Three to four months after hospitalisation for COVID-19 pneumonia, the most frequently described alteration in respiratory function tests (RFTs) is decreased carbon monoxide transfer capacity (DL_CO). Methods: This is a prospective cohort study that included patients hospitalised because of SARS-CoV-2 pneumonia, three months after their discharge. A clinical evaluation, analytical parameters, chest X-ray, six-minute walk test, spirometry and DL_CO–DL_NO analysis were performed. Demographic variables, comorbidities, and variables related to the severity of the admission were recorded. Results: Two hundred patients completed the study; 59.5% men, age 62 years, 15.5% admitted to the intensive care unit. The most frequent functional alteration, in 27% of patients, was in the DL_CO–DL_NO combination. This alteration was associated with age, male sex, degree of dyspnoea, poorer perception of health, and limited ability for physical effort. These patients also presented higher levels of D-Dimer and more residual radiological alterations. In 42% of the patients with diffusion alterations, only reduced DL_NO was presented, along with lower D-Dimer levels and less capillary volume involvement. The severity of the process was associated with the reduction in DL_CO–DL_NO. Conclusions: The most sensitive RFT for the detection of the sequelae of COVID-19 pneumonia was the combined measurement of DL_CO–DL_NO and this factor was related to patient health status and their capacity for physical exertion. In 40% of these cases, there was only a reduction in DL_NO, a finding that may indicate less pulmonary vascular involvement.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Patients with end-stage renal disease (ESRD) and Mycobacterium tuberculosis infection pose a high risk of developing active TB disease. It is therefore important to detect latent TB infection (LTBI) to be able to offer treatment and prevent progression to TB disease. We assessed the value of the tuberculin skin test (TST) and of an interferon-gamma release assay (Quantiferon®-TB Gold in-Tube, QFT) for diagnosing LTBI in ESRD patients, after prolonged exposure to a highly contagious TB case in a haemodialysis unit. As a high number of patients presented erythema without induration in the TST response, this type of reaction was also analysed.

Introduction: There is debate as to whether lung-ultrasound (LUS) can replace lung-auscultation (LA) in the assessment of respiratory diseases. Methodology: The diagnostic validity, safety, and reliability of LA and LUS were analyzed in patients admitted in a pulmonary ward due to decompensated obstructive airway diseases, decompensated interstitial diseases, and pulmonary infections, in a prospective study. Standard formulas were used to calculate the diagnostic sensitivity, specificity, and accuracy. The interobserver agreement with respect to the LA and LUS findings was evaluated based on the Kappa coefficient (ᴋ). Results: A total of 115 patients were studied. LUS was more sensitive than the LA in evaluating pulmonary infections (93.59% vs. 77.02%; p = 0.001) and more specifically in the case of decompensated obstructive airway diseases (95.6% vs. 19.10%; p = 0.001). The diagnostic accuracy of LUS was also greater in the case of pulmonary infections (75.65% vs. 60.90%; p = 0.02). The sensitivity and specificity of the combination of LA and LUS was 95.95%, 50% in pulmonary infections, 76.19%, 100% in case of decompensated obstructive airway diseases, and (100%, 88.54%) in case of interstitial diseases. (ᴋ) was 0.71 for an A-pattern, 0.73 for pathological B-lines, 0.94 for condensations, 0.89 for pleural-effusion, 0.63 for wheezes, 0.38 for rhonchi, 0.68 for fine crackles, 0.18 for coarse crackles, and 0.29 for a normal LA. Conclusions: There is a greater interobserver agreement in the interpretation of LUS-findings compared to that of LA-noises, their combined use improves diagnostic performance in all diseases examined.

Background: Persistent pulmonary dysfunction is common after COVID-19, yet traditional assessments using carbon monoxide diffusing capacity (DLCO) alone may miss alveolar-capillary impairment. Objective: To determine whether combining nitric oxide (DLNO _5s ) and carbon monoxide (DLCO _5s ) diffusing capacities enhances detection of post-COVID-19 lung impairment and whether summed z -scores outperform individual measures in classifying affected individuals. Design and methods: We conducted an individual participant data meta-analysis using hierarchical mixed-effects modelling. The dataset included 572 COVID-19 survivors and 72 matched controls from six European centres. Lung function metrics—including spirometry, total lung capacity, DLNO _5s and DLCO _5s —were standardised into z -scores. Logistic models were compared using Bayesian Information Criterion and Leave-One-Out Information Criterion. Classification accuracy was assessed with Matthews Correlation Coefficient (MCC) and net reclassification improvement (NRI). Principal Component Analysis examined score structures, and dyspnoea severity was correlated with z-scores. Assessments were conducted 32–575 days post-infection (median=130 days). Results: The number of days between SARS-CoV-2 diagnosis and testing did not affect any of the measured z‐scores. Summed DLNO _5s + DLCO _5s z -scores consistently outperformed individual metrics. The combined model improved MCC by 0.06 (95% CI 0.01 to 0.11) and NRI by 37% (95% CI 13 to 62%) over DLCO _5s alone. The top model (summed DLNO _5s + DLCO _5s z-scores model) explained 10% of fixed and 59% of random variance. DLCO _5s alone failed to identify reduced membrane diffusion in approximately 16% of cases. Dyspnoea severity was significantly associated with all diffusion indices (p<0.001), though combined scores showed no stronger correlation than single predictors. Conclusion: Summed DLNO _5s + DLCO _5s z -scores enhance classification of post-COVID-19 pulmonary impairment beyond DLCO _5s alone. The NO-CO double diffusion approach offers improved diagnostic discrimination between previously infected individuals and controls and aligns with symptom severity. These findings support broader clinical integration of combined diffusion metrics in post-COVID assessment.

Pulmonary complications remain a significant challenge for COVID-19 survivors, necessitating advanced diagnostic approaches for long-term assessment. We present a curated, open-access dataset of pulmonary function measurements—including nitric oxide (DLNO) and carbon monoxide (DLCO) diffusing capacities—in 572 post–COVID-19 patients and 72 healthy controls (filtered from an original cohort of 726 survivors and 126 controls). Collected across eight international centres, the data include demographics, spirometry, lung volumes, and 5–6 s single-breath DLNO_5s, DLCO_5s, and alveolar volume (VA_5s). Missing values for total lung capacity were imputed, and low-quality or system-specific (Hyp’Air Compact) measurements were excluded in the filtered dataset. A third subset (333 patients, 54 controls) links these measurements to dyspnoea severity (mMRC scale) for correlation and proportional odds analyses. This resource underpins predictive modeling of post–COVID pulmonary impairment via summed z-scores (DLNO + DLCO) and aims to accelerate validation of NO-CO diagnostics. The freely accessible datasets are provided in both SPSS (.sav) and .csv formats at the Mendeley Data Cloud-based repository and includes nominal, ordinal, and scalar data.