Time-to-survival from COVID-19 disease and its associated factors in Osun State, Nigeria.

Introduction This study focused on estimating the probability of survival and the specific time to survival from COVID-19 among patients who had COVID-19 in Osun state, Nigeria. Also, we examined some factors associated with the time to survival among COVID-19 patients in Osun state, Nigeria. Methods The retrospective data of 2596 records of COVID-19 patients in Osun state were analysed in this study. The outcome variable was the "COVID-19 treatment outcome (survived=1, dead=0)". The time date used in the survival analysis was treatment duration (in days). The explanatory variables were demographic characteristics, type of health facility, vaccination status, symptoms, and mode of admission. The descriptive statistics was computed and presented. Kaplan Meier was used to estimate the median time to survival. Bivariate analysis and multivariate analysis were done using the Log-Rank test and Cox regression, respectively. P values were set at P<0.05. Results The mean age was observed to be 40 (SD=17.51) years, ranging from mostly, 2 months to 98 years old. More (56.1%) of the participants were males. Most (99.5%) of them were Nigerians. Only 1.4% were vaccinated. The survival rate from COVID-19 was 98.1% in Osun State. The median time for survival was 14 (IQR= 14- 16) days. COVID-19 reduces as the number of days for being on treatment increases. Unvaccinated (HR=0.93, 95%CI: 0.43-2.03) and those whose vaccination status was unknown (HR=0.52, 95%CI: 0.37-0.74) were less likely to survive COVID-19 diseases. Conclusion The Survival rate was high, the observed median time to survival was 14 days, and the probability of survival reduces as the number of days of being on treatment for COVID-19 increases. Also, gender, vaccination, type of care, and ethnicity were associated with survival time. Similarly, unvaccinated and inpatients were less likely to rapidly survive COVID-19. This study recommends that the COVID-19 vaccine should be encouraged among patients who have the COVID-19 virus. Also, home care may be further explored to assess its effectiveness in caring for COVID-19 patients. In the same vein, COVID-19 data capturing, and databases need strengthening in Nigeria.

Survival of COVID-19 among the Iranian population before and after the Delta variant spread

COVID-19 has been considered the most important issue in the last two years. Some characteristics and factors can play a pivotal role in the survival time and mortality of COVID-19 patients. The Delta variant was one of the most important variants of COVID-19. This study aimed to investigate the risk factors of COVID-19 survival before and after the spread of the Delta variant. In this historical cohort study, 6,117 hospitalized patients with positive COVID-19 PCR tests between January and September 2021 participated. Some characteristics such as age, sex, death by COVID-19, and presence/absence of some comorbidities were registered for the patients. Log-rank test and Cox proportional hazards model were done to check the effect of the potential risk factors on the survival of COVID-19 patients by considering the onset of symptoms to death as the time variable. The mean age of patients was 47.29 (SD=18.70). 53% of patients were female, 4.6% were admitted to the ICU, and 3.6% died from COVID-19. Age (HR=9.81, p<.001), cardiovascular disease (HR=2.86, p<.001), chronic kidney disease (HR=6.21, p<.001), diabetes (HR=2.16, p=0.002), hypertension (HR=2.67, p<.001), ICU admission (HR=12.92, p<.001), pO2<93% (HR=6.75, p<.001), and intubation (HR=21.54, P<.001) were risk factors that were influential on the survival of COVD-19 patients before the spread of the Delta variant. Although the effect of some variables changed after the spread of the Delta variant, some of them, like chronic kidney disease and hypertension were no longer significant. Although the effect of some comorbidities was significant only in the crude models, they were not influential in the adjusted model. Conversely, in the presence of other risk factors, especially age, most of the comorbidities were not significant in the adjusted model. Older age, ICU admission, intubation, and pO2<93% are the most important variables which played a pivotal role in the survival of individuals infected by COVID-19. © 2023, Mahidol University - ASEAN Institute for Health Development. All rights reserved.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods.

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

Incidence and predictors of attrition rate after children started inpatient treatments for complicated severe acute malnutrition in North West Ethiopia.

