Area Under ROC Curve Research Articles

Circulating exosome long non-coding RNAs are associated with atrial structural remodeling by increasing systemic inflammation in atrial fibrillation patients.

Atrial fibrillation (AF) is the most common cardiac arrhythmia with severe clinical sequelae, but its genetic characteristic implicated in pathogenesis has not been completely clarified. Accumulating evidence has indicated that circulating exosomes and their carried cargoes, such as long non-coding RNAs (lncRNAs), involve in the progress of multiple cardiovascular diseases. However, their potential role as clinical biomarkers in AF diagnosis and prognosis remains unknown. Herein, we conducted the sequence and bioinformatic analysis of circulating exosomes harvested from AF and sinus rhythm patients. A total of 53 differentially expressed lncRNAs were identified, and a total of 6 significantly changed lncRNAs (fold change > 2.0), including NR0046235, NR003045, NONHSAT167247.1, NONHSAT202361.1, NONHSAT205820.1 and NONHSAT200958.1, were verified by qRT-PCR in 215 participants. Moreover, these circulating exosome lncRNA levels were different between paroxysmal and persistent AF patients, which were dramatically associated with abnormal hemodynamics and atrial diameter. Furthermore, we observed that the area under ROC curve (AUC) of six lncRNAs combination for diagnosis of persistent AF was 80.34%. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment pathway analysis indicated these exosome lncRNAs mainly concerning response to chemokine-chemokine receptor interaction, which induced activated inflammation and structural remodeling. In addition, increased plasma levels of CXCR3 ligands, including CXCL4, CXCL9, CXCL10 and CXCL11, were accumulated in AF patient tissues. Our study provides the transcriptome profile revealing pattern of circulating exosome lncRNAs in atrial structural remodeling, which bring valuable insights into improving prognosis and therapeutic targets for AF.

Early prediction models and crucial factor extraction for first-year undergraduate student dropouts

PurposeThis study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.Design/methodology/approachThe real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.FindingsDT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”Practical implicationsThis work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.Originality/valueAlthough first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

Comparison of the efficacy for early warning systems in predicting obstetric critical illness

ObjectiveTo validate the accuracy of four early warning scores for early identification of women at risk. MethodsThis was a retrospective study of pregnant women admitted in obstetrics Critical Care Unit (ICU). Capacity of the Modified Obstetric Early Warning Score (MOEWS), ICNARC Obstetric Early Warning Score (OEWS), Maternal Early Obstetric Warning System (MEOWS chart), and Maternal Early Warning Trigger (MEWT) were compared in predicting severe maternal morbidity. Area under receiver operator characteristic (AUROC) curve was used to evaluate the predictive performance of scoring system. ResultsA total of 352 pregnant women were enrolled and 290 were identified with severe maternal morbidity. MOEWS was more sensitive than MEOWS chart, ICNARC OEWS and MEWT (96.9 % vs. 83.4 %, 66.6 % and 44.8 %). MEWT had the highest specificity (98.4 %), followed by MOEWS (83.9 %), ICNARC OEWS (75.8 %) and MEOWS chart (48.4 %). AUROC of MOEWS, ICNARC OEWS, MEOWS chart, and MEWT for prediction of maternal mortality were 0.91 (95 % CI: 0.874–0.945), 0.765(95 % CI: 0.71–0.82), 0.657(95 % CI: 0.577–0.738), and 0.716 (95 % CI, 0.659–0.773) respectively. MOEWS had the highest AUCs in the discrimination of serious complications in hypertensive disorders, cardiovascular disease, obstetric hemorrhage and infection. For individual vital signs, maximum diastolic blood pressure (DBP), maximum systolic blood pressure (SBP), maximum respiratory rate (RR) and peripheral oxygen saturation (SPO2) demonstrated greater predictive ability. ConclusionMOEWS is more accurate than ICNARC OEWS, MEOWS chart, and MEWT in predicting the deterioration of women. The prediction ability of DBP, SBP, RR and SPO2 are more reliable.

