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1.
Zhongguo Yi Liao Qi Xie Za Zhi ; 45(3): 250-255, 2021 Jun 08.
Artigo em Chinês | MEDLINE | ID: mdl-34096230

RESUMO

Fetal heart rate plays an essential role in maternal and fetal monitoring and fetal health detection. In this study, a method based on Poincare Plot and LSTM is proposed to realize the high performance classification of abnormal fetal heart rate. Firstly, the original fetal heart rate signal of CTU-UHB database is preprocessed via interpolation, then the sequential fetal heart rate signal is converted into Poincare Plot to obtain nonlinear characteristics of the signals, and then SquenzeNet is used to extract the features of Poincare Plot. Finally, the features extracted by SqueezeNet are classified by LSTM. And the accuracy, the true positive rate and the false positive rate are 98.00%, 100.00%, 92.30% respectively on 2 000 test set data. Compared with the traditional fetal heart rate classification method, all respects are improved. The method proposed in this study has good performance in CTU-UHB fetal monitoring database and has certain practical value in the clinical diagnosis of auxiliary fetal heart rate detection.


Assuntos
Monitorização Fetal , Frequência Cardíaca Fetal , Bases de Dados Factuais , Feminino , Feto , Humanos , Gravidez
3.
Artigo em Chinês | MEDLINE | ID: mdl-34074078

RESUMO

Objective: To understand the research status of occupational health risk assessment in recent ten years. Methods: In April 2020, the literatures related to occupational health risk assessment published by CNKI and Web of Science core collection (WoSCC) databases from 2010 to 2019 were searched, and Excel 2016 software was used to organize the literature, CiteSpace 5.6.R2 software was used for visual analysis. Results: A total of 58 Chinese literatures and 407 English literatures were included. The authors of the high frequency posts were Zhang Meibian, and Alessandro Marinaccio, and the publishing institutions were mainly the National Institute for Occupational Health and Poison Control of Chinese Center for Disease Control and Prevention and Finnish Institute Occupational Health. The Chinese journal with the most articles was Chinese Journal of Industrial Hygiene and Occupational Diseases, and the English journal was Safety Science. Chinese high-frequency keywords mainly included risk assessment, occupational health, occupational exposure. English high-frequency keywords mainly included occupational health, risk, risk factor. The prominent words in Chinese literature were occupational health, coal dust, occupational hazards, occupational health and occupational disease hazards; Risk assessment, worker, exposure, heart disease, cardiovascular disease and so on were prominent words in English literature. Conclusion: The main research keywords in the field of occupational health risk assessment at home and abroad focus on occupational health and risk assessment, but the research direction and focus are slightly different.


Assuntos
Bibliometria , Saúde do Trabalhador , China , Bases de Dados Factuais , Publicações , Medição de Risco
4.
Sensors (Basel) ; 21(9)2021 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-34068462

RESUMO

Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. We conduct experiments with X-ITE phasic pain database, which comprises videotaped responses to heat and electrical pain stimuli, each of three intensities. Distinguishing these six stimulation types plus no stimulation was the main 7-class classification task for the human observers and automated approaches. Further, we conducted reduced 5-class and 3-class classification experiments, applied Multi-task learning, and a newly suggested sample weighting method. Experimental results show that the pain assessments of the human observers are significantly better than guessing and perform better than the automatic baseline approach (RFc-BL) by about 1%; however, the human performance is quite poor due to the challenge that pain that is ethically allowed to be induced in experimental studies often does not show up in facial reaction. We discovered that downweighting those samples during training improves the performance for all samples. The proposed RFc and two-CNNs models (using the proposed sample weighting) significantly outperformed the human observer by about 6% and 7%, respectively.


Assuntos
Expressão Facial , Redes Neurais de Computação , Bases de Dados Factuais , Humanos , Dor , Medição da Dor
5.
Sensors (Basel) ; 21(10)2021 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-34069717

RESUMO

Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA's performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with W=3 and V=3 yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.