BACKGROUND: Retaining children for inpatient treatment of complicated severe acute malnutrition (SAM) is a growing challenge until achieved the reference weight of a child. In Ethiopia, there is limited information regarding the time to be lost from the stabilizing centers after initiation of treatment. Thus, this study aimed to identify incidence and predictors of attrition for children suffering from SAM after started inpatient treatment in North West Ethiopia. METHODS: A retrospective cohort study was conducted among under-five children admitted and started inpatient treatment for complicated SAM from 2015/2016 to 2020/2021. Data were entered using Epi-data version 4.2 and then exported to STATA (SE) version R-14 software for further analysis. The analysis was computed using Cox proportional hazard regression model after checking all proportional hazard assumptions. Covariates having < 0.2 of P values in the bi-variable analysis were candidates transferred to the multivariable Cox proportional hazard regression model. Finally, a statistical significance was declared at a P value of < 0.05. RESULT: Overall, 760 files of under-five children were analyzed with a mean (± SD) age of participants 27.8 (± 16.5) months. About 6944 child-days of treatment observation were recorded with the crude incidence of attrition rate of 9.7% (95% CI 7.9-12.6). The overall median time of attrition and half-life time S(t1/2) of survival rates was determined as 14 (IQR = ± 7) days and 91.6% (95% CI 88.2-93.1), respectively. The attrition rate was significantly associated with cases living in rural residents (AHR = 6.03; 95% CI 2.2; 25.2), being re-admitted SAM cases (AHR = 2.99; 95% CI 1.62; 5.5), and caregivers did not have formal education (AHR = :5.6, 95% CI 2.7; 11.7) were all independent predictors for attrition from inpatient treatment. CONCLUSIONS: Nearly one in every ten severely acute malnourished under-five children defaulted at the end of treatment observation with a median time of 14 (IQR = ± 7) days. Living in a rural residence, being re-admitted cases, caregivers who did not have a formal education were significantly associated with the attrition rate. Hence, it is crucial to detect and control the identified causes of defaulting from treatment observation promptly. Furthermore, serious counseling during admission and nutritional provision strategies are essential for virtuous treatment outcomes.

Time-to-Event Analysis of Factors Influencing Delay in Discharge from a Subacute Complex Discharge Unit during the First Year of the Pandemic (2020) in an Irish Tertiary Centre Hospital.

Our study aimed to analyse delaying factors amongst patients with a length of stay (LOS) > 15 days during the COVID-19 pandemic using time-to-event analysis. A total of 390 patients were admitted between March 2020-February 2021 to the subacute complex discharge unit in St James's Hospital: 326 (83.6%) were >65 years of age and 233 (59.7%) were female. The median (IQR) age was 79 (70-86) years with a median (IQR) of 19.4 (10-41) days. A total of 237 (60.7%) events were uncensored, with LOS > 15 days, of which 138 (58.2%) were female and 124 (52.32%) had >4 comorbidities; 153 (39.2%) were censored into LOS ≤ 15 days, and death occurred in 19 (4.8%). Kaplan-Meier's plot compared factors causing a delay in discharge to the single factors: age, gender, and multimorbidity. A multivariate Cox regression analysis adjusted to age, gender, and multimorbidity predicted factors affecting LOS. Further research is required to explore multimorbidity as a risk factor for mortality in patients with prolonged LOS within a complex discharge unit and target gender-specific frailty measures to achieve high-quality patient management.

Cox regression using a calendar time scale was unbiased in simulations of COVID-19 vaccine effectiveness & safety.

