative saliency maps are shown in panel B. In simulations, the positive predictive value ranged from 0.12 [95%CI: 0.10-0.13] at a 3% prevalence level to 0.59 [95%CI: 0.56-0.62] at 25% prevalence. Conclusions We demonstrate that AI applied directly to ECG images represents a promising approach for the screening of ATTR-CM.

On the interpretability of the SVM model for predicting infant mortality in Bangladesh

BackgroundAlthough machine learning (ML) models are well-liked for their outperformance in prediction, greatly avoided due to the lack of intuition and explanation of their predictions. Interpretable ML is, therefore, an emerging research field that combines the performance and interpretability of ML models to create comprehensive solutions for complex decision-making analysis. Conversely, infant mortality is a global public health concern affecting health, social well-being, socio-economic development, and healthcare services. The study employs advanced interpretable ML techniques to anticipate and understand the factors affecting infant mortality in Bangladesh, overcoming the shortcomings of the conventional logistic regression (LR) model.MethodsBy utilizing the global surrogate model and local individual conditional expectation (ICE) interpretability technique, the interpretable support vector machine (SVM) has been used in this study to reveal significant characteristics of infant mortality using data from the Bangladesh Demographic and Health Survey (BDHS) 2017–18. To investigate intricate decision-making analysis of infant mortality, we adapted SVM and LR techniques with the hyperparameter tuning parameters. These models’ performances were initially assessed using the receiver operating characteristics (ROC) curve, run-time, and confusion matrix parameters with 100 permutations. Afterward, the SVM model’s model-agnostic explanation and the LR model’s interpretation were compared to enhance advanced comprehension for further insights.ResultsThe results of the 100 permutations demonstrated that the LR model (Average: accuracy = 0.9105, precision = NaN, sensitivity = 0, specificity = 1, F1-score = 0, area under the ROC curve (AUC) = 0.6780, run-time = 0.0832) outperformed the SVM model (Average: accuracy = 0.8470, precision = 0.1062, sensitivity = 0.0949, specificity = 0.9209, F1-score = 0.1000, AUC = 0.5632, run-time = 0.0254) in predicting infant mortality, but the LR model had a slower run-time and it was unable to predict any positive cases. The interpretation of LR analysis revealed that infant mortality rates decrease when mothers give birth after over two years, with higher educational attainment, overweight or obese mothers, working mothers, and families with polluted cooking fuel having lower rates. The local ICE interpretability technique, which depicts individual influences on the average likelihood of dying within the first birthday, explores the interpretable SVM model that mothers with normal BMIs, giving birth within two years, using less polluted cooking fuel, working mothers, and having male infant were more likely to experience infant death. The interpretable SVM model based on the global surrogate model also reveals that working mothers who used polluted cooking fuel at home and working women who used less polluted cooking fuel but had a longer period between pregnancies than two years would have higher infant death rates. Even among non-working mothers who used polluted cooking fuel and gave birth within two years of the preceding one, infant death rates were higher.ConclusionsThe interpretable SVM model reveals global interpretations help clinicians understand the entire conditional distribution, while local interpretations focus on specific instances, providing different insights into model behavior. Interpretable ML models aid policymakers, stakeholders, and families in understanding and preventing infant deaths by improving policy-making strategies and establishing effective family counseling services.

Association between inflammatory burden index and coronary slow flow phenomenon in patients with chest pain and no obstructive coronary arteries

