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1.
Annals of Thoracic Surgery ; 113(5):1401-1404, 2022.
Article in English | EMBASE | ID: covidwho-1821141
2.
Diagnostic Microbiology and Infectious Disease ; : 115720, 2022.
Article in English | ScienceDirect | ID: covidwho-1819472

ABSTRACT

The duration of antibody persistence following natural infection is unclear. We examined routine SARS-CoV-2 diagnostic and serological testing data on 6,522 persons diagnosed between March 2020 - March 2021 who had at least one antibody test ≥ 30 days after diagnosis at CityMD, an urgent care provider. Using survival analysis, we estimated the median duration of detectable anti-SARS-CoV-2 antibodies and hazard of seroreversion by demographic and clinical characteristics. We found that over 90% (95% CI: 91.8%, 94.8%) of the study population had detectable levels of antibodies at 180 days post diagnosis and that SARS-CoV-2 antibodies persisted at a detectable level for a median duration of 342 days following infection (95% CI: 328, 361). Additionally, there were differences in antibody persistence by age, with older patients less likely to serorevert compared to younger patients. These findings suggest that protection from natural infection may wane with time and differ by demographic factors.

3.
Ieee Transactions on Computational Social Systems ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1816469

ABSTRACT

At the end of 2021 Q2, coronavirus disease 2019 (COVID-19) in Indonesia experienced a continuous increase in positivity and mortality rates. There are limited studies regarding the factors of COVID-19 mortality in Indonesia with a more balanced dataset. The previous studies only implemented logistic regression, sensitive to the imbalanced dataset. Meanwhile, other countries implemented survival analysis to overcome the problem. Most survival analyses using Cox proportional hazard (CPH) model require the variables to be time-independent. To this end, this study aims to identify the risk factors for COVID-19 mortality in Indonesia using a survival analysis approach using Jakarta as a case study. We use the Piecewise Exponential Model (PEM) to overcome the time-dependent problem in CPH. The findings show that the COVID-19 mortality does not differ the gender. In contrast, it differs the elderly with 3.5 times higher to be deceased. Dyspnea, malaise, and pneumonia are the primary symptoms associated with COVID-19 mortality. From the comorbidities, diabetes and chronic disease are related to COVID-19, while hypertension and heart attack are still considerable in clustered contexts. The advanced treatment using intubation and extra corporeal membrane oxygenation (ECMO) produces a relatively large hazard risk of COVID-19 mortality. Based on the findings, we suggest that collaboration among the government, society, and hospitals is vital in overcoming the COVID-19 pandemic and minimizing the COVID-19 death.

4.
Journal of Medical Virology ; n/a(n/a), 2022.
Article in English | Wiley | ID: covidwho-1802454

ABSTRACT

There is a potential risk for SARS-CoV-2 spread through human contact with seafood and the inanimate materials contaminated by the virus. In this study, we examined the stability of the virus in artificial seawater (ASW) and on the surface of selected materials. SARS-CoV-2 (3.75 log10 TCID50) in ASW at 22? maintained infectious about 3 days and at 4? the virus survived more than 7 days. It should be noticed that viable virus at high titer (5.50 log10 TCID50) may survive more than 20 days in ASW at 4? and for 7 days at 22?. SARS-CoV-2 on stainless steel and plastic bag maintained infectious for 3 days, and on non-woven fabric for 1 day at 22?. In addition, the virus remained infectious for 9 days on stainless steel and non-woven fabric, and on plastic bag for 12 days at 4?. It is important to highlight the role of inanimate material surfaces as a source of infection and the necessity for surface decontamination and disinfection.This article is protected by copyright. All rights reserved.

5.
Computational Biology and Chemistry ; : 107681, 2022.
Article in English | ScienceDirect | ID: covidwho-1778061

ABSTRACT

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

6.
Proc Mach Learn Res ; 146:159-170, 2021.
Article in English | PubMed | ID: covidwho-1772436

ABSTRACT

Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.

