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2.
Cardiology Clinics ; 2022.
Article in English | ScienceDirect | ID: covidwho-1767946
3.
Travel Med Infect Dis ; 47: 102318, 2022 Mar 25.
Article in English | MEDLINE | ID: covidwho-1764008

ABSTRACT

BACKGROUND: Guided by the best practices adapted from national and international bodies including the World Health Organization (WHO), the Centers for Disease Control (CDC), and the UK Joint Biosecurity Centre (JBC), this paper aims to develop and provide an empirical risk stratification and assessment framework for advancing the safe resumption of global travel during the COVID-19 pandemic. METHOD: Variables included in our model are categorized into four pillars: (i) incidence of cases, (ii) reliability of case data, (iii) vaccination, and (iv) variant surveillance. These measures are combined based on weights that reflect their corresponding importance in risk assessment within the context of the pandemic to calculate the risk score for each country. As a validation step, the outcome of the risk stratification from our model is compared against four countries. RESULTS: Our model is found to have good agreement with these benchmarked risk designations for 27 out of the top 30 countries with the strongest travel ties to Malaysia (90%). Each factor within this model signifies its importance and can be adapted by governing bodies to address the changing needs of border control policies for the recommencement of international travel. CONCLUSION: In practice, the proposed model provides a turnkey solution for nations to manage transmission risk by enabling stakeholders to make informed, evidence-based decisions to minimize fluctuations of imported cases and serves as a structure to support the improvement, planning, and activation of public health control measures.

4.
Front Cell Infect Microbiol ; 11: 777070, 2021.
Article in English | MEDLINE | ID: covidwho-1742203

ABSTRACT

Background: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population. Objective: Assess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection. Material and Methods: The study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods. Results: Need for O 2 supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18-39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40-64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings. Conclusion: Age and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.


Subject(s)
COVID-19 , Adult , Aged , COVID-19/diagnostic imaging , COVID-19/epidemiology , Child , Female , Humans , Male , Middle Aged , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sexual Behavior , Young Adult
5.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4421-4425, 2021.
Article in English | Scopus | ID: covidwho-1730871

ABSTRACT

In response to the pandemic caused by the rapidly spreading COVID-19 virus, several highly effective vaccines have been developed by Pfizer, Moderna, and Janssen. Despite the promising efficacy of those vaccines, there remains the challenge of properly distributing vaccines to those who need it most in the US. of particular concern are individuals who are at higher risk due to underlying medical conditions which have been shown to exacerbate COVID-19 symptoms and at times lead to fatal illnesses. In addition to this, a variety of socioeconomic factors have been linked to increased COVID-19 rates and increased mortality, such as race, age, income, mobility, and education level.This project aims to develop an information system to help advise vaccine distributors and state governments on how to effectively distribute vaccines to prioritize high risk individuals. The information system incorporates state-level data of the population with underlying medical conditions, demographics, overall state income, education level, and state mobility to formulate a mortality index. State-level data on the number of vaccines available and doses already administered are also incorporated into the information system to generate a vaccine index. The mortality and vaccine indices for each state are coupled to generate a vaccine priority ranking which can be used to advise vaccine distribution.The prototype can successfully link the data described above to a map of the US and then color code states according to the vaccine priority ranking. Implementation of this prototype will enable optimal vaccine distribution and reduce instances of severe or fatal COVID-19 illnesses as well as reduce costs associated with oversupply of vaccines in a single region. Future work will focus on improving the granularity of data down to the county-level, as well as increasing the scope of the system to the global scale. Additionally, the team plans to expand the application space of this information system to other diseases. © 2021 IEEE.

