What is the real incidence of right ventricular affection in patients with acute pulmonary embolism?

BACKGROUND: Echocardiographic markers of right ventricular dysfunction or pressure overload (RVd/PO) have been used in risk assessment of patients with acute pulmonary embolism (APE). Nevertheless, the role of echocardiography in these patients is incompletely determined. We evaluated the right ventricular function using 'non-conventional' markers of RVd/PO in patients with APE. METHODS: This was a prospective, single-arm, single-centre study. Consecutive adult patients hospitalised for APE were included. The RV free wall longitudinal strain (RV-FWLS), the fractional area change (FAC), the ratio tricuspid annular plane systolic excursion (TAPSE)/pulmonary arterial systolic pressure (PASP), and the pulmonary vascular resistance (PVR) were evaluated. RESULTS: One hundred patients (mean age 70.0 ± 13.9 years, female 48%) were screened and 73 had adequate RV-FWLS images. The most common abnormal echocardiographic marker was RV-FWLS (44/73; p < 0.001, for all other echocardiographic indices). Thirty-one patients had either PASP ≥ 36 mmHg or PVR > 2 WU (49.2% of the patients with both indices available). There were significant correlations between RV-FWLS, TAPSE/PASP and PVR with both D-Dimers and B-type natriuretic peptide (BNP), and between FAC and BNP. RF-FWLS differed significantly between patients with a simplified pulmonary embolism severity index (sPESI) score 0 and those with a score ≥1 (p < 0.001). CONCLUSIONS: RVd/PO coexists with APE in a large proportion of patients. RV-FWLS is the most abnormal echocardiographic sign and is related to clinical and biochemical prognostic indices.

Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others. In this literature review, we describe the most prominent applications of AI in ILDs presented approximately within the last five years. We roughly stratify these studies in three categories, namely: (i) screening, (ii) diagnosis and classification, (iii) prognosis.

Effectiveness and quality of life in asthmatic patients treated with budesonide/formoterol via Elpenhaler® device in primary care. The "SKIRON" real world study.

Aim: Inhaled corticosteroid (ICS)/long-acting ß2 agonist (LABA) combination therapy is used for the effective control of asthma. Aim of this study was to collect data on the effectiveness, safety, quality of life, and patient satisfaction from a fixed dose combination of budesonide/formoterol administered with the Elpenhaler® device following 3-months' treatment.Methods: A 3-month real-life, multicentre, one-arm, prospective observational study (SKIRON study-NCT03055793) was conducted, using the following questionnaires: Asthma Control Questionnaire (ACQ-6) for asthma control assessment, MiniAQLQ questionnaire for QoL assessment, and Feeling of Satisfaction with Inhaler questionnaire (FSI-10) for patients' satisfaction with the inhaler device. Comorbidities and safety data were also recorded during the study.Results: We enrolled 1,174 asthmatic patients following standard clinical practice in primary care from 126 sites in urban and rural areas of Greece. The majority of patients (71.5%) had at least one comorbidity. A statistically significant improvement in the ACQ-6 score was noted at 3 months compared to the baseline evaluation (mean ± SD 2.19 ± 0.97 at baseline vs. 0.55 ± 0.56 at 3 months; mean change -1.64 (95%CI -1.69, -1.57), p < 0.0001). MiniAQLQ score was statistically and clinically significantly improved, compared to baseline, (4.55 ± 1.04 at baseline vs. 6.37 ± 0.64 at 3 months; mean change 1.82 (95%CI 1.75, 1.87), p < 0.0001). The mean FSI-10 score of 44.2 ± 5.4 indicated patient satisfaction and ease-of-use of the Elpenhaler® device.Conclusions: In this large real-world study of inadequately-controlled asthma patients in primary care settings, the treatment with budesonide/formoterol FDC with the Elpenhaler® device was associated with significant improvement in patients' asthma control and quality of life.

Adherence and quality of life assessment in patients with asthma treatment with budesonide/formoterol via the Elpenhaler device: the COMPLETE study.

