scholarly journals Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19

2022 ◽  
Vol 27 (1) ◽  
Author(s):  
Georg Marcus Fröhlich ◽  
Marlieke E. A. De Kraker ◽  
Mohamed Abbas ◽  
Olivia Keiser ◽  
Amaury Thiabaud ◽  
...  
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Cox Regression ◽  
Hospital Death ◽  
Rt Pcr ◽  
Hospital Outcomes ◽  
Subdistribution Hazard ◽  
Inverse Probability ◽  
Icu Admission ◽  
Competing Events

Background Since the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data. Aim This study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland. Methods This retrospective multi-centre cohort study includes patients > 18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders. Results In 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p < 0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54–78) than the patients with influenza (median 74 years; IQR: 61–84) (p < 0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p < 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22–4.09; p < 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00–3.00, p < 0.001) for ICU admission. Conclusion Community-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.

scholarly journals COVID-19: more than “a little flu”? Insights from the Swiss hospital-based surveillance of Influenza and COVID-19

2020 ◽  
Author(s):  
Georg Marcus Fröhlich ◽  
Marlieke E. A. De Kraker ◽  
Mohammed Abbas ◽  
Olivia Keiser ◽  
Amaury Thiabaud ◽  
...  
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Cox Regression ◽  
Hospital Death ◽  
Time Dependency ◽  
Prospective Surveillance ◽  
Subdistribution Hazard ◽  
Inverse Probability ◽  
Icu Admission ◽  
Competing Events

AbstractBackgroundCoronavirus disease 19 (COVID-19) has frequently been colloquially compared to the seasonal influenza, but comparisons based on empirical data are scarce.AimsTo compare in-hospital outcomes for patients admitted with community-acquired COVID-19 to patients with community-acquired influenza in Switzerland.MethodsPatients >18 years, who were admitted with PCR proven COVID-19 or influenza A/B infection to 14 participating Swiss hospitals were included in a prospective surveillance. Primary and secondary outcomes were the in-hospital mortality and intensive care unit (ICU) admission between influenza and COVID-19 patients. We used Cox regression (cause-specific models, and Fine & Gray subdistribution) to account for time-dependency and competing events with inverse probability weighting to account for confounders.ResultsIn 2020, 2843 patients with COVID-19 were included from 14 centers and in years 2018 to 2020, 1361 patients with influenza were recruited in 7 centers. Patients with COVID-19 were predominantly male (n=1722, 61% vs. 666 influenza patients, 48%, p<0.001) and were younger than influenza patients (median 67 years IQR 54-78 vs. median 74 years IQR 61-84, p<0.001). 363 patients (12.8%) died in-hospital with COVID-19 versus 61 (4.4%) patients with influenza (p<0.001). The final, adjusted subdistribution Hazard Ratio for mortality was 3.01 (95% CI 2.22-4.09, p<0.001) for COVID-19 compared to influenza, and 2.44 (95% CI, 2.00-3.00, p<0.001) for ICU admission.ConclusionEven in a national healthcare system with sufficient human and financial resources, community-acquired COVID-19 was associated with worse outcomes compared to community-acquired influenza, as the hazards of in-hospital death and ICU admission were ∼3-fold higher.

scholarly journals Insights into the association of ACEIs/ARBs use and COVID-19 prognosis: a multistate modelling study of nationwide hospital surveillance data from Belgium

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e053393
Author(s):  
José L. Peñalvo ◽  
Els Genbrugge ◽  
Elly Mertens ◽  
Diana Sagastume ◽  
Marianne A B van der Sande ◽  
...  
Keyword(s):  
Markov Models ◽  
Transition Probabilities ◽  
Angiotensin Receptor Blockers ◽  
Surveillance Data ◽  
Hospital Death ◽  
Hospital Data ◽  
Health States ◽  
Converting Enzyme Inhibitors ◽  
Icu Admission ◽  
Competing Events

