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1.
2nd Information Technology to Enhance E-Learning and other Application Conference, IT-ELA 2021 ; : 58-63, 2021.
Article in English | Scopus | ID: covidwho-1878962

ABSTRACT

With the continued rise in the number of infected people and deaths from the coronavirus (COVID-19) daily, along with the collapse of the health care systems in many countries of the world, especially in diagnosing the virus, it becomes necessary to devise an achievable and rapid way for diagnosing the virus. Since radiographs like X-ray images and Computed Tomography (CT) scans are broadly available at public health amenities, hospital Emergency Rooms (ERs), as well as at non-urban clinics. Therefore, they might be utilized for the rapid detection of COVID-19 induced lung infections. In this paper, for automating the detection of COVID-19 from X-ray images, deep learning techniques have been used to distinguish between (COVID-19) and normal cases. A dataset used by this work is publicly published, which comprised 5000 Chest X-ray images with their labels. A subset of 2000 X-ray images was used to train two trendy convolutional neural networks, which are AlexNet and ResNet50. While the remaining 3000 images were used for testing. The parameters of these network models have been adjusted precisely to achieve optimum detection decision. Results show these models can achieve an accuracy of nearly 99.6% with F1-Scores of 0.939 for COVID-19 and 0.998 for non-COVID-19 via the AlexNet model, while the ResNet50 model realized an accuracy of 99.3% with F1-Scores of 0.91 and 0.996 for COVID-19 and non-COVID-19, respectively. From these results, the AlexNet model can be an enthralling tool to assist radiologists in the early diagnosis and detection of COVID-19 cases. © 2021 IEEE.

2.
Infection ; 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1872762

ABSTRACT

PURPOSE: To externally validate four previously developed severity scores (i.e., CALL, CHOSEN, HA2T2 and ANDC) in patients with COVID-19 hospitalised in a tertiary care centre in Switzerland. METHODS: This observational analysis included adult patients with a real-time reverse-transcription polymerase chain reaction or rapid-antigen test confirmed severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection hospitalised consecutively at the Cantonal Hospital Aarau from February to December 2020. The primary endpoint was all-cause in-hospital mortality. The secondary endpoint was disease progression, defined as needing invasive ventilation, ICU admission or death. RESULTS: From 399 patients (mean age 66.6 years ± 13.4 SD, 68% males), we had complete data for calculating the CALL, CHOSEN, HA2T2 and ANDC scores in 297, 380, 151 and 124 cases, respectively. Odds ratios for all four scores showed significant associations with mortality. The discriminative power of the HA2T2 score was higher compared to CALL, CHOSEN and ANDC scores [area under the curve (AUC) 0.78 vs. 0.65, 0.69 and 0.66, respectively]. Negative predictive values (NPV) for mortality were high, particularly for the CALL score (≥ 6 points: 100%, ≥ 9 points: 95%). For disease progression, discriminative power was lower, with the CHOSEN score showing the best performance (AUC 0.66). CONCLUSION: In this external validation study, the four analysed scores had a lower performance compared to the original cohorts regarding prediction of mortality and disease progression. However, all scores were significantly associated with mortality and the NPV of the CALL and CHOSEN scores in particular allowed reliable identification of patients at low risk, making them suitable for outpatient management.

