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743), left heart ejection fraction (p = 0.574), and pulmonary artery systolic pressure (p = 0.441). However, the anaerobic threshold, both as an absolute (p = 0.009) and as a % of predicted value (p = 0.047), was significantly lower during follow-up.

In adult CF patients with stable, mild-to-moderate disease, a peak VO2 may be preserved for several years. However, even in these patients, deconditioning is present.

In adult CF patients with stable, mild-to-moderate disease, a peak VO2 may be preserved for several years. However, even in these patients, deconditioning is present.

Acute respiratory distress syndrome (ARDS) is a life-threatening chest disease associated with a poor outcome and increased mortality. It may lead to pulmonary hypertension and, eventually, right ventricular failure. click here These changes can be investigated by transthoracic echocardiography (TTE) which is considered a non-invasive and cost-effective modality. We studied the role of right ventricular function in the prediction of the severity and mortality in ARDS.

In this observational study, 94 patients suffering from ARDS were subjected to TTE to evaluate the parameters of right ventricular function by measuring tricuspid annular plane systolic excursion (TAPSE), right ventricular fractional area change (RV-FAC), myocardial performance index (Tei index), and systolic pulmonary artery pressure (SPAP) to assess their relation to the severity and mortality in ARDS.

TAPSE, SPAP, Tei index, and RV-FAC showed significant differences between survivors and non-survivors after 30 days (all p < 0.001). An increased length of intensive care unit stay was significantly correlated with TAPSE, Tei index, and RV-FAC (p = 0.002′ 0.007′ and 0.013, respectively). Meanwhile, the length of mechanical ventilation days was significantly correlated with the Tei index only (p < 0.001). Multivariate regression analysis found that TAPSE and the Tei index were independent factors affecting mortality (p = 0.004′ and 0.006, respectively). RV-FAC, with a cut-off point ≤ 57%, had the highest sensitivity’ while TAPSE, with a cut-off point ≤ 17 mm, had the highest specificity to predict mortality.

Transthoracic echocardiographic parameters of the right ventricle could be used to predict severity and mortality in patients with ARDS with high sensitivity and specificity.

Transthoracic echocardiographic parameters of the right ventricle could be used to predict severity and mortality in patients with ARDS with high sensitivity and specificity.

Cough is one of the most frequent symptoms reported to pulmonologists. The role of bronchoscopy in the diagnostic work-up of chronic cough is not clearly defined. The aim of this study was to evaluate the utility of fiberoptic bronchoscopy (FOB) and additional testing of samples collected during FOB in the differential diagnosis of chronic cough in adults.

This was a single-center retrospective study. Out of 7115 conventional white light FOB examinations, we finally selected 198 with cough as the only indication.

In 40.9% of bronchoscopic examinations, no visible cause of cough was found. Visual signs of chronic bronchitis (CB) were detected in 57.6% of reports. Only in 3 cases (1.5%) bronchoscopy revealed a potential cause of chronic cough other than CB. Mycobacterium tuberculosis or other mycobacteria were spotted in none of the samples. In 91.1% of bronchoalveolar lavage (BAL) cytologic examinations, at least one cell count abnormality was detected, but only in case of increased percentage of eosinophils, it might be considered clinically relevant. In 53% of bacteriological culture results, at least one potentially pathogenic bacterium was isolated.

The present study results strengthen the evidence that FOB combined with additional testing of airway specimens obtained during FOB is not a powerful tool in the differential diagnosis of chronic cough, and FOB as a diagnostic tool may be overused. The appropriate timing and decision regarding referral for FOB and additional testing of achieved material requires careful clinical consideration.

The present study results strengthen the evidence that FOB combined with additional testing of airway specimens obtained during FOB is not a powerful tool in the differential diagnosis of chronic cough, and FOB as a diagnostic tool may be overused. The appropriate timing and decision regarding referral for FOB and additional testing of achieved material requires careful clinical consideration.

Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics.

Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea’s centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric.

As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period.

Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.

Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.