6.
Arch Cardiovasc Dis ; 2022 Mar 04.
Article in English | MEDLINE | ID: covidwho-1719146

ABSTRACT

BACKGROUND: Coronary artery calcium (CAC) is an independent risk factor for major adverse cardiovascular events; however, its impact on coronavirus disease 2019 (COVID-19) mortality remains unclear, especially in patients without known atheromatous disease. AIMS: To evaluate the association between CAC visual score and 6-month mortality in patients without history of atheromatous disease hospitalized with COVID-19 pneumonia. METHODS: A single-centre observational cohort study was conducted, involving 293 consecutive patients with COVID-19 in Paris, France, between 13 March and 30 April 2020, with a 6-month follow-up. Patients with a history of ischaemic stroke or coronary or peripheral artery disease were excluded. The primary outcome was all-cause mortality at 6 months according to CAC score, which was assessed by analysing images obtained after the first routine non-electrocardiogram-gated computed tomography scan performed to detect COVID-19 pneumonia. RESULTS: A total of 251 patients (mean age 64.8±16.7 years) were included in the analysis. Fifty-one patients (20.3%) died within 6 months. The mortality rate increased with the magnitude of calcifications, and was 10/101 (9.9%), 15/66 (22.7%), 10/34 (29.4%) and 16/50 (32.0%) for the no CAC, mild CAC, moderate CAC and heavy CAC groups, respectively (p=0.004). Compared with the no calcification group, adjusted risk of death increased progressively with CAC: hazard ratio (HR) 2.37 (95% confidence interval [CI] 1.06-5.27), HR 3.1 (95% CI 1.29-7.45) and HR 4.02 (95% CI 1.82-8.88) in the mild, moderate and heavy CAC groups, respectively. CONCLUSIONS: Non-electrocardiogram-gated computed tomography during the initial pulmonary assessment of patients with COVID-19 without atherosclerotic cardiovascular disease showed a high prevalence of mild, moderate and heavy CAC. CAC score was related to 6-month mortality, independent of conventional cardiovascular risk factors. These results highlight the importance of CAC scoring for patients hospitalized with COVID-19, and calls for attention to patients with high CAC.

7.
Intell Based Med ; 6: 100049, 2022.
Article in English | MEDLINE | ID: covidwho-1705741

ABSTRACT

Background: Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. Methods: A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. Results: 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. Conclusion: M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

8.
Diagn Progn Res ; 6(1): 6, 2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1702772

ABSTRACT

BACKGROUND: Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. METHODS: We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. RESULTS: Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. CONCLUSIONS: Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.

9.
Kidney Blood Press Res ; 47(2): 147-150, 2022.
Article in English | MEDLINE | ID: covidwho-1630618

ABSTRACT

BACKGROUND/AIMS: The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a wide spectrum of effects, including acute kidney injury (AKI) in up to 40% of hospitalized patients. Given the established relationship between AKI and poor prognosis, whether AKI might be a prognostic indicator for patients admitted to the hospital for SARS-CoV-2 infection would allow for a straightforward risk stratification of these patients. METHODS: We analyzed data of 623 patients admitted to San Raffaele Hospital (Milan, IT) between February 25 and April 19, 2020, for laboratory-confirmed SARS-CoV-2 infection. Incidence of AKI at hospital admission was calculated, with AKI defined according to the KDIGO criteria. Multivariable Cox regression models assessed the association between AKI and overall mortality and admission to the intensive care unit (ICU). RESULTS: Overall, 108 (17%) patients had AKI at hospital admission for SARS-CoV-2 infection. After a median follow-up for survivors of 14 days (interquartile range: 8, 23), 123 patients died, while 84 patients were admitted to the ICU. After adjusting for confounders, patients who had AKI at hospital admission were at increased risk of overall mortality compared to those who did not have AKI (hazards ratio [HR]: 2.00; p = 0.0004), whereas we did not find evidence of an association between AKI and ICU admission (HR: 0.95; p = 0.9). CONCLUSIONS: These data suggest that AKI might be an indicator of poor prognosis for patients with SARS-CoV-2 infection, and as such, given its readily availability, it might be used to improve risk stratification at hospital admission.


