INTRODUCTION: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their "black box" structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. METHODS: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. RESULTS: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. CONCLUSION: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.

• Publikační typ
• časopisecké články MeSH

BACKGROUND AND OBJECTIVE: While active surveillance (AS) is an alternative to surgical interventions in patients with small renal masses (SRMs), evidence regarding its oncological efficacy is still debated. We aimed to evaluate oncological outcomes for patients with SRMs who underwent AS in comparison to surgical interventions. METHODS: In April 2024, PubMed, Scopus, and Web of Science were queried for comparative studies evaluating AS in patients with SRMs (PROSPERO: CRD42024530299). The primary outcomes were overall (OS) and cancer-specific survival (CSS). A random-effects model was used for quantitative analysis. KEY FINDINGS AND LIMITATIONS: We identified eight eligible studies (three prospective, four retrospective, and one study based on Surveillance, Epidemiology and End Results [SEER] data) involving 4947 patients. Pooling of data with the SEER data set revealed significantly higher OS rates for patients receiving surgical interventions (hazard ratio [HR] 0.73; p = 0.007), especially partial nephrectomy (PN; HR 0.62; p < 0.001). However, in a sensitivity analysis excluding the SEER data set there was no significant difference in OS between AS and surgical interventions overall (HR 0.84; p = 0.3), but the PN subgroup had longer OS than the AS group (HR 0.6; p = 0.002). Only the study based on the SEER data set showed a significant difference in CSS. The main limitations include selection bias in retrospective studies, and classification of interventions in the SEER database study. CONCLUSIONS AND CLINICAL IMPLICATIONS: Patients treated with AS had similar OS to those who underwent surgery or ablation, although caution is needed in interpreting the data owing to the potential for selection bias and variability in AS protocols. Our review reinforces the need for personalized shared decision-making to identify patients with SRMs who are most likely to benefit from AS. PATIENT SUMMARY: For well-selected patients with a small kidney mass suspicious for cancer, active surveillance seems to be a safe alternative to surgery, with similar overall survival. However, the evidence is still limited and more studies are needed to help in identifying the best candidates for active surveillance.

• MeSH
• ablace metody   MeSH
• lidé MeSH
• nádory ledvin * chirurgie   mortalita   patologie   MeSH
• nefrektomie * metody   MeSH
• pozorné vyčkávání * MeSH
• tumor burden MeSH
• výsledek terapie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• srovnávací studie MeSH
• systematický přehled MeSH

BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. METHODS: We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). RESULTS: The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. CONCLUSIONS: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

• MeSH
• dospělí MeSH
• glioblastom * patologie   klasifikace   mortalita   diagnostické zobrazování   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• míra přežití MeSH
• mladý dospělý MeSH
• nádory mozku * patologie   klasifikace   mortalita   diagnostické zobrazování   MeSH
• následné studie MeSH
• prognóza MeSH
• senioři MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH

BACKGROUND: Plant-based diets are gaining popularity due to their well-documented cardiometabolic benefits and environmental sustainability. However, these diets are often lower in specific micronutrients such as iodine, raising concerns about their potential impact on thyroid health. Therefore, we examined the associations between plant-based diets and the risk of hypothyroidism. METHODS: We analysed data from the UK (United Kingdom) Biobank cohort. Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for incident hypothyroidism across vegans, vegetarians, pescatarians, poultry-eaters, low meat-eaters, and high meat-eaters aged 40-69 years. Ancillary to this, we carried out logistic regression analyses to evaluate associations between the diet groups and prevalent hypothyroidism according to International Classification of Diseases (ICD) codes at baseline. RESULTS: We included 466,362 individuals from the UK Biobank, of which 220,514 followed a high meat, 221,554 a low meat, 5242 a poultry-based, 10,598 a pescatarian, 8057 a vegetarian, and 397 a vegan diet. During a median SD (Standard Deviation) follow-up of 12.7 (± 3.2) years, 10,831 participants developed hypothyroidism. In multivariable Cox regression models without adjustment for body mass index (BMI), none of the diets were significantly associated with the risk of hypothyroidism. However, there was a tendency for a higher risk of hypothyroidism among vegetarians compared to people following a high meat diet (HR = 1.13, 95% CI 0.98-1.30). After controlling for BMI, a potential collider, the association for vegetarians (HR = 1.23, 95% CI 1.07-1.42) became stronger and statistically significant. Furthermore, we observed a positive association between low meat-eaters (OR = 1.05, 95% CI 1.03-1.08), poultry-eaters (OR = 1.15, 95% CI 1.04-1.28), pescatarians (OR = 1.10, 95% CI 1.01-1.19) and vegetarian (OR = 1.26, 95% CI 1.15-1.38) with hypothyroidism prevalence. CONCLUSIONS: In the present study, we found a moderately higher risk of hypothyroidism among vegetarians, after controlling for BMI, a potential collider. This slightly higher risk of hypothyroidism among vegetarians requires further investigation, taking iodine status and thyroid hormone levels into account.

