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1.
Journal of Jilin University Medicine Edition ; 48(2):518-526, 2022.
Artigo em Chinês | EMBASE | ID: covidwho-20244896

RESUMO

Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.

2.
Chest ; 162(4):A1854-A1855, 2022.
Artigo em Inglês | EMBASE | ID: covidwho-2060873

RESUMO

SESSION TITLE: Diagnosis of Lung Disease through Pathology Case Posters SESSION TYPE: Case Report Posters PRESENTED ON: 10/19/2022 12:45 pm - 01:45 pm INTRODUCTION: This report describes the case of a patient presenting with pneumothorax and Severe Acute Respiratory Syndrome (SARS) Coronavirus-2 (SARS-cov-2) infection leading to Coronavirus Disease 2019 (COVID-19) pneumonia, with worsening presentation, later found to have underlying Pleuroparenchymal Fibroelastosis (PPFE). CASE PRESENTATION: A 68 year old male with a past medical history of hypertension and type 2 diabetes presented to his primary care clinic with shortness of breath. He underwent a Chest X-Ray as an outpatient which revealed a moderate right-sided pneumothorax (PTX), and he was sent to the Emergency Department by his primary care provider. He was found to be COVID positive on initial workup, also requiring supplemental oxygen. Other routine laboratory tests did not reveal any significant abnormalities. His shortness of breath worsened and on repeat X-rays his pneumothorax increased in size therefore a chest tube was placed by Cardiothoracic Surgery. Computerized Tomography of the chest revealed moderate right pneumothorax, bilateral diffuse ground glass opacities and pulmonary micronodules [Figure 1]. The patient had mild initial improvement and the chest tube was removed but he had recurrence of the PTX and he underwent urgent Video Assisted Thoracoscopic Surgery (VATS), with right upper lobe wedge resection and talc pleurodesis. A biopsy of the resected lung revealed a benign lung with fibroelastotic scarring, diffusely involving subpleural tissue and prominently extending into and entrapping areas of underlying alveolated tissue, with no inflammation, granulomas or pneumonia noted. Workup for tuberculosis, autoimmune disorders, HIV was negative. He eventually was discharged home with close pulmonology and cardiothoracic surgery follow ups, planned for disease surveillance and malignancy workup. DISCUSSION: PPFE is a rare entity, and classified amongst rare causes of idiopathic interstitial pneumonias (IIP) [1]. It is characterized by upper lobe fibrosis, supleural and parenchymal scarring. It can occur at any age, and the usual presentation is of pneumothorax in a thin male, with a shortened anteroposterior diameter of the chest. Radiographic findings typically include subpleural nodular or reticular opacities in the upper lobes, usually sparing the middle and lower lobes. Pathology reveals increased elastic tissue and dense collagen fibers, along with subpleural fibrosis [2]. Pulmonary function testing reveals a restrictive pattern with reduced diffusion capacity and it is usually resistant to steroids [3]. CONCLUSIONS: PPFE is an uncommon cause of insidious, slowly progressive fibrotic lung disease often limited to the upper lobes. It should be suspected in any person presenting with recurrent pneumothorax or blebs without other known inciting causes. Lung biopsy helps establish the diagnosis. Patients with this condition need close pulmonology follow up to assess progression. Reference #1: Travis WD, Costabel U, Hansell DM, King TE Jr, Lynch DA, Nicholson AG, Ryerson CJ, Ryu JH, Selman M, Wells AU, Behr J, Bouros D, Brown KK, Colby TV, Collard HR, Cordeiro CR, Cottin V, Crestani B, Drent M, Dudden RF, Egan J, Flaherty K, Hogaboam C, Inoue Y, Johkoh T, Kim DS, Kitaichi M, Loyd J, Martinez FJ, Myers J, Protzko S, Raghu G, Richeldi L, Sverzellati N, Swigris J, Valeyre D;ATS/ERS Committee on Idiopathic Interstitial Pneumonias. An official American Thoracic Society/European Respiratory Society statement: Update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med. 2013 Sep 15;188(6):733-48. doi: 10.1164/rccm.201308-1483ST. PMID: 24032382;PMCID: PMC5803655. Reference #2: Frankel SK, Cool CD, Lynch DA, Brown KK. Idiopathic pleuroparenchymal fibroelastosis: description of a novel clinicopathologic entity. Chest. 2004 Dec;126(6):2007-13. doi: 10.1378/chest.126.6.2007. PMID: 1559 706. Reference #3: Watanabe K. Pleuroparenchymal Fibroelastosis: Its Clinical Characteristics. Curr Respir Med Rev. 2013 Jun;9(4):299-237. doi: 10.2174/1573398X0904140129125307. PMID: 24578677;PMCID: PMC3933942. DISCLOSURES: No relevant relationships by FNU Amisha No relevant relationships by Perminder Gulani No relevant relationships by Hyomin Lim No relevant relationships by paras malik No relevant relationships by Divya Reddy