Assuntos
Fibrilação Atrial , Algoritmos , Fibrilação Atrial/diagnóstico , Bases de Dados Factuais , Eletrocardiografia , Humanos , Aprendizado de Máquina
6.
BMJ ; 373: n991, 2021 05 11.
Artigo em Inglês | MEDLINE | ID: mdl-33975876

RESUMO

OBJECTIVE: To investigate whether the results of a rhythm control strategy differ according to the duration between diagnosis of atrial fibrillation and treatment initiation. DESIGN: Longitudinal observational cohort study. SETTING: Population based cohort from the Korean National Health Insurance Service database. PARTICIPANTS: 22 635 adults with atrial fibrillation and cardiovascular conditions, newly treated with rhythm control (antiarrhythmic drugs or ablation) or rate control strategies between 28 July 2011 and 31 December 2015. MAIN OUTCOME MEASURE: A composite outcome of death from cardiovascular causes, ischaemic stroke, admission to hospital for heart failure, or acute myocardial infarction. RESULTS: Of the study population, 12 200 (53.9%) were male, the median age was 70, and the median follow-up duration was 2.1 years. Among patients with early treatment for atrial fibrillation (initiated within one year since diagnosis), compared with rate control, rhythm control was associated with a lower risk of the primary composite outcome (weighted incidence rate per 100 person years 7.42 in rhythm control v 9.25 in rate control; hazard ratio 0.81, 95% confidence interval 0.71 to 0.93; P=0.002). No difference in the risk of the primary composite outcome was found between rhythm and rate control (weighted incidence rate per 100 person years 8.67 in rhythm control v 8.99 in rate control; 0.97, 0.78 to 1.20; P=0.76) in patients with late treatment for atrial fibrillation (initiated after one year since diagnosis). No significant differences in safety outcomes were found between the rhythm and rate control strategies across different treatment timings. Earlier initiation of treatment was linearly associated with more favourable cardiovascular outcomes for rhythm control compared with rate control. CONCLUSIONS: Early initiation of rhythm control treatment was associated with a lower risk of adverse cardiovascular outcomes than rate control treatment in patients with recently diagnosed atrial fibrillation. This association was not found in patients who had had atrial fibrillation for more than one year.


Assuntos
Antiarrítmicos/uso terapêutico , Fibrilação Atrial/terapia , Ablação por Cateter/estatística & dados numéricos , Tempo para o Tratamento , Idoso , Fibrilação Atrial/mortalidade , Bases de Dados Factuais , Feminino , Insuficiência Cardíaca/epidemiologia , Frequência Cardíaca/efeitos dos fármacos , Hospitalização/estatística & dados numéricos , Humanos , Incidência , 59375/epidemiologia , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Infarto do Miocárdio/epidemiologia , República da Coreia/epidemiologia , Estudos Retrospectivos
7.
JMIR Public Health Surveill ; 7(6): e27917, 2021 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-33975277

RESUMO

BACKGROUND: The United States of America has the highest global number of COVID-19 cases and deaths, which may be due in part to delays and inconsistencies in implementing public health and social measures (PHSMs). OBJECTIVE: In this descriptive analysis, we analyzed the epidemiological evidence for the impact of PHSMs on COVID-19 transmission in the United States and compared these data to those for 10 other countries of varying income levels, population sizes, and geographies. METHODS: We compared PHSM implementation timing and stringency against COVID-19 daily case counts in the United States and against those in Canada, China, Ethiopia, Japan, Kazakhstan, New Zealand, Singapore, South Korea, Vietnam, and Zimbabwe from January 1 to November 25, 2020. We descriptively analyzed the impact of border closures, contact tracing, household confinement, mandated face masks, quarantine and isolation, school closures, limited gatherings, and states of emergency on COVID-19 case counts. We also compared the relationship between global socioeconomic indicators and national pandemic trajectories across the 11 countries. PHSMs and case count data were derived from various surveillance systems, including the Health Intervention Tracking for COVID-19 database, the World Health Organization PHSM database, and the European Centre for Disease Prevention and Control. RESULTS: Implementing a specific package of 4 PHSMs (quarantine and isolation, school closures, household confinement, and the limiting of social gatherings) early and stringently was observed to coincide with lower case counts and transmission durations in Vietnam, Zimbabwe, New Zealand, South Korea, Ethiopia, and Kazakhstan. In contrast, the United States implemented few PHSMs stringently or early and did not use this successful package. Across the 11 countries, national income positively correlated (r=0.624) with cumulative COVID-19 incidence. CONCLUSIONS: Our findings suggest that early implementation, consistent execution, adequate duration, and high adherence to PHSMs represent key factors of reducing the spread of COVID-19. Although national income may be related to COVID-19 progression, a country's wealth appears to be less important in controlling the pandemic and more important in taking rapid, centralized, and consistent public health action.


