CORONAVIRUS DISEASE SURVIVAL TIME MODEL FOR FORECASTING

The term "survival analysis" refers to statistical techniques for data analysis where the time until the occurrence of the desired event serves as the outcome variable. Time to event analysis is another name for survival analysis. Applications for survival analysis are fairly broad and include things like calculating a population's survival rate or contrasting the survival of two or more groups. Cox regression analysis is a highly well-liked and frequently applied technique among them. Data on disease states are typically obtained at random epochs or at periodic epochs during follow-up in research looking at biological changes between states of Coronavirus infection and the start of COVID-19 in the human immune system. For instance, after the COVID enters a person's bloodstream by a route of transmission, it progresses through numerous stages that are linked to the depletion of B cells before becoming COVID-19. This study presents the Cox's approach for simulating the link between variables influencing the development of two disease states, namely I= the time epoch of COVID infection and P= the time epoch of COVID-19. Incubation period (IP) or survival time is the precise interval of time between "P and I." It is shown how Cox's model works with several personal infective factors and how well it can estimate the percentage of COVID-19 victims with the same completed length of IP. Such forecast values are then established for a synthetically simulated data set. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Survival Evaluation of Hospitalized COVID-19 Patients with Cox Frailty Approach.

INTRODUCTION: The survival cox analysis is becoming more popular in time-to-event data analysis. When there are unobserved /unmeasured individual factors, then the results of this model may not be dependable. Hence, this study aimed to determine the factors associated with Covid-19 patients' survival time with considering frailty factor. METHODS: This study was conducted at 1 of the hospitals in Iran, so that hospitalized patients with COVID-19 were included. Epidemiological, clinical, laboratory, and outcome data on admission were extracted from electronic medical records. Gamma-frailty Cox model was used to identify the effects of the risk factors. RESULTS: A total of 360 patients with COVID-19 enrolled in the study. The median age was 74 years (IQR 61 - 83), 903 (57·7%) were men, and 661 (42·3%) were women; the mortality rate was 17%. The Cox frailty model showed that there is at least a latent factor in the model (P = 0.005). Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of patients. CONCLUSION: The Cox frailty model indicates that in addition to age, the frailty factor is a useful predictor of survival in Covid-19 patients.

The impact of HIV on the risk of COVID-19 death among hospitalized patients.

BACKGROUND: Little is known about the association between Human Immunodeficiency Virus (HIV) infection and risk of death among hospitalized COVID-19 patients. We aimed to investigate this association using a multicenter study. MATERIAL AND METHODS: This multicenter study was conducted using the registry database of Coronavirus Control Operations Headquarter from March 21, 2021 to January 18, 2020 in the province of Tehran, Iran. The interest outcome was COVID-19 death among hospitalized patients living with and without HIV. The Cox regression models with robust standard error were used to estimate the association between HIV infection and risk of COVID-19 death. The subgroup and interaction analysis were also performed in this study. RESULTS: 326052 patients with COVID-19 were included in the study, of whom 127 (0.04%) were living with HIV. COVID-19 patients with HIV were more likely to be female, older, and to have symptoms such as fever, muscular pain, dyspnea and cough. The death proportion due to COVID-19 was 18 (14.17%) and 21595 (6.63%) among HIV and non-HIV patients, respectively. Patients living with HIV had lower mean survival time compared to those without HIV (26.49 vs. 15.31 days, P-value = 0.047). Crude risk of COVID-19 death was higher among HIV patients than in non-HIV group (hazard ratio[HR]: 1.60, 1.08-2.37). Compared to those without HIV, higher risk of COVID-19 death was observed among patients with HIV after adjusting for sex (1.60, 1.08-2.36), comorbidities (1.49, 1.01-2.19), cancer (1.59, 1.08-2.33), and PO2 (1.68, 1.12-2.50). However, the risk of COVID-19 death was similar in patients with and without HIV after adjusting for age (1.46, 0.98-2.16) and ward (1.30, 0.89-1.89). CONCLUSION: We found no strong evidence of association between HIV infection and higher risk of COVID-19 death among hospitalized patients. To determine the true impact of HIV on the risk of COVID-19 death, factors such as age, comorbidities, hospital ward, viral load, CD4 count, and antiretroviral treatment should be considered.

Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19

The proportional hazard Cox model is traditionally used in survival analysis to estimate the effect of several variables on the hazard rate of an event. Recently, neural networks were proposed to improve the flexibility of the Cox model. In this work, we focus on an extension of the Cox model, namely on a non-proportional relative risk model, where the neural network approximates a non-linear time-dependent risk function. We address the issue of the lack of time-varying variables in this model, and to this end, we design a deep neural network model capable of time-varying regression. The target application of our model is the waning of post-vaccination and post-infection immunity in COVID-19. This task setting is challenging due to the presence of multiple time-varying variables and different epidemic intensities at infection times. The advantage of our model is that it enables a fine-grained analysis of risks depending on the time since vaccination and/or infection, all approximated using a single non-linear function. A case study on a data set containing all COVID-19 cases in the Czech Republic until the end of 2021 has been performed. The vaccine effectiveness for different age groups, vaccine types, and the number of doses received was estimated using our model as a function of time. The results are in accordance with previous findings while allowing greater flexibility in the analysis due to a continuous representation of the waning function. © 2022, Springer Nature Switzerland AG.

Reliably Assessing Duration of Protection for Coronavirus Disease 2019 Vaccines.

Decision making about vaccination and boosting schedules for coronavirus disease 2019 (COVID-19) hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also detects plateaus in protective effectiveness more reliably than the standard method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines for which rapid and reliable understanding of vaccine effectiveness over time is desired.

Clinical characteristics and risk factors for mortality in COVID-19 inpatients in Birjand, Iran: a single-center retrospective study.

BACKGROUND: The coronavirus disease 2019(COVID-19) has affected mortality worldwide. The Cox proportional hazard (CPH) model is becoming more popular in time-to-event data analysis. This study aimed to evaluate the clinical characteristics in COVID-19 inpatients including (survivor and non-survivor); thus helping clinicians give the right treatment and assess prognosis and guide the treatment. METHODS: This single-center study was conducted at Hospital for COVID-19 patients in Birjand. Inpatients with confirmed COVID-19 were included. Patients were classified as the discharged or survivor group and the death or non-survivor group based on their outcome (improvement or death). Clinical, epidemiological characteristics, as well as laboratory parameters, were extracted from electronic medical records. Independent sample T test and the Chi-square test or Fisher's exact test were used to evaluate the association of interested variables. The CPH model was used for survival analysis in the COVID-19 death patients. Significant level was set as 0.05 in all analyses. RESULTS: The results showed that the mortality rate was about (17.4%). So that, 62(17%) patients had died due to COVID-19, and 298 (83.6%) patients had recovered and discharged. Clinical parameters and comorbidities such as oxygen saturation, lymphocyte and platelet counts, hemoglobin levels, C-reactive protein, and liver and kidney function, were statistically significant between both studied groups. The results of the CPH model showed that comorbidities, hypertension, lymphocyte counts, platelet count, and C-reactive protein level, may increase the risk of death due to the COVID-19 as risk factors in inpatients cases. CONCLUSIONS: Patients with, lower lymphocyte counts in hemogram, platelet count and serum albumin, and high C-reactive protein level, and also patients with comorbidities may have more risk for death. So, it should be given more attention to risk management in the progression of COVID-19 disease.