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COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collected data that can be utilized to train machine learning models to anticipate COPD exacerbations. The objective of this study is to develop a machine learning model that, based on a patient’s history, predicts the probability of an exacerbation event within the next 3 days. After cleaning and harmonizing the different subsets of data, we split the data along the temporal axis: one subset for model training, another for model selection, and another for model evaluation. We then trained a gradient tree boosting approach as well as neural network-based approaches. After conducting our analysis, we found that the CatBoost algorithm yielded the best results, with an area under the precision-recall curve of 0.53 and an area under the ROC curve of 0.91. Additionally, we assessed the significance of the input variables and discovered that breathing rate, heart rate, and SpO2 were the most informative. The resulting model can operate in a 50% recall and 50% precision regime, which we consider has the potential to be useful in daily practice.

The factors that predispose an individual to a higher risk of death from COVID-19 are poorly understood. The goal of the study was to identify factors associated with risk of death among patients with COVID-19. This is a retrospective cohort study of people with laboratory-confirmed SARS-CoV-2 infection from February to May 22, 2020. Data retrieved for this study included patient sociodemographic data, baseline comorbidities, baseline treatments, other background data on care provided in hospital or primary care settings, and vital status. Main outcome was deaths until June 29, 2020. In the multivariable model based on nursing home residents, predictors of mortality were being male, older than 80 years, admitted to a hospital for COVID-19, and having cardiovascular disease, kidney disease or dementia while taking anticoagulants or lipid-lowering drugs at baseline was protective. The AUC was 0.754 for the risk score based on this model and 0.717 in the validation subsample. Predictors of death among people from the general population were being male and/or older than 60 years, having been hospitalized in the month before admission for COVID-19, being admitted to a hospital for COVID-19, having cardiovascular disease, dementia, respiratory disease, liver disease, diabetes with organ damage, or cancer while being on anticoagulants was protective. The AUC was 0.941 for this model’s risk score and 0.938 in the validation subsample. Our risk scores could help physicians identify high-risk groups and establish preventive measures and better follow-up for patients at high risk of dying.ClinicalTrials.gov Identifier: NCT04463706

IntroductionAn increase in airway isolates of non-tuberculous mycobacteria (NTM) has been observed, particularly in patients with previous lung damage. Inhaled corticosteroids may increase the risk of NTM lung disease. NTM isolation is of therapeutic importance, especially when macrolides are used. There are few data on the actual prevalence of NTM isolation in patients with chronic obstructive pulmonary disease (COPD).ObjectiveTo determine the prevalence of NTM isolation and NTM pulmonary disease according to the ATS/ERS/IDSA 2020 criteria in patients with high-risk COPD. As a secondary objective, we sought to identify risk factors for NTM isolation and developing NTM pulmonary disease in patients with COPD.MethodsProspective multicentre observational study based on the collection of three sputum samples in a year, for standard, mycobacteria and fungi cultures, in patients with high-risk COPD (postbronchodilator forced expiratory volume in 1 s<50% and/or ≥2 exacerbations in the previous year), with a 12-month follow-up. Patients with at least two good-quality samples were included.Results305 patients were initially selected, of which only 258 had at least two valid samples. NTM was isolated in 15% of patients (n=39), though only 8 (3%) met the ATS 2020 criteria for NTM disease. The most commonly isolated species was mycobacterium avium complex (MAC). Multivariate analysis identified the following risk factors for NTM isolation: low body weight, alpha-1 antitrypsin (AAT) deficiency, inhaled corticosteroids and cancer. NTM disease was only associated with body mass index <21.ConclusionsNTM isolation is more common than expected in patients with COPD and may have implications for treatment. It is associated with low body weight, AAT deficiency, inhaled corticosteroid use and cancer.

Background Health-related quality of life (HRQoL) should be seen as a tool that provides an overall view of the general clinical condition of a COPD patient. The aims of this study were to identify variables associated with HRQoL and whether they continue to have an influence in the medium term, during follow-up. Methods Overall, 543 patients with COPD were included in this prospective observational longitudinal study. At all four visits during a 5-year follow-up, the patients completed the Saint George’s Respiratory Questionnaire (SGRQ), pulmonary function tests, the 6-min walk test (6MWT), and a physical activity (PA) questionnaire, among others measurements. Data on hospitalization for COPD exacerbations and comorbidities were retrieved from the personal electronic clinical record of each patient at every visit. Results The best fit to the data of the cohort was obtained with a beta-binomial distribution. The following variables were related over time to SGRQ components: age, inhaled medication, smoking habit, forced expiratory volume in one second, handgrip strength, 6MWT distance, body mass index, residual volume, diffusing capacity of the lung for carbon monoxide, PA (depending on level, 13 to 35% better HRQoL, in activity and impacts components), and hospitalizations (5 to 45% poorer HRQoL, depending on the component). Conclusions Among COPD patients, HRQoL was associated with the same variables throughout the study period (5-year follow-up), and the variables with the strongest influence were PA and hospitalizations.

