BACKGROUND: Mineralocorticoid receptor antagonists (MRA) improve outcomes in patients with heart failure and reduced ejection fraction (HFrEF) but are underused in clinical practice. Observational data suggest that hyperkalemia is the leading obstacle for the suboptimal use of MRA. OBJECTIVES: This study sought to evaluate the effects of sodium zirconium cyclosilicate (SZC) in optimizing use of spironolactone among participants with HFrEF and hyperkalemia. METHODS: REALIZE-K (Study to Assess Efficacy and Safety of SZC for the Management of High Potassium in Patients With Symptomatic HFrEF Receiving Spironolactone) was a prospective, double-blind, randomized- withdrawal trial in participants with HFrEF (NYHA functional class II-IV; left ventricular ejection fraction ≤40%), optimal guideline-directed therapy (except MRA), and prevalent or incident MRA-induced hyperkalemia. During open-label run-in, participants underwent spironolactone titration (target: 50 mg/day); those with hyperkalemia started SZC. Participants with normokalemia (potassium: 3.5-5.0 mEq/L) on SZC and spironolactone ≥25 mg/day were randomized to continued SZC or placebo for 6 months. The primary endpoint was optimal treatment response (normokalemia on spironolactone ≥25 mg/day without rescue therapy for hyperkalemia [months 1-6]). The 5 secondary endpoints were tested hierarchically. Exploratory endpoints included a composite of adjudicated cardiovascular death or worsening heart failure (HF) events (hospitalizations and urgent visits). RESULTS: Overall, 203 participants were randomized (SZC: 102; placebo: 101). Higher percentage of SZC- vs placebo-treated participants had optimal response (71% vs 36%; OR: 4.45; 95% CI: 2.89-6.86; P < 0.001). SZC (vs placebo) improved the first 4 secondary endpoints: normokalemia on randomization dose of spironolactone and without rescue therapy (58% vs 23%; OR: 4.58; 95% CI: 2.78-7.55; P < 0.001); receiving spironolactone ≥25 mg/day (81% vs 50%; OR: 4.33; 95% CI: 2.50-7.52; P < 0.001); time to hyperkalemia (HR: 0.51; 95% CI: 0.37-0.71; P < 0.001); and time to decrease/discontinuation of spironolactone due to hyperkalemia (HR: 0.37; 95% CI: 0.17-0.73; P = 0.006). There was no between-group difference in Kansas City Cardiomyopathy Questionnaire-Clinical Summary Score at 6 months (-1.01 points; 95% CI: -6.64 to 4.63; P = 0.72). Adverse events (64% vs 63%) and serious adverse events (23% vs 22%) were balanced between SZC and placebo, respectively. Composite of cardiovascular (CV) death or worsening HF occurred in 11 (11%) participants in the SZC group (1 with CV death, 10 with HF events) and 3 (3%) participants in the placebo group (1 with CV death, 2 with HF events; log-rank nominal P = 0.034). CONCLUSIONS: In participants with HFrEF and hyperkalemia, SZC led to large improvements in the percentage of participants with normokalemia while on optimal spironolactone dose, and reduced risk of hyperkalemia and down-titration/discontinuation of spironolactone. Although underpowered for clinical outcomes, more participants had HF events with SZC than placebo, which should be factored into the clinical decision making. (Study to Assess Efficacy and Safety of SZC for the Management of High Potassium in Patients With Symptomatic HFrEF Receiving Spironolactone; NCT04676646).

• MeSH
• antagonisté mineralokortikoidních receptorů * terapeutické užití   aplikace a dávkování   škodlivé účinky   MeSH
• dvojitá slepá metoda MeSH
• hyperkalemie * farmakoterapie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• prospektivní studie MeSH
• senioři MeSH
• silikáty * terapeutické užití   aplikace a dávkování   škodlivé účinky   MeSH
• spironolakton * aplikace a dávkování   škodlivé účinky   terapeutické užití   MeSH
• srdeční selhání * farmakoterapie   MeSH
• tepový objem účinky léků   fyziologie   MeSH
• výsledek terapie MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• randomizované kontrolované studie MeSH