3.
Archives of Academic Emergency Medicine ; 10(1):6, 2022.
Article in English | Web of Science | ID: covidwho-1870224

ABSTRACT

Introduction: Outcome prediction of intensive care unit (ICU)-admitted patients is one of the important issues for physicians. This study aimed to compare the accuracy of Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Urea, Respiratory Rate, Blood Pressure and Age Above or Below 65 Years (CURB-65), and Systemic Inflammatory Response Syndrome (SIRS) scores in predicting the in-hospital mortality of COVID-19 patients. Methods: This prognostic accuracy study was performed on 225 ICU-admitted patients with a definitive diagnosis of COVID-19 from July to December 2021 in Tehran, Iran. The patients' clinical characteristics were evaluated at the time of ICU admission, and they were followed up until discharge from ICU. The screening performance characteristics of CURB-65, qSOFA, and SIRS in predicting their mortality was compared. Results: 225 patients with the mean age of 63.27 +/- 14.89 years were studied (56.89% male). The in-hospital mortality rate of this series of patients was 39.10%. The area under the curve (AUC) of SIRS, CURB-65, and qSOFA were 0.62 (95% CI: 0.55 - 0.69), 0.66 (95% CI: 0.59 - 0.73), and 0.61(95% CI: 0.54 - 0.67), respectively (p = 0.508). In cut-off >= 1, the estimated sensitivity values of SIRS, CURB-65, and qSOFA were 85.23%, 96.59%, and 78.41%, respectively. The estimated specificity of scores were 34.31%, 6.57%, and 38.69%, respectively. In cut-off >= 2, the sensitivity values of SIRS, CURB-65, and qSOFA were evaluated as 39.77%, 87.50%, and 15.91%, respectively. Meanwhile, the specificity of scores were 72.99%, 34.31%, and 92.70%. Conclusion: It seems that the performance of SIRS, CURB-65, and qSOFA is similar in predicting the ICU mortality of COVID-19 patients. However, the sensitivity of CURB-65 is higher than qSOFA and SIRS.

4.
2nd International Conference on Computer Science and Software Engineering, CSASE 2022 ; : 107-112, 2022.
Article in English | Scopus | ID: covidwho-1861090

ABSTRACT

To tackle the global pandemic of COVID-19, scholars are looking for accurate and efficient artificial intelligence approaches to screen the chest situation of the X-Ray images of the COVID-Affected people. Developing an accurate deep model is a goal which can be achieved through an ensemble of multiple deep models. Utilizing multiple networks boosts the performance and surpasses utilizing a single model classifier. However, it suffers from a high computational cost of training. To avoid this, we propose a novel deep network model namely ECOVIDNet. The proposed model is based on merging multiple model snapshots for final prediction at the cost of a single training run. The proposed scheme adopts EfficientNet through the transfer learning process with freezing all trainable layers and adding two fully connected layers at the end of the model. The model is trained on an X-ray image dataset with achieving an accuracy of 99.2%, 96.8% for binary (Normal vs COVID-19), and ternary (Normal vs COVID-19 vs Pneumonia) classifications. The model is evaluated with 5-fold cross-validation and obtained precision, sensitivity, and F1-score of 99.5%, 99.5, and 99.4%, respectively. Also, the proposed model yields 96.62% of precision, 96.5% of sensitivity, and 96.48% of F1-score in ternary classification. © 2022 IEEE.

5.
Vaccines (Basel) ; 10(5)2022 May 13.
Article in English | MEDLINE | ID: covidwho-1855850

ABSTRACT

BACKGROUND: in 2020, a new form of coronavirus spread around the world starting from China. The older people were the population most affected by the virus worldwide, in particular in Italy where more than 90% of deaths were people over 65 years. In these people, the definition of the cause of death is tricky due to the presence of numerous comorbidities. OBJECTIVE: to determine whether COVID-19 was the cause of death in a series of older adults residents of nursing care homes. METHODS: 41 autopsies were performed from May to June 2020. External examination, swabs, and macroscopic and microscopic examination were performed. RESULTS: the case series consisted of nursing home guests; 15 men and 26 women, with a mean age of 87 years. The average number of comorbidities was 4. Based only on the autopsy results, the defined cause of death was acute respiratory failure due to diffuse alveolar damage (8%) or (31%) bronchopneumonia with one or more positive swabs for SARS-CoV-2. Acute cardiac failure with one or more positive swabs for SARS-CoV-2 was indicated as the cause of death in in symptomatic (37%) and asymptomatic (10%) patients. Few patients died for septic shock (three cases), malignant neoplastic diseases (two cases), and massive digestive bleeding (one case). CONCLUSIONS: Data from post-mortem investigation were integrated with previously generated Geriatric Index of Comorbidity (GIC), resulting in four different degrees of probabilities: high (12%), intermediate (10%), low (59%), and none (19%), which define the level of strength of causation and the role of COVID-19 disease in determining death.