Assuntos
/prevenção & controle , Saúde Global/estatística & dados numéricos , Saúde Pública/legislação & jurisprudência , /epidemiologia , Bases de Dados Factuais , Humanos , Quarentena , Instituições Acadêmicas/organização & administração , Estados Unidos/epidemiologia , Local de Trabalho/organização & administração
8.
PLoS One ; 16(5): e0249171, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34032800

RESUMO

AIMS OF THE STUDY: The novel coronavirus pandemic has affected emergency department consultations for surgical pathologies. The aim of our study was to compare the number of acute appendicitis cases and the proportion of complicated appendicitis before and during the COVID-19 pandemic. METHODS: We retrospectively analyzed all data collected from a multi-center database of patients presenting to the emergency department for acute appendicitis during the COVID-19 pandemic from March 12 to June 6, 2020, and compared these data with those from the same periods in 2017, 2018, and 2019. The number of acute appendicitis cases, proportion of complicated appendicitis, and pre- and postoperative patient characteristics were evaluated. RESULTS: A total of 306 patients were included in this evaluation. Sixty-five patients presented during the 2020 COVID-19 pandemic lockdown (group A), and 241 patients in previous years (group B: 2017-2019). The number of consultations for acute appendicitis decreased by almost 20 percent during the pandemic compared with previous periods, with a significant increase in complicated appendicitis (52% in group A versus 20% in group B, p < 0,001.). Comparing the two groups, significant differences were also noted in the duration of symptoms (symptoms > 48h in 61% and 26%, p < 0,001), the intervention time (77 vs 61 minutes, p = 0,002), length of hospital stay (hospitalization of > 2 days in 63% and 32%, p < 0.001) and duration of antibiotic treatment (antibiotics > 3 days in 36% and 24% p = 0.001). CONCLUSIONS: The COVID-19 pandemic resulted in a decreased number of consultations for acute appendicitis, with a higher proportion of complicated appendicitis, most likely due to patient delay in consulting the emergency department at symptom onset. Patients and general practitioners should be aware of this problem to avoid a time delay from initial symptoms to consultation.


Assuntos
Apendicite/diagnóstico , /patologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Apendicite/complicações , Apendicite/epidemiologia , Apendicite/cirurgia , Proteína C-Reativa/análise , /epidemiologia , Criança , Bases de Dados Factuais , Diagnóstico Tardio/estatística & dados numéricos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Feminino , Humanos , Tempo de Internação , Masculino , Pessoa de Meia-Idade , Pandemias , Estudos Retrospectivos , Adulto Jovem
9.
PLoS One ; 16(5): e0250958, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33945546