BACKGROUND: To predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a heart failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). OBJECTIVE: We studied the association of the baseline HF Score, determined at algorithm activation, with long-term patient outcomes. METHODS: Data from 9 clinical trials were pooled, including 1841 ICD patients with a preimplantation ejection fraction ≤35%, New York Heart Association class II/III, and no long-standing atrial fibrillation. The primary end point was a composite of death or WHFH. RESULTS: After a median follow-up of 631 days (interquartile range, 385-865 days), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. The primary end point occurred in 265 patients (14.4%). A multivariable time-to-first-event analysis showed that a high baseline HF Score (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of the primary end point (adjusted hazard ratio [HR], 2.05; 95% confidence interval [CI], 1.54-2.71; P < .0001), all-cause death (HR, 2.37; CI, 1.56-3.58; P < .0001), cardiovascular death (HR, 2.19; CI, 1.14-4.22; P = .019), and WHFH (HR, 1.91; CI, 1.35-2.71; P = .0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated event outcomes, the win ratio was 2.47 (CI, 1.89-3.24; P < .0001). CONCLUSION: Based on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.

• MeSH
• algoritmy MeSH
• časové faktory MeSH
• defibrilátory implantabilní * MeSH
• klinické zkoušky jako téma MeSH
• lidé středního věku MeSH
• lidé MeSH
• následné studie MeSH
• senioři MeSH
• srdeční selhání * terapie   patofyziologie   mortalita   MeSH
• technologie dálkového snímání metody   MeSH
• tepový objem fyziologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

AIM: To examine the organisational (i.e., perceived organisational support and psychologically safe environment) and individual (i.e., value, belief and norm) antecedents that strengthen healthcare workers' speaking-up behaviour in a developing economy. DESIGN: The study uses a cross-sectional design to gather the same data from healthcare workers within the Ashanti Region of Ghana. METHODS: The data collection happened between 15 June and 30 August 2023. A sample of 380 healthcare workers was selected from 20 facilities in the Ashanti Region of Ghana. A configurational approach, a fussy-set qualitative comparative analysis, was used to identify the configurations that caused high and low speaking-up behaviour among the study sample. RESULTS: The study results reveal that whereas four configurations generate high speaking-up behaviour, three configurations, by contrast, produce low speaking-up behaviour among healthcare workers. CONCLUSION: Results suggest that in so far as organisational support systems which take the form of a psychologically safe environment and perceived organisational support are vital in relaxing the hierarchical boundaries in a healthcare setting to improve healthcare workers' speaking-up behaviour, the individual value-based factors that take the form of values, beliefs and norms are indispensable as it provides the healthcare workers with the necessary inner drive to regard speaking-up behaviour on patient safety and care as a moral duty. IMPACT: Healthcare workers' speaking-up behaviour is better achieved when organisational support systems complement the individual norms, values and beliefs of the individual. REPORTING METHOD: Adhered to Strengthening Reporting of Observational Studies in Epidemiology guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

• MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• organizační kultura * MeSH
• postoj zdravotnického personálu * MeSH
• průřezové studie MeSH
• zdravotnický personál * psychologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Ghana MeSH

Tento článek zpracovává téma nových trendů a technologií v urologii, a to konkrétně v oblasti telemedicíny a umělé inteligence. Nejprve stručně pojednává o přínosech telemedicíny a jak mění pohled na vztah mezi lékařem a pacientem. Podrobněji se pak text věnuje především umělé inteligenci, jež se v současnosti dostává do popředí zájmu laické i odborné veřejnosti. Její potenciál v urologii je testován v mnoha studiích, především se zaměřením na uroonkologii, v menší míře pak také v oblasti benigních urologických onemocnění. Článek se snaží identifikovat nejvýznamnější pokroky v této rychle se rozvíjející oblasti, a zároveň předkládá současné limity jejího zapojení do klinické praxe.

This article explores the emerging trends and technologies in urology, focusing on telemedicine and artificial intelligence. It provides a brief overview of the benefits of telemedicine and its impact on the patient-physician interactions. The article subsequently explores in detail the use of artificial intelligence, which is currently gaining considerable interest from both general public and medical professionals. Its potential in urology has been tested in a number of clinical studies, particularly in the field of uro-oncology and, to a lesser extent, in benign urological diseases. The aim of this article is to identify the key advances in this rapidly evolving field, while also highlighting the current limitations of its implementation into clinical practice.