BackgroundThe inflammatory burden index (IBI), a novel inflammation-based indicator, to is associated with the presence and prognosis of various diseases. However, few studies have focused on exploring the relationship between IBI and the coronary slow flow phenomenon (CSFP). In this study, we aimed to investigate the predictive value of IBI for CSFP in patients with chest pain and no obstructive coronary artery disease.MethodsA total of 1126 individuals with chest pain and no obstructive coronary arteries were consecutively included in this study. 71 patients developed CSFP were included in the CSFP group. A 1:2 age- and sex-matched patient with normal blood flow and angiographically proven normal coronary arteries was selected as the control group (n = 142). Plasma C-reactive protein (CRP), neutrophil, and lymphocyte counts were measured to determine the value of IBI.ResultsThe IBI were significantly higher in the CSFP group than in the controls (21.1 ± 6.5 vs. 14.5 ± 6.4, P < 0.001). The IBI increasedelevated with the increase of the numbers of vessels affected by CSFP. Multivariate logistic regression analysis revealed that IBI and body mass index (BMI) were independent predictors of CSFP. Receiver operating characteristic (ROC) curve analysis showed that when IBI was > 15.74, the sensitivity and specificity were 77.5% and 67.6%, respectively, and the area under the ROC curve (AUC) was 0.799 (95% CI: 0.737-0.862, P<0.001).ConclusionThe IBI may be an independent predictor of CSFP in patients with chest pain and normal coronary arteries. The IBI could improve the predictive value of CSFP compared with the indicators alone.

Proteomic and serological markers for diagnosing cardia gastric cancer and precursor lesions in a Chinese population

Cardia gastric cancer (CGC) is prevalent in East Asia, and noninvasive, cost-effective screening methods are needed. This study investigated the diagnostic value of serum pepsinogen (PG), gastrin-17 (G-17), Helicobacter pylori (H. pylori) antibodies, and proteomic profiling for CGC and precancerous lesions. We conducted a case-control study involving biopsy-confirmed patients with CGC (n = 60), low-grade intraepithelial neoplasia (CLGD, n = 60), high-grade intraepithelial neoplasia (CHGD, n = 64), and healthy controls (n = 120) matched for age and sex from high-incidence areas in China. Serological markers including PGI, PGII, G-17, and H. pylori were measured using ELISA and Western blot, while plasma protein markers were assessed using Olink® technology. The VSOLassoBag algorithm and nine machine learning (ML) algorithms were employed to identify crucial features and construct predictive models. Various evaluation metrics, including the area under the receiver-operating-characteristic curve (AUC), were utilized to compare predictive performance. Elevated PGII levels, decreased PGR, and H. pylori infection were significantly associated with an increased risk of CGC and precancerous lesions (P for trend < 0.05). The eXtreme Gradient Boosting (XGBoost) model performed best in discriminative ability among the 9 ML models. Following feature reduction based on predictive performance, a final explainable XGBoost model was developed, incorporating five protein biomarkers (CDHR2, ICAM4, PTPRM, CDC27, and FLT1). This model exhibited excellent performance in distinguishing individuals with CGC and precancerous lesions from healthy controls (AUC = 0.931 for CGC, 0.867 for CHGD, and 0.763 for CLGD), surpassing the traditional serological marker-based model. This study underscores the diagnostic potential of serological markers and proteomic profiling in the detection of CGC. Further validation and exploration of combined biomarker approaches are warranted to enhance early diagnosis and improve outcomes in high-risk populations.

Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.

This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction. Data from ICU patients (December 2016 to December 2021), including serum levels of trace elements, vitamins, cholesterol, and prealbumin, were retrospectively analyzed using AI models. Models employed included category boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and multilayer perceptron (MLP). Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1-score. The performance was evaluated using 10-fold crossvalidation. The SHapley Additive exPlanations (SHAP) method provided interpretability. CatBoost emerged as the top-performing individual AI model with an AUROC of 0.756, closely followed by LGBM, MLP, and XGBoost. Furthermore, the ensemble model combining these four models achieved the highest AUROC of 0.776 and more balanced metrics, outperforming all models. SHAP analysis indicated significant influences of prealbumin, Acute Physiology and Chronic Health Evaluation II score, and age on predictions. Notably, the ratios of selenium to age and low-density lipoprotein to total cholesterol also had a notable impact on the models' output. The study underscores the critical role of nutrition-related parameters in ICU patient outcomes. Advanced AI models, particularly in an ensemble approach, demonstrated improved predictive accuracy. SHAP analysis offered insights into specific factors influencing patient survival, highlighting the need for broader consideration of these biomarkers in critical care management.