RESUMO

BACKGROUND: Evidence on the spectrum of risk factors for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among front-line healthcare workers (HCWs) has not been well-described. While several studies evaluating the risk factors associated with SARS-CoV-2 infection among patient-facing and non-patient-facing front-line HCWs have been reported since the outbreak of the coronavirus disease in 2019 (COVID-19), and several more are still underway. There is, therefore, an immediate need for an ongoing, rigorous systematic review that continuously assesses the risk factors of SARS-CoV-2 infection among front-line HCWs. OBJECTIVE: Here, we outline a protocol to serve as a guideline for conducting a living systematic review and meta-analysis to examine the burden of COVID-19 on front-line HCWs and identify risk factors for SARS-CoV-2 infection in patient-facing and non-patient-facing front-line HCWs. METHODS: The protocol was developed and reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). The conduct of the proposed living systematic review and meta-analysis will primarily follow the principles recommended in the Centre for Reviews and Dissemination (CRD) guidance for undertaking systematic reviews in healthcare, and the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. The systematic literature searches will be performed using the EBSCOhost platform by searching the following databases within the platform: Academic search complete, health source: nursing/academic edition, CINAHL with full text, Embase, PubMed, MEDLINE, Science Direct databases, Google Scholar, and; also a search in the China National Knowledge Infrastructure and the World Health Organization library databases for relevant studies will be performed. The searches will include peer-reviewed articles, published in English and Mandarin language irrespective of publication year, evaluating the risk for testing positive for C0VID-19, the risk of developing symptoms associated with SARS-CoV-2 infection, or both, among front-line HCWs. The initial review period will consider articles published since the onset of COVID-19 disease to the present and then updated monthly. Review Manager (RevMan 5.3) will be used to pool the odds ratios or mean differences for individual risk factors where possible. Results will be presented as relative risks and 95% confidence intervals for dichotomous outcomes and mean differences, or standardised mean differences along with 95% confidence intervals, for continuous outcomes. The Newcastle-Ottawa Scale will be used to rate study quality, and the certainty of the evidence will be assessed by using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE). The results of the living systematic review and meta-analysis will be reported per the PRISMA guidelines. DISCUSSION: Though addressing the needs of front-line HCWs during the COVID-19 pandemic is a high priority, data to inform such initiatives are inadequate, particularly data on the risk factor disparities between patient-facing and non-patient-facing front-line HCWs. The proposed living systematic review and meta-analysis anticipate finding relevant studies reporting risk factors driving the SARS-CoV-2 infection rates among patient-facing and non-patient-facing front-line HCWs, thus providing subsidies for public health interventions and occupational health policies. The study results will be disseminated electronically, in print and through conference presentation, and key stakeholder meetings in the form of policy briefs. TRAIL REGISTRATION: PROSPERO registration number: CRD42020193508 available for public comments via the link below https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020193508).


Assuntos
/diagnóstico , /virologia , Bases de Dados Factuais , Pessoal de Saúde , Saúde do Trabalhador , Saúde Pública , Fatores de Risco , /isolamento & purificação
10.
Sensors (Basel) ; 21(9)2021 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-33946998

RESUMO

Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition.


Assuntos
Algoritmos , Redes Neurais de Computação , Idoso , Bases de Dados Factuais , Atividades Humanas , Humanos , Esqueleto
11.
PLoS One ; 16(5): e0251123, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33951100

RESUMO

BACKGROUND: There is a lack of population level data on risk factors, incidence and impact of SARS-CoV-2 infection in pregnant women and their babies. The primary aim of this study was to describe the incidence, characteristics and outcomes of hospitalized pregnant women with symptomatic and asymptomatic SARS-CoV-2 in the UK compared to pregnant women without SARS-CoV-2. METHODS AND FINDINGS: We conducted a national, prospective cohort study of all hospitalized pregnant women with confirmed SARS-CoV-2 from 01/03/2020 to 31/08/2020 using the UK Obstetric Surveillance System. Incidence rates were estimated using national maternity data. Overall, 1148 hospitalized women had confirmed SARS-CoV-2 in pregnancy, 63% of which were symptomatic. The estimated incidence of hospitalization with symptomatic SARS-CoV-2 was 2.0 per 1000 maternities (95% CI 1.9-2.2) and for asymptomatic SARS-CoV-2 was 1.2 per 1000 maternities (95% CI 1.1-1.4). Compared to pregnant women without SARS-CoV-2, women hospitalized with symptomatic SARS-CoV-2 were more likely to be overweight or obese (adjusted OR 1.86, (95% CI 1.39-2.48) and aOR 2.07 (1.53-2.29)), to be of Black, Asian or Other minority ethnic group (aOR 6.24, (3.93-9.90), aOR 4.36, (3.19-5.95) and aOR 12.95, (4.93-34.01)), and to have a relevant medical comorbidity (aOR 1.83 (1.32-2.54)). Hospitalized pregnant women with symptomatic SARS-CoV-2 were more likely to be admitted to intensive care (aOR 57.67, (7.80-426.70)) but the absolute risk of poor outcomes was low. Cesarean births and neonatal unit admission were increased regardless of symptom status (symptomatic aOR 2.60, (1.97-3.42) and aOR 3.08, (1.99-4.77); asymptomatic aOR 2.02, (1.52-2.70) and aOR 1.84, (1.12-3.03)). The risks of stillbirth or neonatal death were not significantly increased, regardless of symptom status. CONCLUSIONS: We have identified factors that increase the risk of symptomatic and asymptomatic SARS-CoV-2 in pregnancy. Clinicians can be reassured that the majority of women do not experience severe complications of SARS-CoV-2 in pregnancy.