BACKGROUND: Observational studies on corona virus disease 2019 (COVID-19) vaccines compare event rates in vaccinated and unvaccinated person time using Poisson or Cox regression. In Cox regression, the chosen time scale needs to account for the time-varying incidence of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection and COVID-19 vaccination.We aimed to quantify bias in person-time based methods, with and without adjustment for calendar time, using simulations and empirical data analysis. METHODS: We simulated 500,000 individuals who were followed for 365 days, and a point exposure resembling COVID-19 vaccination (cumulative incidence 80%). We generated an effectiveness outcome, emulating the incidence of severe acute respiratory syndrome corona virus 2 infection in Denmark during 2021 (risk 10%), and a safety outcome with seasonal variation (myocarditis, risk 1/5,000). Incidence rate ratios (IRRs) were set to 0.1 for effectiveness and 5.0 for safety outcomes. IRRs and hazard ratios (HRs) were estimated using Poisson and Cox regression with a time under observation scale, and a calendar time scale. Bias was defined as estimated IRR or HR-true IRR. Further, we obtained estimates for both outcomes using data from the Danish health registries. RESULTS: Unadjusted IRRs (biaseffectivenes +0.16; biassafety -2.09) and HRs estimated using a time-under-observation scale (+0.28;-2.15) were biased. Adjustment for calendar time reduced bias in Cox (+0.03; +0.33) and Poisson regression (0.00; -0.28). Cox regression using a calendar time scale was least biased (0.00, +0.12). When analyzing empirical data, adjusted Poisson and Cox regression using a calendar time scale yielded estimates in accordance with existing evidence. CONCLUSION: Lack of adjustment for the time-varying incidence of COVID-19 related outcomes may severely bias estimates.

The impact of COVID-19 on mortality after hip fracture : a population cohort study from England.

AIMS: Hip fracture commonly affects the frailest patients, of whom many are care-dependent, with a disproportionate risk of contracting COVID-19. We examined the impact of COVID-19 infection on hip fracture mortality in England. METHODS: We conducted a cohort study of patients with hip fracture recorded in the National Hip Fracture Database between 1 February 2019 and 31 October 2020 in England. Data were linked to Hospital Episode Statistics to quantify patient characteristics and comorbidities, Office for National Statistics mortality data, and Public Health England's SARS-CoV-2 testing results. Multivariable Cox regression examined determinants of 90-day mortality. Excess mortality attributable to COVID-19 was quantified using Quasi-Poisson models. RESULTS: Analysis of 102,900 hip fractures (42,630 occurring during the pandemic) revealed that among those with COVID-19 infection at presentation (n = 1,120) there was a doubling of 90-day mortality; hazard ratio (HR) 2.09 (95% confidence interval (CI) 1.89 to 2.31), while the HR for infections arising between eight and 30 days after presentation (n = 1,644) the figure was greater at 2.51 (95% CI 2.31 to 2.73). Malnutrition (1.45 (95% CI 1.19 to 1.77)) and nonoperative treatment (2.94 (95% CI 2.18 to 3.95)) were the only modifiable risk factors for death in COVID-19-positive patients. Patients who had tested positive for COVID-19 more than two weeks prior to hip fracture initially had better survival compared to those who contracted COVID-19 around the time of their hip fracture; however, survival rapidly declined and by 365 days the combination of hip fracture and COVID-19 infection was associated with a 50% mortality rate. Between 1 January and 30 June 2020, 1,273 (99.7% CI 1,077 to 1,465) excess deaths occurred within 90 days of hip fracture, representing an excess mortality of 23% (99.7% CI 20% to 26%), with most deaths occurring within 30 days. CONCLUSION: COVID-19 infection more than doubles the rate of early hip fracture mortality. Those contracting infection between 8 and 30 days after initial presentation are at even higher mortality risk, signalling the potential for targeted interventions during this period to improve survival.Cite this article: Bone Joint J 2022;104-B(10):1156-1167.

Reduction in Risk of Death Among Patients Admitted With COVID-19 Between the First and Second Epidemic Waves in New York City.

Background: Many regions have experienced successive epidemic waves of coronavirus disease 2019 (COVID-19) since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with heterogeneous differences in mortality. Elucidating factors differentially associated with mortality between epidemic waves may inform clinical and public health strategies. Methods: We examined clinical and demographic data among patients admitted with COVID-19 during the first (March-August 2020) and second (August 2020-March 2021) epidemic waves at an academic medical center in New York City. Results: Hospitalized patients (n = 4631) had lower overall and 30-day in-hospital mortality, defined as death or discharge to hospice, during the second wave (14% and 11%) than the first (22% and 21%). The wave 2 in-hospital mortality decrease persisted after adjusting for several potential confounders. Adjusting for the volume of COVID-19 admissions, a measure of health system strain, accounted for the mortality difference between waves. Several demographic and clinical patient factors were associated with an increased risk of mortality independent of wave: SARS-CoV-2 cycle threshold, do-not-intubate status, oxygen requirement, and intensive care unit admission. Conclusions: This work suggests that the increased in-hospital mortality rates observed during the first epidemic wave were partly due to strain on hospital resources. Preparations for future epidemics should prioritize evidence-based patient risks, treatment paradigms, and approaches to augment hospital capacity.