6.
J Pers Med ; 12(5)2022 May 16.
Article in English | MEDLINE | ID: covidwho-1855698

ABSTRACT

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961-0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

7.
Journal of Dance and Somatic Practices ; 13(1-2):225-229, 2021.
Article in English | Scopus | ID: covidwho-1841159

ABSTRACT

In this practice, I have been examining an ecology of touch through the filter of post-humanism and Karen Barad’s motive concepts of intra-action, re-turn and diffraction. The practice, inevitably affected by COVID-19, has led to a virtual iteration of the process and the continued development of soft matter during the pandemic has allowed me to reflect on the ghostly traces of touch left in materials and sensed in the body. The resulting soft matter objects will be employed as choreographic scores to wear, think through and move within an inherited field of touch, affect and echoes of ghostly matter. © 2021 Intellect Ltd Practice-Research Reflection. English language.

8.
Social Behavior and Personality ; 50(5):1-11, 2022.
Article in English | ProQuest Central | ID: covidwho-1837425

ABSTRACT

Although the Student Career Construction Inventory (SCCI) has been used in many countries to measure the career adapting responses of college students, the applicability of the SCCI for use with Chinese college students has not yet been tested. Thus, we analyzed data from 411 college students who completed the SCCI and other related scales. The results support the second-order factor structure of the SCCI, and we found that the four dimensions, and the SCCI as a whole, were reliable. Students who scored higher on the SCCI scored higher on measures of career exploration, career planning, and career engagement, supporting the high congruent validity of the SCCI. Students with higher SCCI scores also showed stronger vocational identity and higher academic achievement, supporting the high criterion validity of the measure. Finally, the SCCI demonstrated scalar invariance across genders. Generally, the results show that the Chinese version of the SCCI is a valid and reliable scale for assessing college students career adaptation thoughts and behaviors.

9.
International Journal of Environmental Research and Public Health ; 19(9):5480, 2022.
Article in English | ProQuest Central | ID: covidwho-1837148

ABSTRACT

In 2021, over 100,000 people died prematurely from opioid overdoses. Neuropsychiatric and cognitive impairments are underreported comorbidities of reward dysregulation due to genetic antecedents and epigenetic insults. Recent genome-wide association studies involving millions of subjects revealed frequent comorbidity with substance use disorder (SUD) in a sizeable meta-analysis of depression. It found significant associations with the expression of NEGR1 in the hypothalamus and DRD2 in the nucleus accumbens, among others. However, despite the rise in SUD and neuropsychiatric illness, there are currently no standard objective brain assessments being performed on a routine basis. The rationale for encouraging a standard objective Brain Health Check (BHC) is to have extensive data available to treat clinical syndromes in psychiatric patients. The BHC would consist of a group of reliable, accurate, cost-effective, objective assessments involving the following domains: Memory, Attention, Neuropsychiatry, and Neurological Imaging. Utilizing primarily PUBMED, over 36 years of virtually all the computerized and written-based assessments of Memory, Attention, Psychiatric, and Neurological imaging were reviewed, and the following assessments are recommended for use in the BHC: Central Nervous System Vital Signs (Memory), Test of Variables of Attention (Attention), Millon Clinical Multiaxial Inventory III (Neuropsychiatric), and Quantitative Electroencephalogram/P300/Evoked Potential (Neurological Imaging). Finally, we suggest continuing research into incorporating a new standard BHC coupled with qEEG/P300/Evoked Potentials and genetically guided precision induction of “dopamine homeostasis” to diagnose and treat reward dysregulation to prevent the consequences of dopamine dysregulation from being epigenetically passed on to generations of our children.

