BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals.

BACKGROUND AND OBJECTIVE: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available. METHODS: In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds). RESULTS: The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%. CONCLUSION: The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.

An observational study on factors associated with ICU mortality in Covid-19 patients and critical review of the literature.

The novel pandemic caused by SARS-CoV-2 has been associated with increased burden on healthcare system. Recognizing the variables that independently predict death in COVID-19 is of great importance. The study was carried out prospectively in a single ICU in northern Greece. It was based on the collection of data during clinical practice in 375 adult patients who were tested positive for SARS-CoV-2 between April 2020 and February 2022. All patients were intubated due to acute respiratory insufficiency and received Invasive Mechanical Ventilation. The primary outcome was ICU mortality. Secondary outcomes were 28-day mortality and independent predictors of mortality at 28 days and during ICU hospitalization. For continuous variables with normal distribution, t-test was used for means comparison between two groups and one-way ANOVA for multiple comparisons. When the distribution was not normal, comparisons were performed using the Mann-Whitney test. Comparisons between discrete variables were made using the x2 test, whereas the binary logistic regression was employed for the definition of factors affecting survival inside the ICU and after 28 days. Of the total number of patients intubated due to COVID-19 during the study period, 239 (63.7%) were male. Overall, the ICU survival was 49.6%, whereas the 28-day survival reached 46.9%. The survival rates inside the ICU for the four main viral variants were 54.9%, 50.3%, 39.7% and 50% for the Alpha, Beta, Delta and Omicron variants, respectively. Logistic regressions for outcome revealed that the following parameters were independently associated with ICU survival: wave, SOFA @day1, Remdesivir use, AKI, Sepsis, Enteral Insufficiency, Duration of ICU stay and WBC. Similarly, the parameters affecting the 28-days survival were: duration of stay in ICU, SOFA @day1, WBC, Wave, AKI and Enteral Insufficiency. In this observational cohort study of critically ill COVID-19 patients we report an association between mortality and the wave sequence, SOFA score on admission, the use of Remdesivir, presence of AKI, presence of gastrointestinal failure, sepsis and WBC levels. Strengths of this study are the large number of critically ill COVID-19 patients included, and the comparison of the adjusted mortality rates between pandemic waves within a two year-study period.

COVID-19-Associated Pulmonary Aspergillosis (CAPA) in Northern Greece during 2020-2022: A Comparative Study According to the Main Consensus Criteria and Definitions.

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) has emerged as an important complication among patients with acute respiratory failure due to SARS-CoV-2 infection. Almost 2.5 years since the start of the COVID-19 pandemic, it continues to raise concerns as an extra factor that contributes to increased mortality, which is mostly because its diagnosis and management remain challenging. The present study utilises the cases of forty-three patients hospitalised between August 2020 and February 2022 whose information was gathered from ten ICUs and special care units based in northern Greece. The main aim was to describe the gained experience in diagnosing CAPA, according to the implementation of the main existing diagnostic consensus criteria and definitions, and present the different classification of the clinical cases due to the alternative algorithms.

Association of sleep duration and quality with immunological response after vaccination against severe acute respiratory syndrome coronavirus-2 infection.

Growing evidence suggests that sleep could affect the immunological response after vaccination. The aim of this prospective study was to investigate possible associations between regular sleep disruption and immunity response after vaccination against coronavirus disease 2019 (COVID-19). In total, 592 healthcare workers, with no previous history of COVID-19, from eight major Greek hospitals were enrolled in this study. All subjects underwent two Pfizer-BioNTech messenger ribonucleic acid (mRNA) COVID-19 vaccine BNT162b2 inoculations with an interval of 21 days between the doses. Furthermore, a questionnaire was completed 2 days after each vaccination and clinical characteristics, demographics, sleep duration, and habits were recorded. Blood samples were collected and anti-spike immunoglobulin G antibodies were measured at 20 ± 1 days after the first dose and 21 ± 2 days after the second dose. A total of 544 subjects (30% males), with median (interquartile range [IQR]) age of 46 (38-54) years and body mass index of 24·84 (22.6-28.51) kg/m2 were eligible for the study. The median (IQR) habitual duration of sleep was 6 (6-7) h/night. In all, 283 participants (52%) had a short daytime nap. In 214 (39.3%) participants the Pittsburgh Sleep Quality Index score was >5, with a higher percentage in women (74·3%, p < 0.05). Antibody levels were associated with age (r = -0.178, p < 0.001), poor sleep quality (r = -0.094, p < 0.05), insomnia (r = -0.098, p < 0.05), and nap frequency per week (r = -0.098, p < 0.05), but after adjusting for confounders, only insomnia, gender, and age were independent determinants of antibody levels. It is important to emphasise that insomnia is associated with lower antibody levels against COVID-19 after vaccination.

