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3.
Medicine (Baltimore) ; 99(22): e20316, 2020 May 29.
Artigo em Inglês | MEDLINE | ID: mdl-32481403

RESUMO

Stomachache is not only disease name of Traditional Chinese medicine (TCM) but also the clinical symptom. It is a common and multiple diseases. TCM has its particular advantage in clinical treatment of stomachache. Syndrome differentiation is an important concept in TCM practice. The therapeutic process is virtually a nonlinear mapping process from clinical symptom to syndrome diagnosis with processing and seeking rules from mass data. Artificial neutral network has strong learning ability for nonlinear relationship. Artificial neutral network has been widely used to TCM area where the multiple factors, multilevel, nonlinear problem accompanied by a large number of optimization exist.We present an original experimental method to apply the improved third-order convergence LM algorithm to intelligent syndrome differentiation for the first time, and compare the predicted ability of Levenberg-Marquardt (LM) algorithm and the improved third-order convergence LM algorithm in syndrome differentiation.In this study, 2436 cases of stomachache electronic medical data from hospital information system, and then the real world data were normalized and standardized. Afterwards, LM algorithm and the improved third-order convergence LM algorithm were used to build the Back Propagation (BP) neural network model for intelligent syndrome differentiation of stomachache on Matlab, respectively. Finally, the differentiation performance of the 2 models was tested and analyzed.The testing results showed that the improved third-order convergence LM algorithm model has better average prediction and diagnosis accuracy, especially in predicting "liver-stomach disharmony" and "stomach yang deficiency", is above 95%.By effectively using the self-learning and auto-update ability of the BP neural network, the intelligent syndrome differentiation model of TCM can fully approach the real side of syndrome differentiation, and shows excellent predicted ability of syndrome differentiation.


Assuntos
Dor Abdominal/diagnóstico , Medicina Tradicional Chinesa/métodos , Redes Neurais de Computação , Gastropatias/diagnóstico , Dor Abdominal/fisiopatologia , Algoritmos , Diagnóstico Diferencial , Humanos , Gastropatias/fisiopatologia
4.
Korean J Radiol ; 21(7): 859-868, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32524786

RESUMO

OBJECTIVE: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. RESULTS: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. CONCLUSION: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adulto , Algoritmos , Área Sob a Curva , Betacoronavirus , Proteína C-Reativa/análise , China , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Inflamação , Linfócitos/citologia , Masculino , Pessoa de Meia-Idade , Pandemias , Admissão do Paciente , Pró-Calcitonina/sangue , Prognóstico , Curva ROC , Projetos de Pesquisa , Estudos Retrospectivos , Sensibilidade e Especificidade , Índice de Gravidade de Doença
5.
Lancet Haematol ; 7(7): e541-e550, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32589980

RESUMO

Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications in medicine, both as a catalyst for research and as a means of improving clinical care across the cycle of diagnosis, prognosis, and treatment of disease. These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the analysis of electronic health record data. As computational power becomes cheaper and the tools for implementing machine learning become increasingly democratised, it is likely to become increasingly integrated into the research and practice landscape of haematology. As such, machine learning merits understanding and attention from researchers and clinicians alike. This narrative Review describes important concepts in machine learning for unfamiliar readers, details machine learning's current applications in haematological malignancy, and summarises important concepts for clinicians to be aware of when appraising research that uses machine learning.


Assuntos
Neoplasias Hematológicas , Aprendizado de Máquina , Algoritmos , Humanos , Redes Neurais de Computação
6.
Medicine (Baltimore) ; 99(24): e20345, 2020 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-32541456

RESUMO

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with pancreatic cancer (PC) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with PC. METHODS: Databases were searched to identify association studies of SNPs and PC published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database (VIP) and Wanfang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability (FPRP) for noteworthy associations. The methodological quality of data was assessed based on the STREGA statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with pancreatic cancer susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with pancreatic cancer susceptibility.Registration: INPLASY202040023.


