The relationship of early expressed milk quantity and later full breastmilk feeding after very preterm birth: A cohort study.

When infants cannot directly breastfeed after birth, mothers are advised to initiate lactation through mechanical expression. Families are recommended to target an expression volume of at least 500-750 mL by Day 14 after birth, as this is considered a 'critical window' to establish milk supply. This is challenging for many mothers after a very preterm birth. This article explores the relationship of early milk quantity and later full breastmilk feeding as a 'gold standard' outcome, using statistical techniques designed for diagnostic tests. A cohort of 132 mothers of infants born at 23 + 0 to 31 + 6 weeks' gestational age submitted expressing logs on Day 4, 14 and 21 after birth and provided later feeding outcome. Using receiver operating characteristic (ROC) analysis, the following 24-h milk quantities were identified as associated with high probability of full breastmilk at 36 weeks' post-menstrual age (PMA): on Day 4, ≥250 g (specificity 88%; positive predictive value 88%) and on Day 21 ≥650 g (specificity 88%; positive predictive value 91%). The following values were identified as associated with low probability of full breastmilk at 36 weeks' PMA: on Day 4 <50 g (sensitivity 92%; negative predictive value 72%) and on Day 21 <250 g (sensitivity 90%; negative predictive value 70%). Participants exceeding the high thresholds had 3-4 times increased likelihood of full breastmilk, whereas those below the low thresholds had 3-5 times lower likelihood. These thresholds have potential as targets for families, to provide individualised prognostic information and to help clinicians target more intensive lactation support.

Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism.

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist. METHODS: Data from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated. RESULTS: Whole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score. CONCLUSION: Utilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

A discrete time-to-event model for the meta-analysis of full ROC curves.

The development of new statistical models for the meta-analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the "Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy", the authors point to the challenges of this kind of meta-analysis and propose two approaches. However, both of them come with some disadvantages, such as the nonstraightforward choice of priors in Bayesian models or the requirement of a two-step approach where parameters are estimated for the individual studies, followed by summarizing the results. As an alternative, we propose a novel model by applying methods from time-to-event analysis. To this task we use the discrete proportional hazard approach to treat the different diagnostic thresholds, that provide means to estimate sensitivity and specificity and are reported by the single studies, as categorical variables in a generalized linear mixed model, using both the logit- and the asymmetric cloglog-link. This leads to a model specification with threshold-specific discrete hazards, avoiding a linear dependency between thresholds, discrete hazard, and sensitivity/specificity and thus increasing model flexibility. We compare the resulting models to approaches from the literature in a simulation study. While the estimated area under the summary ROC curve is estimated comparably well in most approaches, the results depict substantial differences in the estimated sensitivities and specificities. We also show the practical applicability of the models to data from a meta-analysis for the screening of type 2 diabetes.

Patient-centric thresholding of Cobas® EGFR mutation Test v2 for surveillance of EGFR-mutated metastatic non-small cell lung cancer.

Cobas EGFR mutation Test v2 was FDA-approved as qualitative liquid biopsy for actionable EGFR variants in non-small cell lung cancer (NSCLC). It generates semiquantitative index (SQI) values that correlate with mutant allele levels, but decision thresholds for clinical use in NSCLC surveillance are lacking. We conducted long-term ctDNA monitoring in 20 subjects with EGFR-mutated NSCLC; resulting in a 155 on-treatment samples. We defined optimal SQI intervals to predict/rule-out progression within 12 weeks from sampling and performed orthogonal calibration versus deep-sequencing and digital PCR. SQI showed significant diagnostic power (AUC 0.848, 95% CI 0.782-0.901). SQI below 5 (63% of samples) had 93% (95% CI 87-96%) NPV, while SQI above 10 (25% of samples) had 69% (95% CI 56-80%) PPV. Cobas EGFR showed perfect agreement with sequencing (Kappa 0.860; 95% CI 0.674-1.00) and digital PCR. SQI values strongly (r: 0.910, 95% 0.821-0.956) correlated to mutant allele concentrations with SQI of 5 and 10 corresponding to 6-9 (0.2-0.3%) and 64-105 (1.1-1.6%) mutant allele copies/mL (VAF) respectively. Our dual-threshold classifier of SQI 0/5/10 yielded informative results in 88% of blood draws with high NPV and good overall clinical utility for patient-centric surveillance of metastatic NSCLC.

Immature platelet fraction levels predict the development of prolonged thrombocytopenia after haematopoietic stem cell transplantation.

