The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to ...evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.
Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models.
A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites.
NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A facile method to design wearable sensors is developed by using laser-cut paper as a mold.•PDMS sensors with a sensitivity as high as 0.82 kPa-1 were designed based on channels with different ...geometries.•The interplay of the air and PDMS dielectric properties play a key role in sensor performance.•The sensor demonstrates remarkable performance to monitor human physiological signals like breathing and pulse.•Excellent electromechanical response of the developed sensors opens opportunities for wearable electronics applications.
Wearable sensors and devices capable of simulating human somatosensation are highly desirable to develop the next generation of electronic skins (e-skins), human-machine interfaces, and soft robotics. Herein, the design of flexible and skin-compliant capacitive pressure sensors for monitoring human motion and physiological signals by electrical signal conversion is reported. The introduction of air chambers into elastomeric polydimethylsiloxane (PDMS) substrate led to the development of pressure sensors with high sensitivity (0.82 kPa−1, up to 20% strain), capable of detecting both static and dynamic mechanical stimuli (17 nm/s), with a low Limit of Detection (LOD) of 1 Pa. Furthermore, the pressure sensors present an ultra-fast rise time (31.2 ms) and bandwidth (11.2 Hz), negligible hysteresis, and excellent cycling stability (> 10,000 cycles). Successful monitoring of human movement (e.g., arms bending), haptics, and physiological signals like heart and respiratory rates, demonstrate the potential of these sensors for wearable biosensing applications.
Graphical abstract
Display omitted .
Research suggests that an increased risk of physical comorbidities might have a key role in the association between severe mental illness (SMI) and disability. We examined the association between ...physical multimorbidity and disability in individuals with SMI.
Data were extracted from the clinical record interactive search system at South London and Maudsley Biomedical Research Centre. Our sample (n = 13,933) consisted of individuals who had received a primary or secondary SMI diagnosis between 2007 and 2018 and had available data for Health of Nations Outcome Scale (HoNOS) as disability measure. Physical comorbidities were defined using Chapters II-XIV of the International Classification of Diagnoses (ICD-10).
More than 60 % of the sample had complex multimorbidity. The most common organ system affected were neurological (34.7%), dermatological (15.4%), and circulatory (14.8%). All specific comorbidities (ICD-10 Chapters) were associated with higher levels of disability, HoNOS total scores. Individuals with musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders were found to be associated with significant difficulties associated with more than five HoNOS domains while others had a lower number of domains affected.
Individuals with SMI and musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders are at higher risk of disability compared to those who do not have those comorbidities. Individuals with SMI and physical comorbidities are at greater risk of reporting difficulties associated with activities of daily living, hallucinations, and cognitive functioning. Therefore, these should be targeted for prevention and intervention programs.
ObjectivesThe first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. ...Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD).DesignObservational study.SettingSecondary mental healthcare services from South LondonParticipantsOur maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018.MeasuresWe designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale.ResultsPhysical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses.ConclusionsOur data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.
Skin-inspired materials facilitate the design and fabrication of various biomedical devices capable of real-time monitoring of physiological signals and provide the foundations for mechanically ...compliant next-generation wearables that behave and operate like an extension of our own integumentary system. These devices need to maintain conformal contact with the body while offering very little mechanical impedance and must be able to sustain the accidental damage that comes in normal, dynamic, everyday life. This work demonstrates how dynamic metal-ligand self-healing chemistry can be used to synthesize multi-wall carbon nanotubes (MWCNT) – silicone nanocomposites with skin-like mechanical elasticity. Highly repeatable, room-temperature self-healing nanocomposites were synthesized with various filler concentrations to study the ability of the material to perform in sensing and electronic interconnect scenarios. An increase in mechanical stiffness from 8 kPa to 102 kPa were achieved at a percolation of 4.8 %wt, maintaining extensibility up to 50%. Further, conductivities as high as 2 × 10−2 S/m were achieved whilst maintaining high flexibility, offering tuneability for a broad range of tailored applications. The gauge factor (GF) performance was independent of healing cycles, maintaining a value of 1.4 after several ruptures, and continually sustains mechanical stiffness matching the dermal layer of the human forearm. These results parallel the human skin's ability to both heal damage elements and respond to stimuli of the world around us and provide expandability to current biomedical systems.
