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182 Figure 1Cardiovascular events in COVID-19 Survivors by LGE Status[Figure omitted. See PDF] 182 Figure 2All-cause mortality in COVID-19 Survivors by LGE Status[Figure omitted. See PDF]Conflict of InterestNone
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Background: Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2 virus. Severity of this disease influenced by old age, sex, comorbidities, and other factors. Hypertension and type 2 diabetes mellitus are the most common comorbidities in COVID-19 patients that cause high morbidity and mortality. Objective(s): To analyze the survival of COVID-19 patients with hypertension comorbidity and compare it between diabetes mellitus and non-diabetes mellitus group. Method(s): This retrospective, descriptive study included COVID-19 patients with hypertension comorbidity at Internal Medicine ward Dr. Soetomo Hospital Surabaya from May 2020 to December 2021. Data on age, sex, hypertension, diabetes mellitus type 2, symptoms, vital signs, laboratory finding, length of stay, and outcome were taken from medical records and we carried out kaplan-meier method and log rank test by using SPSS. Result(s): This study obtained 698 sample of confirmed COVID-19 patients and after applying exclusion criteria there were 174 patients with hypertension comorbidity. Most patient were female (60.3%) and age 51-60 years (38.5%). The most common symptoms were shortness of breath (62.1%) and cough (50.6%). There were 50 hypertension and 79 non-hypertension patients died and Survival analysis showed a significant statistical difference between both groups (p=0.042). From 50 deceased hypertensive patients, there are 36 and 14 hypertensive patients with and without diabetes mellitus respectively but survival analysis showed a non-significant statistical difference between both groups (p=0.081) Conclusion(s): There is significant statistical difference in survival analysis in patients with hypertension. We should be aware about COVID-19 patients with hypertension.
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Background/Objectives: SARS-CoV-2 infection clinical manifestations hugely vary among patients, ranging from no symptoms, to life-threatening conditions. This variability is also due to host genetics: COVID-19 Host Genetics Initiative identified six loci associated with COVID-19 severity in a previous case-control genome-wide association study. A different approach to investigate the genetics of COVID-19 severity is looking for variants associated with mortality, e.g. by analyzing the association between genotypes and time-to-event data. Method(s): Here we perform a case-only genome-wide survival analysis, of 1,777 COVID-19 patients from the GEN-COVID cohort, 60 days after infection/hospitalization. Case-only studies has the advantage of eliminating selection biases and confounding related to control subjects. Patients were genotyped using Illumina Infinium Global Screening Arrays. PLINK software was used for data quality check and principal component analysis. GeneAbel R package was used for survival analysis and age, sex and the first four principal components were used as covariates in the Cox proportional hazard model. Result(s): We found four variants associated with COVID-19 patient survival at a nominal P < 1.0 x 10-6. Their minor alleles were associated with a higher mortality risk (i.e. hazard ratios (HR)>1). In detail, we observed: HR=1.03 for rs28416079 on chromosome 19 (P=1.34 x 10-7), HR=1.15 for rs72815354 on chromosome 10 (P=1.66 x 10-7), HR=2.12 for rs2785631 on chromosome 1 (P=5.14 x 10-7), and HR=2.27 for rs2785631 on chromosome 5 (P=6.65 x 10-7). Conclusion(s): The present results suggest that germline variants are COVID-19 prognostic factors. Replication in the remaining HGI COVID-19 patient cohort (EGAS00001005304) is ongoing at the time of submission.
