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- Adult asthma scores—development and validation of multivariable scores to identify asthma in surveysPublication . Sá-Sousa, Ana; Pereira, Ana Margarida; Almeida, Rute; Araújo, Luís; Couto, Mariana; Jacinto, Tiago; Freitas, Alberto; Bousquet, Jean; Fonseca, João A.One of the questions in epidemiology is the identification of adult asthma in studies. To develop and validate multivariable scores for adult asthma identification in epidemiological studies and to explore cutoffs to rule in/rule out asthma, compared with asthma diagnosed by a physician after clinical examination and diagnostic tests, blinded to the self-administered questions. We analyzed data (n = 711 adults) from a nationwide population-based study. The predictors were self-administered questions identified in a literature review (the Adult Asthma Epidemiological Score [A2 score]) and from the Global Allergy and Asthma Network of Excellence (GA2LEN) questionnaire (the GA2LEN Asthma Epidemiological Score [GA2LEN score]). Scores were developed using exploratory factor analysis. Internal consistency, discriminative power, and diagnostic accuracy were assessed. The A2 score comprises 8 questions (including “Did a physician confirm you had asthma?”) and the GA2LEN score comprises 6 questions (including “Have you ever had asthma?”). Both had high Cronbach α (0.89 and 0.85, respectively, for the A2 score and the GA2LEN score) and good area under the receiver-operating characteristic curve (90.4% and 89.0%). The scoring is the sum of positive answers. Asthma is present (rule in) for scores of 4 or more (specificity, 99.2%; PPV, 93.3% and 91.7%; accuracy, 89.4% and 87.4%, respectively, for the A2 score and the GA2LEN score). Asthma is excluded (rule out) for A2 scores of 0 to 1 and a GA2LEN score of 0 (sensitivity, 93.1%; NPV, 98.2% and 98.0%; accuracy 89.4% and 82.8%, respectively, for the A2 score and the GA2LEN score). These practical scores can be used to rule in/rule out asthma in epidemiological studies and clinical screening/triage settings. They may help physicians in primary care or other specialties to screen patients with asthma using a simple score with a high level of discrimination and to identify the best candidates to be referred for a diagnostic workup. Moreover, their use may contribute to reducing the inconsistencies of operational definitions of asthma across studies and surveys.
- 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.
- 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.
- Automatic quality assessment of a forced expiratory manoeuvre acquired with the tablet microphonePublication . Almeida, Rute; Bernardo, Pinho; Jácome, Cristina; Teixeira, João Fonseca; Amaral, Rita; Gonçalves, Ivânia; Lopes, Filipa; Pinheiro, Ana Catarina; Jacinto, Tiago; Paixão, Cátia; Pereira, Mariana; Marques, Alda; Fonseca, João AlmeidaEvaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed.
- Reproducibility of the Vivatmopro measurements for exhaled nitric oxide valuesPublication . Amaral, Rita; Jácome, Cristina; Almeida, Rute; Sá-Sousa, Ana; Pinho, Bernardo; Guedes, Rui; Jacinto, Tiago; Fonseca, JoãoPortable monitoring devices allow fraction exhaled nitric oxide (FeNO) measurements outside clinical settings. However, the reproducibility of the new portable device Vivatmo pro is not yet fully established. In this study, we aimed to assess the reproducibility of this device for FeNO measurements.
- A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: latent class analysis and partitioning around medoids methodsPublication . Amaral, Rita; Jacinto, Tiago; Pereira, Ana; Almeida, Rute; Fonseca, JoãoLatent class analysis (LCA) and partitioning around medoids (PAM) are popular data-driven methods for partitioning objects based on dichotomous data, remaining not clear which is better for large epidemiological datasets. Hence, we compared these methods in the identification of clusters of subjects with airways symptoms, using a large population-based data from the U.S. National Health and Nutrition Examination Surveys (NHANES).
- Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypesPublication . Amaral, Rita; Bousquet, Jean; Pereira, Ana M.; Araújo, Luís M.; Sá‐Sousa, Ana; Jacinto, Tiago; Almeida, Rute; Delgado, Luís; Fonseca, João A.Background Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached. Aim To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis. Methods Seven hundred and twenty‐eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA‐classes. Results A six‐class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non‐allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA‐classes. Conclusion Novel severe phenotypes of participants with co‐occurrence of ocular, nasal and bronchial symptoms, and exacerbation‐prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.
- Combined image-based approach for monitoring the adherence to inhaled medicationsPublication . Vieira-Marques, Pedro; Teixeira, João Fonseca; Valente, José; Pinho, Bernardo; Guedes, Rui; Almeida, Rute; Jácome, Cristina; Pereira, Ana; Jacinto, Tiago; Amaral, Rita; Gonçalves, Ivânia; Sousa, Ana Sá; Couto, Mariana; Magalhães, Manuel; Bordalo, Diana; Silva, Luís Nogueira; Fonseca, J. AlmeidaThe adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.
- FRASIS - Monitorização da função respiratória na asma utilizando os sensores integrados do smartphonePublication . Almeida, Rute; Amaral, Rita; Jacinto, Tiago; Amaral, RitaO projeto FRASIS pretende desenvolver, integrar e validar um conjunto de tecnologias de informação e comunicação (TIC) de saúde móvel (mHealth) para a monitorização remota da função respiratória na asma, usando apenas o smartphone e os seus sensores integrados. As ferramentas atuais de automonitorização e autogestão da asma são complexas, pouco atrativas, não individualizadas e obrigam os profissionais de saúde a análises trabalhosas, desmotivando a sua utilização e integração nos cuidados de saúde. Existe uma clara oportunidade para soluções tecnológicas avançadas económicas para os doentes e atrativas para os diferentes intervenientes do setor da saúde. A estratégia do FRASIS é desenvolver e integrar tecnologias de automonitorização e autogestão sem a necessidade de dispositivos adicionais, fazendo uso da presença dos smartphones na vida diária e da sua futura integração com sensores ambientais em cidades inteligentes. O FRASIS pretende assim contribuir para a concretização dos princípios da saúde digital e inovar nas tecnologias de monitorização remota da função respiratória. Espera - se que os resultados do FRASIS tenham um impacto relevante na inovação em serviços de saúde para a asma, com maior envolvimento e capacitação do doente, fornecendo informação prospetiva e de qualidade para uma melhor decisão clínica, tornando assim os cuidados de saúde mais eficientes e sustentáveis.
- AIRDOC: Smart mobile application for individualized support and monitoring of respiratory function and sounds of patients with chronic osbtructive diseasePublication . Almeida, Rute; Amaral, Rita; Jácome, Cristina; Martinho, Diogo; Vieira-Marques, Pedro; Jacinto, Tiago; Ferreira, Ana; Almeida, Ana; Martins, Constantino; Pereira, Mariana; Pereira, Ana; Valente, José; Almeida, Rafael; Vieira, Ana; Amaral, Rita; Sá-Sousa, Ana; Gonçalves, Ivânia; Rodrigues, Pedro; Alves-Correia, Magna; Freitas, Alberto; Marreiros, GoretiCurrent tools for self-management of chronic obstructive respiratory diseases (CORD) are difficult to use, not individualized and requiring laborious analysis by health professionals, discouraging their use in healthcare. There is an opportunity for cost-effective and easy-to-disseminate advanced technological solutions directed to patients and attractive to different stakeholders. The strategy of AIRDOC is to develop and integrate self-monitoring and self-managing tools, making use of the smartphone's presence in everyday life. AIRDOC intends to innovate on: i) technologies for remote monitoring of respiratory function and computerized lung auscultation; ii) coaching solutions, integrating psychoeducation, gamification and disease management support systems; and iii) management of personal health data, focusing on security, privacy and interoperability. It is expected that AIRDOC results will contribute for the innovation in CORD healthcare, with increased patient involvement and empowerment while providing quality prospective information for better clinical decisions, allowing more efficient and sustainable healthcare delivery.