Search

Contains fulltext : 291740.pdf (Publisher’s version ) (Open Access) BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting. METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated. DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.

Additional file 2. Self-rated change in the six dimensions of Positive Health compared to the year before the COVID-19 pandemic in older individuals living in the Netherlands (n=834).

Additional file 3. Self-rated change (%), compared to before the COVID-19 pandemic, in the six dimensions of Positive Health of older individuals living in the Netherlands depending on sex, age, living situation and self-rated general health (n=834). * = statistically significant.

Additional file 1. Original response categories and conversion of the response categories for data analysis.

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term ‘metabolomics’ refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Additional file 1 Table 1. Correlations of baseline characteristics and cross-sectional outcomes of mental wellbeing.

Additional file 3. Group information for the Pathway for older patients reaching end stage renal disease (POLDER) study group.

Additional file 2 Table 2. Correlations of baseline characteristics and change in longitudinal outcomes of mental wellbeing.

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. In addition, the original version of this Article contained an error in the author affiliations. An affiliation of Abdel Abdellaoui with Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Aims: We investigated the association between quantified metabolite, lipid and lipoprotein measures and incident heart failure hospitalisation (HFH) in the elderly, and examined whether circulating metabolic measures improve HFH prediction. Methods and results: Overall, 80 metabolic measures from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial were measured by proton nuclear magnetic resonance spectroscopy (n = 5341; 182 HFH events during 2.7‐year follow‐up). We repeated the work in FINRISK 1997 (n = 7330; 133 HFH events during 5‐year follow‐up). In PROSPER, the circulating concentrations of 13 metabolic measures were found to be significantly different in those who were later hospitalised for heart failure after correction for multiple comparisons. These included creatinine, phenylalanine, glycoprotein acetyls, 3‐hydroxybutyrate, and various high‐density lipoprotein measures. In Cox models, two metabolites were associated with risk of HFH after adjustment for clinical risk factors and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP): phenylalanine [hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.10–1.53; P = 0.002] and acetate (HR 0.81, 95% CI 0.68–0.98; P = 0.026). Both were retained in the final model after backward elimination. Compared to a model with established risk factors and NT‐proBNP, this model did not improve the C‐index but did improve the overall continuous net reclassification index (NRI 0.21; 95% CI 0.06–0.35; P = 0.007) due to improvement in classification of non‐cases (NRI 0.14; 95% CI 0.12–0.17; P