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Objective: COVID-19 has presented numerous epidemiological and clinical pictures from its beginning and much effort has been paid to detect the behavior of disease and its new types. Therefore, in this study, we aimed to compare the in-hospital survival time of Delta and Omicron variant patients admitted to the intensive care unit. Methods: This was a secondary data analysis of the QCOVICU data registry of 200 COVID-19 patients admitted to the ICU of Shahid Beheshti-Amir Al-Momenin Hospital of Qom City, in 2021. Likewise, time to event data, demographics, and baseline laboratory data was collected. Time of transfer to ICU, survivals, and possible predictors of hazards of death was compared within the variants of Omicron and delta. Results: Two hundred patients (62.98±19.94 years old, 94 females/106 males;100 Delta and 100 Omicron variant) participated in this study. Fifty percent of the population had died. Cross-tabulation showed comparable death rates among variants of delta and omicron (50.5% vs. 51%;p=0.999). There was a statistically significant higher time to ICU admission in Delta variant victims than in Omicron variant victims. The mean survival time of delta variant patients was 21.52 days (95% CI: 17.96 – 25.09) which was statistically higher than the mean survival of omicron patients (17.15 days, 95% CI: 13.65-20.64, p=0.018). The mean survival time of delta variant patients was statistically higher than omicron patients (21.52 vs. 17.15 days, p=0.018). Gender, age (years), and lymphocyte count were significant predictors of mortality based on the Cox regression analysis (P>0.05). There was a 5.9 times higher risk of mortality in females compared with males' gender after adjusting for other variables and a 5.6% increase in death risk with a 1-year increase in age, and a 31.8% decrease in death risk with a 1% lymphocyte percentage increase. Conclusion: Critically patients with Delta variant are getting ICU admitted later and withstand more days at ICU than Omicron patients. It seems that Omicron variant causes sudden deterioration of the patient's condition. © 2023 by SPC (Sami Publishing Company).
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In the article, the author discusses the challenges in the decision making and advance care planning on critical care admission of patients living with dementia. Also cited are the poor understanding of the public of critical care, the effectiveness of using natural language processing of unstructured records and machine learning to identify those at risk of subsequent falls, and the recommended fall prevention strategies like Tai Chi.
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BackgroundAlthough many studies have been conducted on COVID-19 in recent years, there are still unanswered questions regarding breakthrough infections (BTIs), particularly in patients with systemic lupus erythematosus (SLE).ObjectivesThis study aimed to determine the occurrence of breakthrough COVID-19 infections in patients with SLE versus other autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs), and healthy controls (HCs).MethodsThe study was based on data from the COVAD questionnaire which amassed a total of 10,783 complete responses from patients with SLE, AIRD, or nrAIRD, and HCs. After exclusion of individuals who were unvaccinated, those who received one vaccine dose only, and those with uncertain responses regarding the vaccine doses, a total of 9,595 patients formed the study population of the present investigation. If a COVID-19 infection occurred after the initial two vaccine doses and at least one booster dose (at least three doses in total, herein termed full vaccination), it was considered a BTI. Data were analysed using multivariable regression models. Statistically significant results were denoted by p values <0.05.ResultsA total of 7,016/9,595 (73.1%) individuals were fully vaccinated. Among those, 1,002 (14.2%) reported at least one BTI, and 166 (2.3%) reported at least two BTIs. Among SLE patients, 867/1,218 (71.2%) were fully vaccinated. Among fully vaccinated SLE patients, 137 (15.8%) reported at least one BTI while 28 (3.2%) reported at least two BTIs. BTI frequencies in fully vaccinated SLE patients were comparable to those of other AIRDs (OR: 1.0;95% CI: 0.8–1.3;p=0.447) and nrAIDS (OR: 0.9;95% CI: 0.6–1.3;p=0.856) but higher compared with HCs (OR: 1.2;95% CI: 1.0–1.6;p=0.022).For SLE patients with three vaccine doses, 113/137 (82.5%) reported at least one BTI while the corresponding number for four vaccine doses was 24/137 (17.5%). Compared with HCs (OR: 10.6;95% CI: 1.2–93.0;p=0.032) and other AIRDs (OR: 3.5;95% CI: 1.08–11.5;p=0.036), SLE patients showed higher frequencies of hospitalisation.AID multimorbidity was associated with a 15-fold increased risk for a need of advanced treatment for COVID-19 (OR: 15.3;95% CI: 2.6–88.2;p=0.002).ConclusionCOVID-19 BTIs occurred in nearly 1 every 6th fully vaccinated patient with SLE, and 20% more frequently in this patient population compared with fully vaccinated HCs. Moreover, BTIs in SLE patients were more severe compared with BTIs in HCs or patients with AIRDs other than SLE, resulting in a greater need for hospitalisation. AID multimorbidity contributed to a more severe COVID-19 BTI requiring advanced management. These insights call for greater attention to vaccination in the vulnerable group of SLE patients, with appropriate risk stratification towards optimised vaccination strategies.Figure 1.Survival analysis across patients with SLE, AIRDs, or nrAIDs, and HCs. SLE: systemic lupus erythematosus;AIRD: autoimmune rheumatic disease;nrAID: non-rheumatic autoimmune disease;HC: healthy control.