Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.

OBJECTIVE: Stratifying the risk of death in SSc-related interstitial lung disease (SSc-ILD) is a challenging issue. The extent of lung fibrosis on high-resolution CT (HRCT) is often assessed by a visual semiquantitative method that lacks reliability. We aimed to assess the potential prognostic value of a deep-learning-based algorithm enabling automated quantification of ILD on HRCT in patients with SSc. METHODS: We correlated the extent of ILD with the occurrence of death during follow-up, and evaluated the additional value of ILD extent in predicting death based on a prognostic model including well-known risk factors in SSc. RESULTS: We included 318 patients with SSc, among whom 196 had ILD; the median follow-up was 94 months (interquartile range 73-111). The mortality rate was 1.6% at 2 years and 26.3% at 10 years. For each 1% increase in the baseline ILD extent (up to 30% of the lung), the risk of death at 10 years was increased by 4% (hazard ratio 1.04, 95% CI 1.01, 1.07, P = 0.004). We constructed a risk prediction model that showed good discrimination for 10-year mortality (c index 0.789). Adding the automated quantification of ILD significantly improved the model for 10-year survival prediction (P = 0.007). Its discrimination was only marginally improved, but it improved prediction of 2-year mortality (difference in time-dependent area under the curve 0.043, 95% CI 0.002, 0.084, P = 0.040). CONCLUSION: The deep-learning-based, computer-aided quantification of ILD extent on HRCT provides an effective tool for risk stratification in SSc. It might help identify patients at short-term risk of death.

ERS technical standard: Global Lung Function Initiative reference values for exhaled nitric oxide fraction (F ENO50 ).

BACKGROUND: Elevated exhaled nitric oxide fraction at a flow rate of 50 mL·s-1 (F ENO50 ) is an important indicator of T-helper 2-driven airway inflammation and may aid clinicians in the diagnosis and monitoring of asthma. This study aimed to derive Global Lung Function Initiative reference equations and the upper limit of normal for F ENO50 . METHODS: Available individual F ENO50 data were collated and harmonised using consensus-derived variables and definitions. Data collected from individuals who met the harmonised definition of "healthy" were analysed using the generalised additive models of location, scale and shape (GAMLSS) technique. RESULTS: Data were retrospectively collated from 34 782 individuals from 34 sites in 15 countries, of whom 8022 met the definition of healthy (19 sites, 11 countries). Overall, height, age and sex only explained 12% of the between-subject variability of F ENO50 (R2=0.12). F ENO device was neccessary as a predictor of F ENO50 , such that the healthy range of values and the upper limit of normal varied depending on which device was used. The range of F ENO50 values observed in healthy individuals was also very wide, and the heterogeneity was partially explained by the device used. When analysing a subset of data in which F ENO50 was measured using the same device and a stricter definition of health (n=1027), between-site heterogeneity remained. CONCLUSION: Available F ENO50 data collected from different sites using different protocols and devices were too variable to develop a single all-age reference equation. Further standardisation of F ENO devices and measurement are required before population reference values might be derived.

Cardiopulmonary Exercise Testing Provides Prognostic Information in Advanced Cystic Fibrosis Lung Disease.

Rationale: Cardiopulmonary exercise testing (CPET) provides prognostic information in cystic fibrosis (CF); however, its prognostic value for patients with advanced CF lung disease is unknown. Objectives: To determine the prognostic value of CPET on the risk of death or lung transplant (LTX) within 2 years. Methods: We retrospectively collected data from 20 CF centers in Asia, Australia, Europe, and North America on patients with a forced expiratory volume in 1 second (FEV1) ⩽ 40% predicted who performed a cycle ergometer CPET between January 2008 and December 2017. Time to death/LTX was analyzed using mixed Cox proportional hazards regression. Conditional inference trees were modeled to identify subgroups with increased risk of death/LTX. Results: In total, 174 patients (FEV1, 30.9% ± 5.8% predicted) were included. Forty-four patients (25.5%) died or underwent LTX. Cox regression analysis adjusted for age, sex, and FEV1 revealed percentage predicted peak oxygen uptake ([Formula: see text]o2peak) and peak work rate (Wpeak) as significant predictors of death/LTX: adjusted hazard ratios per each additional 10% predicted were 0.60 (95% confidence interval, 0.43-0.90; P = 0.008) and 0.60 (0.48-0.82; P < 0.001). Tree-structured regression models, including a set of 11 prognostic factors for survival, identified Wpeak to be most strongly associated with 2-year risk of death/LTX. Probability of death/LTX was 45.2% for those with a Wpeak ⩽ 49.2% predicted versus 10.9% for those with a Wpeak > 49.2% predicted (P < 0.001). Conclusions: CPET provides prognostic information in advanced CF lung disease, and Wpeak appears to be a promising marker for LTX referral and candidate selection.