7.
Contemp Clin Trials ; : 106758, 2022 Apr 06.
Article in English | MEDLINE | ID: covidwho-1773152

ABSTRACT

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.

8.
Web of Science; 2022.
Preprint in English | Web of Science | ID: ppcovidwho-331396

ABSTRACT

BACKGROUND: Countries have focused research on developing strategies to fight COVID-19, prevent hospitalizations, and maintain economic activities. OBJECTIVE: This study aimed to establish a survival analysis and identify risk factors for patients with COVID-19 in a upper middle-income city in Brazil. METHODS: We performed a retrospective cohort study with 280 hospitalized patients with COVID-19. The eCOVID platform provided data used to monitor COVID-19 cases and help communication between professionals. RESULTS: Survival analysis showed that age ≥ 65 years was associated with decreased survival (54.8%). Females had lower survival rate than males (p=0.01). Regarding risk factors, urea concentration (p<0.001), hospital LOS (p=0.002), oxygen concentration (p=0.005), and age (p=0.02) were associated with death. CONCLUSION: Age, hospital LOS, high blood urea concentration, and low oxygen concentration were associated with death by COVID-19 in the studied population. These findings corroborate with studies conducted in research centers worldwide.

9.
Economies ; 10(3):60, 2022.
Article in English | ProQuest Central | ID: covidwho-1760442

ABSTRACT

The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest.

10.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752369

ABSTRACT

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

11.
Leukemia and Lymphoma ; 62(SUPPL 1):S8-S9, 2021.
Article in English | EMBASE | ID: covidwho-1747040

ABSTRACT

Introduction: The severe acute respiratory syndrome coronavirus (SARS-CoV-2) has become the cause of a worldwide pandemic. The clinical course of COVID-19 was reported to be more severe in patients with cancer, especially with hematological malignancies. Due to the impairment of the immune system, infections are the leading cause of death in patients with chronic lymphocytic leukemia (CLL). Methods: We performed an observational, retrospective study in polish hematological centers within the Polish Adult Leukemia Study Group analyzing the clinical course of SARSCoV- 2 infection in patients with CLL. Results and conclusions: The study group included 188 patients. The median age of the patients was 67.9 years (range 36-87) and 70 (37.2%) were men. The Median Eastern Cooperative Study Group (ECOG) score was 1 (range 0-4). At the time of SARS-CoV-2 infection, 29 (15.4%) patients were treatment-naïve, 41 (21.8%) have ended the treatment, whereas 118 (62.8%) were during the active phase of CLL therapy. The median number of lines of previous treatment regimens was 1 (range 0-7), whereas 24 (12.8%) patients received four or more treatment regimens. At the time of infection 51 patients (27.1%) were treated with Bruton's tyrosine kinase inhibitor (iBTK), 46 (24.5%) with anti-CD20 antibodies while 37 patients (19.7%) were during venetoclax therapy. In the analyzed cohort 111 patients (59.0%) required hospitalization and 50 patients (26.5%) died due to COVID- 19. Patients with poor performance status (ECOG >1), advanced age (≥65 years), low platelet count (<100 G/l), low hemoglobin levels (<10 g/dl), and elevated lactate dehydrogenase (LDH) were at increased risk of death due to SARSCoV- 2 infection. Poor performance status, low platelet count and hemoglobin levels, elevated LDH and advanced Binet stage at diagnosis were associated with the need for hospitalization for the purpose of COVID-19 treatment. Multivariate analysis revealed that independent factors associated with risk of hospitalization due to SARS-CoV-2 infection and its complications were presence of 17p deletion (p = 0.042), anti- CD20 antibody treatment (p = 0.01), low hemoglobin (p = 0.008) and platelet (p = 0.004) levels and elevated LDH (p = 0.0023). Interestingly, the CLL treatment status (treatment naïve vs. treated) or type of administered treatment (BTKi, anti-CD20, or venetoclax) had no impact on SARS-CoV-2 related risk of death. Univariate survival analysis showed that poor performance status (p = 0.02), advanced age (p = 0.04), low platelet count (p = 0.0012), low hemoglobin level (p = 0.0017) and elevated lactate dehydrogenase (p = 0.008) were associated with significantly shorter overall survival. Multivariate Cox regression analysis showed that only the low platelet count (p < 0.0001) and advanced age (p = 0.019) were associated with patients' shorter overall survival. Considering the abovementioned data, SARS-CoV-2 infection in patients with CLL is associated with the poor outcome regardless of administered CLL-directed treatment.