ObjectivesThe widespread use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) by patients with chronic conditions raised early concerns on the potential exacerbation of COVID-19 severity and fatality. Previous studies addressing this question have used standard methods that may lead to biased estimates when analysing hospital data because of the presence of competing events and event-related dependency. We investigated the association of ACEIs/ARBs’ use with COVID-19 disease outcomes using time-to-event data in a multistate setting to account for competing events and minimise bias.SettingNationwide surveillance data from 119 Belgian hospitals.ParticipantsMedical records of 10 866 patients hospitalised from 14 March 2020to 14 June 2020 with a confirmed SARS-CoV-19 infection and information about ACEIs/ARBs’ use.Primary outcome measureMultistate, multivariate Cox-Markov models were used to estimate the hazards of patients transitioning through health states from admission to discharge or death, along with transition probabilities calculated by combining the baseline cumulative hazard and regression coefficients.ResultsAfter accounting for potential confounders, there was no discernable association between ACEIs/ARBs’ use and transfer to intensive care unit (ICU). Contrastingly, for patients without ICU transfer, ACEIs/ARBs’ use was associated with a modest increase in recovery (HR 1.07, 95% CI 1.01 to 1.13, p=0.027) and reduction in fatality (HR 0.83, 95% CI 0.75 to 0.93, p=0.001) transitions. For patients transferred to ICU admission, no evidence of an association between ACEIs/ARBs’ use and recovery (HR 1.16, 95% CI 0.97 to 1.38, p=0.098) or in-hospital death (HR 0.91, 95% CI 0.73 to 1.12, p=0.381) was observed. Male gender and older age were significantly associated with higher risk of ICU admission or death. Chronic cardiometabolic comorbidities were also associated with less recovery.ConclusionsFor the first time, a multistate model was used to address magnitude and direction of the association of ACEIs/ARBs’ use on COVID-19 progression. By minimising bias, this study provided a robust indication of a protective, although modest, association with recovery and survival.

Subdistribution-Based Imputation for Deep Survival Analysis with Competing Events

2021 ◽  
Author(s):  
Shekoufeh Gorgi Zadeh ◽  
Charlotte Behning ◽  
Matthias Schmid
Keyword(s):  
Survival Analysis ◽  
Survival Data ◽  
Cox Regression ◽  
Classical Model ◽  
Event Analysis ◽  
Survival Times ◽  
Subdistribution Hazard ◽  
Time To Event Data ◽  
Competing Events ◽  
Real World Datasets

Abstract With the popularity of deep neural networks (DNNs) in recent years, many researchers have proposed DNNs for the analysis of survival data (time-to-event data). These networks learn the distribution of survival times directly from the predictor variables without making strong assumptions on the underlying stochastic process. In survival analysis, it is common to observe several types of events, also called competing events. The occurrences of these competing events are usually not independent of one another and have to be incorporated in the modeling process in addition to censoring. In classical survival analysis, a popular method to incorporate competing events is the subdistribution hazard model, which is usually fitted using weighted Cox regression. In the DNN framework, only few architectures have been proposed to model the distribution of time to a specific event in a competing events situation. These architectures are characterized by a separate subnetwork/pathway per event, leading to large networks with huge amounts of parameters that may become difficult to train. In this work, we propose a novel imputation strategy for data preprocessing that incorporates the subdistribution weights derived from the classical model. With this, it is no longer necessary to add multiple subnetworks to the DNN to handle competing events. Our experiments on synthetic and real-world datasets show that DNNs with multiple subnetworks per event can simply be replaced by a DNN designed for a single-event analysis without loss in accuracy.

scholarly journals 686. Use of an Influenza-Like Illness School Absenteeism Monitoring System to Identify Seasonal Influenza Outbreaks in the Community: ORCHARDS (Wisconsin, September 2014–June 2017)

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S248-S248
Author(s):  
Jonathan Temte ◽  
Yenlik Zheteyeva ◽  
Shari Barlow ◽  
Maureen Goss ◽  
Emily Temte ◽  
...  
Keyword(s):  
Lead Time ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza B ◽  
Linear Regression Models ◽  
Rt Pcr ◽  
Human Influenza Virus ◽  
Influenza Like Illness ◽  
K 12 ◽  
Influenza Outbreaks

Abstract Background Schools are purported to be primary venues of influenza transmission and amplification with secondary spread to communities. We assessed K—12 student absenteeism monitoring as a means for early detection of influenza activity in the community. Methods. We conducted a 3-year, prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness-associated (a-ILI) absenteeism within the Oregon School District, Oregon, WI (OSD: enrollment = 3,900 students). Absenteeism reporting was facilitated by automated processes within OSD’s electronic student information system. Students were screened for ILI, and, if eligible, visited at home, where pharyngeal specimens were collected for influenza RT-PCR (IVD CDC Human Influenza Virus RT-PCR Diagnostic Panel) and multipathogen testing (Luminex NxTAG RPP). The study definition of a-ILI was validated for 700 children with acute respiratory infections using binomial logistic regression. Surveillance of medically attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining OSD as part of the Wisconsin Influenza Incidence Surveillance Project using the same laboratory testing. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis. Results. Influenza A and B were detected in 54 and 51 of the 700 visited students, respectively. Influenza was significantly associated with a-ILI status (OR = 4.74; 95% CI: 2.78—8.18; P &lt; 0.001). Of MAI patients, 371 had influenza A and 143 had influenza B. a-I was significantly correlated with MAI in the community (r = 0.472; P &lt; 0.001) with a 15-day lead time. a-ILI was significantly correlated with MAI in the community (r = 0.480; P &lt; 0.001) with a 1-day lead time. a-TOT performed poorly (r = 0.278; P &lt; 0.001), following MAI by 9 days (Figure 1). Conclusion. Surveillance using cause-specific absenteeism was feasible to implement in OSD and performed well over a 3-year period marked by diverse presentations of seasonal influenza. Monitoring a-I and a-ILI can detect influenza outbreaks in the community, providing early warning in time for community mitigation efforts for seasonal and pandemic influenza. Disclosures All authors: No reported disclosures.