[Figure omitted. See PDF]AcknowledgementsThe authors thank all survey respondents, as well as patient associations and all members of the COVAD study group for their invaluable role in the data collection.Disclosure of InterestsEmelie Kihlgren Olsson: None declared, Naveen Ravichandran: None declared, Elena Nikiphorou Speakers bureau: EN has received speaker honoraria/participated in advisory boards for Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly., Consultant of: EN has received speaker honoraria/participated in advisory boards for Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly., Grant/research support from: EN holds research grants from Pfizer and Lilly., Julius Lindblom: None declared, Sreoshy Saha: None declared, Syahrul Sazliyana Shaharir: None declared, Wanruchada Katchamart: None declared, Phonpen Akarawatcharangura Goo: None declared, Lisa Traboco: None declared, Yi-Ming Chen: None declared, Kshitij Jagtap: None declared, James B. Lilleker Speakers bureau:
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BACKGROUND: Coronavirus disease 2019 (COVID-19) patients with acute respiratory failure who experience delayed initiation of invasive mechanical ventilation have poor outcomes. The lack of objective measures to define the timing of intubation is an area of concern. We investigated the effect of timing of intubation based on respiratory rate-oxygenation (ROX) index on the outcomes of COVID-19 pneumonia. METHODS: This was a retrospective cross-sectional study performed in a tertiary care teaching hospital in Kerala, India. Patients with COVID-19 pneumonia who were intubated were grouped into early intubation (within 12 hours of ROX index <4.88) or delayed intubation (12 hours or more hours after ROX <4.88). RESULTS: A total of 58 patients was included in the study after exclusions. Among them, 20 patients were intubated early, and 38 patients were intubated 12 hours after ROX index <4.88. The mean age of the study population was 57±14 years, and 55.0% of the patients were male; diabetes mellitus (48.3%) and hypertension (50.0%) were the most common comorbidities. The early intubation group had 88.2% successful extubation, while only 11.8% of the delayed group had successful extubation (P<0.001). Survival was also significantly more frequent in the early intubation group. CONCLUSIONS: Early intubation within 12 hours of ROX index <4.88 was associated with improved extubation and survival in patients with COVID-19 pneumonia.
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INTRODUCTION: Delaying the onset of disability is important for maintaining independence and quality of life in community-dwelling older adults. Given that social isolation is a significant risk factor for disability, effective means associated with social isolation are needed to alleviate disability. Although information and communication technology (ICT) may be a reasonable measure considering the recent social contexts due to the coronavirus disease 2019 pandemic, further insights are required. This study aimed to investigate whether ICT use can alleviate the onset of disability in community-dwelling older adults with and without social isolation. METHODS: This longitudinal cohort study on 4,346 community-dwelling independent Japanese older adults (mean age, 73.5 ± 5.3 years) was conducted between 2017 and 2018. Participants were classified into four groups based on social isolation (the condition where two or more of the following measures were met: domestic isolation, less social contact, and social disengagement) and ICT users (those who had recently used a computer or a smartphone) and followed up to assess disability incidence for 24 months after baseline assessments. Cox proportional-hazards regression models were used to identify the effect of social isolation and ICT use on the risk of disability onset by adjusting for age, sex, education history, number of medications, eye disease, level of annual income, Mini-Mental State Examination, Geriatric Depression Scale 15, and gait speed. RESULTS: The group comprised nonsocial isolation and ICT users (44.7%), social isolation and ICT users (5.4%), nonsocial isolation and ICT nonusers (41.7%), and social isolation and ICT nonusers (8.2%). At the follow-up, 2.2%, 2.4%, 5.5%, and 12.4% of the participants in the above order developed disability (p < 0.01). Cox regression models revealed a significantly higher risk of disability onset in the social isolation and ICT nonusers group than in the social isolation and ICT users group (HR = 2.939; 95% confidence interval (CI) 1.029-8.397; p = 0.044). In the subgroup analysis stratified by social isolation, ICT use significantly reduced the risk of disability onset in the socially isolated group (HR = 0.320; 95% CI 0.109-0.943; p = 0.039), although the same association was not observed in the nonsocially isolated group (HR = 0.845; 95% CI 0.565-1.264; p = 0.411). CONCLUSION: ICT use can alleviate the onset of disability in socially isolated older adults in a community setting. Considering ICT-applied methods for alleviating disability is beneficial for older adults in social isolation.