12.
Open Forum Infectious Diseases ; 8(SUPPL 1):S282-S283, 2021.
Article in English | EMBASE | ID: covidwho-1746636

ABSTRACT

Background. Epicardial adipose tissue (EAT) is a highly inflammatory depot of fat, with high concentrations of IL-6 and macrophages, which can directly reach the myo-pericardium via the vasa vasorum or paracrine pathways. TNF-α and IL-6 diminish cardiac inotropic function, making EAT inflammation a potential cause of cardiac dysfunction. Methods. A retrospective cohort study assessing EAT Thickness and Density from CT scans, without contrast, from adult patients during index admission for COVID-19 infection at Mount Sinai Medical Center from March 2020 to January 2021. A total of 1,644 patients were screened, of which 148 patients were included. Follow-up completed until death or discharge. The descriptive analysis was applied to the general population, parametric test of normality for comparisons between groups. Kaplan survival analysis was conducted after survival distribution was confirmed significant. It was followed by the assumption of normality by Q-Q Plot, prior to performing a multiple regression analysis in the vulnerable group using a K-Matrix input for cofounders. A log-rank test was conducted to determine differences in the survival distributions for the different ranges of EAT thickness. Results. A total of 148 Participants were assigned to two groups based on epicardial adipose tissue in order to classify them as increased or decreased risk of cardiovascular risk: >5mm (n = 99), < 5mm (n = 49). The survival percentage was higher in the group with no EAT inflammation compared to the group with EAT inflammation (95.0% and 65%, respectively). Participants with EAT >5mm had a median day of hospital stay of 18 (95% CI, 16.86 to 29.92). The survival distributions for the two categories were statistically significantly different, χ2(2) = 6.9, p < 0.01. A Bonferroni correction was made with statistical significance accepted at the p < 0.025 level. There was a statistically significant difference in survival distributions for the EAT >5 mm vs EAT < 5 mm, χ2(1) =6.953, p = 0.008. Conclusion. There was an association with increased EAT thickness and increased mortality. These findings suggest that EAT thickness can be used as a prognostic factor and as a risk factor for increased mortality in patients with COVID-19.

13.
Open Forum Infectious Diseases ; 8(SUPPL 1):S323, 2021.
Article in English | EMBASE | ID: covidwho-1746554

ABSTRACT

Background. Mortality from COVID-19 is associated with male sex, older age, black race, and comorbidities including obesity. Our study identified risk factors for in-hospital mortality from COVID-19 using survival analysis at an urban center in Detroit, MI. Methods. This was a single-center historical cohort study. We reviewed the electronic medical records of patients positive for severe acute respiratory syndrome coronavirus 2 (the COVID-19 virus) on qualitative polymerase-chain-reaction assay, who were admitted between 3/8-6/14/20. We assessed risk factors for mortality using Kaplan-Meier analysis and Cox proportional hazards models. Results. We included 565 patients with mean age (standard deviation) 64.4 (16.2) years, 52.0% male (294) and 77.2% (436) black/African American. The overall mean body mass index (BMI) was 32.0 (9.02) kg/m2. At least one comorbidity was present in 95.2% (538) of patients. The overall case-fatality rate was 30.4% (172/565). The unadjusted mortality rate among males was 33.7% compared to 26.9% in females (p=0.08);the median time to death (range) for males was 16.8 (0.3, 33.9) compared to 14.2 (0.32, 47.7) days for females (p=0.04). Univariable survival analysis with Cox proportional hazards models revealed that age (p=< 0.0001), admission from a facility (p=0.002), public insurance (p< 0.0001), respiratory rate ≥ 22 bpm (p=0.02), lymphocytopenia (p=0.07) and serum albumin (p=0.007) were additional risk factors for mortality (Table 1). From multivariable Cox proportional hazards modeling (Table 2), after controlling for age, Charlson score and qSofa, males were 40% more likely to die than females (p=0.03). Conclusion. After controlling for risk factors for mortality including age, comorbidity and sepsis-related organ failure assessment, males continued to have a higher hazard of death. These demographic and clinical factors may help healthcare providers identify risk factors from COVID-19.

