COVID-19 mortality needs age adjusting for international comparisons.
Ontology highlight
ABSTRACT:
SUBMITTER: Garcia-Calavaro C
PROVIDER: S-EPMC8250608 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
| S-EPMC9192605 | biostudies-literature
Project description: Not available
| S-EPMC7928837 | biostudies-literature
Project description:IntroductionIncreased mortality has been demonstrated in older adults with coronavirus disease 2019 (COVID-19), but the effect of frailty has been unclear.MethodsThis multi-centre cohort study involved patients aged 18 years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS) and delirium on risk of increased care requirements on discharge, adjusting for the same variables.ResultsData from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, interquartile range [IQR] 54-83; 55.2% male). The risk of death increased independently with increasing age (>80 versus 18-49: hazard ratio [HR] 3.57, confidence interval [CI] 2.54-5.02), frailty (CFS 8 versus 1-3: HR 3.03, CI 2.29-4.00) inflammation, renal disease, cardiovascular disease and cancer, but not delirium. Age, frailty (CFS 7 versus 1-3: odds ratio 7.00, CI 5.27-9.32), delirium, dementia and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9.ConclusionAge and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age.
| S-EPMC7929433 | biostudies-literature
Project description:COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of covidcompare.io, a web tool built to compare numerous forecasts and offer insight into how each has performed over the course of the pandemic. From its launch in December 2020 to June 2021, we have seen 4600 unique visitors from 85 countries. A study conducted with public health professionals showed high usability overall as formally assessed using a Post-Study System Usability Questionnaire. We find that covidcompare.io is an impactful tool for the comparison of international COVID-19 mortality forecasting models.
| S-EPMC8712244 | biostudies-literature
Project description: Not available
| S-EPMC7442432 | biostudies-literature
Project description:Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs into a range of policies and decision-makers need information about predictive performance. We identified n=386 public COVID-19 forecasting models and included n=8 that were global in scope and provided public, date-versioned forecasts. For each, we examined the median absolute percent error (MAPE) compared to subsequently observed mortality trends, stratified by weeks of extrapolation, world region, and month of model estimation. Models were also assessed for ability to predict the timing of peak daily mortality. The MAPE among models released in July rose from 1.8% at one week of extrapolation to 24.6% at twelve weeks. The MAPE at six weeks were the highest in Sub-Saharan Africa (34.8%), and the lowest in high-income countries (6.3%). At the global level, several models had about 10% MAPE at six weeks, showing surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. The framework and publicly available codebase presented here ( https://github.com/pyliu47/covidcompare ) can be routinely used to compare predictions and evaluate predictive performance in an ongoing fashion.
| S-EPMC7685335 | biostudies-literature
Project description:Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.
| S-EPMC7725386 | biostudies-literature
Project description:Background: The COVID 19 epidemic submerged many health systems in the Amazon. The objective of the present study was to focus on the epidemic curves of the COVID 19 epidemic in different centers, and to look at testing and mortality data. Methods: Publicly available datasets were used. The log10 of the daily cumulated number of cases starting from the day the territory reached 100 cumulated cases was plotted to compare the magnitude, shape and slope of the different curves. The maximum daily testing efforts were plotted for each territory in relation to the maximum daily number of diagnoses. The case fatality rate was computed by dividing the number of COVID 19 deaths by the number of confirmed cases. Results: In the Amazonian regions in general the speed of growth was generally lower than in Europe or the USA, or Southern Brazil. Whereas, countries like South Korea or New Zealand "broke" the curve relatively rapidly the log linear trajectory seemed much longer with signs of a decline in growth rate as of early July 2020. After a very slow start, French Guiana had the lowest slope when compared to other Amazonian territories with significant epidemics. The Amazonian states of Roraima, Amazonas, Parà, and Amapà had among the highest number of cases and deaths per million inhabitants in the world. French Guiana had significantly fewer deaths relative to its number of confirmed cases than other Amazonian territories. French Guiana had a late epidemic surge with intense testing scale-up often exceeding 4,000 persons tested daily per million inhabitants. Brazil was an outlier with low daily testing levels in relation to the number of daily diagnoses. Conclusions: There were marked heterogeneities mortality rates suggesting that socioeconomic, political factors, and perhaps ethnic vulnerability led to striking outcome differences in this Amazonian context.
| S-EPMC7990879 | biostudies-literature
Project description:In this study we profiled 288 new serum proteomics samples measured at admission from patients hospitalized within the Mount Sinai Health System with positive SARS-CoV-2 infection. We first computed Th1 and Th2 pathway enrichment scores by gene set variation analysis and then compared the differences in Th2 and Th1 pathway scores between patients that died compared to those that survived.
2022-06-16 | GSE178399 | GEO
Project description:The first cluster of patients suffering from coronavirus disease 2019 (COVID-19) was identified on December 21, 2019, and as of July 29, 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have been linked with 664,333 deaths and number at least 16,932,996 worldwide. Unprecedented in global societal impact, the COVID-19 pandemic has tested local, national, and international preparedness for viral outbreaks to the limits. Just as it will be vital to identify missed opportunities and improve contingency planning for future outbreaks, we must also highlight key successes and build on them. Concomitant to the emergence of a novel viral disease, there is a 'research and development gap' that poses a threat to the overall pace and quality of outbreak response during its most crucial early phase. Here, we outline key components of an adequate research response to novel viral outbreaks using the example of SARS-CoV-2. We highlight the exceptional recent progress made in fundamental science, resulting in the fastest scientific response to a major infectious disease outbreak or pandemic. We underline the vital role of the international research community, from the implementation of diagnostics and contact tracing procedures to the collective search for vaccines and antiviral therapies, sustained by unique information sharing efforts.
| S-EPMC7546461 | biostudies-literature