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COVID-19 , Quality of Life , Humans , Aged , Longitudinal Studies , COVID-19/epidemiology , Social Isolation , Cohort Studies , Independent Living , Communication , TechnologyABSTRACT
The identification of surrogate markers for gold standard outcomes in clinical trials enables future cost-effective trials that target the identified markers. Due to resource limitations, these surrogate markers may be collected only for cases and for a subset of the trial cohort, giving rise to what is termed the case-cohort design. Motivated by a COVID-19 vaccine trial, we propose methods of assessing the surrogate markers for a time-to-event outcome in a case-cohort design by using mediation and instrumental variable (IV) analyses. In the mediation analysis we decomposed the vaccine effect on COVID-19 risk into an indirect effect (the effect mediated through the surrogate marker such as neutralizing antibodies) and a direct effect (the effect not mediated by the marker), and we propose that the mediation proportions are surrogacy indices. In the IV analysis we aimed to quantify the causal effect of the surrogate marker on disease risk in the presence of surrogatedisease confounding which is unavoidable even in randomized trials. We employed weighted estimating equations derived from nonparametric maximum likelihood estimators (NPMLEs) under semiparametric probit models for the time-to-disease outcome. We plugged in the weighted NPMLEs to construct estimators for the aforementioned causal effects and surrogacy indices, and we determined the asymptotic properties of the proposed estimators. Finite sample performance was evaluated in numerical simulations. Applying the proposed mediation and IV analyses to a mock COVID-19 vaccine trial data, we found that 84.2% of the vaccine efficacy was mediated by 50% pseudovirus neutralizing antibody and that neutralizing antibodies had significant protective effects for COVID-19 risk.
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BACKGROUND: Dengue hemorrhagic fever (DHF) is one of the endemic diseases with the highest cases in Indonesia. According to the World Health Organization data in 2020, the incidence of DHF has increased more than 8 times over the last two decades, from 505,430 cases in 2000, to more than 2.4 million in 2010 and 5.2 million in 2019. AIM: This study aims to analyzed the recovery rate of DHF patients at Dr. M. Djamil Padang Hospital during the COVID-19 period and the factors that influence it. METHOD(S): This study is a quantitative study with a retrospective cohort study design. Data were taken from the medical records of DHF patients during the COVID-19 period (March 2020-February 2022). The sampling technique used was simple random sampling. The analysis used the Kaplan-Meier, Cox Regression method, and the Cox Proportional Hazard analysis. RESULT(S): The average time it takes a patient to recover from DHF at Dr. M. Djamil Padang is 10 days. Patients who experienced the event were 32.7%, with an incidence rate of 0.061%. DHF patients at RSUP Dr. M. Djamil Padang, most of them were >14-years-old, male, duration of fever before hospitalization >4 days, platelet level 100,000, hematocrit level >20%, and patients using insurance. CONCLUSION(S): DHF patients aged >14 years, duration of fever before hospitalization 4 days, hematocrit level 20% had a longer recovery rate. It is recommended to the hospital to be better prepared to handle DHF patients according to risk factors and to collaborate for health education.Copyright © 2023 Masrizal Masrizal, Yudi Pradipta, Sari.