Deep learning-based super-resolution and denoising algorithm improves reliability of dynamic contrast-enhanced MRI in diffuse glioma

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to non-invasively image blood-brain barrier leakage, yet its clinical utility has been hampered by issues such as noise and partial volume artifacts. In this retrospective study involving 306 adult patients with diffuse glioma, we applied deep learning-based super-resolution and denoising (DLSD) techniques to enhance the signal-to-noise ratio (SNR) and resolution of DCE-MRI. Quantitative analysis comparing standard DCE-MRI (std-DCE) and DL-enhanced DCE-MRI (DL-DCE) revealed that DL-DCE achieved significantly higher SNR and contrast-to-noise ratio (CNR) compared to std-DCE (SNR, 52.09 vs 27.21; CNR, 9.40 vs 4.71; P < 0.001 for all). Diagnostic performance assessed by the area under the receiver operating characteristic curve (AUROC) showed improved differentiation of WHO grades based on a pharmacokinetic parameter \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {V}_{{e}}$$\\end{document} (AUC, 0.88 vs 0.83, P = 0.02), while remaining comparable to std-DCE in other parameters. Analysis of arterial input function (AIF) reliability demonstrated that \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {AIF}_{{DL}}$$\\end{document} exhibited superior agreement compared to \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {AIF}_{{std}}$$\\end{document}, as indicated by mostly higher intraclass correlation coefficients (Time to peak, 0.79 vs 0.43, P < 0.001). In conclusion, DLSD significantly enhances both the image quality and reliability of DCE-MRI in patients with diffuse glioma, while maintaining or improving diagnostic performance.

Predicting neonatal mortality prior to discharge from hospital in prenatally diagnosed left congenital diaphragmatic hernia.

To evaluate the association of standardized prenatal imaging parameters and immediate neonatal variables with mortality prior to discharge in infants with isolated left congenital diaphragmatic hernia (LCDH), and to compare the performance of ultrasound- and magnetic resonance imaging (MRI)-based severity grading for the prediction of neonatal mortality. This was a retrospective study of infants with prenatally diagnosed isolated LCDH referred to a single tertiary center between 2008 and 2020. Fetuses with right or bilateral congenital diaphragmatic hernia, additional major structural anomaly or known genetic condition, as well as cases that underwent fetal intervention or declined postnatal intervention, were excluded. Ultrasound and MRI images were reviewed retrospectively. Univariable and multivariable analyses were performed, incorporating prenatal and immediate neonatal factors to analyze the association with neonatal mortality prior to discharge, and a prediction calculator was generated. The performance of ultrasound and that of MRI for the prediction of neonatal mortality were compared. Of 253 pregnancies with fetal CDH, 104 met the inclusion criteria, of whom 77 (74%) neonates survived to discharge. Seventy-five fetuses underwent both prenatal ultrasound and MRI. On multivariable analysis, observed/expected (o/e) lung-to-head ratio and o/e total fetal lung volume were associated independently with neonatal death (adjusted odds ratio, 0.89 (95% CI, 0.83-0.95) and 0.90 (95% CI, 0.84-0.97), respectively), whereas liver position was not. There was no significant difference in predictive performance between using ultrasound and MRI together (area under the receiver-operating-characteristics curve (AUC), 0.85 (95% CI, 0.76-0.93)) compared with using ultrasound alone (AUC, 0.81 (95% CI, 0.72-0.90); P = 0.19). The addition of neonatal parameters (gestational age at birth and small-for-gestational age) did not improve model performance (AUC, 0.87 (95% CI, 0.80-0.95)) compared with the combined ultrasound and MRI model (P = 0.22). There was poor agreement between severity assessment on ultrasound and MRI (Cohen's κ, 0.19). Most discrepancies were seen among cases deemed to be non-severe on ultrasound and severe on MRI, and outcomes were more consistent with MRI-based prognostication. In fetuses with prenatally diagnosed isolated LCDH, mortality prediction using standardized ultrasound and MRI measurements performed reasonably well. In cases classified as non-severe on ultrasound, MRI is recommended, as it may provide more accurate prognostication and assist in the determination of candidacy for fetal intervention. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Validation of serum non-invasive tests of liver fibrosis as prognostic markers of clinical outcomes in people with fatty liver disease in Australia.