To adapt the Test of Adherence to Inhalers (TAI) questionnaire to Basque language and evaluate the psychometric properties. A cross-sectional observational study was carried out. We recruited Basque-speaking adults aged ≥18 years who attended the respiratory outpatient clinics in the last 5 years for follow-up with a diagnosis of asthma (n=249) or chronic obstructive pulmonary disease (COPD) (n=149). Patients were contacted by postal mail and reminders were sent to non-respondents. The TAI was translated into Basque and back-translated into Spanish. The comprehensibility and feasibility of Basque TAI were evaluated through cognitive debriefing interviews. We performed the descriptive of the questionnaire and assessed reliability by Cronbach's alpha, structure validity by confirmatory factor analysis (CFA) and known-groups validity by Wilcoxon and Kruskal-Wallis tests according to disease severity, exacerbations and Charlson Comorbidity Index (CCI). Mean TAI score were 44.82 (standard deviation (SD)=7.26) and 48.52 (SD=3.88) for patients with asthma and COPD, respectively. No floor effect was observed in any cohort. 38.14% of patients with asthma and 67.21% with COPD reported good adherence (maximum score), indicating a ceiling effect. The Cronbach's alphas were 0.881 and 0.836 for the asthma and COPD cohorts, respectively, demonstrating adequate reliability. In the CFA, each item loading was >0.4, suggesting that they fit into a single dimension. Mean TAI score was higher in patients with asthma experiencing exacerbations (p<0.05) and more severe asthma and COPD (p<0.0001) as compared to their counterparts, which demonstrates the known-groups validity. TAI score did not differ according to comorbidities. The Basque TAI is valid, reliable and useful for measuring adherence to inhalers in patients with asthma and COPD in clinical and research settings.

Our aim was to assess the impact of comorbidities on existing COPD prognosis scores. A total of 543 patients with COPD (FEV <80% and FEV /FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index. Among the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV % 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLD , GOLD and cluster analysis for predicting mortality, cluster system was found to be superior compared with GOLD (0.654 vs 0.722, =0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior ( <0.001). Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.

Although subtypes of chronic obstructive pulmonary disease are recognized, it is unknown what happens to these subtypes over time. Our objectives were to assess the stability of cluster-based subtypes in patients with stable disease and explore changes in clusters over 1 year. Multiple correspondence and cluster analysis were used to evaluate data collected from 543 stable patients included consecutively from 5 respiratory outpatient clinics. Four subtypes were identified. Three of them, A, B, and C, had marked respiratory profiles with a continuum in severity of several variables, while the fourth, subtype D, had a more systemic profile with intermediate respiratory disease severity. Subtype A was associated with less dyspnea, better health-related quality of life and lower Charlson comorbidity scores, and subtype C with the most severe dyspnea, and poorer pulmonary function and quality of life, while subtype B was between subtypes A and C. Subtype D had higher rates of hospitalization the previous year, and comorbidities. After 1 year, all clusters remained stable. Generally, patients continued in the same subtype but 28% migrated to another cluster. Together with movement across clusters, patients showed changes in certain characteristics (especially exercise capacity, some variables of pulmonary function and physical activity) and changes in outcomes (quality of life, hospitalization and mortality) depending on the new cluster they belonged to. Chronic obstructive pulmonary disease clusters remained stable over 1 year. Most patients stayed in their initial subtype cluster, but some moved to another subtype and accordingly had different outcomes.

Background Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition, in which taking into consideration clinical phenotypes and multimorbidity is relevant to disease management. Network analysis, a procedure designed to study complex systems, allows to represent connections between the distinct features found in COPD. Methods Network analysis was applied to a cohort of patients with COPD in order to explore the degree of connectivity between different diseases, taking into account the presence of two phenotypic traits commonly used to categorize patients in clinical practice: chronic bronchitis (CB+/CB−) and the history of previous severe exacerbations (Ex+/Ex−). The strength of association between diseases was quantified using the correlation coefficient Phi (ɸ). Results A total of 1726 patients were included, and 91 possible links between 14 diseases were established. Although the four phenotypically defined groups presented a similar underlying comorbidity pattern, with special relevance for cardiovascular diseases and/or risk factors, classifying patients according to the presence or absence of CB implied differences between groups in network density (mean ɸ: 0.098 in the CB− group and 0.050 in the CB+ group). In contrast, between‐group differences in network density were small and of questionable significance when classifying patients according to prior exacerbation history (mean ɸ: 0.082 among Ex− subjects and 0.072 in the Ex+ group). The degree of connectivity of any given disease with the rest of the network also varied depending on the selected phenotypic trait. The classification of patients according to the CB−/CB+ groups revealed significant differences between groups in the degree of conectivity between comorbidities. On the other side, grouping the patients according to the Ex−/Ex+ trait did not disclose differences in connectivity between network nodes (diseases). Conclusions The multimorbidity network of a patient with COPD differs according to the underlying clinical characteristics, suggesting that the connections linking comorbidities between them vary for different phenotypes and that the clinical heterogeneity of COPD could influence the expression of latent multimorbidity. Network analysis has the potential to delve into the interactions between COPD clinical traits and comorbidities and is a promising tool to investigate possible specific biological pathways that modulate multimorbidity patterns. The structure of the multimorbidity network of COPD patients can vary depending on underlying clinical phenotypes. This finding suggests that there are different biological mechanisms that influence the interactions between multimorbidities for different phenotypes.

Introduction: COPD is a highly prevalent chronic disease which is already the 4th cause of death worldwide, and its prevalence will keep increasing. The rate of hospitalizations of COPD patients remains constant as opposed as other chronic diseases such as chronic cardiac failure, in which the hospitalization rates are decreasing.  In our hospital we have developed a telehealth program named telEPOC which is aimed to monitorize COPD patients that have been frequently admitted to hospital. The main goal of this program is to reduce the number of admissions to the hospital, and its results so far have been very satisfactory. It has also been shown that this program improves the quality of life of patients with respect to those in the control group. However, we have not been able to reduce to zero the number of COPD hospitalizations. Therefore, we wondered if it would be possible to predict COPD exacerbations, which would enable us to take appropriate actions to avoid such exacerbations or reduce their negative effects. Machine Learning is the most important branch of Artificial Intelligence  and it is focused in developing software that enables computers to learn complex patterns from data, and use them to predict the outcome of previously unseen events. Therefore, this technology enables us to use Electronic Health Records of patients to make personalized predictions about their future. Objectives: telEPOC database is composed by daily reports sent by the patients. According to these daily reports, an alarm system composed by three levels of exacerbation (green, yellow and red) is established. The telEPOC program presents a great opportunity to apply Machine Learning to predict COPD exacerbations, due to the high quality of the data it generates and the great advantage that such predictions will bring to physicians in daily practice. In this work we show an Early Warning System (EWS), based on Machine Learning, that is capable of predicting when a patient of the telEPOC program is going to exacerbate. Also, we find the configuration to make the system optimal both from the medical and computational points of view. Besides we will identify the most informative factors to predict the exacerbations. Methods: The system records  the following variables for each patient on a daily basis: heart rate, temperature, oxygen saturation, respiratory rate, steps walked and a questionnaire form about symptoms (sputum, disnea, cough,  general health status). On this data the Random Forests Algorithm was applied to predict when a patient will present a red alarm. We used a 10-fold cross validation to estimate the performance of the model. Results and Conclusions: We achieved an Area under the ROC curve of 0.87 for the task of predicting whether a patient will suffer an exacerbation within the next three days. The EWS was capable of making reliable predictions with enough time in advance when a patient is going to present a red alarm. The more informative variables for this prediction were the heart rate and the number of walked steps.

Background Limited data are available regarding fit and healthy patients with pneumonia at different ages. We evaluated the association of age with clinical presentation, serotype and outcomes among healthy and well-functioning patients hospitalized for bacteremic pneumococcal community–acquired pneumonia. Methods We performed a prospective cohort study of consecutive healthy and well-functioning patients hospitalized for this type of pneumonia. Patients were stratified into younger (18 to 64 years) and older (≥65 years) groups. Results During the study period, 399 consecutive patients were hospitalized with bacteremic pneumococcal pneumonia. We included 203 (50.8%) patients who were healthy and well-functioning patients, of whom 71 (35%) were classified as older. No differences were found in antibiotic treatment, treatment failure rate, antibiotic resistance, or serotype, except for serotype 7F that was less common in older patients. In the adjusted multivariate analysis, the older patients had higher 30-day mortality (OR 6.83; 95% CI 1.22–38.22; P  = 0.028), but were less likely to be admitted to the ICU (OR 0.14; 95% CI 0.05–0.39; P <  0.001) and had shorter hospital stays (OR 0.71; 95% CI 0.54–0.94; P  = 0.017). Conclusions Healthy and well-functioning older patients have higher mortality than younger patients, but nevertheless, ICU admission was less likely and hospital stays were shorter. These results suggest that the aging process is a determinant of mortality, beyond the functional status of patients with bacteremic pneumococcal pneumonia.