Holistic management of patients with progressive pulmonary fibrosis.

Progressive pulmonary fibrosis (PF) is a complex interstitial lung disease that impacts substantially on patients' daily lives, requiring personalised and integrated care. We summarise the main needs of patients with PF and their caregivers, and suggest a supportive care approach. Individualised care, education, emotional and psychological support, specialised treatments, and better access to information and resources are necessary. Management should start at diagnosis, be tailored to the patient's needs, and consider end-of-life care. Pharmacological and non-pharmacological interventions should be individualised, including oxygen therapy and pulmonary rehabilitation, with digital healthcare utilised as appropriate. Further research is needed to address technical issues related to oxygen delivery and digital healthcare. Educational aims: To identify the main needs of patients with PF and their caregivers.To describe the components of a comprehensive approach to a supportive care programme for patients with PF.To identify further areas of research to address technical issues related to the management of patients with PF.

COPD Patients with Asthma Features in Vietnam: Prevalence and Suitability for Personalized Medicine.

COPD patients with asthma features usually benefit from inhaled corticosteroids (ICS)-containing regimens, but their burden and diagnostic criteria remain to be established. The aims of this study were to estimate the proportion of patients with asthma features among patients with physician-diagnosed COPD and to investigate differences in clinical characteristics and current medications between COPD patients with asthma features and patients with COPD alone. A cross-sectional study was conducted at two respiratory out-patient clinics at the University Medical Center in Ho Chi Minh City and Bach Mai Hospital in Ha Noi, Vietnam. COPD patients with asthma features were identified by attending physicians following the approach recommended by the GINA/GOLD joint committee. Of the 332 patients screened, 300 were enrolled in the study. The proportion of COPD patients with asthma features was 27.3% (95% confidence interval (95% CI) 22.6-32.6%). COPD patients with asthma features were younger, with higher FEV1 values, a greater proportion of positive bronchodilator reversibility tests, higher blood eosinophil count, and were more often treated with ICS/LABA (ICS/long-acting bronchodilator beta-2 agonist) than patients with COPD alone. The prevalence of COPD patients with asthma features is particularly high in Vietnam thus requiring appropriate action plans in clinical practice.

GINA Implementation Improves Asthma Symptoms Control and Lung Function: A Five-Year Real-World Follow-Up Study.

Symptoms control remains challenging for most patients with asthma. This study was conducted to evaluate the level of asthma symptoms control and lung function over 5 years of GINA (Global INitiative for Asthma) implementation. We included all patients with asthma who had been managed following GINA recommendations at the Asthma and COPD Outpatient Care Unit (ACOCU) of the University Medical Center in Ho Chi Minh City, Vietnam from October 2006 to October 2016. Of 1388 patients with asthma managed following GINA recommendations, the proportion of patients with well-controlled asthma significantly improved from 2.6% at baseline to 66.8% at month 3, 64.8% at year 1, 59.6% at year 2, 58.6% at year 3, 57.7% at year 4, and 59.5% at year 5 (p < 0.0001 for all comparisons). The proportion of patients with persistent airflow limitation significantly decreased from 26.7% at baseline to 12.6% at year 1 (p < 0.0001), 14.4% at year 2 (p < 0.0001), 15.9% at year 3 (p = 0.0006), 12.7% at year 4 (p = 0.0047), and 12.2% at year 5 (p = 0.0011). In patients with asthma managed according to GINA recommendations, asthma symptoms control and lung function improved after 3 months and the improvement was sustained over 5 years.

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.

BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. METHODS: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists' decisions. RESULTS: In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. CONCLUSION: A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone.

Outcome prediction model and prognostic biomarkers for COVID-19 patients in Vietnam.