14.
Int J Popul Data Sci ; 5(4): 1411, 2021 Mar 03.
Article in English | MEDLINE | ID: covidwho-1744428

ABSTRACT

INTRODUCTION: Length of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic. However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change. OBJECTIVES: To investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS. DATA SOURCES: We used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the "Covid-19 Hospitalisation in England Surveillance System" (CHESS) database, collected daily from England's National Health Service, and collated by Public Health England. METHODS: We used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU. RESULTS: Patients admitted before 25 March had significantly longer LoS in ICU (mean = 18.4 days, median = 12), controlling for age, sex, whether the patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean = 15.4, median = 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS. CONCLUSIONS: The skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

15.
Open Access Macedonian Journal of Medical Sciences ; 10:240-244, 2022.
Article in English | Scopus | ID: covidwho-1744865

ABSTRACT

BACKGROUND: COVID-19 has infected and spread over the whole earth. For the time being, there is no cure for COVID-19. Although several medications have the potential to be utilized at various stages of the disease, no therapy has yet been demonstrated to be completely successful. AIM: This study aims to determine survival of COVID-19 patients who received antiviral and antiviral therapy combined with anti-inflammation therapy in a National Referral Hospital, Indonesia. METHODS: COVID-19 patients treated at Dr. M Djamil General Hospital in Padang, Indonesia were the subject of an analytic investigation using a retrospective cohort design. From January to June 2021, data were gathered from patient medical records. Independent sample T test and Chi-square test were used to analyze subject characteristics data. The median survival and survival rates were calculated using Kaplan–Meier survival analysis. It is also subjected to cox-regression analysis to answer the study hypothesis. RESULTS: The mean age of the subjects who received antiviral and anti-inflammatory medication was 60.95 12.11 years, while the average age of those who received antiviral therapy was 56.72 17.80 years, with the highest sex being male in both groups (59.3% and 50.6%). Antiviral and antiviral medication, as well as anti-inflammatory therapy, had no effect on the length of stay of COVID-19 patients (p >0.05). Antiviral and antiviral therapy, as well as anti-inflammatory therapy, play a role in the outcome of COVID-19 patients (p < 0.05), with patients receiving antiviral and anti-inflammatory therapy being a preventive factor in the final outcome of patients compared to patients receiving antiviral therapy HR = 0.69 (95% CI 0.48–0.99). CONCLUSION: When compared to patients who just got antiviral medication, the patients who received antiviral plus anti-inflammatory therapy had a better outcome. © 2022 Afriani Afriani, Sabrina Ermayanti, Irvan Medison, Russilawati Russilawati, Fenty Anggrainy, Yessy Susanty Sabri, Ricvan Dana Nindrea.