• MeSH
• deep learning MeSH
• lidé MeSH
• roboticky asistované výkony MeSH
• strojové učení MeSH
• telemedicína MeSH
• umělá inteligence MeSH
• urologické nádory diagnóza   terapie   MeSH
• urologie * trendy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH

BACKGROUND: Although illness perception (IP) is a widely recognised factor in the psychosocial adjustment to cancer, little is known about the impact of individual dimensions of IP. This study aims to analyse the relationship between individual dimensions of IP and quality of life (QOL) in childhood cancer survivors. METHODS: The sample consisted of 163 long-term survivors aged 11 to 25 who were administered the Brief Illness Perceptions Questionnaire and the Minneapolis-Manchester Quality of Life Scale. RESULTS: In the correlational analysis, all dimensions of IP were associated with individual dimensions of QOL, except for understanding and treatment control. The results of the hierarchical regression analysis controlling for demographic and medical factors showed that IP had predicted individual dimensions of QOL above and beyond these factors, with emotional response, concern, consequences and understanding being the most predictive dimensions. Several age-specific relationships between IP and QOL were also identified. CONCLUSIONS: Illness perceptions significantly contribute to explaining QOL of childhood cancer survivors above and beyond demographic and medical factors. These results may contribute to more effective targeting of psychosocial interventions promoting QOL of survivors.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3D18F-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site. METHOD: A publicly available lesion-annotated dataset of 1014 whole-body FDG-PET/CT images was used to train, validate, and test (70:10:20) eight configurations with 3D U-Net as the backbone architecture. The best-performing model on the test set was further evaluated on 3 different unseen cohorts consisting of osteosarcoma or neuroblastoma (OS cohort) (n = 13), pediatric solid tumors (ST cohort) (n = 14), and adult Pheochromocytoma/Paraganglioma (PHEO cohort) (n = 40). Both lesion-level and patient-level statistical analyses were conducted to validate the performance of the model on different cohorts. RESULTS: The best performing 3D full resolution nnUNet model achieved a lesion-level sensitivity and DISC of 71.70 % and 0.40 for the test set, 97.83 % and 0.73 for ST, 40.15 % and 0.36 for OS, and 78.37 % and 0.50 for the PHEO cohort. For the test set and PHEO cohort, the model has missed small volume and lower uptake lesions (p < 0.01), whereas no statistically significant differences (p > 0.05) were found in the false positive (FP) and false negative lesions volume and uptake for the OS and ST cohort. The predicted total lesion glycolysis is slightly higher than the ground truth because of FP calls, which experts can easily check and reject. CONCLUSION: The developed deep learning-based automated lesion segmentation AI model which utilizes 3D_FullRes configuration of the nnUNet framework showed promising and reliable performance for the whole-body FDG-PET/CT images.

• MeSH
• celotělové zobrazování * metody   MeSH
• deep learning * MeSH
• dítě MeSH
• dospělí MeSH
• fluorodeoxyglukosa F18 * MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• nádory * diagnostické zobrazování   MeSH
• PET/CT * metody   MeSH
• počítačové zpracování obrazu * metody   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• validační studie MeSH

BACKGROUND: Duchenne muscular dystrophy (DMD) patients are monitored periodically for cardiac involvement, including cardiac MRI with gadolinium-based contrast agents (GBCA). Texture analysis (TA) offers an alternative approach to assess late gadolinium enhancement (LGE) without relying on GBCA administration, impacting DMD patients' care. The study aimed to evaluate the prognostic value of selected TA features in the LGE assessment of DMD patients. RESULTS: We developed a pipeline to extract TA features of native T1 parametric mapping and evaluated their prognostic value in assessing LGE in DMD patients. For this evaluation, five independent TA features were selected using Boruta to identify relevant features based on their importance, least absolute shrinkage and selection operator (LASSO) to reduce the number of features, and hierarchical clustering to target multicollinearity and identify independent features. Afterward, logistic regression was used to determine the features with better discrimination ability. The independent feature inverse difference moment normalized (IDMN), which measures the pixel values homogeneity in the myocardium, achieved the highest accuracy in classifying LGE (0.857 (0.572-0.982)) and also was significantly associated with changes in the likelihood of LGE in a subgroup of patients with three yearly examinations (estimate: 23.35 (8.7), p-value = 0.008). Data are presented as mean (SD) or median (IQR) for normally and non-normally distributed continuous variables and numbers (percentages) for categorical ones. Variables were compared with the Welch t-test, Wilcoxon rank-sum, and Chi-square tests. A P-value < 0.05 was considered statistically significant. CONCLUSION: IDMN leverages the information native T1 parametric mapping provides, as it can detect changes in the pixel values of LGE images of DMD patients that may reflect myocardial alterations, serving as a supporting tool to reduce GBCA use in their cardiac MRI examinations.