BACKGROUND: Asthma is a chronic inflammatory disease of the airways that causes recurring episodes of wheezing, breathlessness, chest tightness and coughing. Inhaled drugs on a daily basis are the cornerstone of asthma treatment, therefore, patient adherence is very important. METHODS: We performed a multicenter, open, non-interventional, observational, prospective study of 716 adult patients diagnosed with asthma receiving FDC (Fixed-dose combination) budesonide/formoterol via the Elpenhaler device. We assessed the adherence to treatment at 3 and 6 months (based on the MMAS-8: 8-item Morisky Medication Adherence Scale), the quality of life and change in forced expiratory volume in 1 s (FEV1) from baseline to follow-up. RESULTS: Approximately 80% of the patients showed medium to high adherence throughout the study. The mean (SD) MMAS-8 score at 6 months was 6.85 (1.54) and we observed a statistically significant shift of patients from the low adherence group to the high adherence group at 6 months. Moreover, after 6 months of treatment with FDC budesonide/formoterol, we observed an increase in the patients' quality of life that as expressed by a change 2.01 (95%CI 1.93-2.10) units in Mini AQLQ (p < 0.0001) that was more pronounced in the high adherence group. The same trend was also observed in terms of spirometry (mean FEV1 2.58 L (0.85) at the end of the study, increased by 220 mL from baseline) with a higher improvement in the medium and high adherence groups. CONCLUSIONS: Treatment with FDC of budesonide/formoterol via the Elpenhaler device was associated with improvement in asthma-related quality of life and lung function over 6 months that were more prominent in patients with higher adherence. TRIAL REGISTRATION: 2017-HAL-EL-74 (ClinicalTrials.gov Identifier: NCT03300076).

The Diagnostic Role of Uric Acid to Creatinine Ratio for the Identification of Patients with Adverse Pulmonary Embolism Outcomes.

BACKGROUND: Uric acid (UA) is the final product of purine metabolism and a marker of oxidative stress that may be involved in the pathophysiology of cardiovascular and thromboembolic disease. The aim of the current study is to investigate the potential value of UA to creatinine ratio (UA/Cr) as a diagnostic tool for the outcome of patients admitted with acute pulmonary embolism (PE) and the correlations with other parameters. METHODS: We evaluated 116 patients who were admitted for PE in a respiratory medicine department. PE was confirmed with computed tomography pulmonary angiography. Outcomes evaluated were hospitalization duration, mortality or thrombolysis and a composite endpoint (defined as mortality or thrombolysis). Patients were assessed for PE severity with the PE Severity Index (PESI) and the European Society of Cardiology (ESC) 2019 risk stratification. RESULTS: The median (interquartile range) UA/Cr level was 7.59 (6.3-9.3). UA/Cr was significantly associated with PESI (p < 0.001), simplified PESI (p = 0.019), and ESC 2019 risk stratification (p < 0.001). The area under the curve (AUC) for prediction of 30-day mortality by UA/Cr was 0.793 (95% CI: 0.667-0.918). UA/Cr levels ≥7.64 showed 87% specificity and 94% negative predictive value for mortality. In multivariable analysis UA/Cr was an independent predictor of mortality (HR (95% CI): 1.620 (1.245-2.108), p < 0.001) and composite outcome (HR (95% CI): 1.521 (1.211-1.908), p < 0.001). Patients with elevated UA/Cr levels (≥7.64) had longer hospitalization (median (IQR) 7 (5-11) vs. 6 (5-8) days, p = 0.006)), higher mortality (27.3% vs. 3.2%, p = 0.001) and worse composite endpoint (32.7% vs. 3.4%, p < 0.001). CONCLUSION: Serum UA/Cr ratio levels at the time of PE diagnosis are associated with disease severity and risk stratification, and may be a useful biomarker for the identification of patients at risk of adverse outcomes.

Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease.

BACKGROUND: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE: The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS: We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS: The resulting articles were assessed and organized into four basic contextual categories, namely: i) 'COPD diagnosis', ii) 'COPD prognosis', iii) 'Patient classification', iv) 'COPD management', and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS: We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.

Recurrent bilateral lung infiltrates in a patient with ulcerative colitis.