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Background: Hepatocellular carcinoma (HCC) is the second leading cause of malignancy-related mortality and the fifth most common worldwide. Immuno-cancer microenvironment (ICME) was highlighted recently because scientists want to unlock the detailed mechanism in carcinogenesis pathway and find the novel interactions in ICME. Besides, single cell analysis could mitigate the interrupted signals between cells and tissues. On the other hand, COVID-19 angiotensin I converting enzyme (ACE) previously was reported associated with cancer. However, the robust association between COVID-19 and HCC ICME is still unaddressed. Aim(s): We plan to investigate the COVID-19 ACE relevant genes to HCC ICME regarding survival. Method(s): We used Reactome for COVID-19 ACE gene pathway mapping and explored the positive relevant gene expression. DISCO website was applied for single cell analyses using the above-collected genes from Reactome. Finally, we implanted the biomedical informatics into TIMER 2.0 for ICME survival analyses. Result(s): In Fig. 1, the gene-gene interaction mapping was shown. We collected 13 genes (CPB2, ACE2, AGT, MME, ANPEP, CPA3, ENPEP, GZMH, CTSZ, CTSD, CES1, ATP6AP2, and AOPEP) for further single cell relevant analyses, in Table 1, with detailed expression level (TPM). Among the above 13 genes, AGT, GZMH, CTSZ, CTSD, CES1, and ATP6AP2 were strongly expressed in liver tissue. We then applied the initial 13 genes to TIMER 2.0 for HCC ICME 2-year survival analyses. CPA3 and GZMH low expressions with high macrophage infiltration in HCC ICME showed significantly worse 2-year cumulative survival [hazard ratio (HR):CPA3 2.21, p-value 0.018;GZMH 2.07, p-value 0.0341]. ACE2, CPB2, AGT, MME, ANPEP, ENPEP, CTSZ, CTSD, CES1, and ATP6AP2 high expressions with high macrophage infiltration in HCC ICME revealed significantly worse 2-year cumulative survival. Conclusion(s): We demonstrate that ACE2 was strongly associated with HCC clinical survival with macrophage infiltration. However, the bidirectional translational roles about ACE2 relevant genes in HCC should be documented.
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BACKGROUND: The high prevalence of diabetes mellitus (DM) in the population causes DM to become one of the most common comorbidities of coronavirus disease 2019 (COVID-19). Patients with diabetes have a higher risk of experiencing serious complications from COVID-19 and even death. AIM: This study was aimed to determine the difference in survival probability of COVID-19 patients, based on their DM status and to determine the association between type 2 DM and COVID-19 mortality at Al Ihsan Hospital, West Java Province, Indonesia. METHOD(S): The population of this retrospective cohort study were COVID-19 patients, aged >=18 years and were treated at Al Ihsan Hospital, from March 2020 to December 31, 2021. Differences in survival probability were obtained from survival analysis with Kaplan-Meier. Cox Proportional Hazard regression was used to determine the association between type 2 DM and COVID-19 mortality. RESULT(S): Totally, 308 confirmed positive COVID-19 patients were recruited in this study. During the 21 days of observation, survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM (71.24% vs. 84.13% respectively, with p = 0.0056). There was a statistically significant association between type 2 DM and COVID-19 mortality after controlling for age, cough symptoms, acute respiratory distress syndrome, vaccination, chronic kidney disease, ventilator use, antiviral therapy, and the percentage of bed occupation rate COVID-19 isolation at admission. The adjusted hazard ratio showing association between type 2 DM and COVID-19 mortality in the final model of multivariate analysis was 2.68 (95% CI 1.24-5.73). CONCLUSION(S): The survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM. COVID-19 patients with DM in Al Ihsan Hospital were almost 3 times more likely to be fatal as compared COVID-19 patients without DM.Copyright © 2023 Oka Septiriani, Mondastri Korib Sudaryo, Syahrizal Syarif, Citra Citra.