Background: Accurate prognosis is important either after acute infection or during long-term follow-up of patients infected by severe acute respiratory syndrome coronavirus 2. This study aims to predict coronavirus disease 2019 (COVID-19) severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment. Methods: We included 261 Vietnamese COVID-19 patients, who were classified into moderate and severe groups. Disease severity prediction based on biomarkers and clinical parameters was performed by applying machine learning and statistical methods using the combination of clinical and biological data. Results: The random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to the severe condition. The most important indicators were interleukin (IL)-6, ferritin and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from the analysis to generalise the applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were C-reactive protein (CRP), D-dimer, IL-6, ferritin and dyspnoea. Two different sets of biomarkers (D-dimer, IL-6 and ferritin, and CRP, D-dimer and IL-6) are applicable for the assessment of disease severity and prognosis. The two biomarker sets were further tested through machine learning algorithms and relatively validated on two Danish COVID-19 patient groups (n=32 and n=100). The results indicated that various biomarker sets combined with clinical data can be used for detection of the potential to develop the severe condition. Conclusion: This study provided a simple and reliable model using two different sets of biomarkers to assess disease severity and predict clinical outcomes in COVID-19 patients in Vietnam.

Respiratory recovery trajectories after severe-to-critical COVID-19: a 1-year prospective multicentre study.

BACKGROUND: Survivors of severe-to-critical coronavirus disease 2019 (COVID-19) may have functional impairment, radiological sequelae and persistent symptoms requiring prolonged follow-up. This pragmatic study aimed to describe their clinical follow-up and determine their respiratory recovery trajectories, and the factors that could influence them and their health-related quality of life. METHODS: Adults hospitalised for severe-to-critical COVID-19 were evaluated at 3 months and up to 12 months post-hospital discharge in this prospective, multicentre, cohort study. RESULTS: Among 485 enrolled participants, 293 (60%) were reassessed at 6 months and 163 (35%) at 12 months; 89 (51%) and 47 (27%) of the 173 participants initially managed with standard oxygen were reassessed at 6 and 12 months, respectively. At 3 months, 34%, 70% and 56% of the participants had a restrictive lung defect, impaired diffusing capacity of the lung for carbon monoxide (D LCO) and significant radiological sequelae, respectively. During extended follow-up, both D LCO and forced vital capacity percentage predicted increased by means of +4 points at 6 months and +6 points at 12 months. Sex, body mass index, chronic respiratory disease, immunosuppression, pneumonia extent or corticosteroid use during acute COVID-19 and prolonged invasive mechanical ventilation (IMV) were associated with D LCO at 3 months, but not its trajectory thereafter. Among 475 (98%) patients with at least one chest computed tomography scan during follow-up, 196 (41%) had significant sequelae on their last images. CONCLUSIONS: Although pulmonary function and radiological abnormalities improved up to 1 year post-acute COVID-19, high percentages of severe-to-critical disease survivors, including a notable proportion of those managed with standard oxygen, had significant lung sequelae and residual symptoms justifying prolonged follow-up.

Distal Lung Inflammation Assessed by Alveolar Concentration of Nitric Oxide Is an Individualised Biomarker of Severe COVID-19 Pneumonia.

Pulmonary sequelae as assessed by pulmonary function tests (PFTs) are often reported in patients infected by SARS-CoV-2 during the post-COVID-19 period. Little is known, however, about the status of pulmonary inflammation during clinical recovery after patients' discharge from the hospitals. We prospectively measured PFTs coupled with the exhaled nitric oxide (NO) stemming from the proximal airways (FeNO) and the distal lung (CaNO) in 169 consecutive patients with varying degrees of the severity of COVID-19 six weeks to one year after acute infection by SARS-CoV-2. The proportions of patients with abnormal PFTs, defined as the presence of either obstructive/restrictive patterns or impaired lung gas transfer, or both, increased with the severity of the initial lung disease (15, 30, and 52% in patients with mild, moderate, and severe COVID-19). FeNO values remained within normal ranges and did not differ between the three groups of patients. CaNO, however, was significantly higher in patients with severe or critical COVID-19, compared with patients with milder forms of the disease. There was also an inverse relationship between CaNO and DLCO. We conclude that the residual inflammation of the distal lung is still present in the post-COVID-19 follow-up period, in particular, in those patients with an initially severe form of COVID-19. This long-lasting alveolar inflammation might contribute to the long-term development of pulmonary fibrosis and warrants the regular monitoring of exhaled NO together with PFTs in patients with COVID-19.