The validity of non-invasive tests (NITs) of liver fibrosis for the prediction of liver and mortality outcomes in an Australian cohort is unknown. We aimed to verify the utility of available NITs to predict overall and cause-specific mortality and major adverse liver outcome (MALO). This was an analysis from the Crossroads 1 clinic sub-study of a randomly sampled adult cohort from regional Australia between 2001 and 2003. Baseline variables included demographic details, anthropometry, health and lifestyle data, and laboratory tests. Non-alcoholic fatty liver disease (NAFLD) and metabolic-(dysfunction) associated fatty liver disease (MAFLD) were defined by fatty liver index≥60 and other accepted criteria. Outcomes were defined by the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes for linked hospitalization and death registry data. Available serum-based NITs were analyzed as predictors of overall, cardiovascular disease-related, and cancer-related mortality and MALO in those with fatty liver disease (FLD). In total, 1324 and 1444 participants were included for NAFLD and MAFLD analysis (prevalence 35.4% and 40.7%, respectively). There were 298 deaths (89 cardiovascular disease-related and 98 cancer-related) and 24 MALO over a median 19.7years of follow-up time. In both forms of FLD, fibrosis-4 index, Steatosis-Associated Fibrosis Estimator score, and Forns fibrosis score consistently had the highest area under the receiver operating characteristic curve (AUROC) for overall and cause-specific mortality, with AUROC>0.70 for each outcome. However, all had poor discriminatory ability for determining MALO in each FLD. Several liver fibrosis NITs perform similarly reasonably well in predicting the risk of mortality outcomes in those with FLD but are poorly discriminatory for MALO prediction.

Shear-Wave Elastography Improves Diagnostic Accuracy in Chronic Kidney Disease Compared to Conventional Ultrasound.

Non-invasive tests are increasingly demanded for diagnosing and prognostication of chronic kidney disease (CKD). Shear-wave elastography (SWE), an emerging technique for measuring tissue stiffness, shows promise for distinguishing between individuals with different stages of renal fibrosis. This study aimed to compare the diagnostic accuracy of two-dimensional SWE (2D-SWE) and conventional ultrasound for detecting CKD, employing renal biopsy as the gold standard. From May 2020 to October 2023, this prospective study included 30 healthy volunteers and 169 patients with CKD who had undergone 2D-SWE and conventional ultrasound of both kidneys. Cortical and medullary stiffness, cortical pixel intensity, renal length, parenchymal and cortical thickness, interlobar artery peak systolic velocity, end-diastolic velocity (EDV), and resistive index were measured. The diagnostic accuracy of 2D-SWE and conventional ultrasound was compared using the receiver operating characteristic curve (ROC) and Delong test. For diagnosing CKD, the area under the ROC (AUC) of cortical stiffness (0.96 [95% CI, 0.93, 0.99]) was significantly higher than that of all conventional ultrasound parameters, including EDV (0.78 [95% CI, 0.71, 0.86]) and cortical thickness (0.74 [95% CI, 0.67, 0.80]). The sensitivity of cortical stiffness (91%) was significantly higher than that of EDV (68%) and cortical thickness (53%). No significant difference was found in the specificity of cortical stiffness (96%) compared to that of EDV (79%) and cortical thickness (100%). Two-dimensional SWE showed higher diagnostic accuracy than that of conventional ultrasound for detecting CKD.