Diagnostic Utility of Glial Fibrillary Acidic Protein Beyond 12 Hours After Traumatic Brain Injury: A TRACK-TBI Study.

Blood levels of glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) within 12h of suspected traumatic brain injury (TBI) have been approved by the Food and Drug administration to aid in determining the need for a brain computed tomography (CT) scan. The current study aimed to determine whether this context of use can be expanded beyond 12h post-TBI in patients presenting with Glasgow Coma Scale (GCS) 13-15. The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled TBI participants aged ≥17 years who presented to a United States Level 1 trauma center and received a clinically indicated brain CT scan within 24h post-injury, a blood draw within 24h and at 14 days for biomarker analysis. Data from participants with emergency department arrival GCS 13-15 and biomarker values at days 1 and 14 were extracted for the primary analysis. A subgroup of hospitalized participants with serial biomarkers at days 1, 3, 5, and 14 were analyzed, including plasma GFAP and UCH-L1, and serum neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B). The primary analysis compared biomarker values dichotomized by head CT results (CT+/CT-). Area under receiver-operating characteristic curve (AUC) was used to determine diagnostic accuracy. The overall cohort included 1142 participants with initial GCS 13-15, with mean age 39.8 years, 65% male, and 73% Caucasian. The GFAP provided good discrimination in the overall cohort at days 1 (AUC = 0.82) and 14 (AUC = 0.72), and in the hospitalized subgroup at days 1 (AUC = 0.84), 3 (AUC = 0.88), 5 (AUC = 0.82), and 14 (AUC = 0.74). The UCH-L1, NSE, and S100B did not perform well (AUC = 0.51-0.57 across time points). This study demonstrates the utility of GFAP to aid in decision-making for diagnostic brain CT imaging beyond the 12h time frame in patients with TBI who have a GCS 13-15.

Highest 3-month international normalized ratio (INR): a predictor of bleeding following ultrasound-guided liver biopsy.

To determine whether international normalized ratio (INR), bilirubin, and creatinine predict bleeding risk following percutaneous liver biopsy. A total of 870 consecutive patients (age 53 ± 14years; 53% (459/870) male) undergoing non-targeted, ultrasound-guided, percutaneous liver biopsy at a single tertiary center from 01/2016 to 12/2019 were retrospectively reviewed. Results were analyzed using descriptive statistics and logistic regression models to evaluate the relationship between individual and combined laboratory values, and post-biopsy bleeding risk. Receiver operating characteristic (ROC) curves and area under ROC (AUC) curves were constructed to evaluate predictive ability. Post-biopsy bleeding occurred in 2.0% (17/870) of patients, with 0.8% (7/870) requiring intervention. The highest INR within 3months preceding biopsy demonstrated the best predictive ability for post-biopsy bleeding and was superior to the most recent INR (AUC = 0.79 vs 0.61, p = 0.003). Total bilirubin is an independent predictor of bleeding (AUC = 0.73) and better than the most recent INR (0.61). Multivariate regression analysis of the highest INR and total bilirubin together yielded no improvement in predictive performance compared to INR alone (0.80 vs 0.79). The MELD score calculated using the highest INR (AUC = 0.79) and most recent INR (AUC = 0.74) were similar in their predictive performance. Creatinine is a poor predictor of bleeding (AUC = 0.61). Threshold analyses demonstrate an INR of > 1.8 to have the highest predictive accuracy for bleeding. The highest INR in 3months preceding ultrasound-guided percutaneous liver biopsy is associated with, and a better predictor for, post-procedural bleeding than the most recent INR and should be considered in patient risk stratification. Despite correction of coagulopathic indices, the highest international normalized ratio within the 3months preceding percutaneous liver biopsy is associated with, and a better predictor for, bleeding and should considered in clinical decision-making and determining biopsy approach. • Bleeding occurred in 2% of patients following ultrasound-guided liver biopsy, and was non-trivial in 41% of those patients who needed additional intervention and had an associated 23% 30-day mortality rate. • The highest INR within 3months preceding biopsy (AUC = 0.79) is a better predictor of bleeding than the most recent INR (AUC = 0.61). • The MELD score is associated with post-procedural bleeding, but with variable predictive performance largely driven by its individual laboratory components.