How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients' care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

Open-Source Hardware May Address the Shortage in Medical Devices for Patients with Low-Income and Chronic Respiratory Diseases in Low-Resource Countries.

Respiratory diseases pose an increasing socio-economic burden worldwide given their high prevalence and their elevated morbidity and mortality. Medical devices play an important role in managing acute and chronic respiratory failure, including diagnosis, monitoring, and providing artificial ventilation. Current commercially available respiratory devices are very effective but, given their cost, are unaffordable for most patients in low- and middle-income countries (LMICs). Herein, we focus on a relatively new design option-the open-source hardware approach-that, if implemented, will contribute to providing low-cost respiratory medical devices for many patients in LMICs, particularly those without full medical insurance coverage. Open source reflects a set of approaches to conceive and distribute the comprehensive technical information required for building devices. The open-source approach enables free and unrestricted use of the know-how to replicate and manufacture the device or modify its design for improvements or adaptation to different clinical settings or personalized treatments. We describe recent examples of open-source devices for diagnosis/monitoring (measuring inspiratory/expiratory pressures or flow and volume in mechanical ventilators) and for therapy (non-invasive ventilators for adults and continuous positive airway pressure support for infants) that enable building simple, low-cost (hence, affordable), and high-performance solutions for patients in LMICs. Finally, we argue that the common practice of approving clinical trials by the local hospital ethics board can be expanded to ensure patient safety by reviewing, inspecting, and approving open hardware for medical application to maximize the innovation and deployment rate of medical technologies.

Usefulness of Hospital Admission Chest X-ray Score for Predicting Mortality and ICU Admission in COVID-19 Patients.

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 during the 2021 peak pandemic in HCM City, Vietnam. CXR and clinical data were collected from Vinmec Central Park-hospitalized patients from July to September 2021. Three radiologists independently assessed the day-one CXR score consisting of both severity and extent of lung lesions (maximum score = 24). Among 219 included patients, 28 died and 34 were admitted to the ICU. There was a high consensus for CXR scoring among radiologists (κ = 0.90; CI95%: 0.89-0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85 CI95% 0.69-1) within the first 3 weeks after admission. A multivariate model confirmed a significant effect of an increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allowed for predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89)), respectively, and can be used to identify high-risk patients in needy countries such as Vietnam.

European Respiratory Society statement on long COVID follow-up.

Patients diagnosed with coronavirus disease 2019 (COVID-19) associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently experience symptom burden post-acute infection or post-hospitalisation. We aimed to identify optimal strategies for follow-up care that may positively impact the patient's quality of life (QoL). A European Respiratory Society (ERS) Task Force convened and prioritised eight clinical questions. A targeted search of the literature defined the timeline of "long COVID" as 1-6 months post-infection and identified clinical evidence in the follow-up of patients. Studies meeting the inclusion criteria report an association of characteristics of acute infection with persistent symptoms, thromboembolic events in the follow-up period, and evaluations of pulmonary physiology and imaging. Importantly, this statement reviews QoL consequences, symptom burden, disability and home care follow-up. Overall, the evidence for follow-up care for patients with long COVID is limited.

A Low-Cost, Easy-to-Assemble Device to Prevent Infant Hyperthermia under Conditions of High Thermal Stress.

High ambient temperature and humidity greatly increase the risk of hyperthermia and mortality, particularly in infants, who are especially prone to dehydration. World areas at high risk of heat stress include many of the low- and middle-income countries (LMICs) where most of their inhabitants have no access to air conditioning. This study aimed to design, evaluate, and test a novel low-cost and easy-to-assemble device aimed at preventing the risk of infant hyperthermia in LMICs. The device is based on optimizing negative heat transfer from a small amount of ice and transferring it directly to the infant by airflow of refrigerated air. As a proof of concept, a device was assembled mainly using recycled materials, and its performance was assessed under laboratory-controlled conditions in a climatic chamber mimicking realistic stress conditions of high temperature and humidity. The device, which can be assembled by any layperson using easily available materials, provided sufficient refrigerating capacity for several hours from just 1-2 kg of ice obtained from a domestic freezer. Thus, application of this novel device may serve to attenuate the adverse effects of heat stress in infants, particularly in the context of the evolving climatic change trends.