Assuntos
/epidemiologia , Portador Sadio/epidemiologia , Resultado da Gravidez , Adulto , /diagnóstico , Portador Sadio/diagnóstico , Portador Sadio/virologia , Cesárea , Estudos de Coortes , Bases de Dados Factuais , Feminino , Humanos , Incidência , Unidades de Terapia Intensiva , Grupos Minoritários/estatística & dados numéricos , Obesidade/complicações , Razão de Chances , Gravidez , Gestantes , Estudos Prospectivos , Reino Unido/epidemiologia , Adulto Jovem
12.
Adv Exp Med Biol ; 1187: 493-509, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33983596

RESUMO

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.


Assuntos
Inteligência Artificial , Neoplasias da Mama , Big Data , Mama , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/terapia , Bases de Dados Factuais , Humanos
13.
J Korean Med Sci ; 36(18): e132, 2021 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-33975399

RESUMO

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread around the globe, and it is important to determine the risk factors of death in the general population. Our study aimed to determine the risk factors of death and severe illness requiring supplemental oxygen therapy based on the demographic and clinical characteristics of COVID-19 patients in Korea. METHODS: In this study, we used data provided by the Korea Disease Control and Prevention Agency (KDCA) and analyzed a total of 5,068 patients with COVID-19, excluding 19 pregnant women and 544 individuals with missing data. We performed logistic regression analysis to determine the impact of early symptoms on survival and severe disease. Logistic regression models included sex, age, number of comorbidities, symptoms on admission, blood pressure, heart rate, and body temperature as explanatory variables, and death and oxygen therapy as outcome variables. RESULTS: Logistic regression analyses revealed that the male sex, older age (≥ 60 years), higher number of comorbidities, presence of symptoms on admission, heart rate ≥ 120 bpm, and body temperature ≥ 37.5°C presented with higher risk of in-hospital death and oxygen therapy requirement. Conversely, rhinorrhea and headache were associated with a low risk of death and oxygen therapy requirement. The findings showed that cough, sputum, and fever were the most common symptoms on admission, while 25.3% of patients with COVID-19 were asymptomatic. CONCLUSION: COVID-19 patients with high-risk early symptoms on admission, such as dyspnea and altered mental status, and those without low-risk symptoms of rhinorrhea and headache should be included in priority treatment groups.


Assuntos
/patologia , Resultado do Tratamento , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , /virologia , Comorbidade , Bases de Dados Factuais , Dispneia/epidemiologia , Dispneia/etiologia , Oxigenação por Membrana Extracorpórea , Feminino , Febre/epidemiologia , Febre/etiologia , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Razão de Chances , República da Coreia , Fatores de Risco , Índice de Gravidade de Doença , Adulto Jovem
14.
Database (Oxford) ; 20212021 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-33991092

RESUMO

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the 'covid19census' package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL: https://github.com/c1au6i0/covid19census.