Correlation between Pre-Operative Myocardial Fibrosis and Early Post CABG Dysrhythmia

Background: Cardiac magnetic resonance (CMR) imaging combines the assessment of both the functional and structural aspects of the heart in order to identify the existence, timing, and intensity of ischemic heart disease by analyzing the function of the myocardium and the movement of the heart wall. This study aimed to investigate whether preoperative myocardial fibrosis, measured by CMR imaging, may be used to predict the incidence of rhythm disturbances in the early postoperative phase after coronary artery bypass grafting (CABG) surgery.
 Methods: Two groups of 92 patients who had CABG procedures performed were studied in this retrospective observational single site cohort study: There were 43 patients in Group A who had atrial or ventricular arrhythmia, and 49 patients in Group B who did not.
 Results: There was no correlation between arrhythmia and non-arrhythmia group and age, sex, body mass index, risk factor, CMR timing before surgery, cross clamp time, bypass time, left ventricular end-diastolic volume index (LV EDVI), LV end-systolic volume index (ESVI), stroke volume index (SVI), LV ejection fraction (EF) and territory of scar and were positive correlation between both groups and scar (P <0.001). Scar% was an independent predictor of occurrence of rhythm disturbance (P=0.002) while LV EDVI, LV EF, LV ESVI, SVI, and presence of scar were not. Group A had a statistically significantly lower LV-EF% and lower LV-SVI compared to Group B. Group A had a higher scar percentage compared with group B and this was statistically significant (P <0.001). Rhythm disturbance occurred more often in patients with a scar percentage >14.8% {P=0.0002 and area under ROC curve (AUC)=0.708}.
 Conclusions: CMR has evolved as a gold standard non-invasive imaging tool in cardiovascular medicine. Preoperative CMR imaging may be a promising tool for predicting postoperative cardiac arrhythmia after CABG. Our study showed that preoperative myocardial scarring >14.8%, as determined by CMR imaging, was predictive of early postoperative arrhythmia in patients undergoing CABG.

Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative data and a diagnostic cutoff value.

The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia. Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer's Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female). The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97-0.98]). We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.

Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography.

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. 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. Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Diagnostic accuracy of serum matrix metalloproteinase-7 as a biomarker of biliary atresia in a large North American cohort.

High levels of serum matrix metalloproteinase-7 (MMP-7) have been linked to biliary atresia (BA), with wide variation in concentration cutoffs. We investigated the accuracy of serum MMP-7 as a diagnostic biomarker in a large North American cohort. MMP-7 was measured in serum samples of 399 infants with cholestasis in the Prospective Database of Infants with Cholestasis study of the Childhood Liver Disease Research Network, 201 infants with BA and 198 with non-BA cholestasis (age median: 64 and 59 days, p = 0.94). MMP-7 was assayed on antibody-bead fluorescence (single-plex) and time resolved fluorescence energy transfer assays. The discriminative performance of MMP-7 was compared with other clinical markers. On the single-plex assay, MMP-7 generated an AUROC of 0.90 (CI: 0.87-0.94). At cutoff 52.8ng/mL, it produced sensitivity = 94.03%, specificity = 77.78%, positive predictive value = 64.46%, and negative predictive value = 96.82% for BA. AUROC for gamma-glutamyl transferase = 0.81 (CI: 0.77-0.86), stool color = 0.68 (CI: 0.63-0.73), and pathology = 0.84 (CI: 0.76-0.91). Logistic regression models of MMP-7 with other clinical variables individually or combined showed an increase for MMP-7+gamma-glutamyl transferase AUROC to 0.91 (CI: 0.88-0.95). Serum concentrations produced by time resolved fluorescence energy transfer differed from single-plex, with an optimal cutoff of 18.2ng/mL. Results were consistent within each assay technology and generated similar AUROCs. Serum MMP-7 has high discriminative properties to differentiate BA from other forms of neonatal cholestasis. MMP-7 cutoff values vary according to assay technology. Using MMP-7 in the evaluation of infants with cholestasis may simplify diagnostic algorithms and shorten the time to hepatoportoenterostomy.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Predictive value of whole brain perfusion on admission for shunt-dependent hydrocephalus fllowing aneurysmal subarachnoid hemorrhage