10.
Cureus ; 14(4): e23802, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1835787

ABSTRACT

Introduction In December of the year 2020, the SARS-CoV-2 virus was discovered in Wuhan, China. It extended to over 180 nations around the world. It can manifest in patients who are asymptomatic to those who are symptomatic, with symptoms ranging from anosmia to severe respiratory distress syndrome. It affects both men and women. The existence of comorbidity is also linked to a significant worsening of the infection. Despite the fact that the principal consequences of coronavirus disease 2019 (COVID-19) damage the lungs, the prevalence of current smokers among COVID-19 hospitalized patients has repeatedly been observed to be lower than the prevalence of smokers in the general community. As a result, the evidence from various studies appears to cast doubt on active smoking as a risk factor for COVID-19 pneumonia. Thus, with this background, this study has been conducted with the aim of assessing the influence of smoking as a risk factor for COVID-19 mortality. Methodology An observational study was conducted in a tertiary care center in Tamil Nadu for a period of three months (April 2021 to June 2021). The study participants were all the patients admitted to the COVID-19 ward of the department of general medicine during the study period. Those who were not willing to participate in the study were excluded. The questionnaire contains variables including socio-demographic characteristics, vitals, and investigations, and the outcome variable was death due to COVID-19. The data obtained were entered in Microsoft Excel (Microsoft Corporation, Redmond, WA) and the results were analyzed using SPSS version 21 (IBM Corp., Armonk, NY). Results About 401 individuals participated in the study. The mean age, COVID-19 Reporting and Data System (CO-RADS) score, and CT severity score of the study participants were 50 years, 4.91, and 10.61, respectively. About 63.3% of participants were males, about 92% have not been vaccinated, about 91.8% have a CO-RADS score of 5, about 45.1% were smokers, and about 15.7% have died despite effective treatment. When looking for adverse outcomes, being male (p = 0.047), non-vaccinated for COVID-19 (p = 0.042), and being a smoker (p = 0.008) were the factors that showed statistical significance. Conclusion The mortality due to COVID-19 is high among smokers than non-smokers with statistical significance. Thus, before admitting COVID-19 patients, to classify the patients as mild, moderate, and severe, the risk factor of the habit of smoking can be added. Cigarette smoke is harmful to the lungs in a variety of ways, and further research is needed to understand why there is such a low proportion of current smokers among COVID-19 patients in hospitals. The impact of current smoking on SARS-CoV-2 infection is a delicate and complex topic that should be thoroughly investigated before sending out potentially misunderstood signals.

11.
2nd International Conference on Advanced Research in Computing, ICARC 2022 ; : 19-24, 2022.
Article in English | Scopus | ID: covidwho-1831768

ABSTRACT

The COVID-19 pandemic has caused devastating effects on global health, the economy, and people's daily lives, and timely diagnosis is crucial to control the spread. The need for additional testing methods has increased due to the limitations in current testing methods. This study focused on applying deep learning methods to classify chest X-rays as COVID-19 pneumonia, normal, and non-COVID pneumonia. We developed two deep learning models to ascertain COVID-19 using Posteroanterior (PA) and Anteroposterior (AP) view Chest X-rays. Two datasets of 300 chest X-rays for PA and AP views were used. As the first deep learning model, a new Convolutional Neural Network from scratch was built. Then, VGG16, VGG19, and ResNet50 transfer learning models were used. Finally, the transfer learning models were extended by adding more layers to the top of the existing model. As the first part of this study, we used PA view X-rays and obtained 98% overall accuracy, and 98% precision, 99% recall, and 99% f1-score for the COVID-19 class. In the second part, we used AP view X-rays and obtained 79% overall accuracy, and 96% precision, 83% recall, and 89% f1-score for the COVID-19 class. Finally, gradient-based class activation maps were generated using the proposed extended VGG19 model to visualize the areas that helped the model in detecting COVID-19. This research showed that high performance could be obtained in detecting COVID-19 using extended transfer learning models. In PA view X-rays, the proposed extended VGG-19 model performed the best, and in AP view X-rays, the proposed extended ResNet50 model performed the best. © 2022 IEEE.