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Objectives: Mindfulness meditation apps are used by millions of adults in the USA to improve mental health. However, many new app subscribers quickly abandon their use. The purpose of this study was to determine the behavioral, demographic, and socioeconomic factors associated with the abandonment of meditation apps during the COVID-19 pandemic. Method: A survey was distributed to subscribers of a popular meditation app, Calm, at the start of the COVID-19 pandemic in March 2020 that assessed meditation app behavior and meditation habit strength, as well as demographic and socioeconomic information. App usage data were also collected from the start of each participant's subscription until May 2021. A total of 3275 respondents were included in the analyses. Participants were divided into three cohorts according to their subscription start date: (1) long-term subscribers (> 1 year before pandemic start), (2) pre-pandemic subscribers (< 4 months before pandemic start), and (3) pandemic subscribers (joined during the pandemic). Results: Meditating after an existing routine was associated with a lower risk of app abandonment for pre-pandemic subscribers (hazard ratio = 0.607, 95% CI: 0.422, 0.874; p = 0.007) and for pandemic subscribers (hazard ratio = 0.434, 95% CI: 0.285, 0.66; p < 0.001). Additionally, meditating "whenever I can" was associated with lower risk of abandonment among pandemic subscribers (hazard ratio = 0.437, 95% CI: 0.271, 0.706; p < 0.001), and no behavioral factors were significant predictors of app abandonment among the long-term subscribers. Conclusions: These results show that combining meditation with an existing daily routine was a commonly utilized strategy for promoting persistent meditation app use during the COVID-19 pandemic for many subscribers. This finding supports existing evidence that pairing new behaviors with an existing routine is an effective method for establishing new health habits. Preregistration: This study is not pre-registered.
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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.)
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Introduction: The association of tracheostomy timing and clinical outcomes in ventilated COVID-19 patients remains controversial. Data from the pre-pandemic era has demonstrated the use of tracheostomy for ventilator weaning [1]. However, the use of tracheostomy in COVID- 19 patients was a subject of discussion [2]. Nevertheless, evidence of the impact of tracheostomy on the outcome in critically ill COVID patients is still lacking. This study aims to evaluate the impact on Intensive Care Unit (ICU) outcome (survival) of tracheostomy in COVID- 19 ventilated patients. Method(s): Monocentric descriptive observational study. Demographic and clinical data, timing of tracheostomy and outcome (ICU mortality) from 1st January to 31st December 2021 were registered. Analysis of descriptive statistics for continuous variables and survival analysis (log rank test). Result(s): 115 patients were included (72% males), all mechanically ventilated, 7 (6%) were subjected to tracheostomy. The mean age was 67.2 years (range 36-84 years). The ICU mortality was 62% (71). The group of patients not submitted to tracheostomy had a mean survival time of 24.4 days (SD +/- 1.5) and median survival time of 22 days (SD +/- 1.7). The group of patients that were subjected to tracheostomy, the mean survival time was 68.5 days (SD +/- 12.2) and median survival time was 50 days (SD +/- 2). This comparison is significative (Log Rank test, p = 0.0001). Conclusion(s): The present study demonstrates a better survival likelihood of the tracheostomized subpopulation. Tracheostomy was only done in 6% of patients, which elucidates a need to further prospective, randomized studies to assess the impact on the outcome of tracheostomy in ventilated COVID19 patients.
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Background: Structural barriers to care among people who inject drugs (PWID) raise concerns about disproportionate access to essential services like COVID-19 vaccination. Given the heightened risk of serious complications resulting from SARS-CoV-2 infection, particularly among people living with HIV (PWH) with unsuppressed viral load, its critical to understand the role of HIV care among other factors associated with timely vaccination. We aimed to assess the role of HIV care on COVID-19 vaccination uptake among PWID. Method(s): We included 960 adult PWUD participating in the ALIVE (AIDS Linked to the Intravenous Experience) longitudinal study in Baltimore, Maryland, who were alive and in follow up as of April 2020. We ed COVID-19 vaccination data from electronic medical records linked to participants via the regional health information exchange. We conducted survival analysis to estimate time from broad vaccine eligibility (April 6, 2021) to completion of the COVID-19 vaccination primary series by HIV status (uninfected, virally suppressed PWH [HIV-RNA< 400 copies/mL], unsuppressed PWH [HIV-RNA >400 copies/mL]) and Cox Proportional Hazards regression to adjust for potential confounding by health status and substance use variables. Result(s): Our sample (N=960) was primarily black (77%) and male (65%) with 31% reporting recent injection drug use. Among 265 people living with HIV (PWH) in our sample (27%), 84% were virally suppressed. As of February 22, 2022, 539 (56%) completed the primary series, 131 (14%) received a single dose of mRNA vaccine and 290 (30%) remained unvaccinated. Compared to PWID without HIV, virally suppressed PWH were significantly more likely to complete the primary series (Adjusted Hazard Ratio [AHR]:1.23,95% Confidence Interval [95%CI]:1.07,1.50), while PWH with higher viral loads were less likely (AHR:0.72,95%CI:0.45,1.16). Sensitivity analyses with a subsample restricted to PWH confirmed significant differences in time to vaccination by viral load status (log-rank p-value: 0.016) and modeling with an origin of Dec. 12, 2020, yielded similar adjusted results. Conclusion(s): Among PWID with HIV, viral suppression is associated with quicker vaccination uptake, likely due to HIV care engagement. Alongside interventions targeting social determinants (e.g. low income, homelessness) and substance use behaviors (e.g. active injecting, stimulant use), targeted improvements along the HIV care continuum and other efforts to engage PWID may bolster vaccine uptake. Figure 1. Kaplan-Meier survival curve demonstrating time-to-vaccination (completion of COVID-19 primary series) in weeks by HIV status accounting for viral load (HIV-, HIV+ [VL <= 400 cells/muL], HIV+ [VL > 400 cells/muL]), including results for Log-rank tests for homogeneity among strata (p-value).