Subject(s)
Acute Kidney Injury , COVID-19 , Acute Kidney Injury/diagnosis , Hospital Mortality , Hospitals , Humans , Intensive Care Units , Prognosis , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Triage
10.
Front Cell Infect Microbiol ; 11: 783961, 2021.
Article in English | MEDLINE | ID: covidwho-1630423

ABSTRACT

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.


Subject(s)
COVID-19 , SARS-CoV-2 , Genomics , Humans , Mutation , Risk Assessment
11.
American Journal of the Medical Sciences ; 362(6):553-561, 2021.
Article in English | Web of Science | ID: covidwho-1576802

ABSTRACT

Background: As the Modified Anticoagulation and Risk Factors in Atrial Fibrillation Risk Score (M-ATRIA-RS) encompasses prognostic risk factors of novel coronavirus-2019 (COVID-19), it may be used to predict in-hospital mortality. We aimed to investigate whether M-ATRIA-RS was an independent predictor of mortality in patients hospitalized for COVID-19 and compare its discrimination capability with CHADS, CHA2DS2-VASc, and modified CHA2DS2-VASc (mCHA2DS2-VASc)-RS. Methods: A total of 1,001 patients were retrospectively analyzed and classified into three groups based on M-ATRIA-RS, designed by changing sex criteria of ATRIA-RS from female to male: Group 1 for points 0-1 (n = 448), Group 2 for points 2 -4 (n = 268), and Group 3 for points >= 5 (n = 285). Clinical outcomes were defined as in-hospital mortality, need for high-flow oxygen and/or intubation, and admission to intensive care unit. Results: As the M-ATRIA-RS increased, adverse clinical outcomes significantly increased (Group 1, 6.5%;Group 2, 15.3%;Group 3, 34.4%;p <0.001 mortality for in-hospital). Multivariate logistic regression analysis showed that M-ATRIA-RS, malignancy, troponin increase, and lactate dehydrogenase were independent predictors of in-hospital mortality (p<0.001, per scale possibility rate for ATRIA-RS 1.2). In receiver operating characteristic (ROC) analysis, the discriminative ability of M-ATRIA-RS was superior to mCHA2DS2-VASc-RS and ATRIA-RS, but similar to that Charlson Comorbidity Index (CCI) score (AUC(M-ATRIA) vs AUC(ATRIA) Z-test=3.14 p = 0.002, AUC(M-ATRIA) vs. AUC(mCHA2DS2-VASc) Z-test=2.14, p = 0.03;AUC(M-ATRIA) vs. AUC(CCI) Z-test=1.46 p = 0.14). Conclusions: M-ATRIA-RS is useful to predict in-hospital mortality among patients hospitalized with COVID-19. In addition, it is superior to the mCHA2DS2-VASc-RS in predicting mortality in patients with COVID-19 and is more easily calculable than the CCI score.

12.
Nurs Crit Care ; 2021 Dec 09.
Article in English | MEDLINE | ID: covidwho-1566312

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has spread globally and caused a major worldwide health crisis. Patients who are affected more seriously by COVID-19 usually deteriorate rapidly and need further intensive care. AIMS AND OBJECTIVES: We aimed to assess the performance of the National Early Warning Score 2 (NEWS2) as a risk stratification tool to discriminate newly admitted patients with COVID-19 at risk of serious events. DESIGN: We conducted a retrospective single-centre case-control study on 200 unselected patients consecutively admitted in March 2020 in a public general hospital in Wuhan, China. METHODS: The following serious events were considered: mortality, unplanned intensive care unit (ICU) admission, and non-invasive ventilation treatment. Receiver operating characteristic (ROC) analysis and logistic regression analysis were used to quantify the association between outcomes and NEWS2. RESULTS: There were 12 patients (6.0%) who had serious events, where 7 patients (3.5%) experienced unplanned ICU admissions. The area under the ROC curve (AUROC) and cut-off of NEWS2 for the composite outcome were 0.83 and 3, respectively. For patients with NEWS2 ≥ 4, the odds of being at risk for serious events was 16.4 (AUROC = 0.74), while for patients with NEWS2 ≥ 7, the odds of being at risk for serious events was 18.2 (AUROC = 0.71). CONCLUSIONS: NEWS2 has an appropriate ability to triage newly admitted patients with COVID-19 into three levels of risk: low risk (NEWS2 = 0-3), medium risk (NEWS2 = 4-6), and high risk (NEWS2 ≥ 7). RELEVANCE TO CLINICAL PRACTICE: Using NEWS2 may help nurses in early identification of at-risk COVID-19 patients and clinical nursing decision-making. Using NEWS2 to triage new patients with COVID-19 may help nurses provide more appropriate level of care and medical resources allocation for patients safety.