12.
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 ; : 292-297, 2021.
Article in English | Scopus | ID: covidwho-1831727

ABSTRACT

COVID-19 is breaking out and spreading globally, posing a severe threat to public health and economies worldwide due to its highly transmissible and pathogenic nature. Early, accurate and rapid diagnosis of COVID-19 can effectively stop the spread of the COVID-19 virus. Automatic diagnostic models based on deep learning can detect COVID-19 quickly and accurately. This paper uses a three-dimensional Convolutional Neural Network (3D CNN) to build a COVID-19 diagnostic prediction model for COVID-19 detection. All 192 sets of chest Computed Tomography(CT) data collected are used for this study, including 96 sets of confirmed COVID-19 patients and 96 sets of CT scans of normal human lungs. 5-fold cross-validation is used to train and validate the model. 154 data sets are used to train the model, and 38 sets are used for testing. All experimental data are segmented using a pre-trained SP-V-Net to obtain 3D lung masks fed into 3D CNN for training and validation of the prediction model. In addition, to verify the accuracy of the model predictions and provide interpretability for medical diagnosis, we visualize the experimental results using Class Activation Maps(CAM) to localize the predicted disease regions. The results from several experiments show that the accuracy of our prediction model is 0.911, the Area Under Curve (AUC) 0.976, for no-COVID-19(Precision, 0.902, Recall 0.911, F1-Score 0.900), COVID-19 (Precision, 0.932, Recall 0.911, F1-Score 0.902). The experimental results show that our established diagnostic model can help physicians make a rapid and accurate diagnosis of COVID-19 in response to the spread of COVID-19. © 2021 IEEE.

13.
BMC Pregnancy Childbirth ; 22(1): 362, 2022 Apr 26.
Article in English | MEDLINE | ID: covidwho-1817195

ABSTRACT

BACKGROUND: Maternal morbidity and mortality related to infection is an international public health concern, but detection and assessment is often difficult as part of routine maternity care in many low- and middle-income countries due to lack of easily accessible diagnostics. Front-line healthcare providers are key for the early identification and management of the unwell woman who may have infection. We sought to investigate the knowledge, attitudes, and perceptions of the use of screening tools to detect infectious maternal morbidity during and after pregnancy as part of routine antenatal and postnatal care. Enabling factors, barriers, and potential management options for the use of early warning scores were explored. METHODS: Key informant interviews (n = 10) and two focus group discussions (n = 14) were conducted with healthcare providers and managers (total = 24) working in one large tertiary public hospital in Blantyre, Malawi. Transcribed interviews were coded by topic and then grouped into categories. Thematic framework analysis was undertaken to identify emerging themes. RESULTS: Most healthcare providers are aware of the importance of the early detection of infection and would seek to better identify women with infection if resources were available to do so. In current practice, an early warning score was used in the high dependency unit only. Routine screening was not in place in the antenatal or postnatal departments. Barriers to implementing routine screening included lack of trained staff and time, lack of thermometers, and difficulties with the interpretation of the early warning scores. A locally adapted early warning screening tool was considered an enabler to implementing routine screening for infectious morbidity. Local ownership and clinical leadership were considered essential for successful and sustainable implementation for clinical change. CONCLUSIONS: Although healthcare providers considered infection during and after pregnancy and childbirth a danger sign and significant morbidity, standardised screening for infectious maternal morbidity was not part of routine antenatal or postnatal care. The establishment of such a service requires the availability of free and easy to access rapid diagnostic testing, training in interpretation of results, as well as affordable targeted treatment. The implementation of early warning scores and processes developed in high-income countries need careful consideration and validation when applied to women accessing care in low resource settings.