Assuntos
Neoplasias Pancreáticas/epidemiologia , Neoplasias Pancreáticas/genética , Polimorfismo de Nucleotídeo Único/genética , Algoritmos , Estudos de Casos e Controles , China/epidemiologia , Reações Falso-Positivas , Feminino , Predisposição Genética para Doença , Humanos , Masculino , Metanálise em Rede , Neoplasias Pancreáticas/sangue , Neoplasias Pancreáticas/mortalidade , Risco , Sensibilidade e Especificidade
7.
Medicine (Baltimore) ; 99(24): e20385, 2020 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-32541458

RESUMO

Template matching is a proposed approach for hospital benchmarking, which measures performance based on matching a subset of comparable patient hospitalizations from each hospital. We assessed the ability to create the required matched samples and thus the feasibility of template matching to benchmark hospital performance in a diverse healthcare system.Nationwide Veterans Affairs (VA) hospitals, 2017.Observational cohort study.We used administrative and clinical data from 668,592 hospitalizations at 134 VA hospitals in 2017. A standardized template of 300 hospitalizations was selected, and then 300 hospitalizations were matched to the template from each hospital.There was substantial case-mix variation across VA hospitals, which persisted after excluding small hospitals, hospitals with primarily psychiatric admissions, and hospitalizations for rare diagnoses. Median age ranged from 57 to 75 years across hospitals; percent surgical admissions ranged from 0.0% to 21.0%; percent of admissions through the emergency department, 0.1% to 98.7%; and percent Hispanic patients, 0.2% to 93.3%. Characteristics for which there was substantial variation across hospitals could not be balanced with any matching algorithm tested. Although most other variables could be balanced, we were unable to identify a matching algorithm that balanced more than ∼20 variables simultaneously.We were unable to identify a template matching approach that could balance hospitals on all measured characteristics potentially important to benchmarking. Given the magnitude of case-mix variation across VA hospitals, a single template is likely not feasible for general hospital benchmarking.


Assuntos
Benchmarking/métodos , Prestação Integrada de Cuidados de Saúde/estatística & dados numéricos , Hospitalização/estatística & dados numéricos , Hospitais de Veteranos/estatística & dados numéricos , Idoso , Algoritmos , Benchmarking/normas , Estudos de Coortes , Grupos Diagnósticos Relacionados/tendências , Serviço Hospitalar de Emergência/estatística & dados numéricos , Estudos de Viabilidade , Feminino , Hispano-Americanos/estatística & dados numéricos , Hospitalização/tendências , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Avaliação de Resultados em Cuidados de Saúde/métodos , Qualidade da Assistência à Saúde/estatística & dados numéricos , Centro Cirúrgico Hospitalar/estatística & dados numéricos , Estados Unidos/epidemiologia , United States Department of Veterans Affairs/organização & administração
8.
Medicine (Baltimore) ; 99(24): e20579, 2020 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-32541487

RESUMO

The aim was to compare the effects of metal artifacts from a pacemaker on pulmonary nodule detection among computed tomography (CT) images reconstructed using filtered back projection (FBP), single-energy metal artifact reduction (SEMAR), and forward-projected model-based iterative reconstruction solution (FIRST).Nine simulated nodules were placed inside a chest phantom with a pacemaker. CT images reconstructed using FBP, SEMAR, and FIRST were acquired at low and standard dose, and were evaluated by 2 independent radiologists.FIRST demonstrated the most significantly improved metal artifact and nodule detection on low dose CT (P < .0032), except at 10 mA and 5-mm thickness. At standard-dose CT, SEMAR showed the most significant metal artifact reduction (P < .00001). In terms of nodule detection, no significant differences were observed between FIRST and SEMAR (P = .161).With a pacemaker present, FIRST showed the best nodule detection ability at low-dose CT and SEMAR is comparable to FIRST at standard dose CT.


Assuntos
Artefatos , Marca-Passo Artificial , Nódulo Pulmonar Solitário/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Humanos , Melhoria de Qualidade
9.
Medicine (Baltimore) ; 99(24): e20774, 2020 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-32541529

RESUMO

BACKGROUND: The US Centers for Disease Control and Prevention (CDC) regularly issues "travel health notices" that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps. METHODS: We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country. RESULTS: The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020. CONCLUSION: We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Surtos de Doenças , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Algoritmos , Teorema de Bayes , Centers for Disease Control and Prevention, U.S. , China/epidemiologia , Infecções por Coronavirus/mortalidade , Apresentação de Dados , Visualização de Dados , Europa (Continente)/epidemiologia , Saúde Global , Humanos , Irã (Geográfico)/epidemiologia , Modelos Estatísticos , Pandemias , Pneumonia Viral/mortalidade , República da Coreia/epidemiologia , Medição de Risco , Viagem , Estados Unidos/epidemiologia
10.
Lab Chip ; 20(12): 2075-2085, 2020 06 21.
Artigo em Inglês | MEDLINE | ID: mdl-32490853

RESUMO

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.