In all cases of ILD in patients with UC, drug-induced pneumonitis should be excluded. In patients who receive both anti-TNF-α and mesalazine and develop drug-induced pneumonitis, it is quite difficult to differentiate which is the actual causing agent. https://bit.ly/3AnNJNN.

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

Reduction in Hospitalizations for Respiratory Diseases during the First COVID-19 Wave in Greece.

INTRODUCTION: During the first COVID-19 wave, a considerable decline in hospital admissions was observed worldwide. AIM: This retrospective cohort study aimed to assess if there were any changes in the number of patients hospitalized for respiratory diseases in Greece during the first CO-VID-19 wave. METHODS: In the present study, we evaluated respiratory disease hospitalization rates across 9 tertiary hospitals in Greece during the study period (March-April 2020) and the corresponding period of the 2 previous years (2018-2019) that served as the control periods. Demographic data and discharge diagnosis were documented for every patient. RESULTS: Of the 1,307 patients who were hospitalized during the study period, 444 (35.5%) were males with a mean (±SD) age of 66.1 ± 16.6 years. There was a 47 and 46% reduction in all-cause respiratory morbidity compared to the corresponding periods of 2018 and 2019, respectively. The mean incidence rate for respiratory diseases during the study period was 21.4 admissions per day, and this rate was significantly lower than the rate during the same period in 2018 (40.8 admissions per day; incidence rate ratio [IRR], 0.525; 95% confidence interval [CI], 0.491-0.562; p < 0.001) or the rate during 2019 (39.9 admissions per day; IRR, 0.537; 95% CI, 0.502-0.574; p < 0.001). The greatest reductions (%) in the number of daily admissions in 2020 were observed for sleep apnoea (87% vs. 2018 and 84% vs. 2019) followed by admissions for asthma (76% vs. 2018 and 79% vs. 2019) and chronic obstructive pulmonary disease (60% vs. 2018 and 51% vs. 2019), while the lowest reductions were detected in hospitalizations for pulmonary embolism (6% vs. 2018 and 23% vs. 2019) followed by tuberculosis (25% vs. both 2018 and 2019). DISCUSSION/CONCLUSION: The significant reduction in respiratory admissions in 2020 raises the reasonable question of whether some patients may have avoided seeking medical attention during the COVID-19 pandemic and suggests an urgent need for transformation of healthcare systems during the pandemic to offer appropriate management of respiratory diseases other than COVID-19.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature.

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.

Validation of the portable Bluetooth® Air Next spirometer in patients with different respiratory diseases.

BACKGROUND: Chronic respiratory diseases constitute a considerable part in the practice of pulmonologists and primary care physicians; spirometry is integral for the diagnosis and monitoring of these diseases, yet remains underutilized. The Air Next spirometer (NuvoAir, Sweden) is a novel ultra-portable device that performs spirometric measurements connected to a smartphone or tablet via Bluetooth®. METHODS: The objective of this study was to assess the accuracy and validity of these measurements by comparing them with the ones obtained with a conventional desktop spirometer. Two hundred subjects were enrolled in the study with various spirometric patterns (50 patients with asthma, 50 with chronic obstructive pulmonary disease and 50 with interstitial lung disease) as well as 50 healthy individuals. RESULTS: For the key spirometric parameters in the interpretation of spirometry, i.e. FEV1, FVC, FEV1/FVC and FEF25-75%, Pearson correlation and Interclass Correlation Coefficient were greater than 0.94, exhibiting perfect concordance between the two spirometers. Similar results were observed in an exploratory analysis of the subgroups of patients. Using Bland-Altman plots we have shown good reproducibility in the measurements between the two devices, with small mean differences for the evaluated spirometric parameters and the majority of measurements being well within the limits of agreement. CONCLUSIONS: Our results support the use of Air Next as a reliable spirometer for the screening and diagnosis of various spirometric patterns in clinical practice.

Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data.

Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support. The stratification scores currently employed are mostly generic, i.e. not specifically built for LVAD patients, and are based on pre-implantation patient data. In this work we apply data mining approaches for the prediction of time dependent survival in patients after LVAD implantation. Moreover, the predictions acquired with the use of pre-implantation data are enriched by employing post-implantation data, i.e. follow-up data. Different clinical scenarios have been depicted and the subsequent conditions are tested in order to identify the optimal set of pre- and post-implant features, as well as the most suitable algorithms for feature selection and prediction. The proposed approach is applied to a real dataset of 71 patients, reporting an accuracy of 84.5%, sensitivity of 87% and specificity of 82%. Based on the reported results, expert cardio-surgeons can be supported in planning the treatment of VAD patients.

Prediction of oral cancer recurrence using dynamic Bayesian networks.

We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices. Moreover, the proposed methodology aims to (i) assess the prognosis of patients regarding oral cancer recurrence and at the same time, (ii) provide important information about the underlying biological processes of the disease.

Machine learning applications in cancer prognosis and prediction.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Sequence patterns mediating functions of disordered proteins.

Disordered proteins lack specific 3D structure in their native state and have been implicated with numerous cellular functions as well as with the induction of severe diseases, e.g., cardiovascular and neurodegenerative diseases as well as diabetes. Due to their conformational flexibility they are often found to interact with a multitude of protein molecules; this one-to-many interaction which is vital for their versatile functioning involves short consensus protein sequences, which are normally detected using slow and cumbersome experimental procedures. In this work we exploit information from disorder-oriented protein interaction networks focused specifically on humans, in order to assemble, by means of overrepresentation, a set of sequence patterns that mediate the functioning of disordered proteins; hence, we are able to identify how a single protein achieves such functional promiscuity. Next, we study the sequential characteristics of the extracted patterns, which exhibit a striking preference towards a very limited subset of amino acids; specifically, residues leucine, glutamic acid, and serine are particularly frequent among the extracted patterns, and we also observe a nontrivial propensity towards alanine and glycine. Furthermore, based on the extracted patterns we set off to infer potential functional implications in order to verify our findings and potentially further extrapolate our knowledge regarding the functioning of disordered proteins. We observe that the extracted patterns are primarily involved with regulation, binding and posttranslational modifications, which constitute the most prominent functions of disordered proteins.

A Bayesian Network-based approach for discovering oral cancer candidate biomarkers.

Oral cancer can arise in the head and neck region. Due to the aggressive nature of the disease, which often leads to poor prognosis, Oral Squamous Cell Carcinoma (OSCC) constitutes the 8(th) most common neoplasms in humans. In the present work we formulate gene interaction network from oral cancer genomic data using Dynamic Bayesian Networks (DBNs). Four modules were extracted after applying a clustering technique to the network. We consequently explore them by applying topological and functional analysis methods in order to identify significant network nodes. Our analysis revealed that these important nodes may correspond to candidate biomarkers of the disease.

A multiscale approach for modeling atherosclerosis progression.

Progression of atherosclerotic process constitutes a serious and quite common condition due to accumulation of fatty materials in the arterial wall, consequently posing serious cardiovascular complications. In this paper, we assemble and analyze a multitude of heterogeneous data in order to model the progression of atherosclerosis (ATS) in coronary vessels. The patient's medical record, biochemical analytes, monocyte information, adhesion molecules, and therapy-related data comprise the input for the subsequent analysis. As indicator of coronary lesion progression, two consecutive coronary computed tomography angiographies have been evaluated in the same patient. To this end, a set of 39 patients is studied using a twofold approach, namely, baseline analysis and temporal analysis. The former approach employs baseline information in order to predict the future state of the patient (in terms of progression of ATS). The latter is based on an approach encompassing dynamic Bayesian networks whereby snapshots of the patient's status over the follow-up are analyzed in order to model the evolvement of ATS, taking into account the temporal dimension of the disease. The quantitative assessment of our work has resulted in 93.3% accuracy for the case of baseline analysis, and 83% overall accuracy for the temporal analysis, in terms of modeling and predicting the evolvement of ATS. It should be noted that the application of the SMOTE algorithm for handling class imbalance and the subsequent evaluation procedure might have introduced an overestimation of the performance metrics, due to the employment of synthesized instances. The most prominent features found to play a substantial role in the progression of the disease are: diabetes, cholesterol and cholesterol/HDL. Among novel markers, the CD11b marker of leukocyte integrin complex is associated with coronary plaque progression.