scholarly journals Observational Study of Chlorpromazine in Hospitalized Patients with Covid-19

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Marina Sanchez Rico ◽  
Raphael Vernet ◽  
Anne-Sophie Jannot ◽  
Antoine Neuraz ◽  
...  
Keyword(s):  
Primary Endpoint ◽  
Cox Regression ◽  
Cox Model ◽  
Event Analysis ◽  
Time To Event ◽  
Rt Pcr ◽  
University Hospitals ◽  
Inverse Probability ◽  
Antiviral Effects

On the grounds of its anti-inflammatory and potential antiviral effects, chlorpromazine has been suggested to be effective treatment for Covid-19. We examined the association between chlorpromazine use and respiratory failure among all hospitalized adults with Covid-19 at the 39 Greater Paris University hospitals since the beginning of the epidemic. Study baseline was defined as the date of hospital admission. The primary endpoint was a composite of intubation or death in a time-to-event analysis adjusting for numerous potential confounders. We used a multivariable Cox model with inverse probability weighting according to the propensity score. Of the 12,217 adult inpatients with a positive Covid-19 RT-PCR test included in the analyses, 57 (0.47%) received chlorpromazine. Over a mean follow-up of 20.8 days, the primary endpoint occurred in 29 patients (50.9%) exposed to chlorpromazine and 1,899 patients (15.6%) who were not. In the main analysis, there was a positive significant association between chlorpromazine use and the outcome (HR, 1.67; 95% CI, 1.09 to 2.56, p=0.019), while a Cox regression in a matched analytic sample yielded non-significant association (1.38; 95% CI, 0.91 to 2.09, p=0.123). These findings suggest that chlorpromazine is unlikely to have a clinical efficacy for Covid-19.

scholarly journals Nitrostative stress status during seasonal and pdmH1N1 infection in Iraq

2011 ◽  
Vol 5 (12) ◽  
pp. 863-867 ◽  
Author(s):  
Marwan S. M. Al-Nimer ◽  
Majid M Mahmood ◽  
Saba Saadoon Khazaal
Keyword(s):  
Nitric Oxide ◽  
Virus Infection ◽  
Influenza A Virus ◽  
Healthy Subjects ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Nitrosative Stress ◽  
Rt Pcr ◽  
One Step ◽  
Influenza A Virus Infection

Introduction: Influenza A virus infection is associated with oxidative and nitrosative stress. This study aimed to assess nitrosative stress in pandemic H1N1 (pdmH1N1) and seasonal influenza A infected patients. Methodology: The study included the following subjects:  20 patients infected with seasonal (negative one-step probe RT-PCR) influenza and 12 patients infected with pdmH1N1 (positive, one-step probe RT-PCR) influenza during the 2009 pandemic in Iraq. Twenty healthy subjects served as controls. Serum nitric oxide using Greiss reagent and peroxynitrite were used to assess nitrosative stress status. Results: Serum nitric oxide and peroxynitrite are significantly increased in patients infected with seasonal and pdmH1N1 influenza compared with the levels in healthy subjects. Infected patients with seasonal influenza showed significantly higher numbers of serum nitrogen species than corresponding pdmH1N1 infected patients. The turnover process reflected by the peroxynitrite/nitric oxide ratio was 0.177, 0.313 and 0.214 in healthy subjects, seasonal and pdmH1N1infected patients respectively. Conclusions: Influenza A virus infection is associated with significant nitrosative stress activity which is more pronounced in seasonal than in pdmH1N1 infected patients. The determination of serum nitric oxide and peroxynitrite may serve as biochemical markers.  