Assuntos
/epidemiologia , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Pandemias , Algoritmos , Comorbidade , Demografia , Diabetes Mellitus/mortalidade , Inquéritos Epidemiológicos , Humanos , Itália/epidemiologia , Modelos Teóricos , Software , Estados Unidos/epidemiologia
15.
PLoS One ; 16(5): e0251250, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34003850

RESUMO

OBJECTIVES: Clinical characterisation studies have been essential in helping inform research, diagnosis and clinical management efforts, particularly early in a pandemic. This systematic review summarises the early literature on clinical characteristics of patients admitted to hospital, and evaluates the quality of evidence produced during the initial stages of the pandemic. METHODS: MEDLINE, EMBASE and Global Health databases were searched for studies published from January 1st 2020 to April 28th 2020. Studies which reported on at least 100 hospitalised patients with Covid-19 of any age were included. Data on clinical characteristics were independently extracted by two review authors. Study design specific critical appraisal tools were used to evaluate included studies: the Newcastle Ottawa scale for cohort and cross sectional studies, Joanna Briggs Institute checklist for case series and the Cochrane collaboration tool for assessing risk of bias in randomised trials. RESULTS: The search yielded 78 studies presenting data on 77,443 people. Most studies (82%) were conducted in China. No studies included patients from low- and middle-income countries. The overall quality of included studies was low to moderate, and the majority of studies did not include a control group. Fever and cough were the most commonly reported symptoms early in the pandemic. Laboratory and imaging findings were diverse with lymphocytopenia and ground glass opacities the most common findings respectively. Clinical data in children and vulnerable populations were limited. CONCLUSIONS: The early Covid-19 literature had moderate to high risk of bias and presented several methodological issues. Early clinical characterisation studies should aim to include different at-risk populations, including patients in non-hospital settings. Pandemic preparedness requires collection tools to ensure observational studies are methodologically robust and will help produce high-quality data early on in the pandemic to guide clinical practice and public health policy. REVIEW REGISTRATION: Available at https://osf.io/mpafn.


Assuntos
/patologia , Proteína C-Reativa/análise , /epidemiologia , Tosse/epidemiologia , Tosse/etiologia , Bases de Dados Factuais , Febre/epidemiologia , Febre/etiologia , Cefaleia/epidemiologia , Cefaleia/etiologia , Humanos , Linfopenia/etiologia , Pandemias , /isolamento & purificação
16.
BMC Infect Dis ; 21(1): 451, 2021 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-34011298

RESUMO

BACKGROUND: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. METHODS: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. RESULTS: In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model's S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. CONCLUSIONS: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.


Assuntos
Mortalidade Hospitalar , Influenza Humana/mortalidade , Modelos Biológicos , Idoso , Estado Terminal/epidemiologia , Bases de Dados Factuais , Epidemias , Hospitalização , Humanos , Influenza Humana/epidemiologia , Unidades de Terapia Intensiva , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Escores de Disfunção Orgânica , Prognóstico , Curva ROC , Estudos Retrospectivos , Sepse/diagnóstico
17.
Lancet Diabetes Endocrinol ; 9(6): 350-359, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33932335