• MeSH
• dieta vegetariánská * škodlivé účinky   MeSH
• dieta * škodlivé účinky   MeSH
• dospělí MeSH
• hypotyreóza * epidemiologie   etiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• maso * MeSH
• proporcionální rizikové modely MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• senioři MeSH
• vegetariáni * statistika a číselné údaje   MeSH
• zvířata MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Spojené království MeSH

OBJECTIVES: To assess the ability, as well as factors affecting the ability, of ultrasound examiners with different levels of ultrasound experience to detect correctly infiltration of ovarian cancer in predefined anatomical locations, and to evaluate the inter-rater agreement regarding the presence or absence of cancer infiltration, using preacquired ultrasound videoclips obtained in a selected patient sample with a high prevalence of cancer spread. METHODS: This study forms part of the Imaging Study in Advanced ovArian Cancer multicenter observational study (NCT03808792). Ultrasound videoclips showing assessment of infiltration of ovarian cancer were obtained by the principal investigator (an ultrasound expert, who did not participate in rating) at 19 predefined anatomical sites in the abdomen and pelvis, including five sites that, if infiltrated, would indicate tumor non-resectability. For each site, there were 10 videoclips showing cancer infiltration and 10 showing no cancer infiltration. The reference standard was either findings at surgery with histological confirmation or response to chemotherapy. For statistical analysis, the 19 sites were grouped into four anatomical regions: pelvis, middle abdomen, upper abdomen and lymph nodes. The videoclips were assessed by raters comprising both senior gynecologists (mainly self-trained expert ultrasound examiners who perform preoperative ultrasound assessment of ovarian cancer spread almost daily) and gynecologists who had undergone a minimum of 6 months' supervised training in the preoperative ultrasound assessment of ovarian cancer spread in a gynecological oncology center. The raters were classified as highly experienced or less experienced based on annual individual caseload and the number of years that they had been performing ultrasound evaluation of ovarian cancer spread. Raters were aware that for each site there would be 10 videoclips with and 10 without cancer infiltration. Each rater independently classified every videoclip as showing or not showing cancer infiltration and rated the image quality (on a scale from 0 to 10) and their diagnostic confidence (on a scale from 0 to 10). A generalized linear mixed model with random effects was used to estimate which factors (including level of experience, image quality, diagnostic confidence and anatomical region) affected the likelihood of a correct classification of cancer infiltration. We assessed the observed percentage of videoclips classified correctly, the expected percentage of videoclips classified correctly based on the generalized linear mixed model and inter-rater agreement (reliability) in classifying anatomical sites as being infiltrated by cancer. RESULTS: Twenty-five raters participated in the study, of whom 13 were highly experienced and 12 were less experienced. The observed percentage of correct classification of cancer infiltration ranged from 70% to 100% depending on rater and anatomical site, and the median percentage of correct classification for the 25 raters ranged from 90% to 100%. The probability of correct classification of all 380 videoclips ranged from 0.956 to 0.975 and was not affected by the rater's level of ultrasound experience. The likelihood of correct classification increased with increased image quality and diagnostic confidence and was affected by anatomical region. It was highest for sites in the pelvis, second highest for those in the middle abdomen, third highest for lymph nodes and lowest for sites in the upper abdomen. The inter-rater agreement of all 25 raters regarding the presence of cancer infiltration ranged from substantial (Fleiss kappa, 0.68 (95% CI, 0.66-0.71)) to very good (Fleiss kappa, 0.99 (95% CI, 0.97-1.00)) depending on the anatomical site. It was lowest for sites in the upper abdomen (Fleiss kappa, 0.68 (95% CI, 0.66-0.71) to 0.97 (95% CI, 0.94-0.99)) and highest for sites in the pelvis (Fleiss kappa, 0.94 (95% CI, 0.92-0.97) to 0.99 (95% CI, 0.97-1.00)). CONCLUSIONS: Ultrasound examiners with different levels of ultrasound experience can classify correctly predefined anatomical sites as being infiltrated or not infiltrated by ovarian cancer based on video recordings obtained by an experienced ultrasound examiner, and the inter-rater agreement is substantial. The likelihood of correct classification as well as the inter-rater agreement is highest for sites in the pelvis and lowest for sites in the upper abdomen. However, owing to the study design, our results regarding diagnostic accuracy and inter-rater agreement are likely to be overoptimistic. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