Prolonged thrombocytopenia (PT) is a serious complication after haematopoietic stem cell transplantation (HSCT). PT has been suggested to be associated with an increased platelet transfusion requirement and poor outcomes after transplantation. Due to the complex mechanism of PT development, it is difficult to diagnose in the early post-transplant period. Our study aimed to identify an early predictive marker for PT after HSCT. Previous studies showed that the clinical utility of immature platelet fraction (IPF) predicts platelet recovery after chemotherapy and successful engraftment. However, the relationship between IPF and PT after HSCT remains unclear. Fifty-two patients with malignant haematological diseases who underwent HSCT were included in the study. We observed the kinetics of recovery of haematological parameters after transplantation and performed receiver operating characteristics (ROC) curve analysis using data from the 52 HSCT patients. The days to rise and peak of IPF, absolute IPF count (A-IPF) and highly fluorescent IPF (H-IPF) were almost synchronised in all patients, at day 10 and day 15, respectively. The begin to rise levels of IPF, H-IPF and A-IPF were all significantly lower in the PT group than in the good engraftment (GE) group (p=0.0016, p=0.0094, p=0.0086, respectively). The peak levels of IPF were significantly lower in the PT group than the GE group (p=0.0036). However, the peaks of H-IPF and A-IPF were not statistically significant between the two groups (p=0.3383, p=0.0887, respectively). The area under the ROC curve (AUC) of IPF rise was 0.739 (95% CI 0.583-0.896; p<0.05) and the cut-off value was 3.5%, while the AUC of IPF peak was 0.800 (95% CI 0.637-0.962; p<0.01) and the cut-off value was 8.0%. In conclusion, early low levels of IPF predict the development of PT after HSCT. These findings may help improve the management and treatment strategies for PT after HSCT.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM.

BACKGROUND: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.

Corrigendum: Analysis of factors associated with positive surgical margins and the five-year survival rate after prostate cancer resection and predictive modeling.

[This corrects the article DOI: 10.3389/fonc.2024.1360404.].

Exosomal miRNA-21-5p and miRNA-21-3p as key biomarkers of myocardial infarction.

Objective: Coronary artery disease (CAD) is a debilitating condition that can lead to myocardial infarction (MI). Exosomal miRNAs (exo-miRNA) can be diagnostic biomarkers for detecting MI. Here, we conduct a study to evaluate the efficacy of exo-miRNA-21-5p/3p for early detection of MI. Methods: A total of 135 CAD patients and 150 healthy subjects participated in this study. Additionally, we randomly divided 26 male Wistar rats (12 weeks old) into two groups: control and induced MI. Angiographic images were used to identify patients and healthy individuals of all genders. In the following, serum exosomes were obtained, and exo-miRNA-21-5p/3p was measured by reverse-transcriptase polymerase chain reaction. Results: We observed an upregulation of exo-miRNA-21-5p/3p in CAD patient and MI-induced animal groups compared to controls. Analysis of the ROC curves defined 82% and 88% of the participants' exo-miRNA-21-5p and exo-miRNA-21-3p diagnostic power, respectively, which in the animal model was 92 and 82. Conclusion: This study revealed that the mean expression levels of exo-miRNA-21-5p/3p were significantly increased in CAD patients and animal models of induced MI. Also, these results are associated with the atherogenic lipid profile of CAD patients, which may play an important role in the progression of the disease. Therefore, they can be considered as novel biomarkers.

Validation and refinement of a predictive nomogram using artificial intelligence: assessing in-hospital mortality in patients with large hemispheric cerebral infarction.

Background: Large Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients. Methods: Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group. Results: This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range. Conclusion: We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Expression levels and clinical significance of serum miR-19a/CCL20 in patients with acute cerebral infarction.

Acute cerebral infarction (ACI) is a lethal disease whose early diagnosis is critical for treatment. microRNA (miR)-19a targets CC chemokine ligand 20 (CCL20) in myocardial infarction. We investigated the expression patterns of serum miR-19a and CCL20 of ACI patients and assessed their clinical values. Serum samples of 50 healthy subjects and110 ACI patients were collected. Serum levels of miR-19a, CCL20 mRNA, and biochemical indexes were assessed. miR-19a downstream target gene and the binding relationship between miR-19a and CCL20 were predicted and verified. miR-19a and CCL20 mRNA were subjected to correlation and diagnostic efficiency analysis. miR-19a was poorly expressed in the serum of ACI patients, especially in patients with unstable plaque and large infarction. tumor necrosis factor-α, low-density lipoprotein, and platelet/lymphocyte ratio negatively correlated with serum miR-19a level and positively correlated with CCL20. Dual-luciferase assay revealed that miR-19a could negatively regulate CCL20 expression. CCL20 was highly expressed in the serum of ACI patients. The area under receiver-operating characteristic curve of miR-19a combined with CCL20 was 0.9741 (98.00% specificity, 90.91% sensitivity), higher than their single diagnosis. Collectively, miR-19a had high diagnostic value for ACI and could target to restrain CCL20. The combination of miR-19a and CCL20 improved diagnostic value for ACI.

