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- Allergen sensitization associates with worse lung function parametersPublication . Gonçalves, I.; Pereira, A. M.; Jacinto, Tiago; Amaral, Rita; Fonseca, J. de Almeida Lopes daTo assess the association between the number of allergen sensitizations and lung function variables in individuals with airway symptoms. Methods. Retrospective study with all individuals who performed lung function and skin-prick tests at CUF-Porto (01/2011-06/2016). Six allergen groups were considered. % predicted Pre-Bronchodilator test (BD) and % change after BD were analysed for spirometry and plethysmography parameters. Results. A total of 1293 individuals were included, 54% (n = 698) adults and 69% (n = 891) with sensitization to ≥ 1 allergen group. % FEV1 was significantly higher and % change in FEV1 significantly lower in non-sensitized individuals. % sRaw was higher in polysensitized (vs non-sensitized). Conclusions. The presence of allergen sensitizations was significantly associated with worse key lung function parameters.
- Feasibility and acceptability of an asthma app to monitor medication adherence: mixed methods studyPublication . Jácome, Cristina; Almeida, Rute; Pereira, Ana Margarida; Amaral, Rita; Mendes, Sandra; Alves-Correia, Magna; Vidal, Carmen; Freire, Sara López; Brea, Paula Méndez; Araújo, Luís; Couto, Mariana; Antolín-Amérigo, Darío; Caballer, Belén de la Hoz; Castro, Alicia Barra; Gonzalez-De-Olano, David; Bom, Ana Todo; Azevedo, João; Pinto, Paula Leiria; Pinto, Nicole; Neves, Ana Castro; Palhinha, Ana; Bom, Filipa Todo; Costa, Alberto; Loureiro, Cláudia Chaves; Santos, Lilia Maia; Arrobas, Ana; Valério, Margarida; Cardoso, João; Emiliano, Madalena; Gerardo, Rita; Rodrigues, José Carlos Cidrais; Oliveira, Georgeta; Carvalho, Joana; Mendes, Ana; Lozoya, Carlos; Santos, Natacha; Menezes, Fernando; Gomes, Ricardo; Câmara, Rita; Alves, Rodrigo Rodrigues; Moreira, Ana Sofia; Bordalo, Diana; Alves, Carlos; Ferreira, José Alberto; Lopes, Cristina; Silva, Diana; Vasconcelos, Maria João; Teixeira, Maria Fernanda; Ferreira-Magalhães, Manuel; Taborda-Barata, Luís; Cálix, Maria José; Alves, Adelaide; Fonseca, João AlmeidaPoor medication adherence is a major challenge in asthma, and objective assessment of inhaler adherence is needed. The InspirerMundi app aims to monitor adherence while providing a positive experience through gamification and social support. This study aimed to evaluate the feasibility and acceptability of the InspirerMundi app to monitor medication adherence in adolescents and adults with persistent asthma (treated with daily inhaled medication). A 1-month mixed method multicenter observational study was conducted in 26 secondary care centers from Portugal and Spain. During an initial face-to-face visit, physicians reported patients’ asthma therapeutic plan in a structured questionnaire. During the visits, patients were invited to use the app daily to register their asthma medication intakes. A scheduled intake was considered taken when patients registered the intake (inhaler, blister, or other drug formulation) by using the image-based medication detection tool. At 1 month, patients were interviewed by phone, and app satisfaction was assessed on a 1 (low) to 5 (high) scale. Patients were also asked to point out the most and least preferred app features and make suggestions for future app improvements. A total of 107 patients (median 27 [P25-P75 14-40] years) were invited, 92.5% (99/107) installed the app, and 73.8% (79/107) completed the 1-month interview. Patients interacted with the app a median of 9 (P25-P75 1-24) days. At least one medication was registered in the app by 78% (77/99) of patients. A total of 53% (52/99) of participants registered all prescribed inhalers, and 34% (34/99) registered the complete asthma therapeutic plan. Median medication adherence was 75% (P25-P75 25%-90%) for inhalers and 82% (P25-P75 50%-94%) for other drug formulations. Patients were globally satisfied with the app, with 75% (59/79) scoring ≥4,; adherence monitoring, symptom monitoring, and gamification features being the most highly scored components; and the medication detection tool among the lowest scored. A total of 53% (42/79) of the patients stated that the app had motivated them to improve adherence to inhaled medication and 77% (61/79) would recommend the app to other patients. Patient feedback was reflected in 4 major themes: medication-related features (67/79, 85%), gamification and social network (33/79, 42%), symptom monitoring and physician communication (21/79, 27%), and other aspects (16/79, 20%). The InspirerMundi app was feasible and acceptable to monitor medication adherence in patients with asthma. Based on patient feedback and to increase the registering of medications, the therapeutic plan registration and medication detection tool were redesigned. Our results highlight the importance of patient participation to produce a patient-centered and engaging mHealth asthma app.
- Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in PortugalPublication . Portela, Diana ; Amaral, Rita; Rodrigues, Pedro P. ; Freitas, Alberto ; Costa, Elísio ; Fonseca, João A. ; Sousa-Pinto, BernardoQuantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.
- Measurement of respiratory function with a mobile application: comparison with a conventional spirometer and evaluation of usabilityPublication . Pinheiro, Catarina; Viana, Paulo; Amaral, Rita; Jacinto, TiagoMobile apps can improve home measurements of pulmonary function via built-in phone sensors, (e.g. microphone). This could promote greater access to health interventions for patients with respiratory diseases, reducing the need of face-to-face visits..
- Identification of asthma phenotypes in the US general population: A latent class analysis approachPublication . Amaral, Rita; Pereira, Ana M; Jacinto, Tiago; Malinovsch, A; Janson, C; Alving, K; Fonseca, J. A.Combining clinical and physiological data from adults with asthma by means of unsupervised classification methods could provide a better taxonomy among the general asthma population. Therefore, we aimed to identify distinct phenotypes using latent class analysis (LCA), in adults with current asthma from the general population.
- Where do we stand with asthma phenotypes derived from data-driven methods? A systematic reviewPublication . Amaral, Rita; Jacinto, Tiago; Sousa-Pinto, Bernardo; Fonseca, JoãoAsthma phenotypes can be refined using methods without a priori assumptions (data-driven). We aimed to describe asthma phenotypes derived with data-driven methods, using variables easily measurable in a clinical setting, and to summarize their consistency.
- Multidisciplinary development and initial validation of a clinical knowledge base on chronic respiratory diseases for mHealth decision support systemsPublication . Pereira, Ana Margarida; Jácome, Cristina; Jacinto, Tiago; Amaral, Rita; Pereira, Mariana; Sá-Sousa, Ana; Couto, Mariana; Vieira-Marques, Pedro; Martinho, Diogo; Vieira, Ana; Almeida, Ana; Martins, Constantino; Marreiros, Goreti; Freitas, Alberto; Almeida, Rute; Fonseca, João A.Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.
- The use of remote care during the Coronavirus disease 2019 pandemic: a perspective of Portuguese and Spanish physiciansPublication . Jácome, C.; Pereira, A. M.; Amaral, Rita; Alves-Correia, M.; Almeida, R.; Mendes, S.; Mendes, S.; Fonseca, J. Almeida; INSPIRERS groupThis study aimed to characterise how the follow-up of outpatients was done during the first months of COVID-19 pandemic by a multidisciplinary group of physicians involved in an asthma mHealth project. A cross-sectional study based on a web survey was conducted. The survey was sent to 123 physicians working at secondary care centres of Portugal and Spain, that participate in the INSPIRERS project. A total of 65 physicians completed the survey (53% response rate). They had a mean of 18 (11) years of clinical practice and 14% were residents. More than half were allergists (58%), 22% pulmonologists and 20% paediatricians. Most were working in Portugal (89%) and in public hospitals (88%). All were conducting consultations: 71% presential (median [p25 , p75] duration 30 [20, 30] min), 91% telephonic (15 [10, 20] min) and 20% video consultations (20 [10, 28] min). The median duration of presential consultations was significantly higher than pre-COVID-19 (20 [20, 30] min; p = 0.021). From the physicians conducting video consultations, 92% were allergists and only 54% considered that their institution provided adequate conditions. The physicians of the INSPIRERS group used telephonic consultations as the main alternative to presential ones and 1/5 used video consultations. These results suggest the need to rethink clinical follow-up services for outpatients in the near future increasing the use of telemedicine, especially
- Having concomitant asthma phenotypes is common and independently relates to poor lung function in NHANES 2007–2012Publication . Amaral, Rita; Fonseca, João A.; Jacinto, Tiago; Pereira, Ana M.; Malinovschi, Andrei; Janson, Christer; Alving, KjellEvidence for distinct asthma phenotypes and their overlap is becoming increasingly relevant to identify personalized and targeted therapeutic strategies. In this study, we aimed to describe the overlap of five commonly reported asthma phenotypes in US adults with current asthma and assess its association with asthma outcomes. Data from the National Health and Nutrition Examination Surveys (NHANES) 2007–2012 were used (n = 30,442). Adults with current asthma were selected. Asthma phenotypes were: B-Eos-high [if blood eosinophils (B-Eos) ≥ 300/mm3]; FeNO-high (FeNO ≥ 35 ppb); B-Eos&FeNO-low (B-Eos < 150/mm3 and FeNO < 20 ppb); asthma with obesity (AwObesity) (BMI ≥ 30 kg/m2); and asthma with concurrent COPD. Data were weighted for the US population and analyses were stratified by age (< 40 and ≥ 40 years old). Of the 18,619 adults included, 1059 (5.6% [95% CI 5.1–5.9]) had current asthma. A substantial overlap was observed both in subjects aged < 40 years (44%) and ≥ 40 years (54%). The more prevalent specific overlaps in both age groups were AwObesity associated with either B-Eos-high (15 and 12%, respectively) or B-Eos&FeNO-low asthma (13 and 11%, respectively). About 14% of the current asthma patients were “non-classified”. Regardless of phenotype classification, having concomitant phenotypes was significantly associated with (adjusted OR, 95% CI) ≥ 2 controller medications (2.03, 1.16–3.57), and FEV1 < LLN (3.21, 1.74–5.94), adjusted for confounding variables. A prevalent overlap of commonly reported asthma phenotypes was observed among asthma patients from the general population, with implications for objective asthma outcomes. A broader approach may be required to better characterize asthma patients and prevent poor asthma outcomes.
- Quality assessment and feedback of Smart Device Microphone Spirometry executed by childrenPublication . Almeida, Rute; Pinho, Bernardo; Jácome, Cristina; Teixeira, Joao Fonseca; Amaral, Rita; Lopes, Filipa; Jacinto, Tiago; Guedes, Rui; Pereira, Mariana; Goncalves, Ivania; Fonseca, Joao AlmeidaSmart device microphone spirometry, based on the audio recording of forced expiratory maneuver (FEM), can be a simple, ubiquitous and easy tool for patients to self-monitor their asthma. Automatic validity assessment is crucial to guarantee that the global effort of the FEM fulfil the admissible minimum or if the maneuver needs to be repeated. In this work an automatic method to classify the sounds from FEM with respect to global effort was developed and evaluated using data from 54 children (5-10 years). The method proposed was able to correctly classify the microphone spirometry with respect to admissible minimum of effort with an accuracy of 86% (specificity 87% and sensitivity 86%). This method can be used to provide immediate feedback of the correct execution of the maneuver, improving the clinical value and utility of this self-monitoring tool.