Subject(s)
Health Knowledge, Attitudes, Practice , Maternal Health Services , Female , Health Personnel , Humans , Malawi , Pregnancy , Qualitative Research
14.
Rev Esp Quimioter ; 2022 Apr 21.
Article in English | MEDLINE | ID: covidwho-1812162

ABSTRACT

Two years after the COVID-19 pandemic, many uncertainties persist about the causal agent, the disease and its future. This document contains the reflection of the COVID-19 working group of the Official College of Physicians of Madrid (ICOMEM) in relation to some questions that remain unresolved. The document includes considerations on the origin of the virus, the current indication for diagnostic tests, the value of severity scores in the onset of the disease and the added risk posed by hypertension or dementia. We also discuss the possibility of deducing viral behavior from the examination of the structure of the complete viral genome, the future of some drug associations and the current role of therapeutic resources such as corticosteroids or extracorporeal oxygenation (ECMO). We review the scarce existing information on the reality of COVID 19 in Africa, the uncertainties about the future of the pandemic and the status of vaccines, and the data and uncertainties about the long-term pulmonary sequelae of those who suffered severe pneumonia.

15.
J Pers Med ; 12(4)2022 Apr 14.
Article in English | MEDLINE | ID: covidwho-1809987

ABSTRACT

(1) Background: The aim was screening the performance of nine Early Warning Scores (EWS), to identify patients at high-risk of premature impairment and to detect intensive care unit (ICU) admissions, as well as to track the 2-, 7-, 14-, and 28-day mortality in a cohort of patients diagnosed with an acute neurological condition. (2) Methods: We conducted a prospective, longitudinal, observational study, calculating the EWS [Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), VitalPAC Early Warning Score (ViEWS), Modified Rapid Emergency Medicine Score (MREMS), Early Warning Score (EWS), Hamilton Early Warning Score (HEWS), Standardised Early Warning Score (SEWS), WHO Prognostic Scored System (WPSS), and Rapid Acute Physiology Score (RAPS)] upon the arrival of patients to the emergency department. (3) Results: In all, 1160 patients were included: 808 patients were hospitalized, 199 cases (17%) required ICU care, and 6% of patients died (64 cases) within 2 days, which rose to 16% (183 cases) within 28 days. The highest area under the curve for predicting the need for ICU admissions was obtained by RAPS and MEWS. For predicting mortality, MREMS obtained the best scores for 2- and 28-day mortality. (4) Conclusions: This is the first study to explore whether several EWS accurately identify the risk of ICU admissions and mortality, at different time points, in patients with acute neurological disorders. Every score analyzed obtained good results, but it is suggested that the use of RAPS, MEWS, and MREMS should be preferred in the acute setting, for patients with neurological impairment.

16.
Front Med (Lausanne) ; 9: 829423, 2022.
Article in English | MEDLINE | ID: covidwho-1809419

ABSTRACT

Background and Aims: We investigated the association between liver fibrosis scores and clinical outcomes in patients with COVID-19. Methods: We performed a post-hoc analysis among patients with COVID-19 from the trial study Outcomes Related to COVID-19 treated with Hydroxychloroquine among Inpatients with symptomatic Disease (ORCHID) trial. The relationship between aspartate aminotransferase (AST) to platelet ratio index (APRI), non-alcoholic fatty liver disease fibrosis score (NFS), Fibrosis-4 index (FIB-4), and discharge and death during the 28-days of hospitalization was investigated. Results: During the 28 days after randomization, 237 (80.6%) patients were discharged while 31 (10.5%) died among the 294 patients with COVID-19. The prevalence for advanced fibrosis was estimated to be 34, 21.8, and 37.8% for FIB-4 (>2.67), APRI (>1), and NFS (>0.676), respectively. In multivariate analysis, FIB-4 >2.67 [28-days discharge: hazard ratio (HR): 0.62; 95% CI: 0.46-0.84; 28-days mortality: HR: 5.13; 95% CI: 2.18-12.07], APRI >1 (28-days discharge: HR: 0.62; 95% CI: 0.44-0.87; 28-days mortality: HR: 2.85, 95% CI: 1.35-6.03), and NFS >0.676 (28-days discharge: HR: 0.5; 95% CI: 0.35-0.69; 28-days mortality: HR: 4.17; 95% CI: 1.62-10.72) was found to significantly reduce the discharge rate and increase the risk of death. Additionally, FIB-4, APRI, and NFS were found to have good predictive ability and calibration performance for 28-day death (C-index: 0.74 for FIB-4, 0.657 for APRI, and 0.745 for NFS) and discharge (C-index: 0.649 for FIB-4, 0.605 for APRI, and 0.685 for NFS). Conclusion: In hospitalized patients with COVID-19, FIB-4, APRI, and NFS may be good predictors for death and discharge within 28 days. The link between liver fibrosis and the natural history of COVID-19 should be further investigated.