3.
Topics in Antiviral Medicine ; 30(1 SUPPL):250, 2022.
Artigo em Inglês | EMBASE | ID: covidwho-1880741

RESUMO

Background: The World Health Organization (WHO) ordinal scale (OS) is used to evaluate participant outcomes in clinical trials. We modified the WHO OS to enable assessment of patient outcomes associated with various treatment agents using the National COVID Cohort Collaborative (N3C), a national database containing electronic Health Record (EHR) data from > 2.7 million persons with a COVID-19 diagnosis from > 55 U.S. sites. Methods: Modified OS severity scores (Table 1) were assigned in the first through fourth weeks following COVID-19 diagnosis for a sample of patients in N3C. To adjust for disease severity at patient hospitalization, we developed separate models to examine OS levels of 3, 5, 7, and 9. Elastic net penalized multinomial logistic regression was used to simultaneously identify risk factors and predict the probability of each level of the ordinal scale at week 4. We studied groups of anticoagulants (AC), steroids, antibiotics, antiviral agents (AA), monoclonal antibodies (MA), and a miscellaneous group that included all other treatments. Other factors considered were presence of comorbid conditions using the Charlson Comorbidity Index (CCI), ethnicity, age, gender, and time of diagnosis (by quarter). Results: We included 1,489,191 COVID-19 (161,385 outpatients were excluded) patients. Patient characteristics and treatment approaches applied to each OS level were analyzed (Table 1). For hospitalized patients with a Week 1 OS score of 3,5,7, or 9, we found that increased CCI values are associated with higher probabilities of a worsened OS score at Week 4. Given that MAs are a standard treatment for patients at OS levels 3 and 5, and that steroids are typically used at OS 7 and 9, we studied treatment combinations related to MA and steroids given during Week 1. Improved outcomes by Week 4 were demonstrated with AA+MA for OS 3 and for AC+MA for OS 5 (Table 1). Patients at OS 7 in Week 1 had improved Week 4 outcomes with steroids alone while OS 7 patients with CCI>10 had better outcomes with steroids+AC. OS 9 patients treated with steroids+MA had better outcomes compared with those not given that combination. Conclusion: Our analyses identify relationships between COVID-19 serverity, specific treatments and outcomes at 4 weeks after diagnosis. Use of MA at lower levels of severity, and steroids at higher severity levels were associated with survival to hospital discharge.

4.
Endocrine Practice ; 27(12 SUPPL):S11, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1768063

RESUMO

Objective: Glycemic variability (GV) is a well-established and important metric when assessing glycemic control in clinical practice. The objective of this study was to identify factors associated with high GV in patients with diabetes mellitus (DM) hospitalized with COVID-19. Methods: This retrospective, observational study done at three different sites included 685 consecutive patients with DM hospitalized with COVID-19 from March 2020 to December 2020. Demographic characteristics, DM history, comorbidities, laboratory and COVID-19 treatment data were collected. All blood glucose levels, by laboratory serum measurement or by point-of-care testing were collected for the entire hospital stay. GV was expressed as the percentage coefficient of variation for glucose (%CV), derived from the following formula: ([SD of glucose] / [mean glucose]) x 100. High GV was defined as %CV ≥ 36%. We used elastic-net regression model (R version 4.1.0, package “glmnet”) to select the most important covariates affecting GV of the cohort. Out of the potential 34 variables, 13 had nonzero coefficients and were included in a logistic regression model with high GV as a dependent variable. Results: A total of 685 hospitalized patients were included in the analysis, with a mean age of 67.4±14.1, mean BMI of 32.2±8.3, median (IQR) hemoglobin A1c of 7.4 (6.6-9.0), 323 (47.2%) were male and 425 (62%) were African-American race. The total number of glucose values for the study population was 41,335. A total of 239 (34.9%) patients had %CV of glucose greater than 36%. In the multivariable analysis, the use of systemic steroids [adjusted OR 3.33 (95% CI, 2.21-5.08)], outpatient treatment with insulin [adjusted OR 2.10 (95% CI, 1.42-3.14)], African American race [adjusted OR 1.94 (95% CI, 1.31-2.89)], ICU admission [adjusted OR 1.85 (95% CI, 1.20-2.85)] and CKD greater than stage 3 [adjusted OR 1.81 (95% CI, 1.12-2.94)] were independent factors associated with higher GV. On the other hand, every 5-point increase in BMI was inversely associated with higher GV [adjusted OR 0.80 (95% CI, 0.70-0.90)]. Discussion/Conclusion: Our results show that in hospitalized patients with DM and COVID-19, use of steroids, outpatient treatment with insulin, African-American race, ICU admission and CKD greater than stage 3 were independently associated with higher GV. Otherwise, BMI was inversely associated with higher GV.