Evaluation of Pain Scales and Outcome in Critically Ill Patients of a Greek ICU.

The purpose of the study was to evaluate painful procedures in ICU patients and to investigate their effect as well as the role of analgesia in the outcome. We measured pain level and vital signs before, during and after potentially painful procedures by using the Behavioral Pain Scale (BPS) and the Critical Care Pain Observation Tool (CPOT). We analyzed the correlation of these measurements and of analgesia with the outcome. Twenty-eight patients were subjected to 160 stimuli. There were statistically significant differences in pain scores and most vital signs between the different timepoints (before-during, during-after). Most of them were significantly correlated with each other. Physiotherapy proved to be the most painful procedure. Regarding the outcome, the administration of extra analgesia predicted less days of mechanical ventilation (p = 0.015) and of ICU stay (p = 0.016). The higher change in BPS was correlated with more days of mechanical ventilation [B (95% CI) = 3.640 (1.001-6.280), p = 0.007] and of ICU stay [B (95% CI) = 3.645 (1.035-6.254), p = 0.006]. The higher change in CPOT and the nonuse of extra analgesia were related to increased mortality [OR (95% CI) = 1.492 (1.107-2.011), p = 0.009 and OR (95% CI) = 2.626 (1.013-6.806), p = 0.047]. Increased pain in ICU patients was successfully assessed by the BPS and CPOT and correlated to worse outcomes, which the administration of extra analgesia might improve.

CoCross: An ICT Platform Enabling Monitoring Recording and Fusion of Clinical Information Chest Sounds and Imaging of COVID-19 ICU Patients.

Monitoring and treatment of severely ill COVID-19 patients in the ICU poses many challenges. The effort to understand the pathophysiology and progress of the disease requires high-quality annotated multi-parameter databases. We present CoCross, a platform that enables the monitoring and fusion of clinical information from in-ICU COVID-19 patients into an annotated database. CoCross consists of three components: (1) The CoCross4Pros native android application, a modular application, managing the interaction with portable medical devices, (2) the cloud-based data management services built-upon HL7 FHIR and ontologies, (3) the web-based application for intensivists, providing real-time review and analytics of the acquired measurements and auscultations. The platform has been successfully deployed since June 2020 in two ICUs in Greece resulting in a dynamic unified annotated database integrating clinical information with chest sounds and diagnostic imaging. Until today multisource data from 176 ICU patients were acquired and imported in the CoCross database, corresponding to a five-day average monitoring period including a dataset with 3477 distinct auscultations. The platform is well accepted and positively rated by the users regarding the overall experience.

Classification of Electrical Impedance Tomography Data Using Machine Learning.

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non- invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and nonhealthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation).

Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients.

Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in noisy environments. This method will give health professionals a new feature to assess the potential deterioration of critically ill patients.

International prevalence and risk factors evaluation for drug-resistant Streptococcus pneumoniae pneumonia.

OBJECTIVE: Streptococcus pneumoniae is the most frequent bacterial pathogen isolated in subjects with Community-acquired pneumonia (CAP) worldwide. Limited data are available regarding the current global burden and risk factors associated with drug-resistant Streptococcus pneumoniae (DRSP) in CAP subjects. We assessed the multinational prevalence and risk factors for DRSP-CAP in a multinational point-prevalence study. DESIGN: The prevalence of DRSP-CAP was assessed by identification of DRSP in blood or respiratory samples among adults hospitalized with CAP in 54 countries. Prevalence and risk factors were compared among subjects that had microbiological testing and antibiotic susceptibility data. Multivariate logistic regressions were used to identify risk factors independently associated with DRSP-CAP. RESULTS: 3,193 subjects were included in the study. The global prevalence of DRSP-CAP was 1.3% and continental prevalence rates were 7.0% in Africa, 1.2% in Asia, and 1.0% in South America, Europe, and North America, respectively. Macrolide resistance was most frequently identified in subjects with DRSP-CAP (0.6%) followed by penicillin resistance (0.5%). Subjects in Africa were more likely to have DRSP-CAP (OR: 7.6; 95%CI: 3.34-15.35, p<0.001) when compared to centres representing other continents. CONCLUSIONS: This multinational point-prevalence study found a low global prevalence of DRSP-CAP that may impact guideline development and antimicrobial policies.