16.
American Journal of Public Health ; 112(3):345-347, 2022.
Article in English | ProQuest Central | ID: covidwho-1738293

ABSTRACT

Most commonly, such measures have captured social capital: the resources that are rooted in social networks such as social connectedness, civic engagement, norms of reciprocity, and trust in others that facilitate cooperation for mutual benefit.1 Recently, health scholars have aggregated individual responses to national survey data to capture area-level attitudes of anti-Black racism, xenophobia, and homophobia to examine their associations with mortality.2-4 Other studies have used aggregate individual data from Google searches and tweets as indicators of prejudicial social environments.5,6 Questions remain about what a population-level measure of hope captures and how it should be applied in the future. [...]hope significantly increased in 2020. According to Riley et al., even before the pandemic levels of hope varied across the country, with some parts of the country experiencing declines in hope. In the past few years, the phrase "deaths of despair" has been used to describe the declines in US life expectancy and increases in deaths from suicide, drug overdose, and alcohol use.7 Despair-the absence of hope-has been linked to numerous poor health outcomes and increased mortality. [...]tracking despair has emerged as a barometer of risk of poor mental health, unhealthy behaviors, and preventable mortality.

17.
Current Traditional Medicine ; 7(6), 2021.
Article in English | EMBASE | ID: covidwho-1736625

ABSTRACT

Background: The present study analysed the impact of the integrated medical care of Hydroxychloroquine (HCQ) and Siddha herbal preparation KSK on asymptomatic COVID-19 patients based on their body constitution. Objective: The present study aimed to analyse the duration of the hospital stay of asymptomatic COVID-19 patients treated with the integrated medical care of hydroxychloroquine (HCQ) and herbal decoction of Kaba Sura Kudineer (KSK). Design: The study included a retrospective case series of 19 asymptomatic confirmed SARS-Cov-2 patients from District COVID Care Centre, Tirupati, India, between 23rd May to 7th June 2020. Methods: Clinical data were collected using a standardised case report form containing demographic information, length of hospital stays, and Siddha Yakkai Ilakkanam (body constitution) from the records. The association between the length of hospital stay, age, gender, and Siddha YI for the confirmed patients after admission was analysed by the Kaplan Meier survival analysis method. Results: Patients belonging to the Aiyam group stayed for at least nine days in the hospital, and 80% took ten or more days to cure the disease. About 71.4% took more than four days and three days of hospital stay in the Azhal and Vali groups, respectively. It was observed that 75% of females and 73.3% of males took nine days or more of hospital stay, respectively. The range of hospital stay was between 2-15 days for patients aged between 19 – 40 years. Conclusion: The present study explored the significance of integrating Siddha medicine with Western medicine in the management of SARS Cov-2 infection. An overall median of 9 days in the length of stay and 8.5 days in the overall mean survival time was documented. The patients of the present study on integrative treatment recovered about nine days earlier in comparison to the patients studied in Vietnam and China.