Objective: To explore the value of predicting shunt-dependent hydrocephalus (SDHC) in patients with aneurysmal subarachnoid hemorrhage (aSAH) based on whole brain CT perfusion(CTP) and clinical data within 24 hours at admission. Methods: The clinical and imaging data of aSAH patients who received interventional embolization in our hospital were retrospectively collected from March 2018 to August 2022. All patients underwent one-stop whole brain CT examination within 24 hours after symptom onset, and the qualitative and quantitative CTP parameters were obtained after post-processing. Follow-up was conducted once every 2 months by consulting electronic medical records or by telephone for 6 months. According to whether SDHC occurred or not, the patients were divided into SDHC group and non-SDHC group. The differences between the two groups were compared. Logistic regression model was used to analyze and determine the predictive factors of SDHC, and the SDHC predictive model was established. The effectiveness of the predictive model was evaluated by drawing the receiver operating characteristic (ROC) curve of the subjects. Results: A total of 414 patients were included, including 132 males and 282 females, aged (59±11) years. 17.6%(73/414) patients had SDHC. There were significant differences in the occurrence of acute hydrocephalus, the World Neurosurgical League Scale (WFNS), the Hunt-Hess scale, the modified Fisher score (mFS), and the qualitative and quantitative parameters of CTP between the two groups (both P<0.001). Multivariate logistic regression analysis showed that acute hydrocephalus (OR=8.621, 95%CI: 4.237-17.542),old age (OR=1.107, 95%CI: 1.068-1.148), high mFS and high Hunt-Hess classification (OR=3.740, 95%CI: 1.352-10.342) were the risk factors of SDHC in aSAH patients, and high mean cerebral blood flow (mCBF) (OR=0.931, 95%CI: 0.885-0.980) was a protective factor of SDHC.The area under ROC curve (AUC) of the prediction model constructed by these five variables was 0.923(95%CI: 0.89-0.95), with 84.5% sensitivity and 87.7% specificity. Conclusion: The mCBF and acute hydrocephalus, age, mFS and Hunt-Hess classification within 24 hours at admission can be used to predict SDHC for aSAH patients.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.

This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU). Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions

ObjectivesTo differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters.MethodsA total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model.ResultsSUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets.ConclusionThe SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group.

Despite ongoing efforts to minimize sex bias in diagnosis and treatment of acute coronary syndrome (ACS), data still shows outcomes differences between sexes including higher risk of all-cause mortality rate among females. Hence, the aim of the current study was to examine sex differences in ACS in-hospital mortality, and to implement artificial intelligence (AI) models for prediction of in-hospital mortality among females with ACS. All ACS patients admitted to a tertiary care center intensive cardiac care unit (ICCU) between July 2019 and July 2023 were prospectively enrolled. The primary outcome was in-hospital mortality. Three prediction algorithms, including gradient boosting classifier (GBC) random forest classifier (RFC), and logistic regression (LR) were used to develop and validate prediction models for in-hospital mortality among females with ACS, using only available features at presentation. A total of 2,346 ACS patients with a median age of 64 (IQR: 56-74) were included. Of them, 453 (19.3%) were female. Female patients had higher prevalence of NSTEMI (49.2% vs. 39.8%, p < 0.001), less urgent PCI (<2 h) rates (40.2% vs. 50.6%, p < 0.001), and more complications during admission (17.7% vs. 12.3%, p = 0.01). In-hospital mortality occurred in 58 (2.5%) patients [21/453 (5%) females vs. 37/1,893 (2%) males, HR = 2.28, 95% CI: 1.33-3.91, p = 0.003]. GBC algorithm outscored the RFC and LR models, with area under receiver operating characteristic curve (AUROC) of 0.91 with proposed working point of 83.3% sensitivity and 82.4% specificity, and area under precision recall curve (AUPRC) of 0.92. Analysis of feature importance indicated that older age, STEMI, and inflammatory markers were the most important contributing variables. Mortality and complications rates among females with ACS are significantly higher than in males. Machine learning algorithms for prediction of ACS outcomes among females can be used to help mitigate sex bias.