Incidence of Death and Its Predictors of COVID-19 in Amhara Region, Ethiopia: A Retrospective Follow Up Study.

Background: Risk factors associated with COVID-19 incidence of death would aid to notify the most favorable management strategies, hang about undecided, Moreover, studies regarding this issue are limited in Ethiopia and no region-wise study is conducted. Hence, the study investigated the COVID-19 incidence of death and its predictors in the Amhara regional state, Ethiopia. Methods: A facility-based retrospective survey was conducted at all Amhara regional state COVID-19 treatment centers from 13 March 2020, through 13 January 2022. Epidata version 3.1 and STATA version 14 were used for data entry and analysis, respectively. Linearized survey analysis in a stratified Cox regression model was fitted to identify independent risk factors. P-value with 95% CI for hazard ratio was used for testing the significance at alpha 0.05. Results: A total of 28,533 study participants were analyzed in this study. Of these, 2873 (11.2%) died and 25,660 (88.8%) were recovered from COVID-19. The death rate was 11.78 per 1000 person-days of observation with a median survival time of 32 days with IQR [12, 44]. Patients with co-morbidities (AHR = 1.54: 95% CI: 1.51-1.55), patients with age <5-year (AHR = 1.69: 95% CI: 1.78-1.81) and patients with age 60+ years (AHR = 2.91: 95% CI: 1.79-3.99), patients with asymptomatic diseases condition (AHR =1.15: 95% CI: 1.01-1.19), and being male (AHR = 1.22: 95% CI: 1.18-1.27) were independent significant risk factors of death from COVID-19. Conclusion: A relatively high incidence of death from COVID-19 was found in this study. Significant risk factors were identified as patients with age <5 years, patients with age 60+ Years, being male, patients having at least one comorbid condition, and patients with asymptomatic disease conditions. These factors should be taken into consideration for a strategy of quarantining and treating COVID-19 patients.

Survival Analysis of COVID-19 Patients With Symptoms Information by Machine Learning Algorithms

In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The analysis of a dataset from the East Asian region with a focus on data from the Philippines revealed that the hazard of time to death was associated with the symptoms and background variables of patients. Machine learning algorithms, i.e., dimensionality reduction and boosting, were used along with conventional Cox regression. Machine learning algorithms solved the diverging problem observed when using traditional Cox regression and improved performance by maximizing the concordance index (C-index). Logistic principal component analysis for dimensionality reduction was significantly efficient in addressing the collinearity problem. In addition, to address the nonlinear pattern, a higher C-index was achieved using extreme gradient boosting (XGBoost). The results of the analysis showed that the symptoms were statistically significant for the hazard rate. Among the symptoms, respiratory and pneumonia symptoms resulted in the highest hazard level, which can help in the preliminary identification of high-risk patients. Among various background variables, the influence of age, chronic disease, and their interaction were identified as significant. The use of XGBoost revealed that the hazards were minimized during middle age and increased for younger and older people without any chronic diseases, with only the elderly having a higher risk of chronic disease. These results imply that patients with respiratory and pneumonia symptoms or older patients should be given medical attention.

International variation in length of stay in intensive care units and the impact of patient-to-nurse ratios.