13.
Front Med (Lausanne) ; 7: 585003, 2020.
Article in English | MEDLINE | ID: covidwho-1556373

ABSTRACT

Background: Identifying clinical-features or a scoring-system to predict a benefit from hospital admission for patients with COVID-19 can be of great value for the decision-makers in the health sector. We aimed to identify differences in patients' demographic, clinical, laboratory, and radiological findings of COVID-19 positive cases to develop and validate a diagnostic-model predicting who will develop severe-form and who will need critical-care in the future. Methods: In this observational retrospective study, COVID-19 positive cases (total 417) diagnosed in Al Kuwait Hospital, Dubai, UAE were recruited, and their prognosis in terms of admission to the hospital and the need for intensive care was reviewed until their tests turned negative. Patients were classified according to their clinical state into mild, moderate, severe, and critical. We retrieved all the baseline clinical data, laboratory, and radiological results and used them to identify parameters that can predict admission to the intensive care unit (ICU). Results: Patients with ICU admission showed a distinct clinical, demographic as well as laboratory features when compared to patients who did not need ICU admission. This includes the elder age group, male gender, and presence of comorbidities like diabetes and history of hypertension. ROC and Precision-Recall curves showed that among all variables, D dimers (>1.5 mg/dl), Urea (>6.5 mmol/L), and Troponin (>13.5 ng/ml) could positively predict the admission to ICU in patients with COVID-19. On the other hand, decreased Lymphocyte count and albumin can predict admission to ICU in patients with COVID-19 with acceptable sensitivity (59.32, 95% CI [49.89-68.27]) and specificity (79.31, 95% CI [72.53-85.07]). Conclusion: Using these three predictors with their cut of values can identify patients who are at risk of developing critical COVID-19 and might need aggressive intervention earlier in the course of the disease.

14.
Front Biosci (Landmark Ed) ; 26(11): 1312-1339, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1552205

ABSTRACT

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.


Subject(s)
Arteries/diagnostic imaging , Atherosclerosis/diagnostic imaging , COVID-19/physiopathology , Cardiovascular Diseases/diagnostic imaging , Nutritional Status , Algorithms , COVID-19/diagnostic imaging , COVID-19/virology , Humans , Risk Factors , SARS-CoV-2/isolation & purification
15.
Respir Care ; 2021 Nov 23.
Article in English | MEDLINE | ID: covidwho-1534394

