Pedagoginen perusta kielenoppimisessa

Non peer reviewe

Predictors of hospitalisation and death due to SARS-CoV-2 infection in Finland: A population-based register study with implications to vaccinations

Introduction: The aim of this study was to investigate how age and underlying medical conditions affect the risk of severe outcomes following SARS-CoV-2 infection and how they should be weighed while prioritising vaccinations against COVID-19. Methods: This population-based register study includes all SARS-CoV-2 PCR-test-positive cases until 24 Feb 2021, based on the Finnish National Infectious Diseases Register. The cases were linked to other registers to identify presence of predisposing factors and severe outcomes (hospitalisation, intensive care treatment, death). The odds of severe outcomes were compared in those with and without the pre-specified predisposing factors using logistic regression. Furthermore, population-based rates were compared between those with a given predisposing factor and those without any of the specified predisposing factors using negative binomial regression. Results: Age and various comorbidities were found to be predictors of severe COVID-19. Compared to 60–69-year-olds, the odds ratio (OR) of death was 7.1 for 70–79-year-olds, 26.7 for 80–89-year-olds, and 55.8 for ≥ 90-year-olds. Among the 20–69-year-olds, chronic renal disease (OR 9.4), malignant neoplasms (5.8), hematologic malignancy (5.6), chronic pulmonary disease (5.4), and cerebral palsy or other paralytic syndromes (4.6) were strongly associated with COVID-19 mortality; severe disorders of the immune system (8.0), organ or stem cell transplant (7.2), chronic renal disease (6.7), and diseases of myoneural junction and muscle (5.5) were strongly associated with COVID-19 hospitalisation. Type 2 diabetes and asthma, two very common comorbidities, were associated with all three outcomes, with ORs from 2.1 to 4.3. The population-based rate of SARS-CoV-2 infection decreased with age. Taking the 60–69-year-olds as reference, the rate ratio was highest (3.0) for 20–29-year-olds and Conclusion: Comorbidities predispose for severe COVID-19 among younger ages. In vaccine prioritisation both the risk of infection and the risk of severe outcomes, if infected, should be considered. © 2022 The AuthorsAuthor keywordsChronically ill (max 6); COVID-19; Elderly; Risk factors; SARS-CoV-2</p

Virtuaalimaailmojen ja pelien käyttömahdollisuuksia opetuksessa

Non peer reviewe

Tulevaisuudennäkymiä

Non peer reviewe

Analysis of neurodevelopmental outcomes of preadolescents born with extremely low weight revealed impairments in multiple developmental domains despite absence of cognitive impairment

Background and aimsChildren with extremely low‐birth weight (ELBW) have a high risk for cognitive, motor, and attention impairments and learning disabilities. Longitudinal follow‐up studies to a later age are needed in order to increase understanding of the changes in neurodevelopmental trajectories in targeting timely intervention. The aims of this study were to investigate cognitive and motor outcomes, attention‐deficit hyperactivity (ADHD) behaviour, school performance, and overall outcomes in a national cohort of ELBW children at preadolescence, and minor neuromotor impairments in a subpopulation of these children and to compare the results with those of full‐term controls. The additional aim was to report the overall outcome in all ELBW infants born at 22 to 26 gestational weeks.MethodsThis longitudinal prospective national cohort study included all surviving ELBW (birth weight ResultsOf 206 ELBW survivors 122 (73% of eligible) children and 30 (100%) full‐term control children participated in assessments. ELBW children had lower full‐scale intellectual quotient than controls (t‐test, 90 vs 112, P P = .021, r = .20) and needed more educational support (47% vs 17%, OR 4.5, 95% CI 1.6‐12.4, P = .02). In the subpopulation, the incidences of DCD were 30% in ELBW and 7% in control children (P = .012, OR 6.0 CI 1.3‐27.9), and complex MND 12.5% and 0%, (P = .052; RR 1.1 95% CI 1.04‐1.25), respectively. Of survivors born in 24 to 26 gestational weeks, 29% had normal outcome.ConclusionAs the majority of the extremely preterm born children had some problems, long‐term follow‐up is warranted to identify those with special needs and to design individual multidisciplinary support programs.</p

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 +/- 0.23 1/min, and the absolute amplitude error of 0.24 +/- 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.</p

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms

In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artefact removal, in total 119 minutes of AFib data and 126 minutes of sinus rhythm data were considered for automated atrial fibrillation detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on SCG and needs no complementary electrocardiography (ECG) to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme which takes 5 randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9% and an average true negative rate of 96.4% for detecting atrial fibrillation in leave-one-out cross-validation. The presented work facilitates adoption of MEMS-based heart monitoring devices for arrhythmia detection.</p

Adherence to risk-assessment protocols to guide computed tomography pulmonary angiography in patients with suspected pulmonary embolism

AimsThe use of computed tomography pulmonary angiography (CTPA) in the detection of pulmonary embolism (PE) has considerably increased due developing technology and better availability of imaging. The underuse of pre-test probability scores and overuse of CTPA has been previously reported. We sought to investigate the indications for CTPA at a University Hospital emergency clinic and seek for factors eliciting the potential overuse of CTPA.Methods and resultsAltogether 1001 patients were retrospectively collected and analysed from the medical records using a structured case report form. PE was diagnosed in 222/1001 (22.2%) of patients. Patients with PE had more often prior PE/deep vein thrombosis, bleeding/thrombotic diathesis and less often asthma, chronic obstructive pulmonary disease, coronary artery disease, or decompensated heart failure. Patients were divided into three groups based on Wells PE risk-stratification score and two groups based on the revised Geneva score. A total of 9/382 (2.4%), 166/527 (31.5%), and 47/92 (52.2%) patients had PE in the CTPA in the low, intermediate, and high pre-test likelihood groups according to Wells score, and 200/955 (20.9%) and 22/46 (47.8%) patients had PE in the CTPA in the low-intermediate and the high pre-test likelihood groups according to the revised Geneva score, respectively. D-dimer was only measured from 568/909 (62.5%) and 597/955 (62.5%) patients who were either in the low or the intermediate-risk group according to Wells score and the revised Geneva score. Noteworthy, 105/1001 (10.5%) and 107/1001 (10.7%) of the CTPAs were inappropriately ordered according to the Wells score and the revised Geneva score. Altogether 168/1001 (16.8%) could theoretically be avoided.ConclusionsThis study highlights scant utilization of guideline-recommended risk-stratification tools in CTPA use at the emergency department.</p

Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this study, we present a comprehensive time-frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG-GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm (SR) cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib=150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib=40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained respectively for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.</p