OBJECTIVE: To assess variation in ICU length of stay between countries with varying patient-to-nurse ratios; to compare ICU length of stay of individual countries against an international benchmark. DESIGN: Secondary analysis of the DecubICUs trial (performed on 15 May 2018). SETTING: The study cohort included 12,794 adult ICU patients (57 countries). Only countries with minimally twenty patients discharged (or deceased) within 30 days of ICU admission were included. MAIN OUTCOME MEASURE: Multivariate Cox regression was used to evaluate ICU length of stay, censored at 30 days, across countries and for patient-to-nurse ratio, adjusted for sex, age, admission type and Simplified Acute Physiology Score II. The resulting hazard ratios for countries, indicating longer or shorter length of stay than average, were plotted on a forest plot. Results by country were benchmarked against the overall length of stay using Kaplan-Meier curves. RESULTS: Patients had a median ICU length of stay of 11 days (interquartile range, 4-27). Hazard ratio by country ranged from minimally 0.42 (95% confidence interval 0.35-0.51) for Greece, to maximaly1.94 (1.28-2.93) for Lithuania. The hazard ratio for patient-to-nurse was 0.96 (0.94-0.98), indicating that higher patient-to-nurse ratio results in longer length of stay. CONCLUSIONS: Despite adjustment for case-mix, we observed significant heterogeneity of ICU length of stay in-between countries, and a significantly longer length of stay when patient-to-nurse ratio increases. Future studies determining underlying characteristics of individual ICUs and broader organisation of healthcare infrastructure within countries may further explain the observed heterogeneity in ICU length of stay.

Risk factors associated with hospitalization in patients with asymptomatic or mild COVID-19 in public accommodation facilities in Tokyo.

INTRODUCTION: In Japan, patients with coronavirus disease 2019 (COVID-19) who do not require medical intervention are provided care in recovery accommodation facilities (RAFs). However, some patients may require hospitalization if their symptoms become more severe during their stay. We conducted an observational study using epidemiological data of patients with COVID-19 admitted to RAFs in Tokyo. METHODS: This was an observational cohort study using data from COVID-19 patients admitted to one of the RAFs in Tokyo from December 2020 to November 2021. Admissions to the facilities were limited to patients with asymptomatic or mild COVID-19 with no underlying disease or at least stable underlying disease at the time of admission. Patients were hospitalized when they required oxygen administration or when they had, or persistent fever, or severe respiratory symptoms. We evaluated the association between hospitalization and the risk factors for hospitalization using a Cox regression model. RESULTS: The number of patients with COVID-19 admitted to the RAF was 6176. The number of hospitalized patients was 393 (6.4%), and the median length of stay was 5.50 days (IQR: 4.50, 6.50). In the Cox regression analysis, the hazard ratio increased with age and was significantly higher among patients aged >60 years (HR = 10.23, 95% CI: 6.72-15.57) than those in other age groups. This trend is similar to that observed in the sensitivity analysis. CONCLUSION: Patients with diabetes, the elderly, obesity, and medications for gout and psychiatric diseases may be at a high risk of hospitalization. In particular, an age over 60 years was strongly associated with hospitalization.

Estimation of vaccine efficacy for variants that emerge after the placebo group is vaccinated.

SARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work, we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emerge after placebo vaccination. The key requirements are that the surveillance strain distribution accurately reflects the strain distribution for a placebo group throughout follow-up after placebo group vaccination, and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards model to address staggered entry, staggered crossover, and smoothly varying strain specific vaccine efficacy. We evaluate these methods by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against misspecification of the strain distribution.

An alternative to Cox's regression for multiple survival curves comparison: A random forest-based approach using covariate structure

There are several established methods for comparing more than two survival curves, namely the scale-rank test or Cox's proportional hazard model. However, when their statistical assumptions are not met, their results' validity is affected. In this study, we address the mentioned issue and propose a new statistical approach on how to compare more than two survival curves using a random forest algorithm, which is practically assumption-free. The repetitive generating of many decision trees covered by one random forest model enables to calculate of a proportion of trees with sufficient complexity classifying into all groups (depicted by their survival curves), which is the p-value estimate as an analogy of the classical Wald's t-test output of the Cox's regression. Furthermore, a level of the pruning of decision trees the random forest model is built with, can modify both the robustness and statistical power of the random forest alternative. The discussed results are confirmed using COVID-19 survival data with varying the tree pruning level. The introduced method for survival curves comparison, based on random forest algorithm, seems to be a valid alternative to Cox's regression;however, it has no statistical assumptions and tends to reach higher statistical power. © 2021 IEEE

UK poSt Arthroplasty Follow-up rEcommendations (UK SAFE): what does analysis of linked, routinely collected national data sets tell us about mid-late term revision risk after hip replacement? Retrospective cohort study.