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

BackgroundBreast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes.MethodsWe integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification.ResultsThe FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN.ConclusionOur study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

Machine Learning–Driven Prognostication in Traumatic Subdural Hematoma: Development of a Predictive Web Application

BACKGROUND AND OBJECTIVES: Our focus was on creating an array of machine learning (ML) models to predict unfavorable in-hospital outcomes after acute traumatic subdural hematoma (atSDH). Our subsequent aim was to deploy these models in an accessible web application, showcasing their practical value. METHODS: Data from the American College of Surgeons Trauma Quality Program database were used to identify patients with atSDH. In-hospital mortality was the primary outcome of interest. Secondary outcomes were (1) nonhome discharges, (2) prolonged length of stay (LOS), (3) prolonged length of stay in the intensive care unit (ICU-LOS), and (4) major complications. Feature selection was performed with least absolute shrinkage and selection operator algorithm. Five ML algorithms, including TabPFN, TabNET, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS: There were 104 055 patients included in the analysis for the outcome mortality, 82 988 for the outcome nonhome discharges, 104 207 for the outcome prolonged LOS, 62 543 for the outcome prolonged ICU-LOS, and 100 241 for the outcome major complications. The models with the highest area under receiver operating characteristic curve (AUROC) values included TabPFN for mortality and major complications, and LightGBM for nonhome discharges, prolonged LOS, and ICU-LOS. The TabPFN model for the primary outcome of our study, in-hospital mortality, showed an AUROC of 0.934. The models with the highest AUROC values were integrated into an application to predict the outcomes of interest. CONCLUSION: Our findings show that ML tools aid in predicting various outcomes for patients with atSDH. We developed a web application that has the potential to integrate the developed models into clinical practice.

Evaluation of systemic immune inflammation index as a predictor of lethal outcome in patients with thoracic empyema

Purpose: No study to date has investigated the prognostic ability of the recently introduced “systemic immune-inflammation index” (SII) in thoracic empyema. Therefore, we aimed to evaluate the association between SII and outcome in such patients. Methods: The single-center retrospective study involved 84 patients with thoracic empyema admitted to the Clinic of Thoracic Surgery at a University Hospital Stara Zagora between January 2021 and October 2023. Clinical and laboratory parameters were recorded at admission. We analyzed the area under receiver operating characteristics (AUROC) curves of SII, qSOFA, and SIRS and compared their prognostic value. Results: The observed in-hospital mortality was 10.7%. As a predictor of death, SII showed the best prognostic ability compared to SIRS (AUROC = 0.782 vs. 0.676) and qSOFA (AUROC = 0.782 vs. 0.747). At the optimal cut-off value of &gt; 3269 x 109/L, we identified a sensitivity of 71.4% and a specificity of 78.7%. Conclusion: In thoracic empyema, SII could be routinely used as a simple, low-cost, and easily assessable indicator for patients at higher risk of poor outcome.