Assessment of ID family proteins expression in colorectal cancer of Iraqi patients.

BACKGROUND: Colorectal cancer (CRC) is the second most deathly worldwide and third most common cancer, CRC is a very heterogeneous disease where tumors can form by both environmental and genetic risk factors and includes epigenetic and genetic alternations. Inhibitors of DNA binding proteins (ID) are a class of helix-loop-helix transcription regulatory factors; these proteins are considered a family of four highly preserved transcriptional regulators (ID1-4), shown to play significant roles in many processes that are associated with tumor development. ID family plays as negatively dominant antagonists of other essential HLH proteins, concluding the creation of non-functional heterodimers and regulation of the transcription process. MATERIALS AND METHODS: 120 Fresh tissue and blood samples Forty (40) samples of fresh tissue and blood were collected from patients diagnosed with CRC, twenty (20) samples were collected from a patient diagnosed as healthy. The (qRT-PCR) method is a sensitive technique for the quantifying of steady-state mRNA levels that used to evaluation the expression levels of ID (1-4) gene. RESULTS: The findings indicate downregulation in ID1 in tissue with a highly significant change between patients and control groups, where upregulation in the ID1 gene is shown in blood samples.ID2 gene also demonstrated high significant change where show upregulation in tissue and downregulation in blood sample. ID3 and ID4 genes show downregulation in tissue and blood samples with a significant change in ID3 blood samples between patient and blood groups. CONCLUSION: Because of the regulation function of the ID family in many processes, the up or down regulation of IDs genes in tumors Proves how important its tumor development, and therefore those proteins can be used as an indicator for CRC.

Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer's continuum.

BACKGROUND: Enlarged choroid plexus (ChP) volume has been reported in patients with Alzheimer's disease (AD) and inversely correlated with cognitive performance. However, its clinical diagnostic and predictive value, and mechanisms by which ChP impacts the AD continuum remain unclear. METHODS: This prospective cohort study enrolled 607 participants [healthy control (HC): 110, mild cognitive impairment (MCI): 269, AD dementia: 228] from the Chinese Imaging, Biomarkers, and Lifestyle study between January 1, 2021, and December 31, 2022. Of the 497 patients on the AD continuum, 138 underwent lumbar puncture for cerebrospinal fluid (CSF) hallmark testing. The relationships between ChP volume and CSF pathological hallmarks (Aß42, Aß40, Aß42/40, tTau, and pTau181), neuropsychological tests [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory (NPI), and Activities of Daily Living (ADL) scores], and multimodal neuroimaging measures [gray matter volume, cortical thickness, and corrected cerebral blood flow (cCBF)] were analyzed using partial Spearman's correlation. The mediating effects of four neuroimaging measures [ChP volume, hippocampal volume, lateral ventricular volume (LVV), and entorhinal cortical thickness (ECT)] on the relationship between CSF hallmarks and neuropsychological tests were examined. The ability of the four neuroimaging measures to identify cerebral Aß42 changes or differentiate among patients with AD dementia, MCI and HCs was determined using receiver operating characteristic analysis, and their associations with neuropsychological test scores at baseline were evaluated by linear regression. Longitudinal associations between the rate of change in the four neuroimaging measures and neuropsychological tests scores were evaluated on the AD continuum using generalized linear mixed-effects models. RESULTS: The participants' mean age was 65.99 ± 8.79 years. Patients with AD dementia exhibited the largest baseline ChP volume than the other groups (P < 0.05). ChP volume enlargement correlated with decreased Aß42 and Aß40 levels; lower MMSE and MoCA and higher NPI and ADL scores; and lower volume, cortical thickness, and cCBF in other cognition-related regions (all P < 0.05). ChP volume mediated the association of Aß42 and Aß40 levels with MMSE scores (19.08% and 36.57%), and Aß42 levels mediated the association of ChP volume and MMSE or MoCA scores (39.49% and 34.36%). ChP volume alone better identified cerebral Aß42 changes than LVV alone (AUC = 0.81 vs. 0.67, P = 0.04) and EC thickness alone (AUC = 0.81 vs.0.63, P = 0.01) and better differentiated patients with MCI from HCs than hippocampal volume alone (AUC = 0.85 vs. 0.81, P = 0.01), and LVV alone (AUC = 0.85 vs.0.82, P = 0.03). Combined ChP and hippocampal volumes significantly increased the ability to differentiate cerebral Aß42 changes and patients among AD dementia, MCI, and HCs groups compared with hippocampal volume alone (all P < 0.05). After correcting for age, sex, years of education, APOE ε4 status, eTIV, and hippocampal volume, ChP volume was associated with MMSE, MoCA, NPI, and ADL score at baseline, and rapid ChP volume enlargement was associated with faster deterioration in NPI scores with an average follow-up of 10.03 ± 4.45 months (all P < 0.05). CONCLUSIONS: ChP volume may be a novel neuroimaging marker associated with neurodegenerative changes and clinical AD manifestations. It could better detect the early stages of the AD and predict prognosis, and significantly enhance the differential diagnostic ability of hippocampus on the AD continuum.

Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma.

BACKGROUND: The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm. METHODS: Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software. RESULTS: We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis. CONCLUSIONS: In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

The receiver operating characteristic curve accurately assesses imbalanced datasets.

Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.

Reliability of EuroSCORE II on Prediction of Thirty-Day Mortality and Long-Term Results in Patients Treated with Sutureless Valves.

Background: EuroSCORE II (ES2) is a reliable tool for preoperative cardiac surgery mortality risk prediction; however, a patient's age, a surgical procedure's weight and the new devices available may cause its accuracy to drift. We sought to investigate ES2 performance related to the surgical risk and late mortality estimation in patients who underwent aortic valve replacement (AVR) with sutureless valves. Methods: Between 2012 and 2021, a total of 1126 patients with isolated aortic stenosis who underwent surgical AVR by means of sutureless valves were retrospectively collected from six European centers. Patients were stratified into three groups according to the EuroSCORE II risk classes (ES2 < 4%, ES2 4-8% and ES2 > 8%). The accuracy of ES2 in estimating mortality risk was assessed using the standardized mortality ratio (O/E ratio), ROC curves (AUC) and Hosmer-Lemeshow (HL) test for goodness-of-fit. Results: The overall observed mortality was 3.0% (predicted mortality ES2: 5.39%) with an observed/expected (O/E) ratio of 0.64 (confidential interval (CI): 0.49-0.89). In our population, ES2 showed a moderate discriminating power (AUC 0.65, 95%CI 0.56-0.72, p < 0.001; HL p = 0.798). Good accuracy was found in patients with ES2 < 4% (O/E ratio 0.54, 95%CI 0.23-1.20, AUC 0.75, p < 0.001, HL p = 0.999) and for patients with an age < 75 years (O/E ratio 0.98, 95%CI 0.45-1.96, AUC 0.76, p = 0.004, HL p = 0.762). Moderate discrimination was observed for ES2 in the estimation of long-term risk of mortality (AUC 0.64, 95%CI: 0.60-0.68, p < 0.001). Conclusions: EuroSCORE II showed good accuracy in patients with an age < 75 years and patients with ES2 < 4%, while overestimating risk in the other subgroups. A recalibration of the model should be taken into account based on the complexity of actual patients and impact of new technologies.

An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.

Because the conventional binormal ROC curve parameters are in terms of the underlying normal diseased and nondiseased rating distributions, transformations of these values are required for the user to understand what the corresponding ROC curve looks like in terms of its shape and size. In this paper I propose an alternative parameterization in terms of parameters that explicitly describe the shape and size of the ROC curve. The proposed two parameters are the mean-to-sigma ratio and the familiar area under the ROC curve (AUC), which are easily interpreted in terms of the shape and size of the ROC curve, respectively. In addition, the mean-to-sigma ratio describes the degree of improperness of the ROC curve and the AUC describes the ability of the corresponding diagnostic test to discriminate between diseased and nondiseased cases. The proposed parameterization simplifies the sizing of diagnostic studies when conjectured variance components are used and simplifies choosing the binormal a and b parameter values needed for simulation studies.

Comparative evaluation of transverse width indices for diagnosing maxillary transverse deficiency.

OBJECTIVES: This study aimed to compare and evaluate different transverse width indices for diagnosing maxillary transverse deficiency (MTD), a common malocclusion characterized by uncoordinated dental arches, crossbites, and tooth crowding. MATERIALS AND METHODS: Sixty patients aged 7-12 years were included in the study, with 20 patients diagnosed with MTD and 40 normal controls. Transverse width indices, including maxillary width at the buccal alveolar crest and lingual midroot level, as well as at the jugal process width, were measured. Differences between these indices and their corresponding mandibular indices were used as standardized transverse width indices. The reference range of these indices was determined and evaluated. Receiver operating characteristic (ROC) analysis was performed to evaluate their diagnostic ability. RESULTS: The transverse width indices and standardized transverse width indices of the MTD group were significantly smaller than those of the control group, except for the jugal process width. The evaluation of the reference range and ROC analysis revealed that the difference of the maxillomandibular width at buccal alveolar crest was the most accurate diagnostic method. CONCLUSIONS: The jugal point analysis method may not be suitable for diagnosing MTD. Instead, measuring the difference in maxillomandibular width at the buccal alveolar crest proves to be a more reliable and accurate diagnostic method for MTD.