Assuntos
Infecções por Coronavirus/diagnóstico , Sistemas de Apoio a Decisões Clínicas/organização & administração , Pneumonia Viral/diagnóstico , Sistemas Automatizados de Assistência Junto ao Leito , Algoritmos , Biomarcadores , Comorbidade , Infecções por Coronavirus/fisiopatologia , Cuidados Críticos , Humanos , Processamento de Imagem Assistida por Computador , Imunoensaio/métodos , Aprendizado de Máquina , Pandemias , Pneumonia Viral/fisiopatologia , Valor Preditivo dos Testes , Fatores de Risco , Índice de Gravidade de Doença , Software , Resultado do Tratamento
11.
Am Fam Physician ; 101(12): 721-729, 2020 06 15.
Artigo em Inglês | MEDLINE | ID: mdl-32538597

RESUMO

Despite dramatic reductions in the rates of bacteremia and meningitis since the 1980s, febrile illness in children younger than 36 months continues to be a concern with potentially serious consequences. Factors that suggest serious infection include age younger than one month, poor arousability, petechial rash, delayed capillary refill, increased respiratory effort, and overall physician assessment. Urinary tract infections are the most common serious bacterial infection in children younger than three years, so evaluation for such infections should be performed in those with unexplained fever. Abnormal white blood cell counts have poor sensitivity for invasive bacterial infections; procalcitonin and C-reactive protein levels, when available, are more informative. Chest radiography is rarely recommended for children older than 28 days in the absence of localizing signs. Lumbar puncture is not recommended for children older than three months without localizing signs; it may also be considered for those from one to three months of age with abnormal laboratory test results. Protocols such as Step-by-Step, Laboratory Score, or the Rochester algorithms may be helpful in identifying low-risk patients. Rapid influenza testing and tests for coronavirus disease 2019 (COVID-19) may be of value when those diseases are circulating. When empiric treatment is appropriate, suggested antibiotics include ceftriaxone or cefotaxime for infants one to three months of age and ampicillin with gentamicin or with cefotaxime for neonates. For children three months to three years of age, azithromycin or amoxicillin is recommended if pneumonia is suspected; for urinary infections, suggested antibiotics are cefixime, amoxicillin/clavulanate, or trimethoprim/sulfamethoxazole. Choice of antibiotics should reflect local patterns of microbial resistance.


Assuntos
Tomada de Decisão Clínica , Febre/etiologia , Influenza Humana/diagnóstico , Pneumonia Bacteriana/diagnóstico , Infecções Urinárias/diagnóstico , Algoritmos , Combinação Amoxicilina e Clavulanato de Potássio , Antibacterianos/uso terapêutico , Betacoronavirus , Hemocultura , Proteína C-Reativa/metabolismo , Pré-Escolar , Técnicas de Laboratório Clínico , Infecções por Coronavirus/complicações , Infecções por Coronavirus/diagnóstico , Técnicas de Cultura , Humanos , Lactente , Recém-Nascido , Influenza Humana/complicações , Influenza Humana/tratamento farmacológico , Influenza Humana/epidemiologia , Contagem de Leucócitos , Pandemias , Pneumonia Bacteriana/complicações , Pneumonia Bacteriana/tratamento farmacológico , Pneumonia Bacteriana/epidemiologia , Pneumonia Viral , Pró-Calcitonina/metabolismo , Radiografia Torácica , Punção Espinal , Combinação Trimetoprima e Sulfametoxazol , Urinálise , Infecções Urinárias/complicações , Infecções Urinárias/tratamento farmacológico , Infecções Urinárias/epidemiologia
12.
Artigo em Inglês | MEDLINE | ID: mdl-32545581

RESUMO

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.


Assuntos
Infecções por Coronavirus/epidemiologia , Redes Neurais de Computação , Pneumonia Viral/epidemiologia , Algoritmos , Betacoronavirus , Sistemas de Informação Geográfica , Humanos , Incidência , Modelos Logísticos , Aprendizado de Máquina , Modelos Estatísticos , Pandemias , Saúde Pública , Fatores de Risco , Análise Espacial , Estados Unidos/epidemiologia
13.
Comput Math Methods Med ; 2020: 5714714, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32565882

RESUMO

Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group-deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.