5.
European Neuropsychopharmacology ; 53:S60-S61, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1595854

RESUMO

Introduction: The COVID-19 pandemic has led to profound mental health consequences observed during acute infection and at short, medium, and long-term follow-up [1–3]. When considering long-term sequelae, a prevalent proportion of patients infected by SARS-CoV-2 experience a “Post-COVID-19 Syndrome” characterized by fatigue, depressive symptoms, sleep disturbances, and myalgia. In this context, fatigue is recognized as one of the leading complaints in COVID-19 survivors [4]. Long-term health consequences following COVID-19 and their impact on daily quality of life are largely unknown and need further investigation. Thus, questions about possible effects of mental health on fatigue, and of COVID-19 clinical severity on both, remained unanswered. We aim to predict long-term fatigue symptoms basing on clinical and psychopathological predictors through a machine learning approach. Methods: We evaluated the fatigue syndrome and the psychopathological status of 122 adult COVID-19 survivors (80 male, mean age 59.8±12.9) six months after hospital discharge for COVID-19. Clinical and psychopathological predictors were collected for the entire sample. Fatigue at six months was assessed using the Fatigue Severity Scale (FSS). Descriptive statistical analyses to compare means and frequencies were performed. To better disentangle the relationship between somatic and psychopathological predictors and the development of fatigue, we explored the effect of each predictor in affecting fatigue by implementing 5000 non-parametric bootstraps enhanced elastic net penalized logistic regression. The model's accuracy was estimated by 5-folds stratified nested cross-validations in the outer loop to define balance accuracy value (BA), class accuracies, and area under the receiver operator curve (AUC) (for a complete description of the method see [5]). Results: Six months after hospital discharge, 28%, 29%, and 24% of the total sample showed respectively depression (according to Zung Self-Rating Depression Scale), anxiety (according to State-Trait Anxiety Inventory form Y), and sleep disturbances (according to Women's Health Initiative Insomnia Rating Scale). Fatigue was present in 19% of the patients. When entering demographical, clinical, and psychopathological predictors in the elastic net penalized logistic regression, only depressive symptomatology significantly predicted the presence of fatigue at six months (Log Odds Ratio: 2.33;Standard deviation: 1.58;Lower and Upper 95% CI: -0.78 - 5.43;Variable Inclusion Probability: 96.7%). The 10-folds cross-validated elastic net model predicted fatigue with a BA of 65%, an AUC of 77%, and a specificity for the absence of fatigue of 74%, and a sensitivity for the presence of fatigue of 55%, showing good performances in excluding fatigue syndrome. Discussion: Besides confirming a high rate of long-term neuropsychiatric sequelae, our main finding is the strict association between fatigue and depression. We fear that, rather independent of pneumonia severity, major depression after COVID-19 is associated with persistent fatigue, thus worsening the burden of a non-communicable condition triggered by infection and by infection-related systemic inflammation, but then persisting on its own. Post-COVID syndrome, mainly characterized by fatigue, depression, and sleep disturbances, will affect COVID-19 survivors' daily functioning and place additional burden on the healthcare system. Clarifying the mechanisms and risk factors underlying such long-term symptomatology is essential to identify target population and to tailor specific treatment and rehabilitation interventions to foster recovery. No conflict of interest

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