OBJECTIVE: To identify patients at risk of mid-late term revision of hip replacement to inform targeted follow-up. DESIGN: Analysis of linked national data sets from primary and secondary care (Clinical Practice Research Datalink (CPRD-GOLD); National Joint Registry (NJR); English Hospital Episode Statistics (HES); Patient-Reported Outcome Measures (PROMs)). PARTICIPANTS: Primary elective total hip replacement (THR) aged≥18. EVENT OF INTEREST: Revision surgery≥5 years (mid-late term) after primary THR. STATISTICAL METHODS: Cox regression modelling to ascertain risk factors of mid-late term revision. HR and 95% CI assessed association of sociodemographic factors, comorbidities, medication, surgical variables and PROMs with mid-late term revision. RESULTS: NJR-HES-PROMs data were available from 2008 to 2011 on 142 275 THR; mean age 70.0 years and 61.9% female. CPRD GOLD-HES data covered 1995-2011 on 17 047 THR; mean age 68.4 years, 61.8% female. Patients had minimum 5 years postprimary surgery to end 2016. In NJR-HES-PROMS data, there were 3582 (2.5%) revisions, median time-to-revision after primary surgery 1.9 years (range 0.01-8.7), with 598 (0.4%) mid-late term revisions; in CPRD GOLD, 982 (5.8%) revisions, median time-to-revision 5.3 years (range 0-20), with 520 (3.1%) mid-late term revisions.Reduced risk of mid-late term revision was associated with older age at primary surgery (HR: 0.96; 95% CI: 0.95 to 0.96); better 6-month postoperative pain/function scores (HR: 0.35; 95% CI: 0.27 to 0.46); use of ceramic-on-ceramic (HR: 0.73; 95% CI: 0.56 to 0.95) or ceramic-on-polyethylene (HR: 0.76; 95% CI: 0.58 to 1.00) bearing surfaces.Increased risk of mid-late term revision was associated with the use of antidepressants (HR: 1.32; 95% CI: 1.09 to 1.59), glucocorticoid injections (HR: 1.33; 95% CI: 1.06 to 1.67) and femoral head size≥44 mm (HR: 2.56; 95% CI: 1.09 to 6.02)No association of gender, obesity or Index of Multiple Deprivation was observed. CONCLUSION: The risk of mid-late term THR is associated with age at primary surgery, 6-month postoperative pain and function and implant factors. Further work is needed to explore the associations with prescription medications observed in our data.

The effect of underlying diabetes disease on clinical outcome and survival in patients with Covid-19: a propensity score matching study.

PURPOSE: Coronavirus increases mortality rate in people with underlying disease. The purpose of the present research was to compare the clinical outcomes in Covid-19 patients with and without underlying diabetes disease using propensity score matching. METHODS: A matched case-control study was conducted on 459 diabetic patients with Covid-19 (case group) and 459 non-diabetic patients with Covid-19 (control group). Matching in two groups was performed using propensity score matching method. The effect of covariates on the clinical outcome of the patients (recovery-death) was assessed using logistic regression and the associations of factors with the patients' survival were determined using Cox proportional hazards regression model. Data were analyzed using R software. RESULTS: The mean (standard deviation) age of patients in the case and control groups were 65.77 (12.2) and 65.8 (12.24), respectively. 196 patients (43%) in the case group, and 249 patients (54%) in the control group were male (with P-value < 0.05). The logistic regression model showed that the variables of age, level of blood oxygen (SpO2), ICU admission, length of hospitalization, cancer and diabetes affected patients' death. Furthermore, the resuts of the Cox regression showed that the variables of age, level of blood oxygen (SpO2), ICU admission,cancer and diabetes were related to survival of the patients. It was found that diabetes was significantly associated with mortality from COVID-19 with odds ratio of 2.88 (95% CI: 1.80-4.69; P < 0.01) and hazard ratio of 1.45 (95% CI: 1.01-2.03; P = 0.05). CONCLUSION: The underlying diabetes significantly increases the mortality among patients with Covid-19, so special care should be taken for this high risk group if they develop Covid-19.