Assuntos
Infecções por Coronavirus/transmissão , Modelos Biológicos , Redes Neurais de Computação , Pneumonia Viral/transmissão , Algoritmos , Biologia Computacional , Infecções por Coronavirus/epidemiologia , Bases de Dados Factuais , Humanos , Conceitos Matemáticos , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Análise de Regressão
14.
Medicine (Baltimore) ; 99(23): e20543, 2020 Jun 05.
Artigo em Inglês | MEDLINE | ID: mdl-32502015

RESUMO

An axial-volume scan with adaptive statistical iterative reconstruction-V (ASIR-V) is newly developed. Our goal was to identify the influence of axial-volume scan and ASIR-V on accuracy of automated nodule volumetry.An "adult' chest phantom containing various nodules was scanned using both helical and axial-volume modes at different dose settings using 256-slice CT. All CT scans were reconstructed using 30% and 50% blending of ASIR-V and filtered back projection. Automated nodule volumetry was performed using commercial software. The image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured.The axial-volume scan reduced radiation dose by 19.7% compared with helical scan at all radiation dose settings without affecting the accuracy of nodule volumetric measurement (P = .375). Image noise, CNR, and SNR were not significantly different between two scan modes (all, P > .05).The use of axial-volume scan with ASIR-V achieved effective radiation dose reduction while preserving the accuracy of nodule volumetry.


Assuntos
Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Imagens de Fantasmas , Nódulo Pulmonar Solitário/diagnóstico por imagem , Algoritmos , Humanos , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador , Razão Sinal-Ruído , Tomografia Computadorizada Espiral , Tomografia Computadorizada por Raios X
15.
JAMA ; 323(21): 2160-2169, 2020 06 02.
Artigo em Inglês | MEDLINE | ID: mdl-32484534

RESUMO

Importance: Antibiotic overuse drives antibiotic resistance. Gram-negative bacteremia is a common infection that results in substantial antibiotic use. Objective: To compare the clinical effectiveness of C-reactive protein (CRP)-guided, 7-day, and 14-day antibiotic durations 30, 60, and 90 days after treatment initiation. Design, Setting, and Participants: Multicenter, noninferiority, point-of-care randomized clinical trial including adults hospitalized with gram-negative bacteremia conducted in 3 Swiss tertiary care hospitals between April 2017 and May 2019, with follow-up until August 2019. Patients and physicians were blinded between randomization and antibiotic discontinuation. Adults (aged ≥18 years) were eligible for randomization on day 5 (±1 d) of microbiologically efficacious therapy for fermenting, gram-negative bacteria in blood culture(s) if they were afebrile for 24 hours without evidence for complicated infection (eg, abscess) or severe immunosuppression. Intervention: Randomization in a 1:1:1 ratio to an individualized CRP-guided antibiotic treatment duration (discontinuation once CRP declined by 75% from peak; n = 170), fixed 7-day treatment duration (n = 169), or fixed 14-day treatment duration (n = 165). Main Outcomes and Measures: The primary outcome was the clinical failure rate at day 30, defined as the presence of at least 1 of the following, with a non-inferiority margin of 10%: recurrent bacteremia, local suppurative complication, distant complication (growth of the same organism causing the initial bacteremia), restarting gram-negative-directed antibiotic therapy due to clinical worsening suspected to be due to the initial organism, or death due to any cause. Secondary outcomes included the clinical failure rate on day 90 of follow-up. Results: Among 504 patients randomized (median [interquartile range] age, 79 [68-86] years; 306 of 503 [61%] were women), 493 (98%) completed 30-day follow-up and 448 (89%) completed 90-day follow-up. Median antibiotic duration in the CRP group was 7 (interquartile range, 6-10; range, 5-28) days; 34 of the 164 patients (21%) who completed the 30-day follow-up had protocol violations related to treatment assignment. The primary outcome occurred in 4 of 164 (2.4%) patients in the CRP group, 11 of 166 (6.6%) in the 7-day group, and 9 of 163 (5.5%) in the 14-day group (difference in CRP vs 14-day group, -3.1% [1-sided 97.5% CI, -∞ to 1.1]; P < .001; difference in 7-day vs 14-day group, 1.1% [1-sided 97.5% CI, -∞ to 6.3]; P < .001). By day 90, clinical failure occurred in 10 of 143 patients (7.0%) in the CRP group, 16 of 151 (10.6%) in the 7-day group, and 16 of 153 (10.5%) in the 14-day group. Conclusions and Relevance: Among adults with uncomplicated gram-negative bacteremia, 30-day rates of clinical failure for CRP-guided antibiotic treatment duration and fixed 7-day treatment were noninferior to fixed 14-day treatment. However, interpretation is limited by the large noninferiority margin compared with the low observed event rate, as well as low adherence and wide range of treatment durations in the CRP-guided group. Trial Registration: ClinicalTrials.gov Identifier: NCT03101072.


Assuntos
Antibacterianos/administração & dosagem , Bacteriemia/tratamento farmacológico , Duração da Terapia , Infecções por Bactérias Gram-Negativas/tratamento farmacológico , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Antibacterianos/efeitos adversos , Bacteriemia/microbiologia , Bacteriemia/mortalidade , Proteína C-Reativa/análise , Esquema de Medicação , Feminino , Bactérias Gram-Negativas , Infecções por Bactérias Gram-Negativas/mortalidade , Humanos , Análise de Intenção de Tratamento , Masculino , Testes de Sensibilidade Microbiana , Pessoa de Meia-Idade , Análise Multivariada , Recidiva , Análise de Regressão , Falha de Tratamento
16.
Emerg Microbes Infect ; 9(1): 1397-1406, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32490731

RESUMO

The ongoing severe acute respiratory syndrome pandemic caused by the novel coronavirus 2 (SARS-CoV-2) is associated with high morbidity and mortality rates, and it has created a pressing global need for effective antiviral therapies against it. COVID-19 disease pathogenesis is characterized by an initial virus-mediated phase, followed by inappropriate hyperactivation of the immune system leading to organ damage. Targeting of the SARS-CoV-2 viral receptors is being explored as a therapeutic option for these patients. In this paper, we summarize several potential receptors associated with the infectivity of SARS-CoV-2 and discuss their association with the immune-mediated inflammatory response. The potential for the development of resistance towards antiviral drugs is also presented. An algorithm-based platform to improve the efficacy of and overcome resistance to viral receptor blockers through the introduction of personalized variability is described. This method is designed to ensure sustained antiviral effectiveness when using SARS-CoV-2 receptor blockers.


Assuntos
Antivirais/farmacologia , Betacoronavirus/fisiologia , Infecções por Coronavirus/imunologia , Farmacorresistência Viral , Pneumonia Viral/imunologia , Receptores Virais/antagonistas & inibidores , Algoritmos , Animais , Betacoronavirus/efeitos dos fármacos , Betacoronavirus/genética , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/genética , Infecções por Coronavirus/virologia , Humanos , Pandemias , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/genética , Pneumonia Viral/virologia , Receptores Virais/genética , Receptores Virais/imunologia
17.
Bone Joint J ; 102-B(6_Supple_A): 101-106, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32475275

RESUMO

AIMS: The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. METHODS: A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. RESULTS: The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient's history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. CONCLUSION: This study showed that machine learning can detect prosthetic loosening from radiographs. Its accuracy is enhanced when using highly trained public algorithms, and when adding clinical data to the algorithm. While this algorithm may not be sufficient in its present state of development as a standalone metric of loosening, it is currently a useful augment for clinical decision making. Cite this article: Bone Joint J 2020;102-B(6 Supple A):101-106.


Assuntos
Algoritmos , Prótese do Joelho , Aprendizado de Máquina , Complicações Pós-Operatórias/diagnóstico , Falha de Prótese , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Complicações Pós-Operatórias/diagnóstico por imagem , Radiografia
18.
Nature ; 582(7811): 230-233, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32499650

RESUMO

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Internacionalidade , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Opinião Pública , Mídias Sociais/estatística & dados numéricos , Vacinação/psicologia , Algoritmos , Análise por Conglomerados , Infecções por Coronavirus/psicologia , Humanos , Fatores de Tempo , Vacinas Virais
20.
Arch Esp Urol ; 73(5): 360-366, 2020 Jun.
Artigo em Inglês, Espanhol | MEDLINE | ID: mdl-32538805

RESUMO

The COVID-19 pandemic caused by SARS-CoV-2 virus has caused an important health impact that has affected renal cell carcinoma management, among other urology areas. The high cancellation rate of surgeries, including those related to renal cancer, will cause an inevitable healthcare overload and probably a potential negative impact on its oncological outcomes, especially in locally advanced and metastatic renal cancer. Kidney cancer scenarios are quite different depending on their stage, distinguishing mainly between low priority of localized disease or high priority of locally advanced and metastatic under active treatment. The unknown pandemic duration and possibly fluctuating prevalence of the virus are likely to force an adaptation in the management of renal cell carcinoma among urology and oncology departments, ideally individualized ona case-by-case basis within multidisciplinary units. To this end, we present algorithms and tables regarding renal cell carcinoma management adapted to the COVID-19 period and stratified according to oncological stage, which might be useful for specialists dedicated to this uro-oncology area.


Assuntos
Betacoronavirus , Carcinoma de Células Renais , Infecções por Coronavirus , Neoplasias Renais , Pandemias , Pneumonia Viral , Algoritmos , Carcinoma de Células Renais/terapia , Infecções por Coronavirus/epidemiologia , Humanos , Neoplasias Renais/terapia , Pneumonia Viral/epidemiologia
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