18.
Blood ; 138:4996, 2021.
Article in English | EMBASE | ID: covidwho-1736317

ABSTRACT

Introduction: The COVID-19 pandemic is a global public health challenge that has affected more than 30 million people and taken more than 4 lakh lives in India. The first and second COVID waves have greatly impacted the lives of a vast majority and vaccination of the masses remains a struggle. Although SARS -CoV-2 infections in patients with hematological diseases are expected to have an adverse outcomes, only limited reports are available from India. Hence, our study aims to identify the outcome in terms of severity and mortality in this group and the risk factors involved in developing severe COVID-19 and death. Methodology: This is a cross sectional analytical study done in a tertiary care hospital in Southern India for a period of 11 months. All hematological patients irrespective of age, who were infected with SARS-CoV-2 during the first wave (June -December 2020) and second wave (March - June 2021) were consecutively enrolled for the study after IRB approval. The patients were then categorized as neoplastic (acute and chronic leukemia, lymphoma, myeloma, MPN and MDS ) and non-neoplastic (ITP, aplastic anemia, hemolytic anemia, MGUS and TTP ) diseases. The clinical data was collected retrospectively from the electronic medical records and by direct telephonic contact. Patients were categorized as having mild (spO2 > 94 % symptomatic /asymptomatic), moderate (spO2 90 - 94 %) and severe (spO2 < 90 %) disease based on their severity of infection, each category of patients received appropriate clinical management. Treatment details, mortality and other outcomes were recorded for 30 days. The continuous variables were represented as mean (± SD)/median (IQR) and categorical variables as frequency and percentage. The association of the outcome variable with selected variables were calculated using Chi-square tests and kaplan meier survival analysis. The data sets were analyzed (SPSS version 21) and a p value of < 0.05 was considered statistically significant. Results: The study was conducted with 70 patients (n=70). Demographic details of patients are summarized in Table 1.Seventeen (24.3%) out of 49 (70%) hospitalized patients required ICU care. There were 13 (18.6%)deaths. in the patients who survived, prolonged antigen positivity of COVID on testing after 21 days was seen in 9 patients (16.1%). In 35 patients (50%)hematological treatment was restarted with a mean delay of 9.2 +/- 10.72 days. Predictors of severity of the disease is summarized in Table 2. Age more than 50 years (P=0.002)(Figure 1a), severe COVID (P=<0.001) and D dimer value of >2 times normal (P=0.047) were associated with a 30-day mortality. Additionally, patients on active treatment for hematological disease were at greater risk of severe COVID (P=0.012). There was no significant difference in severity (P=0.197) or mortality (P=0.556)in patients with neoplastic vs. non-neoplastic disorders Conclusion: COVID-19 patients with malignant and non-malignant hematological diseases showed an increased mortality. Age > 50 years and high D dimer values (>2N) were identified as predictors of mortality. Active treatment for haematological disease predisposed to severe disease.The study needs to be validated further on a larger cohort of patients. Preventive strategies including vaccination is warranted in patients with hematological disorders. [Formula presented] Disclosures: No relevant conflicts of interest to declare.

19.
7th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2021 ; : 322-327, 2021.
Article in English | Scopus | ID: covidwho-1730931

ABSTRACT

The Weibull regression model is one of the most important parametric regression models. Because of the knowledge of the probability distribution of the response variable following the Weibull distribution, which facilities the possibility of estimating the regression parameters based on the baseline hazard function. It is estimated by estimating the parameters of the Weibull distribution using the maximum likelihood estimation method. The R software was used for the purpose of estimating the regression coefficients and identifying the most significant features that model the outcome. In this paper, we investigate the factors affecting the progression of Corona virus patients from Al-Shiffa Hospital in the city of Mosul. In addition, this study focused on patients who were in a critical condition, and whose cases necessitated their monitoring during their stay under the artificial respiration machine Continues Positive Airway Pressure (CPAP). The six variables were taken as the most influential on the injury case and it was found that the most influential variables were Remdesivir and O2 using some statistical criteria. © 2021 IEEE.

20.
Vaccines (Basel) ; 10(3)2022 Mar 09.
Article in English | MEDLINE | ID: covidwho-1732284

ABSTRACT

By the end of 2021, the COVID-19 pandemic resulted in over 54 million cases and more than 800,000 deaths in the United States, and over 350 million cases and more than 5 million deaths worldwide. The uniqueness and gravity of this pandemic have been reflected in the public health guidelines poorly received by a growing subset of the United States population. These poorly received guidelines, including vaccine receipt, are a highly complex psychosocial issue, and have impacted the successful prevention of disease spread. Given the intricate nature of this important barrier, any single statistical analysis methodologically fails to address all convolutions. Therefore, this study utilized different analytical approaches to understand vaccine motivations and population-level trends. With 12,975 surveys from a state-wide year-long surveillance initiative, we performed three robust statistical analyses to evaluate COVID-19 vaccine hesitancy: principal component analysis, survival analysis and spatial time series analysis. The analytic goal was to utilize complementary mathematical approaches to identify overlapping themes of vaccine hesitancy and vaccine trust in a highly conservative US state. The results indicate that vaccine receipt is influenced by the source of information and the population's trust in the science and approval process behind the vaccines. This multifaceted statistical approach allowed for methodologically rigorous results that public health professionals and policy makers can directly use to improve vaccine interventions.

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