BACKGROUND: Adherence to rhinitis treatment has been insufficiently assessed. We aimed to use data from the MASK-air mHealth app to assess adherence to oral antihistamines (OAH), intra-nasal corticosteroids (INCS) or azelastine-fluticasone in patients with allergic rhinitis. METHODS: We included regular European MASK-air users with self-reported allergic rhinitis and reporting at least 1 day of OAH, INCS or azelastine-fluticasone. We assessed weeks during which patients answered the MASK-air questionnaire on all days. We restricted our analyses to data provided between January and June, to encompass the pollen seasons across the different assessed countries. We analysed symptoms using visual analogue scales (VASs) and the combined symptom-medication score (CSMS), performing stratified analyses by weekly adherence levels. Medication adherence was computed as the proportion of days in which patients reported rhinitis medication use. Sensitivity analyses were performed considering all weeks with at most 1 day of missing data and all months with at most 4 days of missing data. RESULTS: We assessed 8212 complete weeks (1361 users). Adherence (use of medication > 80% days) to specific drug classes ranged from 31.7% weeks for azelastine-fluticasone to 38.5% weeks for OAH. Similar adherence to rhinitis medication was found in users with or without self-reported asthma, except for INCS (better adherence in asthma patients). VAS and CSMS levels increased from no adherence to full adherence, except for INCS. A higher proportion of days with uncontrolled symptoms was observed in weeks with higher adherence. In full adherence weeks, 41.2% days reported rhinitis co-medication. The sensitivity analyses displayed similar results. CONCLUSIONS: A high adherence was found in patients reporting regular use of MASK-air. Different adherence patterns were found for INCS compared to OAH or azelastine-fluticasone that are likely to impact guidelines.

• Klíčová slova
• allergic rhinitis, mobile health, treatment adherence,
• Publikační typ
• časopisecké články MeSH

The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients' resources and abilities to be experts in their own lives based on their lived experiences. Improving healthcare safety, quality, and coordination, as well as quality of life, is an important aim in the care of patients with chronic conditions. Person-centered care needs to ensure that people's values and preferences guide clinical decisions. This paper reviews current knowledge to develop (1) digital care pathways for rhinitis and asthma multimorbidity and (2) digitally enabled, person-centered care.1 It combines all relevant research evidence, including the so-called real-world evidence, with the ultimate goal to develop digitally enabled, patient-centered care. The paper includes (1) Allergic Rhinitis and its Impact on Asthma (ARIA), a 2-decade journey, (2) Grading of Recommendations, Assessment, Development and Evaluation (GRADE), the evidence-based model of guidelines in airway diseases, (3) mHealth impact on airway diseases, (4) From guidelines to digital care pathways, (5) Embedding Planetary Health, (6) Novel classification of rhinitis and asthma, (7) Embedding real-life data with population-based studies, (8) The ARIA-EAACI (European Academy of Allergy and Clinical Immunology) strategy for the management of airway diseases using digital biomarkers, (9) Artificial intelligence, (10) The development of digitally enabled, ARIA person-centered care, and (11) The political agenda. The ultimate goal is to propose ARIA 2024 guidelines centered around the patient to make them more applicable and sustainable.

• Klíčová slova
• ARIA, Artificial intelligence, Asthma, Evidence-based medicine, Person-centered care, Rhinitis, mHealth,
• MeSH
• alergická rýma * terapie   MeSH
• bronchiální astma * terapie   MeSH
• kritické cesty MeSH
• lidé MeSH
• péče orientovaná na pacienta * MeSH
• směrnice pro lékařskou praxi jako téma MeSH
• telemedicína * MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

RATIONALE: Persistent respiratory symptoms following Coronavirus Disease 2019 (COVID-19) are associated with residual radiological changes in lung parenchyma, with a risk of development into lung fibrosis, and with impaired pulmonary function. Previous studies hinted at the possible efficacy of corticosteroids (CS) in facilitating the resolution of post-COVID residual changes in the lungs, but the available data is limited. AIM: To evaluate the effects of CS treatment in post-COVID respiratory syndrome patients. PATIENTS AND METHODS: Post-COVID patients were recruited into a prospective single-center observational study and scheduled for an initial (V1) and follow-up visit (V2) at the Department of Respiratory Medicine and Tuberculosis, University Hospital Olomouc, comprising of pulmonary function testing, chest x-ray, and complex clinical examination. The decision to administer CS or maintain watchful waiting (WW) was in line with Czech national guidelines. RESULTS: The study involved 2729 COVID-19 survivors (45.7% male; mean age: 54.6). From 2026 patients with complete V1 data, 131 patients were indicated for CS therapy. These patients showed significantly worse radiological and functional impairment at V1. Mean initial dose was 27.6 mg (SD ± 10,64), and the mean duration of CS therapy was 13.3 weeks (SD ± 10,06). Following therapy, significantly better improvement of static lung volumes and transfer factor for carbon monoxide (DLCO), and significantly better rates of good or complete radiological and subjective improvement were observed in the CS group compared to controls with available follow-up data (n = 894). CONCLUSION: Better improvement of pulmonary function, radiological findings and subjective symptoms were observed in patients CS compared to watchful waiting. Our findings suggest that glucocorticoid therapy could benefit selected patients with persistent dyspnea, significant radiological changes, and decreased DLCO.