• MeSH
• audiovizuální záznam MeSH
• břicho diagnostické zobrazování   patologie   MeSH
• invazivní růst nádoru diagnostické zobrazování   MeSH
• klinické kompetence * MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory vaječníků * diagnostické zobrazování   patologie   MeSH
• odchylka pozorovatele MeSH
• pánev diagnostické zobrazování   patologie   MeSH
• prevalence MeSH
• senioři MeSH
• ultrasonografie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• pozorovací studie MeSH
• práce podpořená grantem MeSH

Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.

• MeSH
• čtení MeSH
• dítě MeSH
• dyslexie * patofyziologie   diagnóza   klasifikace   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• oční fixace * fyziologie   MeSH
• pohyby očí fyziologie   MeSH
• technologie sledování pohybu očí * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques-area under the ECG curve and wave transformation into the unit circle-to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches' functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS.

• MeSH
• dospělí MeSH
• elektrokardiografie * metody   MeSH
• genotyp MeSH
• lidé MeSH
• strojové učení * MeSH
• support vector machine MeSH
• syndrom dlouhého QT * genetika   diagnóza   patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

BACKGROUND: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age. METHODS: We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses. RESULTS: We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors. LIMITATIONS: Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting. CONCLUSION: Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.

• MeSH
• diabetes mellitus * epidemiologie   patologie   MeSH
• hypertenze * epidemiologie   patologie   MeSH
• kardiometabolické riziko * MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mozek * diagnostické zobrazování   patologie   MeSH
• obezita * epidemiologie   patologie   MeSH
• stárnutí patologie   MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH

To evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional endoanal ultrasound (EAUS). A prospective, comparative, multicentre, international study. Poland, Czechia, Slovakia, and Spain. 152 women between the first moments up to 8 weeks after vaginal delivery. Participants underwent EAUS and were allocated to groups based on OASIS classification: A (no perineal tear), B (1st or 2nd degree tear), or C (3rd or 4th degree, anal sphincters affected). Electric impedance was measured in the anal canal using the ONIRY system. The primary endpoint was the diagnostic outcome of impedance spectroscopy versus EAUS. Adverse events were collected. Part II involved in silico modelling and 10-time 10-fold cross-validation for automated analysis. Accuracy, sensitivity, and specificity. 30 women were allocated to group A, 61 to group B, and 61 to group C. The diagnostic outcome was determined for 147 participants. The accuracy, sensitivity, and specificity of the ML-assisted impedance spectroscopy were 87.0 ± 0.5%, 90.6 ± 2.0%, and 84.6 ± 1.9%, respectively, compared with EAUS. After data cleaning, the performance metrics of the proposed final ML model for ONIRY were: 90.0 ± 0.4%, 90.0 ± 1.2%, and 90.0 ± 0.7%, respectively. No adverse device effects or deficiencies were observed. By enabling early identification of sphincter injuries, ML-assisted impedance spectroscopy facilitates timely diagnosis and intervention, potentially reducing long-term complications such as faecal incontinence. Its rapid, bedside application in obstetric settings supports immediate postpartum care, complementing digital rectal examination and optimizing clinical decision-making.

• MeSH
• anální kanál * zranění   diagnostické zobrazování   MeSH
• dospělí MeSH
• impedanční spektroskopie * metody   MeSH
• komplikace porodu diagnóza   diagnostické zobrazování   MeSH
• lidé MeSH
• prospektivní studie MeSH
• strojové učení * MeSH
• těhotenství MeSH
• vedení porodu * škodlivé účinky   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• srovnávací studie MeSH

Kontext: Procento nezralých granulocytů (immature granulocytes, iG%) je časným markerem zánětu s prognostickou hodnotou u řady onemocnění. Cíl: V této studii jsme se pokusili zjistit, zda má procento nezralých granulocytů prognostickou hodnotu z hlediska 28denní mortality pacientů s akutním koronárním syndromem (AKS). Metoda: Jednalo se o retrospektivní studii provedenou v období mezi 1. lednem 2019 a 30. červnem 2019 na Klinice urgentní medicíny lékařské fakulty univerzity v tureckém Mersinu. Do studie byli zařazeni všichni pacienti ve věku nad 18 let, kteří byli dopraveni na kliniku urgentní medicíny s bolestí na hrudi a hospitalizováni s předběžnou diagnózou AKS. Pacienti byli rozděleni do dvou skupin, na ty, kteří přežili, a ty, kteří nepřežili. Byly zaznamenány hodnoty iG% a dalších laboratorních parametrů a následně byl analyzován vztah mezi hodnotami iG% a 28denní mortalitou. Kromě toho byla pro srovnání diagnostické přesnosti hodnot iG% a dalších proměnných provedena analýza roc. Výsledky: Do studie bylo zařazeno celkem 617 pacientů, z tohoto počtu bylo 423 (68,6 %) mužů. Průměrný věk pacientů dosahoval 63,9 ± 12,7 roku. hodnota iG% byla vyšší u nepřeživších pacientů (1,2 ± 1,4) než u přeživších (0,5 ± 0,5 ) (p = 0,007). V predikci 28denní mortality, pokud byla mezní hodnota iG% > 0,6, byla zjištěna specificita ve výši 93,70 % a senzitivita 54,55 % (Auc = 0,717; p = 0,000). V predikci 28denní mortality na AKS představovalo iG% nezávislý rizikový faktor (poměr rizik [hazard ratio, hr] 632,962; 95% interval spolehlivosti 3,389–118 206,572; p = 0,016). Závěr: u pacientů s AKS může hodnota iG% souviset s 28denní mortalitou.

Background: The percentage of immature granulocytes (IG%) is an early marker of inflammation and has a prognostic significance in many diseases. Objective: In this study, we tried to investigate whether the percentage of immature granulocytes has a prognostic value in 28-day mortality in patients with acute coronary syndrome (ACS). Method: This study was carried out retrospectively between 1.1.2019 and 30.6.2019 at Mersin University Faculty of Medicine, Department of Emergency Medicine. Patients older than 18 years who applied to the emergency department with chest pain and were hospitalized with a preliminary diagnosis of ACS were included in the study. The patients were divided into two groups as survivors and non-survivors. IG% and other laboratory parameters were recorded. The relationship between IG% and 28-day mortality was analyzed. In addition, ROC analysis was performed to compare the diagnostic accuracy of IG% and other variables. Results: A total of 617 patients, including 423 (68.6%) men, were included in the study. The mean age of the patients were 63.9 ± 12.7. IG% was higher in non-survivor patients (1.2 ± 1.4) than in surviving patients (0.5 ± 0.5) (p = 0.007). In predicting 28-day mortality, when the cut-off value for IG% was >0.6, the specifi- city was found to be 93.70% and the sensitivity to be 54.55% (AUC = 0.717, p = 0.000). In predicting 28-day mortality for ACS, IG% was an independent risk factor (hazard ratio [HR] 632.962, 95% confidence interval 3.389-118206.572, p = 0.016). Conclusion: IG% may be associated with a 28-day mortality in patients with ACS.

• MeSH
• akutní koronární syndrom * krev   mortalita   MeSH
• biologické markery MeSH
• granulocyty * patologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• prognóza MeSH
• proporcionální rizikové modely MeSH
• ROC křivka MeSH
• senioři MeSH
• statistika jako téma MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• klinická studie MeSH