• MeSH
• dítě MeSH
• Duchennova muskulární dystrofie * diagnostické zobrazování   patologie   MeSH
• gadolinium MeSH
• kontrastní látky MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mladiství MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.

• MeSH
• dospělí MeSH
• elastografie * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory štítné žlázy diagnostické zobrazování   klasifikace   patologie   diagnóza   MeSH
• prospektivní studie MeSH
• radiomika MeSH
• senioři MeSH
• štítná žláza diagnostické zobrazování   patologie   MeSH
• strojové učení * MeSH
• support vector machine MeSH
• tenkojehlová biopsie MeSH
• uzly štítné žlázy * diagnostické zobrazování   patologie   klasifikace   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.

• MeSH
• deep learning * MeSH
• dítě MeSH
• dospělí MeSH
• fenotyp * MeSH
• genetická variace MeSH
• genetické asociační studie MeSH
• genomika metody   MeSH
• hormony štítné žlázy metabolismus   genetika   MeSH
• lidé MeSH
• mentální retardace vázaná na chromozom X genetika   metabolismus   MeSH
• mladiství MeSH
• mutace ztráty funkce MeSH
• předškolní dítě MeSH
• přenašeče monokarboxylových kyselin * genetika   metabolismus   MeSH
• stupeň závažnosti nemoci MeSH
• svalová atrofie genetika   metabolismus   patologie   MeSH
• svalová hypotonie genetika   metabolismus   MeSH
• symportéry * genetika   metabolismus   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: COVID-19 vaccine hesitancy (CVH) has become a critical public health issue, with attitudes toward vaccines emerging as a broader social issue. Public debates surrounding vaccines have expanded beyond health considerations to include issues of trust, misinformation, and societal values, making CVH a complex challenge that requires multifaceted solutions. Analyzing the various determinants of CVH is crucial for developing targeted strategies to improve vaccine acceptance in specific countries and to better prepare for future public health crises. However, no study to date has evaluated the determinants of CVH in a representative sample of the Czech population. METHODS: A multiple hierarchical logistic regression was used to analyze the associations between various sociodemographic, trust and attitudinal factors with COVID-19 vaccine acceptance (CVA). The analysis utilized survey data from a representative longitudinal sample of the Czech population (N = 1,407). RESULTS: After controlling for all other factors, trust in official statements from the Ministry of Health was the strongest predictor of CVA, followed by prior positive attitudes toward COVID-19 vaccination (prior to vaccine availability) and older age. Lower trust in COVID-19 misinformation also predicted CVA, while lower interest in COVID-19 media content was associated with CVA. Higher income initially predicted CVA but lost statistical significance after controlling for other variables. Interestingly, education did not play a role in CVA. CONCLUSION: CVH was primarily driven by distrust in government-provided information. Notably, vaccine refusers demonstrated a higher motivation to seek information on the topic, offering a promising opportunity for health policy interventions. Our findings suggest that strategies to reduce CVH should prioritize building trust in state institutions and effectively combating misinformation.

• MeSH
• COVID-19 * prevence a kontrola   MeSH
• dospělí MeSH
• důvěra * MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• odkládání očkování * psychologie   statistika a číselné údaje   MeSH
• pacientův souhlas se zdravotní péčí statistika a číselné údaje   psychologie   MeSH
• průzkumy a dotazníky MeSH
• senioři MeSH
• vakcíny proti COVID-19 * aplikace a dávkování   MeSH
• zdraví - znalosti, postoje, praxe MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH