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Institute
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.
Background
Based on low-quality evidence, current nutrition guidelines recommend the delivery of high-dose protein in critically ill patients. The EFFORT Protein trial showed that higher protein dose is not associated with improved outcomes, whereas the effects in critically ill patients who developed acute kidney injury (AKI) need further evaluation. The overall aim is to evaluate the effects of high-dose protein in critically ill patients who developed different stages of AKI.
Methods
In this post hoc analysis of the EFFORT Protein trial, we investigated the effect of high versus usual protein dose (≥ 2.2 vs. ≤ 1.2 g/kg body weight/day) on time-to-discharge alive from the hospital (TTDA) and 60-day mortality and in different subgroups in critically ill patients with AKI as defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria within 7 days of ICU admission. The associations of protein dose with incidence and duration of kidney replacement therapy (KRT) were also investigated.
Results
Of the 1329 randomized patients, 312 developed AKI and were included in this analysis (163 in the high and 149 in the usual protein dose group). High protein was associated with a slower time-to-discharge alive from the hospital (TTDA) (hazard ratio 0.5, 95% CI 0.4–0.8) and higher 60-day mortality (relative risk 1.4 (95% CI 1.1–1.8). Effect modification was not statistically significant for any subgroup, and no subgroups suggested a beneficial effect of higher protein, although the harmful effect of higher protein target appeared to disappear in patients who received kidney replacement therapy (KRT). Protein dose was not significantly associated with the incidence of AKI and KRT or duration of KRT.
Conclusions
In critically ill patients with AKI, high protein may be associated with worse outcomes in all AKI stages. Recommendation of higher protein dosing in AKI patients should be carefully re-evaluated to avoid potential harmful effects especially in patients who were not treated with KRT.
Trial registration: This study is registered at ClinicalTrials.gov (NCT03160547) on May 17th 2017.
Cardiac surgery (CSX) can be lifesaving in elderly patients (age ≥ 80 years) but may still be associated with complications and functional decline. Frailty represents a determinant to outcomes in critically ill patients, but little is known about its influence on elderly CSX-patients. This is a secondary exploratory analysis of a multi-center, prospective observational cohort study of 610 elderly patients admitted to the ICU and followed for one year to document long-term outcomes. CSX-ICU-patients (n = 49) were compared to surgical ICU patients (n = 184) with regard to demographics, frailty, and outcomes. Of all surgical patients, 102 (43%) were considered vulnerable or frail. The subdistribution hazard ratio (SHR) of time to discharge home (TTDH) for vulnerable/frail vs. fit/well patients was 0.54 (95% confidence interval (CI), 0.34, 0.86, p = 0.007). The p-value for effect modification between surgery group (CSX vs. surgical ICU patients) and Clinical Frailty Scale (CFS) group was not significant (p = 0.37) suggesting that the observed difference in the CFS effect between the CSX and surgical ICU patients is consistent with random error. A further subgroup analysis shows that among surgical ICU patients, the SHR of time to discharge home (TTDH) for vulnerable/frail vs. fit/well patients was 0.49 (95% CI, 0.29, 0.83) while the corresponding SHR for CSX patients was 0.77 (0.32–1.88). In conclusion, preoperative frailty reduced the rate of discharge to home in both surgical and CSX patients, but a larger sample of CSX patients is needed to adequately address this question in this patient group.