Monocyte-to-Hdl-Cholesterol Ratio as a Prognostic Marker in Covid-19

Background: There is an urgent need for mortality predictors for COVID-19 so that clinicians can diagnose severe cases and triage them as soon as possible. Many studies have suggested using hematologic markers to predict mortality and severity of COVID-19 disease. This study investigates the use of monocyte-to-high density lipoprotein cholesterol ratio (MHR) as a predictive marker for COVID-19 severity and mortality. Methods: This retrospective cross-sectional study was performed on 81 PCR-confirmed COVID-19 patients between 25 March 2020 to 26 June 2020. Patients were classified into two presentation categories: the non-severe group (n=37) and the severe group (n=44). Patients in the severe group were also divided into two subgroups: severe survivors (n=14) and severe non-survivors (n=30). In the receiver operating characteristic (ROC) analysis, optimal cut-off values of the monocyte count, high-density lipoprotein cholesterol (HDL-C), and MHR were calculated for the differentiation of severe and non-severe COVID-19 patients, as well as survivors and non-survivors. Results: A total of 81 patients, 29 (35.8%) males and 52 (64.2) females, with a median age of 71 (IQR 63-81) years. Both HDL-C and MHR showed a reasonable ability to distinguish severe disease from non-severe disease, while MHR had a higher area under curve (AUC) than HDL-C (0.799, 95%CI 0.704-0.894, p 0.001 vs 0.734, 95% Cl 0.626-0.843, p 0.001). Only MHR could distinguish survivors from non-survivors with an ROC AUC of 0.735 (95%Cl 0.619-0.850). The optimal cut-off values of MHR for predicting severe disease were 0.0061 (sensitivity: 66% and specificity: 66%) and 0.0066 (sensitivity: 70% and specificity: 62%) for predicting mortality. The optimal cut-off value of MHR for predicting severe disease was 0.0061 (sensitivity: 66% and specificity: 66%), and it was 0.0066 for predicting mortality among patients with severe disease (sensitivity: 70% and specificity: 62%). Conclusion: Our results showed that MHR was observed to be able to distinguish severe COVID-19 patients from non-severe patients as well as survivors from non-survivors.

Unraveling atherosclerotic cardiovascular disease risk factors through conditional probability analysis with Bayesian networks: insights from the AZAR cohort study

This study aimed at modelling the underlying predictor of ASCVD through the Bayesian network (BN). Data for the AZAR Cohort Study, which evaluated 500 healthcare providers in Iran, was collected through examinations, and blood samples. Two BNs were used to explore a suitable causal model for analysing the underlying predictor of ASCVD; Bayesian search through an algorithmic approach and knowledge-based BNs. Results showed significant differences in ASCVD risk factors across background variables’ levels. The diagnostic indices showed better performance for the knowledge-based BN (Area under ROC curve (AUC) = 0.78, Accuracy = 76.6, Sensitivity = 62.5, Negative predictive value (NPV) = 96.0, Negative Likelihood Ratio (LR−) = 0.48) compared to Bayesian search (AUC = 0.76, Accuracy = 72.4, Sensitivity = 17.5, NPV = 93.2, LR− = 0.83). In addition, we decided on knowledge-based BN because of the interpretability of the relationships. Based on this BN, being male (conditional probability = 63.7), age over 45 (36.3), overweight (51.5), Mets (23.8), diabetes (8.3), smoking (10.6), hypertension (12.1), high T-C (28.5), high LDL-C (23.9), FBS (12.1), and TG (25.9) levels were associated with higher ASCVD risk. Low and normal HDL-C levels also had higher ASCVD risk (35.3 and 37.4), while high HDL-C levels had lower risk (27.3). In conclusion, BN demonstrated that ASCVD was significantly associated with certain risk factors including being older and overweight male, having a history of Mets, diabetes, hypertension, having high levels of T-C, LDL-C, FBS, and TG, but Low and normal HDL-C and being a smoker. The study may provide valuable insights for developing effective prevention strategies for ASCVD in Iran.

Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs

PurposeTo develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs. DesignMulti-center retrospective study Subjects851 fundus photographs from 235 children (age <18 years) with pseudopapilledema and true papilledema MethodsFour pediatric neuro-ophthalmologists at four different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tri-branch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared to two masked human expert pediatric neuro-ophthalmologists, who performed the same classification task. Main Outcome MeasuresAccuracy, sensitivity, and specificity of the AI model compared to human experts. ResultsThe area under receiver operating curve (AUC) of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model’s accuracy was significantly higher than human experts on the cross validation set (p<0.002), and the model’s sensitivity was significantly higher on the external test set (p<0.0002). The specificity of the AI model and human experts was similar (56.4-67.3%). Moreover, the AI model was significantly more sensitive at detecting mild papilledema than human experts, whereas AI and humans performed similarly on photographs of moderate-to-severe papilledema. On review of the external test set, only one child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema. ConclusionsWhen classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved over 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions.