Idiopathic pulmonary fibrosis (IPF) is the severest form of idiopathic interstitial pneumonia, with a median survival time estimated at 2–5 years from the time of diagnosis. It occurs mainly in elderly adults, suggesting a strong link between the fibrosis process and aging. Although chest high-resolution computed tomography (HRCT) is currently the method of choice in IPF assessment, diagnostic imaging with typical usual interstitial pneumonia (UIP) provides definitive results in only 55%, requiring an invasive surgical procedure such as lung biopsy or cryobiopsy for the final diagnostic analysis. Lung ultrasound (LUS) as a noninvasive, non-radiating examination is very sensitive to detect subtle changes in the subpleural space. The evidence of diffuse, multiple B-lines defined as vertical, hyperechoic artifacts is the hallmark of interstitial syndrome. A thick, irregular, fragmented pleura line is associated with subpleural fibrotic scars. The total numbers of B-lines are correlated with the extension of pulmonary fibrosis on HRCT, being an LUS marker of severity. The average distance between two adjacent B-lines is an indicator of a particular pattern on HRCT. It is used to appreciate a pure reticular fibrotic pattern as in IPF compared with a predominant ground glass pattern seen in fibrotic nonspecific interstitial pattern. The distribution of the LUS artifacts has a diagnostic value. An upper predominance of multiple B-lines associated with the thickening of pleura line is an LUS feature of an inconsistent UIP pattern, excluding the IPF diagnosis. LUS is a repeatable, totally radiation-free procedure, well tolerated by patients, very sensitive in detecting early changes of fibrotic lung, and therefore a useful imaging technique in monitoring disease progression in the natural course or after initiation of treatment.

COVID-19 has been associated with cardiovascular consequences, including myocardial infarction, thromboembolic events, arrhythmia, and heart failure. Numerous overlapping mechanisms, such as the IL-6 dependent cytokine storm and unopposed angiotensin II stimulation, could be responsible for these consequences. Cardiac damage is hypothesized to be a consequence of the direct viral infection of cardiomyocytes, resulting in increased metabolic demand, immunological activation, and microvascular dysfunction. Patients with pre-existing chronic heart failure are therefore at increased risk of decompensation, further heart damage, and significant health deterioration. Based on the aforementioned assumptions, we developed a study aiming to provide a detailed description of changes in biological parameters and cardiac injury markers of patients with heart failure and SARS-CoV-2 infection by correlating them with the clinical presentation and COVID-19 vaccination status, to predict the probability of ICU admission based on their initial hospital presentation. A two-year retrospective study was performed on heart failure patients with a history of SARS-CoV-2 infection and detailed records of biological biomarkers; a total of 124 eligible patients with COVID-19 and 236 without COVID-19 were recruited. Patients with heart failure and SARS-CoV-2 infection had significantly elevated baseline biological parameters and cardiac markers compared to those without COVID-19. Several cardiac injury markers were identified as significant independent risk factors for ICU admission: CK-MB (HR = 4.1, CI[2.2–6.9]), myoglobin (HR = 5.0, CI[2.3–7.8]), troponin-I (HR = 7.1[4.4–9.6]) troponin-T (HR = 4.9, CI[1.7–7.4]). The elevation of a basic panel of acute inflammation markers (CRP, IL-6, fibrinogen), D-dimers, and BNP was also a significant risk factor. The follow-up of survivors at four weeks after viral clearance determined a worsened clinical picture by NYHA classification, worsened cardiac ultrasound findings, and a mild improvement in cardiac and inflammatory markers. Increased levels of myocardial damage parameters in association with cardiac ultrasound findings and basic inflammatory markers may enable early risk assessment and triage in hospitalized heart failure patients infected with SARS-CoV-2.

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease. Key Points • Deep learning algorithms are used in pattern recognition of different interstitial lung diseases. • High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease. • Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.

IntroductionIdiopathic pulmonary fibrosis (IPF) is a relentlessly progressive lung disease with a fatal prognosis to whose rapid evolution multiple comorbidities may contribute, one of the most common being obstructive sleep apnea (OSA). There are several potential factors and conditions for the emergence of a cognitive deficit in relation to IPF or associated morbidities.ObjectivesThe goals of this study were to assess cognition in patients with IPF in stable phase and to identify clinical cognition modifiers.MethodsIn a cross-sectional study, 23 patients with IPF were evaluated using Montreal Cognitive Assessment (MoCA), an instrument for detecting mild cognitive impairments and were screened for OSA through overnight cardiorespiratory polygraphy and for anxiety and depression with three specific scale (Generalized Anxiety Disorder 7-item scale: GAD-7; the Patient Health Questionnaire: PHQ-9; Hospital Anxiety and Depression Scale: HADS).ResultsMoCA score was lower in patients with IPF when compared to controls subjects (24 [21,26] vs. 27 [26,28], p = 0.003) but not as significantly as in COPD patients (21 [18.8,23.3], p<0.0001). OSA was diagnosed in 19 (82.6%) IPF patients, 12 patients showed the presence of moderate-severe forms (63.15%). IPF patients with cognitive impairment (MoCA<23) exhibit a higher severity of OSA (apneea hypopnea index–AHI: 33.0±19.1 vs. 12.44±8.2, p = 0.018), and a higher Epworth score (7.1±3.3 vs. 4.3±1.8, p = 0.013). Anxiety and depression scores were not correlated with MoCA results.ConclusionsImpaired cognition in patients with IPF is mild and affect the areas of visuospatial abilities, language and working memory. OSA could be a possible predictor of IPF cognition deficit. Given the high prevalence of multiple types of sleep disorders in IPF patients, these should be investigated at least by cardiorespiratory polygraphy.

During the COVID-19 pandemic, it was observed that patients with heart disease are more likely to be hospitalized and develop severe COVID-19. Cardiac disease takes the top position among patient comorbidities, heart failure (HF) prevalence reaching almost 5% in the general population older than 35 years in Romania. This retrospective study aimed to determine the potential use of the NYHA classification for HF in hospitalized patients with COVID-19 as prognostic tool for in-hospital mortality, length of hospitalization, and probability of rehospitalization for HF decompensation. We observed that patients with advanced HF had a history of significantly more comorbid conditions that are associated with worse disease outcomes than the rest of patients classified as NYHA I and II. However, regardless of existing diseases, NYHA III, and, especially, NYHA IV, patients were at greatest risk for mortality following SARS-CoV-2 infection. They required significantly longer durations of hospitalization, ICU admission for mechanical ventilation, and developed multiple severe complications. NYHA IV patients required a median duration of 20 days of hospitalization, and their in-hospital mortality was as high as 47.8%. Cardiac biomarkers were significantly altered in patients with SARS-CoV-2 and advanced HF. Although the study sample was small, all patients with NYHA IV who recovered from COVID-19 required a rehospitalization in the following month, and 65.2% of the patients at initial presentation died during the next six months. The most significant risk factor for mortality was the development of severe in-hospital complications (OR = 4.38), while ICU admission was the strongest predictor for rehospitalization (OR = 5.19). Our result highlights that HF patients continue to be vulnerable post SARS-CoV-2 infection. Physicians and policymakers should consider this population’s high likelihood of hospital readmissions when making discharge, hospital capacity planning, and post-discharge patient monitoring choices.