G4 & the balanced metric family – a novel approach to solving binary classification problems in medical device validation & verification studies

BackgroundIn medical device validation and verification studies, the area under the receiver operating characteristic curve (AUROC) is often used as a primary endpoint despite multiple reports showing its limitations. Hence, researchers are encouraged to consider alternative metrics as primary endpoints. A new metric called G4 is presented, which is the geometric mean of sensitivity, specificity, the positive predictive value, and the negative predictive value. G4 is part of a balanced metric family which includes the Unified Performance Measure (also known as P4) and the Matthews’ Correlation Coefficient (MCC). The purpose of this manuscript is to unveil the benefits of using G4 together with the balanced metric family when analyzing the overall performance of binary classifiers.ResultsSimulated datasets encompassing different prevalence rates of the minority class were analyzed under a multi-reader-multi-case study design. In addition, data from an independently published study that tested the performance of a unique ultrasound artificial intelligence algorithm in the context of breast cancer detection was also considered. Within each dataset, AUROC was reported alongside the balanced metric family for comparison. When the dataset prevalence and bias of the minority class approached 50%, all three balanced metrics provided equivalent interpretations of an AI’s performance. As the prevalence rate increased / decreased and the data became more imbalanced, AUROC tended to overvalue / undervalue the true classifier performance, while the balanced metric family was resistant to such imbalance. Under certain circumstances where data imbalance was strong (minority-class prevalence < 10%), MCC was preferred for standalone assessments while P4 provided a stronger effect size when evaluating between-groups analyses. G4 acted as a middle ground for maximizing both standalone assessments and between-groups analyses.ConclusionsUse of AUROC as the primary endpoint in binary classification problems provides misleading results as the dataset becomes more imbalanced. This is explicitly noticed when incorporating AUROC in medical device validation and verification studies. G4, P4, and MCC do not share this limitation and paint a more complete picture of a medical device’s performance in a clinical setting. Therefore, researchers are encouraged to explore the balanced metric family when evaluating binary classification problems.

Machine learning-based prediction of sarcopenia in community-dwelling middle-aged and older adults: findings from the CHARLS.

Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user-friendly calculator for community-dwelling middle-aged and older adults. We used two waves (2011 and 2013) of the China Health and Retirement Longitudinal Study (CHARLS) to predict sarcopenia, defined by the Asian Working Group for Sarcopenia 2019 criteria. We restricted the analytical sample to adults aged 45 or above (N = 2934). Five machine learning models were used to construct Q-based (only questionnaire variables), Bio-based (only biomarker variables), and combined (questionnaire plus biomarker variables) models. Area under the receiver operating characteristic curve (AUROC) was used for performance assessment. Temporal external validation was performed based on two datasets from CHARLS. Important predictors were identified by Shapley values and coefficients. Extreme gradient boosting (XGBoost), considering both questionnaire and biomarker characteristics, emerged as the optimal model, and its AUROC was 0.759 (95% CI: 0.747-0.771) at a decision threshold of 0.20 on the test set. Models also performed well on the external datasets. We found that cognitive function was the most important predictor in both Q-based and combined models, and blood urea nitrogen was the most important predictor in the Bio-based model. Other key predictors included education, haematocrit, total cholesterol, drinking, number of chronic diseases, and instrumental activities of daily living score. Our findings offer a potential for early screening and targeted prevention of sarcopenia among middle-aged and older adults in the community setting.