17.
2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2021 ; : 249-253, 2022.
Article in English | Scopus | ID: covidwho-1806948

ABSTRACT

Higher education is one of the fields that is affected by the COVID-19 pandemic. One of the regulations in Indonesian higher education is Merdeka Learning-Independent Campus (MB-KM). In this research article we are aiming to do an analysis of the sentiment using Twitter as the dataset in order to find out the sentiment of university students towards the program. The analysis was being done using RoBERTa Base IndoLEM Sentiment Classifier Model. With the total number of more than 30 thousand positive sentiments and almost 20 thousand negative sentiments found. The result of the model shows that it achieved an accuracy of 91.73%, with a precision of 83.33%, recall 89.74%, and a harmonic mean of the two (F1 score) of 86.42%. Based on the analysis, it also shows the distribution of the sentiment is 63.0% of positive sentiment and 37.0% of negative sentiments. This paper shows that there are more positive sentiments than the negative one. © 2022 IEEE.

18.
Am J Emerg Med ; 57: 54-59, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1803390

ABSTRACT

INTRODUCTION: Noninvasive risk assessment is crucial in patients with COVID-19 in emergency department. Since limited data is known about the role of noninvasive parameters, we aimed to evaluate the role of a noninvasive parameter 'SpO2/FiO2' in independently predicting 30-day mortality in patients with COVID-19 and its prognostic utility in combination with a noninvasive score 'CRB-65'. METHODS: A retrospective study was performed in a tertiary training and research hospital, which included 272 patients with COVID-19 pneumonia diagnosed with polymerase chain reaction in emergency department. Data on characteristics, vital signs, and laboratory parameters were recorded from electronic medical records. The primary outcome of the study was 30-day mortality, and we assessed the discriminative ability of SpO2/FiO2 in predicting mortality in patients with COVID-19 pneumonia and its prognostic utility in combination with conventional pneumonia risk assessment scores. RESULTS: Multivariate analysis revealed that only SpO2/FiO2 level was found to be an independent parameter associated with 30-day mortality (OR:0.98, 95% CI: 0.98-0.99, p = 0.003). PSI and CURB-65 were found to be better scores than CRB-65 in predicting 30-day mortality (AUC: 0.79 vs 0.72, p = 0.04; AUC: 0.76 vs 0.72, p = 0.01 respectively). Both SpO2/FiO2 combined with CRB-65 and SpO2/FiO2 combined with CURB-65 have good discriminative ability and seemed to be more favorable than PSI in predicting 30-days mortality (AUC: 0.83 vs 0.75; AUC: 0.84 vs 0.75), however no significant difference was found (p = 0.21 and p = 0.06, respectively). CONCLUSION: SpO2/FiO2 is a promising index in predicting mortality. Addition of SpO2/FiO2 to CRB-65 improved the role of CRB-65 alone, however it performed similar to PSI. The combined noninvasive model of SpO2/FiO2 and CRB-65 may help physicians quickly stratify COVID-19 patients on admission, which is expected to be particularly important in hospitals still stressed by pandemic volumes.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnosis , Hospital Mortality , Humans , Pandemics , Pneumonia/diagnosis , Prognosis , Retrospective Studies , Severity of Illness Index
19.
Physician Leadership Journal ; 7(3):72-74, 2020.
Article in English | ProQuest Central | ID: covidwho-1801208