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Background: Patients with COVID-19 can develop a cytokine storm, with a mortality rate of up to 45%. Because IL-6 is a relevant cytokine, early identification of patients at-risk can help reduce mortality. Objective(s): To determine whether serum IL-6 levels on admission can predict the outcome in terms of all-cause mortality in hospitalized COVID-19 patients. Material(s) and Method(s): This observational, single-center retrospective cohort study was conducted in patients admitted with COVID-19 disease at Chest Disease Hospital, Srinagar from May 2021 to October 2021. Investigations like IL-6, HS-CRP and D-Dimer, were collected, once after hospitalization (median 1.53 days). Multivariable logistic and linear regressions and survival analysis were performed depending on outcomes, primary end point being all-cause mortality, secondary outcomes being, need for HFNC, NIV or IMV. Result(s): 198 patients, 55.5% males, median age was 67 years. Mean IL-6 levels in patients who were discharged were 77.4 pg/ml while those who died had 132.56 pg/ml, corresponding with the severity of the disease. Need for HFNC 25.3% vs 13.9% (P = 0.44), NIV 17.3% vs 4.1% (P = 0.002) and IMV 2.7% vs 1.6% (P = 0.614) was also higher in patients with high levels of IL-6. Conclusion(s): Baseline IL-6 greater than 90 pg/mL is a sensitive indicator of progression to severe disease in COVID-19 patients manifesting in terms of higher mortality, need of HFNC, NIV and IMV. Hence early identification of patients at risk may result in early intervention and hence reduce mortality.
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Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes. DESIGN: Retrospective observational study. SETTING: We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada. PATIENTS: Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU. CONCLUSIONS: It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.
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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.
ABSTRACT
Before-after analysis methods in traffic safety often aggregate traffic crashes into crash frequencies using relatively long aggregation time periods, such as a year. The implicit assumption is that the treatment effect is temporally stable over the aggregation period. However, certain "treatments”, such as the COVID-19 pandemic, may result in fast-evolving changes to road safety. By aggregating individual crashes, it is difficult to investigate the temporal characteristics of crashes and capture the potential temporal instability in treatment effect at detailed temporal levels, such as within a year. Therefore, this study exploits the disaggregated nature of crashes and proposes a survival analysis with random parameter (SARP) before-after analysis approach that can flexibly accommodate the temporal instability in treatment effect at various temporal levels. To validate and test the proposed approach, a statistical simulation study and an empirical case study that investigates the safety impact of COVID-19 lockdown in Manhattan, New York, are conducted. The statistical simulation study shows that the SARP method can unbiasedly estimate different patterns of temporally instable treatment effect at various temporal levels. The estimated monthly crash modification factors from the case study display an increasing trend after the largest decrease in the first month after the lockdown, which implies that traffic safety conditions are gradually returning to normal and provides evidence of temporal instability in treatment effect. The proposed SARP approach is promising to investigate the evolving safety impact of emerging technologies in transportation, such as the deployment of connected and autonomous vehicles.