scholarly journals MO206MALE SEX IS ASSOCIATED WITH IN-HOSPITAL DEATH IN NON-DIALYSIS CKD PATIENTS WITH COVID-19

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Armando Coca ◽  
Carla Burballa ◽  
Francisco Javier Centellas Pérez ◽  
Isabel Acosta-Ochoa ◽  
María Dolores Arenas ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Organ Injury ◽  
Hospital Death ◽  
Cox Regression Analysis ◽  
Icu Admission ◽  
Matched Design ◽  
Male Sex ◽  
Age And Sex

Abstract Background and Aims Coronavirus disease (COVID-19), caused by Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) can lead to significant organ injury. CKD has been associated with increased mortality in previous epidemics, and male sex has been correlated with worse outcomes during COVID-19 in the general population. Our aim was to describe the differential effect of sex as a risk factor for in-hospital mortality among non-dialysis CKD subjects. Method Multicenter, observational cohort study including 136 adult patients with CKD and 136 age- and sex-matched controls who required admission for COVID-19 in three academic hospitals in Spain. Viral infection was confirmed by real-time RT-qPCR and/or serologic testing in all cases. Disease severity on admission was classified according to the WHO—China Joint Mission Report on COVID-19. The presence of CKD was defined as sustained eGFR &lt;60 and &gt;15 ml/min/1.73m2 within the 6 months prior to COVID-19 hospitalization. Demographic and clinical data were gathered from medical records. Outcomes were recorded during the following 28 days after admission. We applied Cox proportional hazards models, adjusted for age, sex, hypertension, diabetes and severe or critical disease at presentation. Results Due to the matched design, no differences were found regarding age and sex between cohorts. CKD patients suffered more frequently from hypertension and diabetes and presented higher 28-day mortality after hospital admission due to COVID-19 compared with age- and sex-matched controls (40.4 vs. 24.3%; P=0.004). In adjusted Cox regression analysis among CKD patients, only age (HR: 1.087, 95% CI: 1.047-1.128) and male sex (HR: 1.883, 95% CI: 1.045-3.391) were independent predictors of 28-day mortality. Comparatively, among patients without CKD, only age acted as an independent predictor for 28-day mortality (HR: 1.082, 95% CI: 1.033-1.133). None of the variables included in adjusted regression was able to predict ICU admission in any of the cohorts. Conclusion Male sex is associated with increased mortality, but not with ICU admission, after hospitalization due to COVID-19 among non-dialysis CKD patients. That effect was not observed among hospitalized controls without CKD.

scholarly journals Influence of comorbidity on hospital outcomes among patients with COVID-19 using electronic records from a nationwide Healthcare System in Peru

2021 ◽  
Author(s):  
Janeth Tenorio-Mucha ◽  
Percy Soto-Becerra ◽  
Roger V. Araujo-Castillo ◽  
YAMILEE HURTADO-ROCA
Keyword(s):  
Hospital Mortality ◽  
Cox Regression ◽  
Social Health Insurance ◽  
Rank Test ◽  
Hospital Death ◽  
Middle Income ◽  
Significance Level ◽  
Icu Admission ◽  
Log Rank Test ◽  
Context Specific

Abstract Background Large cases reported that older age and comorbidity are predictors for poor prognosis in COVID-19 patients. Nevertheless, context-specific evidence relevant in low-and middle-income countries is still pending. Methods Retrospective cohort study using electronic health records of confirmed cases admitted in hospitalization areas from the Peruvian Social Health Insurance. The main variable was the presence of comorbidities and the outcomes were in-hospital mortality or intensive care unit admission, and in/out hospital mortality. We used Kaplan-Meier survival curves with the Log-Rank test to compare time-to-event outcomes between comorbidities groups. Crude and adjusted Cox regression models were used to estimate hazard ratios (HR). Statistical analyses were conducted with a significance level of 5%. Results In patients with ICU admission or in-hospital death, 45.99% had one comorbidity and 50.26% had two or more comorbidities. Using in/out hospital deaths up to 60 days as the outcome, the overall survival of patients with two comorbidities is lower than patients with one comorbidity, and both are lowest than a patient without comorbidities (Log-rank test p = 0.001). After adjusting for sex, age, severity, and hospital care network patients with one comorbidity (HR: 1.16; IC 95 %: 1.04–1.31) and with two or more comorbidities (HR: 1.13; IC 95%: 1.01–1.26) are at higher risk to die compared with those without comorbidities. Conclusion The presence of comorbidities in hospitalized patients with COVID-19 are risk factors for ICU admission and mortality. Proper identification of these factors can help to identify patients at higher risk in hospital admission and provide specialized care to prevent deaths.

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