Display omitted
Estimating redundancy in clinical text Searle, Thomas; Ibrahim, Zina; Teo, James ...
Journal of biomedical informatics,
December 2021, 2021-12-00, 20211201, Letnik:
124
Journal Article
Recenzirano
Odprti dostop
Display omitted
•Natural language processing methods for redundancy analysis of clinical text.•Deep learning for redundancy analysis of clinical text.•Electronic health record redundancy ...measures.•Deep pre-trained transformer based language models for clinical text analysis.
The current mode of use of Electronic Health Records (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to propagation of errors, inconsistencies and misreporting of care. Therefore, measures to quantify information redundancy play an essential role in evaluating innovations that operate on clinical narratives.
This work is a quantitative examination of information redundancy in EHR notes. We present and evaluate two methods to measure redundancy: an information-theoretic approach and a lexicosyntactic and semantic model. Our first measure trains large Transformer-based language models using clinical text from a large openly available US-based ICU dataset and a large multi-site UK based Hospital. By comparing the information-theoretic efficient encoding of clinical text against open-domain corpora, we find that clinical text is ∼1.5× to ∼3× less efficient than open-domain corpora at conveying information. Our second measure, evaluates automated summarisation metrics Rouge and BERTScore to evaluate successive note pairs demonstrating lexicosyntactic and semantic redundancy, with averages from ∼43 to ∼65%.
Wearable technologies offer the opportunity to record human physiological signals in real time, in a noninvasive way, and the data can be used to aid in the early detection of abnormal health ...conditions. Here, we demonstrate how the interconnected porosity can be used to increase the sensitivity and linearity of capacitive pressure sensors. The finite element analysis supports the experimental observation that the movement of air during the dynamic mechanical loading is responsible for the high sensitivity observed (0.18 ± 0.01 kPa
) when compared with the solid poly(glycerol sebacate) sensor (0.0042 ± 0.0002 kPa
). The porous sensors present strain insensitivity and remarkable linearity over the entire range of applied mechanical pressure (0-6 kPa), capable of detecting both static and dynamic mechanical stimuli (17 nm/s), and a response time of 50 ms, without evidence of fatigue or electrical hysteresis over 10,000 mechanical cycles. The outstanding features of the porous sensors can find a broad range of applications in real-time health monitoring, from demanding movements like walking/running, to small deformations resulting from breathing or heart beating. The ultrasensitive microcellular structures synthesized in this study can be applied to other types of sensing transductions to obtain tunable and function-specific sensors with high sensitivity.
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a ...patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets.
Display omitted
•Discharge summary summarisation via clinically guided deep learning.•Deep learning natural language processing models with clinical concept guidance.•Inpatient discharge summary text summarisation with natural language processing.•Ensemble extractive/abstractive text summarisation for discharge summary text.
•High performance clinical information extraction supports pertinent clinical research.•Multi-site hospital natural language processing models scale across settings.•Flexible informatics empowers ...fast clinician lead research and analysis.•Fast, scalable, flexible electronic health record information extraction.
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: (a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; (b) a feature-rich annotation interface for customizing and training IE models; and (c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets (F1:0.448–0.738 vs 0.429–0.650). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ∼8.8B words from ∼17M clinical records and further fine-tuning with ∼6K clinician annotated examples. We show strong transferability (F1 > 0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.
Aims
The SARS‐CoV‐2 virus binds to the angiotensin‐converting enzyme 2 (ACE2) receptor for cell entry. It has been suggested that angiotensin‐converting enzyme inhibitors (ACEi) and angiotensin II ...receptor blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise tissue ACE2 levels, could increase the risk of severe COVID‐19 infection.
Methods and results
We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID‐19 at two hospitals with a multi‐ethnic catchment population in London (UK). The mean age was 68 ± 17 years (57% male) and 74% of patients had at least one comorbidity. Overall, 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21 days of symptom onset. A total of 399 patients (33.3%) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co‐morbidities, was 0.63 (95% confidence interval 0.47–0.84, P < 0.01).
Conclusions
There was no evidence for increased severity of COVID‐19 in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta‐analyses and randomised clinical trials.
PDF
