Has the chief complaint of patients with COVID-19 disease changed over time?
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ABSTRACT:
SUBMITTER: Dehghani Firouzabadi M
PROVIDER: S-EPMC7276133 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Has the chief complaint of patients with COVID-19 disease changed over time?
Dehghani Firouzabadi Mohammad M Dehghani Firouzabadi Fatemeh F Goudarzi Sogand S Jahandideh Hesam H Roomiani Maryam M
Medical hypotheses 20200607
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Project description: Not available
| S-EPMC7328546 | biostudies-literature
Project description:Background and aimNew York City (NYC) is an epicenter of the COVID-19 pandemic in the United States. Proper triage of patients with possible COVID-19 via chief complaint is critical but not fully optimized. This study aimed to investigate the association between presentation by chief complaints and COVID-19 status.MethodsWe retrospectively analyzed adult emergency department (ED) patient visits from five different NYC hospital campuses from March 1, 2020 to May 13, 2020 of patients who underwent nasopharyngeal COVID-19 RT-PCR testing. The positive and negative COVID-19 cohorts were then assessed for different chief complaints obtained from structured triage data. Sub-analysis was performed for patients older than 65 and within chief complaints with high mortality.ResultsOf 11,992 ED patient visits who received COVID-19 testing, 6524/11992 (54.4%) were COVID-19 positive. 73.5% of fever, 67.7% of shortness of breath, and 65% of cough had COVID-19, but others included 57.5% of weakness/fall/altered mental status, 55.5% of glycemic control, and 51.4% of gastrointestinal symptoms. In patients over 65, 76.7% of diarrhea, 73.7% of fatigue, and 69.3% of weakness had COVID-19. 45.5% of dehydration, 40.5% of altered mental status, 27% of fall, and 24.6% of hyperglycemia patients experienced mortality.ConclusionA novel high risk COVID-19 patient population was identified from chief complaint data, which is different from current suggested CDC guidelines, and may help triage systems to better isolate COVID-19 patients. Older patients with COVID-19 infection presented with more atypical complaints warranting special consideration. COVID-19 was associated with higher mortality in a unique group of complaints also warranting special consideration.
| S-EPMC7648523 | biostudies-literature
Project description:COVID-19, the disease caused by SARS-CoV-2 infection, can assume a highly variable disease course, ranging from asymptomatic infection, which constitutes the majority of cases, to severe respiratory failure. This implies a diverse host immune response to SARS-CoV-2. However, the immunological underpinnings underlying these divergent disease courses remain elusive. We therefore set out to longitudinally characterize immune signatures of convalescent COVID-19 patients stratified according to their disease severity. Our unique convalescent COVID-19 cohort consists of 74 patients not confounded by comorbidities. This is the first study of which we are aware that excludes immune abrogations associated with non-SARS-CoV-2 related risk factors of disease severity. Patients were followed up and analyzed longitudinally (2, 4 and 6 weeks after infection) by high-dimensional flow cytometric profiling of peripheral blood mononuclear cells (PBMCs), in-depth serum analytics, and transcriptomics. Immune phenotypes were correlated to disease severity. Convalescence was overall associated with uniform immune signatures, but distinct immune signatures for mildly versus severely affected patients were detectable within a 2-week time window after infection.
2021-07-30 | GSE181032 | GEO
Project description:Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper, we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naïve Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, and then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score, and we measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses, the LSTM classifier performs best, though all models exhibit an F1 score above 96.00. Using chief complaints, the GRU performs best (F1 = 47.38), and MNB with bigrams performs worst (F1 = 39.40). We also note that certain syndrome types are easier to detect than others. For example, chief complaints using the GRU model predicts alcohol-related disorders well (F1 = 78.91) but predicts influenza poorly (F1 = 14.80). In all instances, the RNN models outperformed the bag-of-words classifiers suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.
| S-EPMC10563436 | biostudies-literature
Project description:BackgroundUnderstanding heterogeneity of the respiratory rate (RR) as a risk stratification marker across chief complaints is important to reduce misinterpretation of the risk posed by outcome events and to build accurate risk stratification tools. This study was conducted to investigate the associations between RR and clinical outcomes according to the five most frequent chief complaints in an emergency department (ED): fever, shortness of breath, altered mental status, chest pain, and abdominal pain.MethodsThis retrospective cohort study examined ED data of all adult patients who visited the ED of a tertiary medical center during April 2018-September 2019. The primary exposure was RR at the ED visit. Outcome measures were hospitalization and mechanical ventilation use. We used restrictive cubic spline and logistic regression models to assess the association of interest.ResultsOf 16 956 eligible ED patients, 4926 (29%) required hospitalization; 448 (3%) required mechanical ventilation. Overall, U-shaped associations were found between RR and the risk of hospitalization (eg, using RR = 16 as the reference, the odds ratio [OR] of RR = 32, 6.57 [95% CI 5.87-7.37]) and between RR and the risk of mechanical ventilation. This U-shaped association was driven by patients' association with altered mental status (eg, OR of RR = 12, 2.63 [95% CI 1.25-5.53]). For patients who have fever or shortness of breath, the risk of hospitalization increased monotonously with increased RR.ConclusionsU-shaped associations of RR with the risk of overall clinical outcomes were found. These associations varied across chief complaints.