Mortality incidence for SARS-COV-2 non-survivor infected in Colombia: A potential vaccination priority guide based on Comorbidities

The SARS-CoV-2 vaccination plan development in Colombia, set to begin in February 2021, included a comprehensive assessment of the spread to set population priorities in rank-ordered phases. In Phase 3 of the plan, populations between 16 and 59 years with a set of specific comorbidities will be vaccinated. Our study aims to evaluate the comorbidities incidence in the survival probability to assess the population at most risk if infected and assist in the assignation on this phase. In this study, multivariate Cox regression and Kaplan-Meier curves were performed to determine risk predictors of mortality for 610 reports of up to 15-day decay non-survivor SARS-CoV-2 infected in Colombia. After implementation, higher hazard ratios were associated with diabetes. Kaplan-Meier curves indicate that patients with diabetes that have an older age and hypertension are at a higher risk of earlier death. © IEOM Society International.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters.

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

COVID-19 cases with delayed absorption of lung lesion.

OBJECTIVE: Over 90% of the COVID-19 patients with computed tomographic (CT) manifestations showed radiological improvement on dissipating stage. Cases with refractory pulmonary infiltration were discussed in this study. METHODS: During hospitalization, chest CT scan and reverse transcriptase polymerase chain reaction (RT-PCR) test were repeatedly performed. While drawing parallels to RT-PCR, the impact of delayed absorption of lung lesions on length of hospital stay (LOS) and medical expense was investigated. Features for delayed absorption of lung lesions were identified using cox proportional hazard regression model. RESULTS: Cases with delayed absorption of lung lesions had a prolonged LOS (18.00 ± 4.90 vs 9.25 ± 4.20, p < 0.01) and increased medical expense (9124.55 ± 2421.31 vs 4923.88 ± 2218.56, p < 0.01). Time interval from admission to a negative RT-PCR (ATN) was also prolonged (13.29 ± 4.72 vs 9.25 ± 4.20, p = 0.03). The cox proportional hazard regression model indicated that imported cases bore high risk of delayed absorption of lung lesions (hazard ratio = 2.54, 95% confidence interval 1.05, 6.11, p = 0.04). Sensitivity analysis revealed similar pattern (hazard ratio = 6.64, 95% confidence interval 1.62, 27.18, p = 0.01). CONCLUSION: Imported cases of COVID-19 were more likely to have refractory pulmonary infiltration, which subsequently prolongs LOS and increases medical expense.

Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model.

Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that hospital places are not available for all patients. Therefore, most hospitals accept patients whose recovery rate is high. Machine learning techniques and artificial intelligence have been deployed for computing infection risks, performing survival analysis and classification. Survival analysis (time-to-event analysis) is widely used in many areas such as engineering and medicine. This paper presents two systems, Cox_COVID_19 and Deep_ Cox_COVID_19 that are based on Cox regression to study the survival analysis for COVID-19 and help hospitals to choose patients with better chances of survival and predict the most important symptoms (features) affecting survival probability. Cox_COVID_19 is based on Cox regression and Deep_Cox_COVID_19 is a combination of autoencoder deep neural network and Cox regression to enhance prediction accuracy. A clinical dataset for COVID-19 patients is used. This dataset consists of 1085 patients. The results show that applying an autoencoder on the data to reconstruct features, before applying Cox regression algorithm, would improve the results by increasing concordance, accuracy and precision. For Deep_ Cox_COVID_19 system, it has a concordance of 0.983 for training and 0.999 for testing, but for Cox_COVID_19 system, it has a concordance of 0.923 for training and 0.896 for testing. The most important features affecting mortality are, age, muscle pain, pneumonia and throat pain. Both Cox_COVID_19 and Deep_ Cox_COVID_19 prediction systems can predict the survival probability and present significant symptoms (features) that differentiate severe cases and death cases. But the accuracy of Deep_Cox_Covid_19 outperforms that of Cox_Covid_19. Both systems can provide definite information for doctors about detection and intervention to be taken, which can reduce mortality.