ABSTRACT

CORE COVID-19 CALCS Overall Hospital Management * Brescia-COVID Severity Scale/Algorithm - Italian step-wise approach to managing all COVID-19 inpatients. * MuLBSTA Score - Only score specific for viral pneumonia;not yet externally validated. * PSI/PORT Score - Well-studied pneumonia score for all-comers. * Absolute Lymphocyte Count - Lymphopenia appears to suggest COVID infection. Through a partnership with TheNNT.com, MDCalc has incorporated relevant information regarding the most up-to-date knowledge on mortality and risk factors odds ratios for COVID-19, which combine both laboratory and field measurements to provide a comprehensive overview of how these factors affect the risk to patients (https://www.mdcalc.com/covid-19/indicators-mortality-data-china-south-korea). COVID-19 Resource Center Calculators (mdcalc.com/covid-19) Core COVID-19 Calcs Overall Hospital Management * Brescia-COVID Severity Scale/Algorithm - https:// www.mdcalc.com/brescia-covid-respiratory-severityscale-bcrss-algorithm * MuLBSTA Score-https://www.mdcalc.com/mulbstascore * PSI/PORT Score - https://www.mdcalc.com/psiport-score-pneumonia-severity-index-cap * Absolute Lymphocyte Count - https://www.mdcalc .com/absolute-lymphocyte-count-alc ICU - Respiratory * A-a O2 Gradient - https://www.mdcalc.com/a-a-o2gradient * Rapid Shallow Breathing Index (RSBI) - https://www .mdcalc.com/rapid-shallow-breathing-index-rsbi ARDS and ECMO Outcomes * Horowitz Index for Lung Function (P/F Ratio) - https://www.mdcalc.com/horowitz-index-lungfunction-p-f-ratio * HScore - https://www.mdcalc.com/hscore-reactivehemophagocytic-syndrome * Murray Score for Acute Lung Injury - https://www .mdcalc.com/murray-score-acute-lung-injury * RESP Score - https://www.mdcalc.com/resp -respiratory-ecmo-survival-prediction-score Scarce Resource Allocation * SOFA Score - https://www.mdcalc.com/sequential -organ-failure-assessment-sofa-score * mSOFA - https://www.mdcalc.com/modifiedsequential-organ-failure-assessment-msofa-score * Charlson Comorbidity Index - https://www .mdcalc.com/charlson-comorbidity-index-cci * RESP Score - https://www.mdcalc.com/resp-respi ratory-ecmo-survival-prediction-score Other COVID-19 Calcs Scarce Resource Situations * MuLBSTA Score for Viral Pneumonia Severity - https://www.mdcalc.com/mulbsta-score * PSI/PORT Score: Pneumonia Severity Index for CAP - https://www.mdcalc.com/psi-port-score-pneumonia -severity-index-cap * CURB-65 Score for Pneumonia Severity - https:// www.mdcalc.com/curb-65-score-pneumonia -severity Additional Calcs * SMART-COP Score for Pneumonia Severity - https://www.mdcalc.com/smart-cop-score-pneu monia-severity * Severe Community Acquired Pneumonia (SCAP) - https://www.mdcalc.com/severe-community -acquired-pneumonia-scap-score.

20.
Front Med (Lausanne) ; 9: 779516, 2022.
Article in English | MEDLINE | ID: covidwho-1798934

ABSTRACT

SARS-CoV-2 infection has a wide spectrum of presentations, from asymptomatic to pneumonia and sepsis. Risk scores have been used as triggers for protocols that combine several interventions for early management of sepsis. This study tested the accuracy of the score SIRS, qSOFA, and NEWS in predicting outcomes, including mortality and bacterial infection, in patients admitted to the emergency department (ED) during the COVID-19 pandemic. We described 2,473 cases of COVID-19 admitted to the ED of the largest referral hospital for severe COVID-19 in Brazil during the pandemic. SIRS, qSOFA and NEWS scores showed a poor performance as prognostic scores. However, NEWS score had a high sensitivity to predict in-hospital death (0.851), early bacterial infection (0.851), and ICU admission (0.868), suggesting that it may be a good screening tool for severe cases of COVID-19, despite its low specificity.

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