An open access database for the evaluation of respiratory sound classification algorithms.

OBJECTIVE: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. APPROACH: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. MAIN RESULTS: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. SIGNIFICANCE: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

Development/Testing of a Monitoring System Assisting MCI Patients: The European Project INLIFE.

INLIFE is a project cofounded from the European Union aiming in prolonging independent living of elderly people with cognitive impairment based on open, seamless ICT services supporting communication, daily activities, providing health services and professional care to the elderly. The main innovation stems from ICT solutions offering 19 different services adapted on specific characteristics elderly people with mild cognitive impairment, early and later stages of Dementia, cognitive impairment and co-morbid condition, as well as their formal and informal caregivers. All services have different focus areas and are incorporated into a unified system based on cloud architecture implemented in patients of 6 European countries, including Greece. More than 1200 patients, caregivers and healthcare providers participate in the pilot testing of the project. Primary parameter for assessing the effectiveness of the interventions is their impact on the quality of life of the elderly patients and their caregivers, contributing to prolonging independent living of the affected. A special digital platform has been developed in the Greek pilot site aiming to adapt and monitor all the implemented applications. This includes a medical decision support system that receives biosignals from patients and interaction interfaces in which all participants are involved. Recruitment and patients' participation has already started in the pilot site of Thessaloniki for the services that are to be tested in Greece.

Estimating the abundance of airborne pollen and fungal spores at variable elevations using an aircraft: how high can they fly?

Airborne pollen and fungal spores are monitored mainly in highly populated, urban environments, for allergy prevention purposes. However, their sources can frequently be located outside cities' fringes with more vegetation. So as to shed light to this paradox, we investigated the diversity and abundance of airborne pollen and fungal spores at various environmental regimes. We monitored pollen and spores using an aircraft and a car, at elevations from sea level to 2,000 m above ground, in the region of Thesssaloniki, Greece. We found a total of 24 pollen types and more than 15 spore types. Pollen and spores were detected throughout the elevational transect. Lower elevations exhibited higher pollen concentrations in only half of plant taxa and higher fungal spore concentrations in only Ustilago. Pinaceae and Quercus pollen were the most abundant recorded by airplane (>54% of the total). Poaceae pollen were the most abundant via car measurements (>77% of the total). Cladosporium and Alternaria spores were the most abundant in all cases (aircraft: >69% and >17%, car: >45% and >27%, respectively). We conclude that pollen and fungal spores can be diverse and abundant even outside the main source area, evidently because of long-distance transport incidents.

Features of a Mobile Support App for Patients With Chronic Obstructive Pulmonary Disease: Literature Review and Current Applications.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a serious long-term lung disease in which the airflow from the lungs is progressively reduced. By 2030, COPD will become the third cause of mortality and seventh cause of morbidity worldwide. With advances in technology and mobile communications, significant progress in the mobile health (mHealth) sector has been recently observed. Mobile phones with app capabilities (smartphones) are now considered as potential media for the self-management of certain types of diseases such as asthma, cancer, COPD, or cardiovascular diseases. While many mobile apps for patients with COPD are currently found on the market, there is little published material on the effectiveness of most of them, their features, and their adoption in health care settings. OBJECTIVES: The aim of this study was to search the literature for current systems related to COPD and identify any missing links and studies that were carried out to evaluate the effectiveness of COPD mobile apps. In addition, we reviewed existing mHealth apps from different stores in order to identify features that can be considered in the initial design of a COPD support tool to improve health care services and patient outcomes. METHODS: In total, 206 articles related to COPD management systems were identified from different databases. Irrelevant materials and duplicates were excluded. Of those, 38 articles were reviewed to extract important features. We identified 214 apps from online stores. Following exclusion of irrelevant apps, 48 were selected and 20 of them were downloaded to review some of their common features. RESULTS: Our review found that out of the 20 apps downloaded, 13 (65%, 13/20) had an education section, 5 (25%, 5/20) consisted of medication and guidelines, 6 (30%, 6/20) included a calendar or diary and other features such as reminders or symptom tracking. There was little published material on the effectiveness of the identified COPD apps. Features such as (1) a social networking tool; (2) personalized education; (3) feedback; (4) e-coaching; and (5) psychological motivation to enhance behavioral change were found to be missing in many of the downloaded apps. CONCLUSIONS: This paper summarizes the features of a COPD patient-support mobile app that can be taken into consideration for the initial design of an integrated care system to encourage the self-management of their condition at home.