ABSTRACT

BACKGROUND: As lung ultrasound (LUS) has emerged as a diagnostic tool in patients with COVID-19, we sought to investigate the association between LUS findings and the composite in-hospital outcome of ARDS incidence, ICU admission, and all-cause mortality. METHODS: In this prospective, multi-center, observational study, adults with laboratory-confirmed SARS-CoV-2 infection were enrolled from non-ICU in-patient units. Subjects underwent an LUS evaluating a total of 8 zones. Images were analyzed off-line, blinded to clinical variables and outcomes. A LUS score was developed to integrate LUS findings: ≥ 3 B-lines corresponded to a score of 1, confluent B-lines to a score of 2, and subpleural or lobar consolidation to a score of 3. The total LUS score ranged from 0-24 per subject. RESULTS: Among 215 enrolled subjects, 168 with LUS data and no current signs of ARDS or ICU admission (mean age 59 y, 56% male) were included. One hundred thirty-six (81%) subjects had pathologic LUS findings in ≥ 1 zone (≥ 3 B-lines, confluent B-lines, or consolidations). Markers of disease severity at baseline were higher in subjects with the composite outcome (n = 31, 18%), including higher median C-reactive protein (90 mg/L vs 55, P < .001) and procalcitonin levels (0.35 µg/L vs 0.13, P = .033) and higher supplemental oxygen requirements (median 4 L/min vs 2, P = .001). However, LUS findings and score did not differ significantly between subjects with the composite outcome and those without, and were not associated with outcomes in unadjusted and adjusted logistic regression analyses. CONCLUSIONS: Pathologic findings on LUS were common a median of 3 d after admission in this cohort of non-ICU hospitalized subjects with COVID-19 and did not differ among subjects who experienced the composite outcome of incident ARDS, ICU admission, and all-cause mortality compared to subjects who did not. These findings should be confirmed in future investigations. The study is registered at Clinicaltrials.gov (NCT04377035).

16.
J Am Coll Emerg Physicians Open ; 2(6): e12575, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1508651

ABSTRACT

STUDY OBJECTIVE: We sought to determine the ability of lung point-of-care ultrasound (POCUS) to predict mechanical ventilation and in-hospital mortality in patients with coronavirus disease 2019 (COVID-19). METHODS: This was a prospective observational study of a convenience sample of patients with confirmed COVID-19 presenting to 2 tertiary hospital emergency departments (EDs) in Iran between March and April 2020. An emergency physician attending sonographer performed a 12-zone bilateral lung ultrasound in all patients. Research associates followed the patients on their clinical course. We determined the frequency of positive POCUS findings, the geographic distribution of lung involvement, and lung severity scores. We used multivariable logistic regression to associate lung POCUS findings with clinical outcomes. RESULTS: A total of 125 patients with COVID-like symptoms were included, including 109 with confirmed COVID-19. Among the included patients, 33 (30.3%) patients were intubated, and in-hospital mortality was reported in 19 (17.4%). Lung POCUS findings included pleural thickening 95.4%, B-lines 90.8%, subpleural consolidation 86.2%, consolidation 46.8%, effusions 19.3%, and atelectasis 18.3%. Multivariable logistic regression incorporating binary and scored POCUS findings were able to identify those at highest risk for need of mechanical ventilation (area under the curve 0.80) and in-hospital mortality (area under the curve 0.87). In the binary model ultrasound (US) findings in the anterior lung fields were significantly associated with a need for intubation and mechanical ventilation (odds ratio [OR] 3.67; 0.62-21.6). There was an inverse relationship between mortality and posterior lung field involvement (OR 0.05; 0.01-0.23; and scored OR of 0.57; 0.40-0.82). Anterior lung field involvement was not associated with mortality. CONCLUSIONS: In patients with COVID-19, the anatomic distribution of findings on lung ultrasound is associated with outcomes. Lung POCUS-based models may help clinicians to identify those patients with COVID-19 at risk for clinical deterioration.Key Words: COVID-19; Lung Ultrasound; Mechanical ventilation; Prediction; ICU admission; Mortality; Clinical outcome; Risk stratification; Diagnostic accuracy.