• Klíčová slova
• Corticosteroids, Post-covid syndrome, Pulmonary fibrosis, Pulmonary function, Watchful waiting,
• Publikační typ
• časopisecké články MeSH

Excessive daytime sleepiness (EDS) is a common symptom of sleep disorders such as narcolepsy, obstructive sleep apnea, and hypersomnia. The most common tools for assessing EDS are various specialized questionnaires such as Epworth Sleepiness Scale (ESS) and Stanford Sleepiness Scale (SSS). However, the scores obtained from self-rating questionnaires do not seem to measure physiological sleepiness but rather a more complex phenomenon of subjective sleepiness modulated by other factors such as motivation, expectation, and capability of self-perception. The golden standard for measuring physiological sleepiness and assessing EDS is the Multiple Sleep Latency Test (MSLT). However, MSLT is very time consuming and requires trained personnel and expensive equipment. Different method modifications are employed in various medical and industrial fields for different purposes. The infrared pupillography in darkness has the potential to measure objective physiological sleepiness, especially the Pupillographic Sleepiness Test (PST), which is the method of choice for pupillographic measurement of daytime sleepiness. The method has also been employed in several specific sleep disorders, outlining possible future usage. This narrative review summarizes the current state of knowledge on the relevance and usefulness of pupillography in sleep medicine.

• Publikační typ
• časopisecké články MeSH

Biomarkers for the diagnosis, treatment and follow-up of patients with rhinitis and/or asthma are urgently needed. Although some biologic biomarkers exist in specialist care for asthma, they cannot be largely used in primary care. There are no validated biomarkers in rhinitis or allergen immunotherapy (AIT) that can be used in clinical practice. The digital transformation of health and health care (including mHealth) places the patient at the center of the health system and is likely to optimize the practice of allergy. Allergic Rhinitis and its Impact on Asthma (ARIA) and EAACI (European Academy of Allergy and Clinical Immunology) developed a Task Force aimed at proposing patient-reported outcome measures (PROMs) as digital biomarkers that can be easily used for different purposes in rhinitis and asthma. It first defined control digital biomarkers that should make a bridge between clinical practice, randomized controlled trials, observational real-life studies and allergen challenges. Using the MASK-air app as a model, a daily electronic combined symptom-medication score for allergic diseases (CSMS) or for asthma (e-DASTHMA), combined with a monthly control questionnaire, was embedded in a strategy similar to the diabetes approach for disease control. To mimic real-life, it secondly proposed quality-of-life digital biomarkers including daily EQ-5D visual analogue scales and the bi-weekly RhinAsthma Patient Perspective (RAAP). The potential implications for the management of allergic respiratory diseases were proposed.

• Klíčová slova
• ARIA, EAACI, apps, digital health, rhinitis,
• MeSH
• alergická rýma * diagnóza   terapie   MeSH
• biologické markery MeSH
• bronchiální astma * diagnóza   terapie   MeSH
• lidé MeSH
• péče orientovaná na pacienta MeSH
• poruchy dýchání * MeSH
• rinitida * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Názvy látek
• biologické markery MeSH

OBJECTIVE: The study worked with depressive symptoms, anxiety score and cognitive functions in obstructive sleep apnea (OSA) patients treated with CPAP. METHODS: Eighty-one subjects with OSA and without psychiatric comorbidity were treated with CPAP for one year and completed the following scales and cognitive tests: Trail Making Test, Verbal Fluency Test, d2 Test, Beck Depression Inventory-II and Beck Anxiety Inventory. MINI ruled out psychiatric disorder. At the two months check-up, subjects were re-evaluated for depressive and anxiety symptoms, and after one year of CPAP treatment, subjects repeated cognitive tests and scales. Data about therapy adherence and effectiveness were obtained from the patient's CPAP machines. RESULTS: The study was completed by 59 CPAP adherent patients and eight non-adherent patients. CPAP therapy effectiveness was verified in all patients by decreasing the apnea-hypopnoea index below 5 and/or 10% of baseline values. The adherent patients significantly improved depressive and anxiety symptoms. There was also an improvement in overall performance in the attention test; however, performance in many individual items did not change. The adherent patients also improved verbal fluency and in the Part B of the Trail making test. The non-adherent group significantly increased the number of mistakes made in the d2 test; other results were non-significant. CONCLUSION: According to our results, OSA patients' mood, anxiety and certain cognitive domains improved during the one-year therapy with CPAP. TRIAL REGISTRATION NUMBER: NCT03866161.