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

BackgroundCardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine learning (ML) model to predict CVD risk based on VOC exposure and demographic data using SHapley Additive exPlanations (SHAP) for interpretability. MethodsWe utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018, comprising 5098 participants. VOC exposure was assessed through 15 urinary metabolite metrics. The dataset was split into a training set (70 %) and a test set (30 %). Six ML models were developed, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM). Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC), accuracy, balanced accuracy, F1 score, J-index, kappa, Matthew's correlation coefficient (MCC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (sens), specificity (spec) and SHAP was applied to interpret the best-performing model. ResultsThe RF model exhibited the highest predictive performance with an ROC of 0.8143. SHAP analysis identified age and ATCA as the most significant predictors, with ATCA showing a protective effect against CVD, particularly in older adults and those with hypertension. The study found a significant interaction between ATCA levels and age, indicating that the protective effect of ATCA is more pronounced in older individuals due to increased oxidative stress and inflammatory responses associated with aging. E-values analysis suggested robustness to unmeasured confounders. ConclusionsThis study is the first to utilize VOC exposure data to construct an ML model for predicting CVD risk. The findings highlight the potential of combining environmental exposure data with demographic information to enhance CVD risk prediction, supporting the development of personalized prevention and intervention strategies.

The 123 COVID SCORE: A simple and reliable diagnostic tool to predict in-hospital death in COVID-19 patients on hospital admission.

Patients hospitalized due to Coronavirus disease 2019 (COVID-19) are still burdened with high risk of death. The aim of this study was to create a risk score predicting in-hospital mortality in COVID-19 patients on hospital admission. Independent mortality predictors identified in multivariate logistic regression analysis were used to build the 123 COVID SCORE. Diagnostic performance of the score was evaluated using the area under the receiver-operating characteristic curve (AUROC). Data from 673 COVID-19 patients with median age of 70 years were used to build the score. In-hospital death occurred in 124 study participants (18.4%). The final score is composed of 3 variables that were found predictive of mortality in multivariate logistic regression analysis: (1) age, (2) oxygen saturation on hospital admission without oxygen supplementation and (3) percentage of lung involvement in chest computed tomography (CT). Four point ranges have been identified: 0-5, 6-8, 9-11 and 12-17, respectively corresponding to low (1.5%), moderate (13.4%), high (28.4%) and very high (57.3%) risk of in-hospital death. The 123 COVID SCORE accuracy measured with the AUROC was 0.797 (95% CI 0.757-0.838; p<0.0001) in the study population and 0.774 (95% CI 0.728-0.821; p<0.0001) in an external validation cohort consisting of 558 COVID-19 patients. The 123 COVID SCORE containing merely 3 variables: age, oxygen saturation, and percentage of lung involvement assessed with chest CT is a simple and reliable tool to predict in-hospital death in COVID-19 patients upon hospital admission.

Relationship Between White Blood Cell Count and Bacteremia Using Interval Likelihood Ratios in Hospitalized Patients.

Patients with bacteremia often have elevated white blood cell (WBC) and neutrophil counts, yet these alone are poor predictors of bacteremia. Data on the continuous relationship between WBC response and bacteremia are lacking. This study aims to characterize the relationship of WBC count, neutrophil percentage, and absolute neutrophil count (ANC) to bacteremia using interval likelihood ratios (ILRs) derived from a large sample of hospitalized patients. Retrospective cohort study in a large healthcare system from 2017 to 2018. This study included non-surgical inpatients who had at least one complete blood count (CBC) with differential up to 24hours after admission and a blood culture. Patients with immunosuppression and malignancy or who received antibiotics before negative blood cultures were excluded. Predictors were WBC count, ANC, and neutrophil percentage. The outcome was bacteremia. We compared test discrimination using the area under the receiver operating characteristics curve (AUROC). We calculated ILRs for bacteremia across test value intervals. As a practical example, we assumed a 5% pre-test probability of bacteremia and calculated the post-test probability for each interval. We compared this approach to a threshold approach using a threshold of 70% neutrophils. Of 25,776 patients with a CBC with differential and blood culture, 1160 had bacteremia. AUROC was highest for neutrophil percentage (0.74), followed by ANC (0.63) and WBC count (0.58). Probability of bacteremia increased exponentially from neutrophil percentage 80 to 100%. Odds of bacteremia varied 35-fold based on neutrophil percentage. A threshold approach with a cut-off of 70% significantly underestimated bacteremia risk at higher levels. ILRs offered a more discriminating approach to estimating the probability of bacteremia than a single threshold. Physicians assessing risk of bacteremia should pay attention to the magnitude of abnormality because very high and very low values have much stronger predictive power than dichotomized results.

Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning.

Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort. The cohort consisted of 2596 individuals between 40-65 years of age in 2007, who were followed up for 10 years. Of them, 179 developed hard CV diseases (CVD) by 2017. We developed three CV risk prediction models named model 1, 2 and 3 using ML. We compared predictive performances between models and the WHO risk charts using receiver operating characteristic curves (ROC). The most predictive and practical model for use in primary care, model 3 was named "SLCVD score" which used age, sex, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level in the calculation. We developed an online platform to calculate the SLCVD score. Predictions of SLCVD score were validated in an external hospital-based cohort. Model 1, 2, SLCVD score and the WHO risk charts predicted 173, 162, 169 and 10 of 179 observed events and the area under the ROC (AUC) were 0.98, 0.98, 0.98 and 0.52 respectively. During external validation, the SLCVD score and WHO risk charts predicted 56 and 18 respectively of 119 total events and AUCs were 0.64 and 0.54 respectively. SLCVD score is the first and only CV risk prediction model specific for Sri Lankans. It predicts the 10-year risk of developing a hard CVD in Sri Lankans. SLCVD score was more effective in predicting Sri Lankans at high CV risk than WHO risk charts.

Comparison of the utility of SARC-F, SARC-CalF, and calf circumference as screening tools for sarcopenia in patients with osteoporosis.

Patients with osteoporosis who also have sarcopenia are at a high risk for falls and fractures. Early detection of sarcopenia is crucial for these patients. This study aimed to compare the effectiveness of SARC-F, SARC-CalF, and calf circumference (CC) as screening tools for sarcopenia in patients with osteoporosis. This cross-sectional study was retrospectively conducted on patients who attended the outpatient clinic for Osteoporosis and Sarcopenia at Kyoto Medical Center. Sarcopenia was determined based on low skeletal muscle mass and weak handgrip strength. Sensitivity and specificity analyses were conducted on SARC-F, SARC-CalF, and CC. The diagnostic utility of these three tools was compared using the receiver-operating characteristic (ROC) curves and the area under the ROC curves (AUC). A total of 225 patients (men/women: 33/192) with a median age of 69.0 years (interquartile range: 61.0‒75.0) were enrolled. The prevalence of sarcopenia was found to be 11.6%. CC had the highest sensitivity (80.8%), while SARC-F had the highest specificity (93.0%) for detecting sarcopenia. ROC analysis revealed that all three tools had significant potential for sarcopenia diagnosis, with SARC-CalF having the highest AUC compared to SARC-F and CC (0.753 vs. 0.619 and 0.700). A multivariate logistic regression, incorporating other confounders as explanatory variables, revealed that SARC-CalF was independently related to sarcopenia (odds ratio: 14.80, 95% confidence interval: 3.83-57.30, p < 0.001). In patients with osteoporosis, SARC-CalF is more effective in the early detection of sarcopenia than SARC-F and CC.

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes. This information included warning signs of hypoglycemia, such as incoherent speech, exhaustion, weakness, and other clinically relevant cases of low blood sugar. Researchers used supervised, unsupervised, and hybrid techniques. In supervised techniques, researchers applied regression, while in hybrid classification ML techniques were used. In a 5-fold cross-validation approach, the prediction performance of seven models was examined using the area under the receiver operating characteristic curve (AUROC). We analyzed the data of 290 diabetic patients with low blood sugar episodes. Our investigation discovered that gradient boosting and neural networks performed better in regression, with accuracies of 0.416 and 0.417, respectively. In classification models, gradient boosting, AdaBoost, and random forest performed better overall, with AUC scores of 0.821, 0.814, and 0.821, individually. Precision values were 0.779, 0.775, and 0.776 for gradient boosting, AdaBoost, and random forest, respectively. AdaBoost and Gradient Boosting models, in particular, outperformed all others in predicting the probability of clinically severe hypoglycemia. These techniques enable community health nurses to predict hypoglycemia at an early stage and provide the necessary therapies to patients to prevent complications resulting from hypoglycemia.

Comparative analysis of SARC-F-EBM, Ishii test, and six other screening tools for sarcopenia in Chinese community-dwelling older adults: a cross-sectional diagnostic study

To assess the relative performance of simple screening methods for sarcopenia in Chinese community-dwelling older adults. Data of older adults aged ≥ 60 were collected through a cross-sectional investigation. Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. The accuracy of screening methods was evaluated using sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the ROC curves (AUC). The AUC value greater than 0.8 represented the good screening ability. A total of 918 older adults (44.3% men, mean age 70.4 ± 6.5 years) were included in this study. The overall prevalence rates of possible sarcopenia, confirmed sarcopenia, and severe sarcopenia were 59.5%, 12.8%, and 5.9%, respectively. In men, the SARC-F-EBM and Ishii tests indicated good screening capabilities for confirmed sarcopenia, with an AUC of 0.81 (95% CI: 0.77–0.85) and 0.80 (95% CI: 0.76–0.84), respectively. In women, the highest AUC was also achieved using the SARC-F-EBM at 0.79 (95% CI: 0.75–0.82), followed by the Ishii test at 0.77 (95% CI: 0.74–0.81), showing the moderate efficacy. A ranking diagram showed that SARC-F-EBM was most likely to be considered the best method for diagnosing sarcopenia in terms of AUC and sensitivity, regardless of sex. We recommend the SARC-F-EBM for sarcopenia screening in community-dwelling Chinese older adults when respondents are able to answer the questionnaire accurately; otherwise, the Ishii test consisting entirely of objective measures could be used.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

Differentiate adrenal lipid-poor adenoma from nodular hyperplasia with CT quantitative parameters: a feasibility study.

To explore the potential of CT quantitative parameters in differentiating adrenal lipid-poor adenoma (LPA) from nodular hyperplasia and evaluate diagnostic performance. Patients with LPA or nodular hyperplasia who underwent contrast-enhanced CT before adrenalectomy were analyzed retrospectively. The study included 128 patients (83 with LPA and 45 with nodular hyperplasia). Each lesion's unenhanced attenuation, portal-venous phase attenuation (CTp), and the portal-venous phase attenuation of the abdominal aorta were evaluated. We subsequently calculated absolute enhancement [a lesion's portal-venous phase attenuation minus unenhanced attenuation (in HUs)], relative enhancement (absolute enhancement divided by unenhanced attenuation), and the relative enhancement ratio [(absolute enhancement divided by abdominal aorta's portal-venous phase attenuation) ×100%]. Lesion number and size were recorded. Volume was assessed by ITK-snap software and the ratio of lesion volume to ipsilateral adrenal volume (volume ratio) was determined. Intergroup differences were analyzed using Student's t-test and chi-squared test. Logistic regression models were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC), sensitivity, and specificity. The model's performance was then compared against radiologists' subjective assessments, and the inter- and intra-reader agreement values among radiologists were calculated. Portal-venous phase attenuation, volume ratio, and lesion number were independent predictors of LPA. The logistic regression model incorporating CTp, volume ratio, and lesion number achieved an AUC of 0.835, with a sensitivity of 73.5% and a specificity of 80.0%. The radiologists' diagnostic specificity and accuracy appeared to be inferior to the model. The inter-reader agreement among radiologists ranged from 0.082 to 0.535, and the intra-reader agreement of two radiologists were 0.734 and 0.583. The portal-venous phase CT demonstrated potential in distinguishing LPA from nodular hyperplasia. The diagnostic performance of the model integrating CTp, volume ratio, and lesion number outperformed radiologists in terms of variability and reproducibility.