17.
World J Clin Cases ; 9(28): 8388-8403, 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1513223

ABSTRACT

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2. AIM: To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission. METHODS: The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models. RESULTS: There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A. CONCLUSION: Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

18.
J Am Coll Emerg Physicians Open ; 2(5): e12579, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1490771

ABSTRACT

OBJECTIVE: In US emergency departments (EDs), the physician has limited ability to evaluate for common and serious conditions of the gastrointestinal (GI) mucosa such as a bleeding peptic ulcer. Although many bleeding lesions are self-limited, the majority of these patients require emergency hospitalization for upper endoscopy (EGD). We conducted a clinical trial to determine if ED risk stratification with video capsule endoscopy (VCE) reduces hospitalization rates for low-risk to moderate-risk patients with suspected upper GI bleeding. METHODS: We conducted a randomized controlled trial at 3 urban academic EDs. Inclusion criteria included signs of upper GI bleeding and a Glasgow Blatchford score <6. Patients were randomly assigned to 1 of the following 2 treatment arms: (1) an experimental arm that included VCE risk stratification and brief ED observation versus (2) a standard care arm that included admission for inpatient EGD. The primary outcome was hospital admission. Patients were followed for 7 and 30 days to assess for rebleeding events and revisits to the hospital. RESULTS: The trial was terminated early as a result of low accrual. The trial was also terminated early because of a need to repurpose all staff to respond to the coronavirus disease 2019 pandemic. A total of 24 patients were enrolled in the study. In the experimental group, 2/11 (18.2%) patients were admitted to the hospital, and in the standard of care group, 10/13 (76.9%) patients were admitted to the hospital (P = 0.012). There was no difference in safety on day 7 and day 30 after the index ED visit. CONCLUSIONS: VCE is a potential strategy to decrease admissions for upper GI bleeding, though further study with a larger cohort is required before this approach can be recommended.

19.
J Clin Med ; 10(21)2021 Oct 24.
Article in English | MEDLINE | ID: covidwho-1480825

ABSTRACT

The Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has rapidly spread to become a global pandemic, putting a strain on health care systems. SARS-CoV-2 infection may be associated with mild symptoms or, in severe cases, lead patients to the intensive care unit (ICU) or death. The critically ill patients suffer from acute respiratory distress syndrome (ARDS), sepsis, thrombotic complications and multiple organ failure. For optimization of hospital resources, several molecular markers and algorithms have been evaluated in order to stratify COVID-19 patients, based on the risk of developing a mild, moderate, or severe disease. Here, we propose the soluble urokinase receptor (suPAR) as a serum biomarker of clinical severity and outcome in patients who are hospitalized with COVID-19. In patients with mild disease course, suPAR levels were increased as compared to healthy controls, but they were dramatically higher in severely ill patients. Since early identification of disease progression may facilitate the individual management of COVID-19 symptomatic patients and the time of admission to the ICU, we suggest paying more clinical attention on patients with high suPAR levels.

20.
ESC Heart Fail ; 8(6): 4465-4483, 2021 12.
Article in English | MEDLINE | ID: covidwho-1449921

ABSTRACT

Acute heart failure (AHF) affects millions of people worldwide, and it is a potentially life-threatening condition for which the cardiologist is more often brought into play. It is crucial to rapidly identify, among patients presenting with dyspnoea, those with AHF and to accurately stratify their risk, in order to define the appropriate setting of care, especially nowadays due to the coronavirus disease 2019 (COVID-19) outbreak. Furthermore, with physical examination being limited by personal protective equipment, the use of new alternative diagnostic and prognostic tools could be of extreme importance. In this regard, usage of biomarkers, especially when combined (a multimarker approach) is beneficial for establishment of an accurate diagnosis, risk stratification and post-discharge monitoring. This review highlights the use of both traditional biomarkers such as natriuretic peptides (NP) and troponin, and emerging biomarkers such as soluble suppression of tumourigenicity (sST2) and galectin-3 (Gal-3), from patients' emergency admission to discharge and follow-up, to improve risk stratification and outcomes in terms of mortality and rehospitalization.


Subject(s)
COVID-19 , Heart Failure , Acute Disease , Aftercare , Biomarkers , Heart Failure/diagnosis , Heart Failure/therapy , Humans , Patient Discharge , SARS-CoV-2
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