COPD care delivery pathways in five European Union countries: mapping and health care professionals' perceptions.

BACKGROUND: COPD is among the leading causes of chronic morbidity and mortality in the European Union with an estimated annual economic burden of €25.1 billion. Various care pathways for COPD exist across Europe leading to different responses to similar problems. Determining these differences and the similarities may improve health and the functioning of health services. OBJECTIVE: The aim of this study was to compare COPD patients' care pathway in five European Union countries including England, Ireland, the Netherlands, Greece, and Germany and to explore health care professionals' (HCPs) perceptions about the current pathways. METHODS: HCPs were interviewed in two stages using a qualitative, semistructured email interview and a face-to-face semistructured interview. RESULTS: Lack of communication among different health care providers managing COPD and comorbidities was a common feature of the studied care pathways. General practitioners/family doctors are responsible for liaising between different teams/services, except in Greece where this is done through pulmonologists. Ireland and the UK are the only countries with services for patients at home to shorten unnecessary hospital stay. HCPs emphasized lack of communication, limited resources, and poor patient engagement as issues in the current pathways. Furthermore, no specified role exists for pharmacists and informal carers. CONCLUSION: Service and professional integration between care settings using a unified system targeting COPD and comorbidities is a priority. Better communication between health care providers, establishing a clear role for informal carers, and enhancing patients' engagement could optimize current care pathways resulting in a better integrated system.

Qualitative investigation into a wearable system for chronic obstructive pulmonary disease: the stakeholders' perspective.

OBJECTIVES: To ascertain the stakeholders' views and devise recommendations for further stages of the Wearable Sensing and Smart Cloud Computing for Integrated Care to Chronic Obstructive Pulmonary Disease (COPD) Patients with Co-morbidities (WELCOME) system development. This system aims to create a wearable vest to monitor physiological signals for patients concerned incorporating an inhaler adherence monitoring, weight, temperature, blood pressure and glucose metres, and a mobile health application for communication with healthcare professionals (HCPs). DESIGN: A study of qualitative data derived from focus groups and semistructured interviews. SETTING: 4 participating clinical sites in Greece, the UK, Ireland and the Netherlands. PARTICIPANTS: Purposive sampling was used to recruit 32 patients with COPD with heart failure, diabetes, anxiety or depression, 27 informal carers and 23 HCPs from 4 European Union (EU) countries for focus groups and interviews. RESULTS: Most patients and HCPs described the WELCOME system as 'brilliant and creative' and felt it gave a sense of safety. Both users and HCPs agreed that the duration and frequency of vest wear should be individualised as should the mobile application functions. The parameters and frequency of monitoring should be personalised using a multidisciplinary approach. A 'traffic light' alert system was proposed by HCPs for abnormal results. Patients were happy to take actions in response. CONCLUSIONS: WELCOME stakeholders provided valuable views on the development of the system, which should take into account patient's individual comorbidities, circumstances and concerns. This will enable the development of the individualised system in each member state concerned.

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

INTRODUCTION: Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS: Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS: A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION: We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS: Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION: ClinicalTrials.gov NCT01161381.

Pulmonary thromboendarterectomy after treatment with treprostenil in a chronic thromboembolic pulmonary hypertension patient: a case report.

In recent years, there has been a major advance in the treatment of pulmonary hypertension. New medications are continually added to the therapeutic arsenal. The prostanoids are among the first agents used to treat pulmonary hypertension and are currently considered the most effective. This case study describes a 63-year-old man who was diagnosed with chronic thromboembolic pulmonary hypertension and successfully treated with subcutaneously administered treprostenil for 6 months before a successful pulmonary thromboendarterectomy. Treatment of chronic thromboembolic pulmonary hypertension often requires a multidisciplinary approach before surgery. Further evaluation of prostanoids is needed to define their role and time of initiation of medical therapy in these patients.

Dynamic composition of semantic pathways for medical computational problem solving by means of semantic rules.

This paper presents a semantic rule-based system for the composition of successful algorithmic pathways capable of solving medical computational problems (MCPs). A subset of medical algorithms referring to MCP solving concerns well-known medical problems and their computational algorithmic solutions. These solutions result from computations within mathematical models aiming to enhance healthcare quality via support for diagnosis and treatment automation, especially useful for educational purposes. Currently, there is a plethora of computational algorithms on the web, which pertain to MCPs and provide all computational facilities required to solve a medical problem. An inherent requirement for the successful construction of algorithmic pathways for managing real medical cases is the composition of a sequence of computational algorithms. The aim of this paper is to approach the composition of such pathways via the design of appropriate finite-state machines (FSMs), the use of ontologies, and SWRL semantic rules. The goal of semantic rules is to automatically associate different algorithms that are represented as different states of the FSM in order to result in a successful pathway. The rule-based approach is herein implemented on top of Knowledge-Based System for Intelligent Computational Search in Medicine (KnowBaSICS-M), an ontology-based system for MCP semantic management. Preliminary results have shown that the proposed system adequately produces algorithmic pathways in agreement with current international medical guidelines.

Screening of patients with Obstructive Sleep Apnea Syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep.

AIM: To classify patients with possible diagnosis of Obstructive Sleep Apnea Syndrome (OSAS) into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of 3 respiratory signals instead of the use of full polysomnography. PATIENTS-METHODS: Eighty-six consecutive patients referred to the Sleep Unit of a Pulmonology Department underwent full polysomnography and their tests were manually scored. Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula flow-F and thoracic belt-T). The oxygen saturation signal (SpO(2)) was also selected. The above measurements provided data to the C4.5 algorithm using a data mining application. RESULTS: Two decision trees were produced using linear and nonlinear data from 3 respiratory signals. The discrimination between normal subjects and sufferers from OSAS presented an accuracy of 84.9% and a recall of 90.3% using the variables age, sex, DFA from F and Time with SpO(2)<90% (T90). The classification of patients into severity groups had an accuracy of 74.2% and a recall of 81.1% using the variables APEN from F, DFA from F and T90. CONCLUSION: It is possible to have reliable predictions of the severity of OSAS using linear and nonlinear indices from only two respiratory signals during sleep instead of performing full polysomnography. The proposed algorithm could be used for screening patients suspected to suffer from OSAS.

Health related quality of life in Greek patients with sleep apnea-hypopnea syndrome treated with continuous positive airway pressure.

Sleep apnea-hypopnea syndrome (SAHS) causes serious symptoms and may induce patients' quality of life impairment. The aim of this study was to assess health related quality of life (HRQOL) in Greek patients with various severity SAHS before and after CPAP implementation in comparison with conservative therapy and Greek population normative data. In 180 patients (152 males, 28 females) with SAHS (apnea-hypopnea index, AHI 56 +/- 25.4/h), daytime sleepiness was assessed with the Epworth sleepiness scale (ESS) and measurements of health status were performed using the short form-36 health survey (SF-36) questionnaire and the General Health Questionnaire (GHQ-30). One hundred and thirty five patients underwent CPAP treatment and 45 were assigned to a conservative therapy. After 3 months the measurements were repeated in 105 patients under CPAP treatment and in 15 patients from the conservative group. HRQOL in all patients was lower than Greek normative data before any treatment. In patients with CPAP therapy the ESS decreased (p<0.01) and a significant improvement was observed in SF-36 dimensions (p<0.01). The improvement was more obvious in patients with severe SAHS (p<0.05) than in patients with moderate disease severity. In the majority of patients (60.9%), GHQ-30 score was high and it was negatively related to some SF-36 dimensions and positively to ESS. In patients under conservative therapy, no significant changes were observed in any measure. HRQOL in patients with SAHS at the time of diagnosis was low and reached general population levels in patients treated with CPAP. The improvement was greater in those with severe syndrome.