- Publikační typ
- abstrakt z konference MeSH
Nocturnal hypertension is a significant risk factor for cardiovascular mortality and morbidity. Its determination requires the use of 24-hour blood pressure monitoring (ABPM). However, the examination results can be less reliable in patients with insomnia, habitual short sleep or other sleep disorders. In these patients, it is possible to detect falsely high values of blood pressure in the night interval. At the same time, the treatment of sleep disorders itself could adjust the nighttime values of blood pressure and thus prevent unreasonably intensive treatment of high blood pressure. The article summarizes current knowledge about the effect of sleep quality on the accuracy of diagnosis using ABMP. At the same time, we present the study "How to diagnose true nocturnal hypertension?", which will analyze this clinical problem.
Noční hypertenze je významným rizikovým faktorem pro mortalitu i morbiditu z kardiovaskulárních příčin. K její diagnostice je potřeba použít 24hodinové měření krevního tlaku (AMTK). Výsledky vyšetření však mohou být zkresleny u pacientů s nespavostí, habituálním krátkým spánkem či jinými poruchami spánku. U těchto pacientů je možné zachytit falešně vysoké hodnoty krevního tlaku v nočním intervalu. Přitom léčba samotných poruch spánku by mohla hodnoty nočního tlaku upravit, a tím zabránit nepřiměřeně intenzivní léčbě vysokého krevního tlaku. Článek shrnuje aktuální poznatky o vlivu kvality spánku na přesnost diagnostiky pomocí AMTK. Současně prezentujeme studii „Jak diagnostikovat pravou noční hypertenzi?“, která bude tento klinický problém analyzovat.
This article presents a comprehensive and multistage approach to the development of the user experience (UX) for an mHealth application targeting older adult patients with chronic diseases, specifically chronic heart failure and chronic obstructive pulmonary disease. The study adopts a mixed methods approach, incorporating both quantitative and qualitative components. The underlying hypothesis posits that baseline medicine adherence knowledge (measured by the MARS questionnaire), beliefs about medicines (measured by the BMQ questionnaire), and level of user experience (measured by the SUS and UEQ questionnaires) act as predictors of adherence change after a period of usage of the mHealth application. However, contrary to our expectations, the results did not demonstrate the anticipated relationship between the variables examined. Nevertheless, the qualitative component of the research revealed that patients, in general, expressed satisfaction with the application. It is important to note that the pilot testing phase revealed a notable prevalence of technical issues, which may have influenced participants' perception of the overall UX. These findings contribute to the understanding of UX development in the context of mHealth applications for older adults with chronic diseases and emphasise the importance of addressing technical challenges to enhance user satisfaction and engagement.
- Publikační typ
- časopisecké články MeSH
- MeSH
- abnormality ústního a čelistního systému dietoterapie klasifikace terapie MeSH
- lidé MeSH
- telemedicína MeSH
- vzácné nemoci diagnóza terapie MeSH
- zdravotnické informační systémy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.
- Publikační typ
- časopisecké články MeSH
- Publikační typ
- abstrakt z konference MeSH
- MeSH
- informovaný souhlas pacienta zákonodárství a právo MeSH
- komunikace MeSH
- lidé MeSH
- osobní údaje etika zákonodárství a právo MeSH
- telemedicína * ekonomika etika zákonodárství a právo MeSH
- vztahy mezi lékařem a pacientem etika MeSH
- zabezpečení počítačových systémů etika zákonodárství a právo MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