| S-EPMC8245737 | biostudies-literature
Project description:COVID-19, the disease caused by SARS-CoV-2 infection, can assume a highly variable disease course, ranging from asymptomatic infection, which constitutes the majority of cases, to severe respiratory failure. This implies a diverse host immune response to SARS-CoV-2. However, the immunological underpinnings underlying these divergent disease courses remain elusive. We therefore set out to longitudinally characterize immune signatures of convalescent COVID-19 patients stratified according to their disease severity. Our unique convalescent COVID-19 cohort consists of 74 patients not confounded by comorbidities. This is the first study of which we are aware that excludes immune abrogations associated with non-SARS-CoV-2 related risk factors of disease severity. Patients were followed up and analyzed longitudinally (2, 4 and 6 weeks after infection) by high-dimensional flow cytometric profiling of peripheral blood mononuclear cells (PBMCs), in-depth serum analytics, and transcriptomics. Immune phenotypes were correlated to disease severity. Convalescence was overall associated with uniform immune signatures, but distinct immune signatures for mildly versus severely affected patients were detectable within a 2-week time window after infection.
| S-EPMC8634761 | biostudies-literature
Project description:Changes in a river's width reflect natural and anthropogenic impacts on local and upstream/downstream hydraulic and hydrologic processes. Temporal variation of river width also impacts biogeochemical exchange and reflects geomorphologic evolution. However, while global maps of mean river width and dynamic water surface extent exist, there is currently no standardized global assessment of river widths that documents changes over time. Therefore, we made repeated width measurements from Landsat images for all rivers wider than 90 m collected from 1984 to 2020 (named Global LOng-term river Width, GLOW), which consists of ∼1.2 billion cross-sectional river width measurements, with an average of 3,000 width measurements per 10-km reach. With GLOW, we investigated the temporal variations of global river width, quantified by the interquartile range (IQR) and temporal trend. We found that 85% of global rivers have a width IQR <150 m. We also found that 37% of global river segments show significant temporal trends in width over the past 37 years, and this number is higher (46%) for human-regulated rivers. Further, we leveraged machine learning to identify the most important factors explaining river width variations and revealed that these driving factors are significantly different between free-flowing and non-free-flowing rivers. Specifically, the most important factor driving temporal variations in river width is the climate for free-flowing rivers, and is soil condition for human-impacted rivers. Finally, we anticipate that this study and the public release of GLOW will improve the understanding of river dynamics and catalyze additional interdisciplinary studies.
| S-EPMC9541693 | biostudies-literature
Project description:Anterior shoulder dislocation is a disabling injury affecting all ages, young and old alike. Recently, the treatment of traumatic shoulder dislocation has included immobilisation for varying periods of time followed by physiotherapy. This study is the first in this country to address the demographic data and recurrence rates of shoulder dislocation. Three hundred and eight patients (170 men and 138 women) were followed up for an average of 5.9 years. The most frequent mechanism of injury was a fall (65.66% of cases), and in 92.1% of the patients, the shoulder was reduced in the Emergency Department without the need for sedation or general anaesthesia. The overall recurrence rate in all ages was 50%, but rose to 88.9% in the 14-20-year age group. The duration of immobilisation did not affect the rate of re-dislocation of the humeral head. We believe that conventional shoulder immobilisation in a sling offers no benefits, and it would be preferable not to immobilise the shoulder at all.
| S-EPMC2267594 | biostudies-other
Project description:Convalescent COVID-19 patients without comorbidities display similar immunophenotypes over time despite divergent disease severities
| PRJNA750442 | ENA
Project description:BackgroundThe World Health Organization characterizes novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as a pandemic. Here, we investigated the clinical, cytokine levels; T-cell proportion; and related gene expression occurring in patients with COVID-19 on admission and after initial treatment.MethodsEleven patients diagnosed with COVID-19 with similar initial treatment regimens were enrolled in the hospital. Plasma cytokine, peripheral T cell proportions, and microfluidic quantitative polymerase chain reaction analyses for gene expression were conducted.ResultsFive patients with mild and 6 with severe disease were included. Cough and fever were the primary symptoms in the 11 COVID-19 cases. Older age, higher neutrophil count, and higher C-reactive protein levels were found in severe cases. IL-10 level significantly varied with disease progression and treatment. Decreased T-cell proportions were observed in patients with COVID-19, especially in severe cases, and all were returned to normal in patients with mild disease after initial treatment, but only CD4+ T cells returned to normal in severe cases. The number of differentially expressed genes (DEGs) increased with the disease progression, and decreased after initial treatment. All downregulated DEGs in severe cases mainly involved Th17-cell differentiation, cytokine-mediated signaling pathways, and T-cell activation. After initial treatment in severe cases, MAP2K7 and SOS1 were upregulated relative to that on admission.ConclusionsOur findings show that a decreased T-cell proportion with downregulated gene expression related to T-cell activation and differentiation occurred in patients with severe COVID-19, which may help to provide effective treatment strategies for COVID-19.
| S-EPMC7188184 | biostudies-literature