• Klíčová slova
• CPAP treatment, Trail Making Test, Verbal Fluency Test, cognitive functions, depression, obstructive sleep apnoea, treatment efficacy,
• Publikační typ
• časopisecké články MeSH

AIMS: The study analysed post-acute COVID-19 symptoms and the pulmonary function test (PFT) results in patients surviving the native strain of the virus. METHODS: The study was prospective; the inclusion criteria were positive PCR test for SARS-CoV-2 and age 18-100. Exclusion criteria were active respiratory infection, known or suspicious pre-existing pulmonary disease, cardiac failure, recent or acute pulmonary embolism, anaemia, and neuromuscular diseases. The recruitment period was 1st March 2020 - 25th December 2020. The initial examination was performed 4-12 weeks after the disease onset. All subjects underwent physical examination, anamnesis, chest x-ray and PFT. RESULTS: The study involved 785 subjects (345 male) mean age 53.8 (SD 14.6). The disease severity groups were: mild (G1), moderate (G2) and severe/critical (G3). Anosmia was present in the acute disease phase in 45.2% of G1 patients, but only in 4.5% of G3 patients. Dyspnoea occurred frequently in more severe groups (40%, 51.8% and 63.7% for G1, G2 and G3 respectively), while cough and fatigue showed no relationship to disease severity. Females were more likely to experience persistent symptoms. PFT results were significantly decreased in more severe groups compared to the mild COVID-19 patients, diffusing capacity was 86.3%, 79% and 68% of predicted values in G1, G2 and G3 respectively. CONCLUSION: Anosmia during the acute phase was associated with mild disease, persisting dyspnoea was more frequent after more severe COVID-19. Females tended to have persisting symptoms in post-acute phase more frequently. PFT results showed decrease with disease severity.

• Klíčová slova
• COVID-19, clinical presentation, post-acute phase, pulmonary function tests,
• MeSH
• anosmie MeSH
• COVID-19 * komplikace   diagnóza   MeSH
• dospělí MeSH
• dyspnoe etiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• prospektivní studie MeSH
• respirační funkční testy MeSH
• SARS-CoV-2 MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články 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.

• Klíčová slova
• artificial intelligence, corticosteroids, eHealth, personalised medication recommendation algorithms, post-COVID syndrome, prediction model, respiratory system,
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Severe respiratory failure is one of the most serious complications of coronavirus disease 2019 (COVID-19). In a small proportion of patients, mechanical ventilation fails to provide adequate oxygenation and extracorporeal membrane oxygenation (ECMO) is needed. The surviving individuals need long-term follow-up as it is not clear what their prognosis is. AIM: To provide a complex clinical picture of patients during follow-up exceeding one year after the ECMO therapy due to severe COVID-19. METHODS: All subjects involved in the study required ECMO in the acute stage of COVID-19. The survivors were followed-up for over one year at a specialized respiratory medical center. RESULTS: Of the 41 patients indicated for ECMO, 17 patients (64.7% males) survived. The average age of survivors was 47.8 years, and the average BMI was 34.7 kg·m-2. The duration of ECMO support was 9.4 days. A mild decrease in vital capacity (VC) and transfer factor (DLCO) was observed on the initial follow-up visit (82.1% and 60%, respectively). VC improved by 6.2% and by an additional 7.5% after 6 months and 1 year, respectively. DLCO improved by 21.1% after 6 months and remained stable after 1 year. Post-intensive care consequences included psychological problems and neurological impairment in 29% of patients; 64.7% of the survivors got vaccinated against SARS-CoV-2 within 12 months of hospitalization and 17.6% experienced reinfection with a mild course. CONCLUSION: The COVID-19 pandemic has significantly increased the need for ECMO. Patients' quality of life after ECMO is temporarily significantly reduced but most patients do not experience permanent disability.

• Klíčová slova
• COVID-19, ECMO, long-term outcome,
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms. METHODS: We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. RESULTS: We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed a higher frequency of rhinitis co-medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. CONCLUSIONS: We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.

• Klíčová slova
• mobile health, patient-reported outcomes, real-world data, rhinitis,
• MeSH
• lidé MeSH
• longitudinální studie MeSH
• průzkumy a dotazníky MeSH
• rinitida * epidemiologie   MeSH
• telemedicína * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH