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INTRODUCTION: Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults. METHODS: We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool. RESULTS: Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/c statistic 0.70-0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent. CONCLUSIONS: The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination.
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Hogares para Ancianos , Casas de Salud , Admisión del Paciente , Humanos , Anciano , Medición de Riesgo/métodos , Admisión del Paciente/estadística & datos numéricos , Casas de Salud/estadística & datos numéricos , Hogares para Ancianos/estadística & datos numéricos , Evaluación Geriátrica/métodos , Factores de Riesgo , Anciano de 80 o más Años , Masculino , Factores de TiempoRESUMEN
BACKGROUND: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.
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Multimorbilidad , Medicina Estatal , Humanos , Femenino , Persona de Mediana Edad , Estudios Transversales , Fuentes de Información , Prevalencia , Enfermedad CrónicaRESUMEN
Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90 and 93%, respectively), with packing quality strongly influenced by backbone (â¼70% relative importance) and functional mode (â¼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, for example, by focusing on improvements of backbone and functional mode to obtain optimized packings.
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Aprendizaje Automático , Cinética , Tamaño de la Partícula , PorosidadRESUMEN
MOTIVATION: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art of keyword searches from annotations, methods for content-based retrieval have been proposed. In the context of gene expression experiments, most methods retrieve gene expression profiles, requiring each experiment to be expressed as a single profile, typically of case versus control. A more general, recently suggested alternative is to retrieve experiments whose models are good for modelling the query dataset. However, for very noisy and high-dimensional query data, this retrieval criterion turns out to be very noisy as well. RESULTS: We propose doing retrieval using a denoised model of the query dataset, instead of the original noisy dataset itself. To this end, we introduce a general probabilistic framework, where each experiment is modelled separately and the retrieval is done by finding related models. For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples. The suggested metric for retrieval using clusterings is the normalized information distance. Empirical results finally suggest that inference for the full probabilistic model can be approximated with good performance using computationally faster heuristic clustering approaches (e.g. k-means). The method is highly scalable and straightforward to apply to construct a general-purpose gene expression experiment retrieval method. AVAILABILITY AND IMPLEMENTATION: The method can be implemented using standard clustering algorithms and normalized information distance, available in many statistical software packages. CONTACT: paul.blomstedt@aalto.fi or samuel.kaski@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Expresión Génica , Modelos Genéticos , Algoritmos , Análisis por Conglomerados , Perfilación de la Expresión GénicaRESUMEN
MOTIVATION: Over the recent years, the field of whole-metagenome shotgun sequencing has witnessed significant growth owing to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster and with better coverage than before. This technical advancement has initiated the trend of sequencing multiple samples in different conditions or environments to explore the similarities and dissimilarities of the microbial communities. Examples include the human microbiome project and various studies of the human intestinal tract. With the availability of ever larger databases of such measurements, finding samples similar to a given query sample is becoming a central operation. RESULTS: In this article, we develop a content-based exploration and retrieval method for whole-metagenome sequencing samples. We apply a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. We evaluate the performance of the proposed approach on two human gut metagenome datasets as well as human microbiome project metagenomic samples. We observe significant enrichment for diseased gut samples in results of queries with another diseased sample and high accuracy in discriminating between different body sites even though the method is unsupervised. AVAILABILITY AND IMPLEMENTATION: A software implementation of the DSM framework is available at https://github.com/HIITMetagenomics/dsm-framework. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Metagenómica/métodos , Algoritmos , Minería de Datos , Diabetes Mellitus Tipo 2/microbiología , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Enfermedades Inflamatorias del Intestino/microbiología , Microbiota , Análisis de Secuencia de ADNRESUMEN
BACKGROUND: Frailty is characterised by a reduced resilience to adversity. In this analysis we examined changes in frailty in people aged 50+ before and during a period of austere public spending in England. METHODS: Data from the English Longitudinal Study of Ageing 2002-2018 were analysed. Associations between austerity and frailty were examined using (1) Multilevel interrupted times series analysis (ITSA); and (2) Accelerated longitudinal modelling comparing frailty trajectories in people of the same age in 2002 and 2012. RESULTS: The analysis included 16,410 people (mean age 67 years, 55% women), with mean frailty index score of 0.16. Mean scores in women (0.16) where higher than in men (mean 0.14), and higher in the poorest tertile (mean 0.20) than the richest (mean 0.12). In the ITSA, frailty index scores increased more quickly during austerity than before, with the additional increase in frailty 2012-2018 being similar in magnitude to the difference in mean frailty score between people aged 65-69 and 70-74 years. Steeper increases in frailty after 2012 were experienced across the wealth-spectrum and in both sexes but were greater in the very oldest (80+). In the accelerated longitudinal analysis, frailty was lower in 2012 than 2002, but increased more rapidly in the 2012 cohort compared to the 2002 cohort; markedly so in people aged 80+. CONCLUSION: The period of austerity politics was associated with steeper increases in frailty with age compared to the pre-austerity period, consistent with previously observed increases in mortality.
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Fragilidad , Humanos , Anciano , Masculino , Femenino , Estudios Longitudinales , Anciano Frágil , Factores de Tiempo , EnvejecimientoRESUMEN
Fibre-optic based time-resolved fluorescence spectroscopy (TRFS) is an advanced spectroscopy technique that generates sample-specific spectral-temporal signature, characterising variations in fluorescence in real-time. As such, it can be used to interrogate tissue autofluorescence. Recent advancements in TRFS technology, including the development of devices that simultaneously measure high-resolution spectral and temporal fluorescence, paired with novel analysis methods extracting information from these multidimensional measurements effectively, provide additional insight into the underlying autofluorescence features of a sample. This study demonstrates, using both simulated data and endogenous fluorophores measured bench-side, that the shape of the spectral fluorescence lifetime, or fluorescence lifetimes estimated over high-resolution spectral channels across a broad range, is influenced by the relative abundance of underlying fluorophores in mixed systems and their respective environment. This study, furthermore, explores the properties of the spectral fluorescence lifetime in paired lung tissue deemed either abnormal or normal by pathologists. We observe that, on average, the shape of the spectral fluorescence lifetime at multiple locations sampled on 14 abnormal lung tissue, compared to multiple locations sampled on the respective paired normal lung tissue, shows more variability; and, while not statistically significant, the average spectral fluorescence lifetime in abnormal tissue is consistently lower over every wavelength than the normal tissue.
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BACKGROUND: Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals. METHODS: Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing, wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N=4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological well-being, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores. RESULTS: Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled 'mobility impairment and physical morbidity', 'difficulties in daily activities', 'mental health' and 'disorientation in time'. The four subdimensions were a better predictor of quality of life than frailty index scores. CONCLUSIONS: Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has a potential to provide a more nuanced understanding of an individual's state of health across multiple deficits.
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Mortality prediction models support identifying older adults with short life expectancy for whom clinical care might need modifications. We systematically reviewed external validations of mortality prediction models in older adults (ie, aged 65 years and older) with up to 3 years of follow-up. In March, 2023, we conducted a literature search resulting in 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 50 (68%) of 74 validations not reporting calibration. Morbidities (most commonly cardiovascular diseases) were used as predictors by 45 (70%) of 64 of models. For 1-year prediction, 31 (67%) of 46 models had acceptable discrimination, but only one had excellent performance. Models with more than 20 predictors were more likely to have acceptable discrimination (risk ratio [RR] vs <10 predictors 1·68, 95% CI 1·06-2·66), as were models including sex (RR 1·75, 95% CI 1·12-2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95% CI 1·12-3·07). Development and validation of better-performing mortality prediction models in older people are needed.
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Mortalidad , Anciano , Humanos , Enfermedades Cardiovasculares , Pronóstico , Evaluación GeriátricaRESUMEN
Innovations in complementary metal-oxide semiconductor (CMOS) single-photon avalanche diode (SPAD) technology has featured in the development of next-generation instruments for point-based time-resolved fluorescence spectroscopy (TRFS). These instruments provide hundreds of spectral channels, allowing the collection of fluorescence intensity and fluorescence lifetime information over a broad spectral range at a high spectral and temporal resolution. We present Multichannel Fluorescence Lifetime Estimation, MuFLE, an efficient computational approach to exploit the unique multi-channel spectroscopy data with an emphasis on simultaneous estimation of the emission spectra, and the respective spectral fluorescence lifetimes. In addition, we show that this approach can estimate the individual spectral characteristics of fluorophores from a mixed sample.
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Colorantes Fluorescentes , Semiconductores , Análisis Espectral , Colorantes Fluorescentes/química , Fotones , Óxidos/químicaRESUMEN
We present an endoscopic probe that combines three distinct optical fibre technologies including: A high-resolution imaging fibre for optical endomicroscopy, a multimode fibre for time-resolved fluorescence spectroscopy, and a hollow-core fibre with multimode signal collection cores for Raman spectroscopy. The three fibers are all enclosed within a 1.2 mm diameter clinical grade catheter with a 1.4 mm end cap. To demonstrate the probe's flexibility we provide data acquired with it in loops of radii down to 2 cm. We then use the probe in an anatomically accurate model of adult human airways, showing that it can be navigated to any part of the distal lung using a commercial bronchoscope. Finally, we present data acquired from fresh ex vivo human lung tissue. Our experiments show that this minimally invasive probe can deliver real-time optical biopsies from within the distal lung - simultaneously acquiring co-located high-resolution endomicroscopy and biochemical spectra.
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Endoscopía , Espectrometría Raman , Humanos , Espectrometría Raman/métodos , Espectrometría de Fluorescencia , Diagnóstico por Imagen , BiopsiaRESUMEN
Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.
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Espectrometría Raman , Espectrometría Raman/métodosRESUMEN
BACKGROUND: It is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions. METHODS: We did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use. FINDINGS: Between March 1, 2020, and Oct 25, 2021, 33â580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1-30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0-14·8), with the number increasing to 35·6% (34·9-36·3) patients at 1 year. Among the 33â580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18â772 [55·9%]); minimal admissions (n=12â057 [35·9%]); recently high admissions (n=1931 [5·8%]), and persistently high admissions (n=820 [2·4%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 [95% CI 2·35-2·81]; p<0·0001) and the risk of readmission was highest in the persistently high admissions group (3·23 [2·89-3·61]; p<0·0001). INTERPRETATION: Long-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support. FUNDING: Chief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation.
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Vacunas contra la COVID-19 , COVID-19 , Adulto , Humanos , Estudios de Cohortes , Estudios Retrospectivos , COVID-19/epidemiología , COVID-19/terapia , HospitalesRESUMEN
Estimating conditional dependence between two random variables given the knowledge of a third random variable is essential in neuroscientific applications to understand the causal architecture of a distributed network. However, existing methods of assessing conditional dependence, such as the conditional mutual information, are computationally expensive, involve free parameters, and are difficult to understand in the context of realizations. In this letter, we discuss a novel approach to this problem and develop a computationally simple and parameter-free estimator. The difference between the proposed approach and the existing ones is that the former expresses conditional dependence in terms of a finite set of realizations, whereas the latter use random variables, which are not available in practice. We call this approach conditional association, since it is based on a generalization of the concept of association to arbitrary metric spaces. We also discuss a novel and computationally efficient approach of generating surrogate data for evaluating the significance of the acquired association value.
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Algoritmos , Simulación por Computador , Frecuencia Cardíaca/fisiología , Síndromes de la Apnea del Sueño/fisiopatología , Humanos , RespiraciónRESUMEN
Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.
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Potenciales de Acción/fisiología , Modelos Neurológicos , Neuronas/fisiología , Reconocimiento Visual de Modelos/fisiología , Simulación por Computador , Probabilidad , Procesamiento de Señales Asistido por Computador , Factores de TiempoRESUMEN
Knowledge of population distribution is critical for building infrastructure, distributing resources, and monitoring the progress of sustainable development goals. Although censuses can provide this information, they are typically conducted every 10 years with some countries having forgone the process for several decades. Population can change in the intercensal period due to rapid migration, development, urbanisation, natural disasters, and conflicts. Census-independent population estimation approaches using alternative data sources, such as satellite imagery, have shown promise in providing frequent and reliable population estimates locally. Existing approaches, however, require significant human supervision, for example annotating buildings and accessing various public datasets, and therefore, are not easily reproducible. We explore recent representation learning approaches, and assess the transferability of representations to population estimation in Mozambique. Using representation learning reduces required human supervision, since features are extracted automatically, making the process of population estimation more sustainable and likely to be transferable to other regions or countries. We compare the resulting population estimates to existing population products from GRID3, Facebook (HRSL) and WorldPop. We observe that our approach matches the most accurate of these maps, and is interpretable in the sense that it recognises built-up areas to be an informative indicator of population.
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Censos , Imágenes Satelitales , Países en Desarrollo , Humanos , Mozambique , Dinámica Poblacional , Imágenes Satelitales/métodosRESUMEN
Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.
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INTRODUCTION: SARS-CoV-2 infection rarely causes hospitalisation in children and young people (CYP), but mild or asymptomatic infections are common. Persistent symptoms following infection have been reported in CYP but subsequent healthcare use is unclear. We aim to describe healthcare use in CYP following community-acquired SARS-CoV-2 infection and identify those at risk of ongoing healthcare needs. METHODS AND ANALYSIS: We will use anonymised individual-level, population-scale national data linking demographics, comorbidities, primary and secondary care use and mortality between 1 January 2019 and 1 May 2022. SARS-CoV-2 test data will be linked from 1 January 2020 to 1 May 2022. Analyses will use Trusted Research Environments: OpenSAFELY in England, Secure Anonymised Information Linkage (SAIL) Databank in Wales and Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 in Scotland (EAVE-II). CYP aged ≥4 and <18 years who underwent SARS-CoV-2 reverse transcription PCR (RT-PCR) testing between 1 January 2020 and 1 May 2021 and those untested CYP will be examined.The primary outcome measure is cumulative healthcare cost over 12 months following SARS-CoV-2 testing, stratified into primary or secondary care, and physical or mental healthcare. We will estimate the burden of healthcare use attributable to SARS-CoV-2 infections in the 12 months after testing using a matched cohort study of RT-PCR positive, negative or untested CYP matched on testing date, with adjustment for confounders. We will identify factors associated with higher healthcare needs in the 12 months following SARS-CoV-2 infection using an unmatched cohort of RT-PCR positive CYP. Multivariable logistic regression and machine learning approaches will identify risk factors for high healthcare use and characterise patterns of healthcare use post infection. ETHICS AND DISSEMINATION: This study was approved by the South-Central Oxford C Health Research Authority Ethics Committee (13/SC/0149). Findings will be preprinted and published in peer-reviewed journals. Analysis code and code lists will be available through public GitHub repositories and OpenCodelists with meta-data via HDR-UK Innovation Gateway.
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COVID-19 , Niño , Humanos , Adolescente , COVID-19/epidemiología , SARS-CoV-2 , Prueba de COVID-19 , Estudios de Cohortes , Gales/epidemiología , Atención a la Salud , Estudios Observacionales como AsuntoRESUMEN
COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms.
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COVID-19 , Confusión , Tos , Disnea , Fatiga , Femenino , Fiebre , Humanos , Estudios ProspectivosRESUMEN
The power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging owing to technical factors such as sparsity, low number of cells, and batch effect. To address these challenges, we developed scID (Single Cell IDentification), which uses the Fisher's Linear Discriminant Analysis-like framework to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.