RESUMO

BACKGROUND: Obesity is a major risk factor for adverse outcomes after infection with SARS-CoV-2. We aimed to examine this association, including interactions with demographic and behavioural characteristics, type 2 diabetes, and other health conditions. METHODS: In this prospective, community-based, cohort study, we used de-identified patient-level data from the QResearch database of general practices in England, UK. We extracted data for patients aged 20 years and older who were registered at a practice eligible for inclusion in the QResearch database between Jan 24, 2020 (date of the first recorded infection in the UK) and April 30, 2020, and with available data on BMI. Data extracted included demographic, clinical, clinical values linked with Public Health England's database of positive SARS-CoV-2 test results, and death certificates from the Office of National Statistics. Outcomes, as a proxy measure of severe COVID-19, were admission to hospital, admission to an intensive care unit (ICU), and death due to COVID-19. We used Cox proportional hazard models to estimate the risk of severe COVID-19, sequentially adjusting for demographic characteristics, behavioural factors, and comorbidities. FINDINGS: Among 6 910 695 eligible individuals (mean BMI 26·78 kg/m2 [SD 5·59]), 13 503 (0·20%) were admitted to hospital, 1601 (0·02%) to an ICU, and 5479 (0·08%) died after a positive test for SARS-CoV-2. We found J-shaped associations between BMI and admission to hospital due to COVID-19 (adjusted hazard ratio [HR] per kg/m2 from the nadir at BMI of 23 kg/m2 of 1·05 [95% CI 1·05-1·05]) and death (1·04 [1·04-1·05]), and a linear association across the whole BMI range with ICU admission (1·10 [1·09-1·10]). We found a significant interaction between BMI and age and ethnicity, with higher HR per kg/m2 above BMI 23 kg/m2 for younger people (adjusted HR per kg/m2 above BMI 23 kg/m2 for hospital admission 1·09 [95% CI 1·08-1·10] in 20-39 years age group vs 80-100 years group 1·01 [1·00-1·02]) and Black people than White people (1·07 [1·06-1·08] vs 1·04 [1·04-1·05]). The risk of admission to hospital and ICU due to COVID-19 associated with unit increase in BMI was slightly lower in people with type 2 diabetes, hypertension, and cardiovascular disease than in those without these morbidities. INTERPRETATION: At a BMI of more than 23 kg/m2, we found a linear increase in risk of severe COVID-19 leading to admission to hospital and death, and a linear increase in admission to an ICU across the whole BMI range, which is not attributable to excess risks of related diseases. The relative risk due to increasing BMI is particularly notable people younger than 40 years and of Black ethnicity. FUNDING: NIHR Oxford Biomedical Research Centre.


Assuntos
Índice de Massa Corporal , Diabetes Mellitus Tipo 2/epidemiologia , Vida Independente/tendências , Índice de Gravidade de Doença , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Bases de Dados Factuais , Diabetes Mellitus Tipo 2/diagnóstico , Inglaterra/epidemiologia , Feminino , Seguimentos , Hospitalização/tendências , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Fatores de Risco , Adulto Jovem
18.
PLoS Med ; 18(5): e1003571, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-34014945

RESUMO

BACKGROUND: Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. METHODS AND FINDINGS: In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. CONCLUSIONS: In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.


Assuntos
/mortalidade , Mortalidade , Afro-Americanos/estatística & dados numéricos , Idoso , Comorbidade , Estudos Transversais , Bases de Dados Factuais , Diabetes Mellitus/epidemiologia , Escolaridade , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Hispano-Americanos/estatística & dados numéricos , Humanos , Renda , Fatores Raciais , Estados Unidos/epidemiologia
19.
Stud Health Technol Inform ; 281: 512-513, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042626

RESUMO

In this study, an attempt has been made to differentiate Drug Resistant Tuberculosis (DR-TB) in chest X-rays using projection profiling and mediastinal features. DR-TB is a condition which is non-responsive to at least one of anti-TB drugs. Mediastinum variations can be considered as significant image biomarkers for detection of DR-TB. Images are obtained from a public database and are contrast enhanced using coherence filtering. Projection profiling is used to obtain the feature lines from which the mediastinal and thoracic indices are computed. Classification of Drug Sensitive (DS-TB) and DR-TB is performed using three classifiers. Results show that the mediastinal features are found to be statistically significant. Support vector machine with quadratic kernel is able to provide better classification performance values of greater than 93%. Hence, the automated analysis of mediastinum could be clinically significant in differentiation of DR-TB.


Assuntos
Tuberculose Resistente a Múltiplos Medicamentos , Gerenciamento de Dados , Bases de Dados Factuais , Humanos , Máquina de Vetores de Suporte , Tuberculose Resistente a Múltiplos Medicamentos/diagnóstico por imagem , Tuberculose Resistente a Múltiplos Medicamentos/tratamento farmacológico , Raios X
20.
Stud Health Technol Inform ; 281: 555-559, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042637

RESUMO

Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.


Assuntos
Informática Médica , Farmacovigilância , Ciência de Dados , Bases de Dados Factuais , Registros Eletrônicos de Saúde , Humanos , Software
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