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Background:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.
Results:
Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.
Conclusions:
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment
(2010)
The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia
(2019)
The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.
Specialisation and diversity of multiple trophic groups are promoted by different forest features
(2019)
While forest management strongly influences biodiversity, it remains unclear how the structural and compositional changes caused by management affect different community dimensions (e.g. richness, specialisation, abundance or completeness) and how this differs between taxa. We assessed the effects of nine forest features (representing stand structure, heterogeneity and tree composition) on thirteen above- and belowground trophic groups of plants, animals, fungi and bacteria in 150 temperate forest plots differing in their management type. Canopy cover decreased light resources, which increased community specialisation but reduced overall diversity and abundance. Features increasing resource types and diversifying microhabitats (admixing of oaks and conifers) were important and mostly affected richness. Belowground groups responded differently to those aboveground and had weaker responses to most forest features. Our results show that we need to consider forest features rather than broad management types and highlight the importance of considering several groups and community dimensions to better inform conservation.
In order to predict which ecosystem functions are most at risk from biodiversity loss, meta-analyses have generalised results from biodiversity experiments over different sites and ecosystem types. In contrast, comparing the strength of biodiversity effects across a large number of ecosystem processes measured in a single experiment permits more direct comparisons. Here, we present an analysis of 418 separate measures of 38 ecosystem processes. Overall, 45 % of processes were significantly affected by plant species richness, suggesting that, while diversity affects a large number of processes not all respond to biodiversity. We therefore compared the strength of plant diversity effects between different categories of ecosystem processes, grouping processes according to the year of measurement, their biogeochemical cycle, trophic level and compartment (above- or belowground) and according to whether they were measures of biodiversity or other ecosystem processes, biotic or abiotic and static or dynamic. Overall, and for several individual processes, we found that biodiversity effects became stronger over time. Measures of the carbon cycle were also affected more strongly by plant species richness than were the measures associated with the nitrogen cycle. Further, we found greater plant species richness effects on measures of biodiversity than on other processes. The differential effects of plant diversity on the various types of ecosystem processes indicate that future research and political effort should shift from a general debate about whether biodiversity loss impairs ecosystem functions to focussing on the specific functions of interest and ways to preserve them individually or in combination.
Hundreds of experiments have now manipulated species richness (SR) of various groups of organisms and examined how this aspect of biological diversity influences ecosystem functioning. Ecologists have recently expanded this field to look at whether phylogenetic diversity (PD) among species, often quantified as the sum of branch lengths on a molecular phylogeny leading to all species in a community, also predicts ecological function. Some have hypothesized that phylogenetic divergence should be a superior predictor of ecological function than SR because evolutionary relatedness represents the degree of ecological and functional differentiation among species. But studies to date have provided mixed support for this hypothesis. Here, we reanalyse data from 16 experiments that have manipulated plant SR in grassland ecosystems and examined the impact on above-ground biomass production over multiple time points. Using a new molecular phylogeny of the plant species used in these experiments, we quantified how the PD of plants impacts average community biomass production as well as the stability of community biomass production through time. Using four complementary analyses, we show that, after statistically controlling for variation in SR, PD (the sum of branches in a molecular phylogenetic tree connecting all species in a community) is neither related to mean community biomass nor to the temporal stability of biomass. These results run counter to past claims. However, after controlling for SR, PD was positively related to variation in community biomass over time due to an increase in the variances of individual species, but this relationship was not strong enough to influence community stability. In contrast to the non-significant relationships between PD, biomass and stability, our analyses show that SR per se tends to increase the mean biomass production of plant communities, after controlling for PD. The relationship between SR and temporal variation in community biomass was either positive, non-significant or negative depending on which analysis was used. However, the increases in community biomass with SR, independently of PD, always led to increased stability. These results suggest that PD is no better as a predictor of ecosystem functioning than SR.Synthesis. Our study on grasslands offers a cautionary tale when trying to relate PD to ecosystem functioning suggesting that there may be ecologically important trait and functional variation among species that is not explained by phylogenetic relatedness. Our results fail to support the hypothesis that the conservation of evolutionarily distinct species would be more effective than the conservation of SR as a way to maintain productive and stable communities under changing environmental conditions.
Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.
Land-use intensification is a key driver of biodiversity change. However, little is known about how it alters relationships between the diversities of different taxonomic groups, which are often correlated due to shared environmental drivers and trophic interactions. Using data from 150 grassland sites, we examined how land-use intensification (increased fertilization, higher livestock densities, and increased mowing frequency) altered correlations between the species richness of 15 plant, invertebrate, and vertebrate taxa. We found that 54% of pairwise correlations between taxonomic groups were significant and positive among all grasslands, while only one was negative. Higher land-use intensity substantially weakened these correlations(35% decrease in rand 43% fewer significant pairwise correlations at high intensity), a pattern which may emerge as a result of biodiversity declines and the breakdown of specialized relationships in these conditions. Nevertheless, some groups (Coleoptera, Heteroptera, Hymenoptera and Orthoptera) were consistently correlated with multidiversity, an aggregate measure of total biodiversity comprised of the standardized diversities of multiple taxa, at both high and lowland-use intensity. The form of intensification was also important; increased fertilization and mowing frequency typically weakened plant-plant and plant-primary consumer correlations, whereas grazing intensification did not. This may reflect decreased habitat heterogeneity under mowing and fertilization and increased habitat heterogeneity under grazing. While these results urge caution in using certain taxonomic groups to monitor impacts of agricultural management on biodiversity, they also suggest that the diversities of some groups are reasonably robust indicators of total biodiversity across a range of conditions.
Intransitive competition is widespread in plant communities and maintains their species richness
(2015)
Intransitive competition networks, those in which there is no single best competitor, may ensure species coexistence. However, their frequency and importance in maintaining diversity in real-world ecosystems remain unclear. We used two large data sets from drylands and agricultural grasslands to assess: (1) the generality of intransitive competition, (2) intransitivity-richness relationships and (3) effects of two major drivers of biodiversity loss (aridity and land-use intensification) on intransitivity and species richness. Intransitive competition occurred in >65% of sites and was associated with higher species richness. Intransitivity increased with aridity, partly buffering its negative effects on diversity, but was decreased by intensive land use, enhancing its negative effects on diversity. These contrasting responses likely arise because intransitivity is promoted by temporal heterogeneity, which is enhanced by aridity but may decline with land-use intensity. We show that intransitivity is widespread in nature and increases diversity, but it can be lost with environmental homogenisation.
Global change, especially land-use intensification, affects human well-being by impacting the delivery of multiple ecosystem services (multifunctionality). However, whether biodiversity loss is a major component of global change effects on multifunctionality in real-world ecosystems, as in experimental ones, remains unclear. Therefore, we assessed biodiversity, functional composition and 14 ecosystem services on 150 agricultural grasslands differing in land-use intensity. We also introduce five multifunctionality measures in which ecosystem services were weighted according to realistic land-use objectives. We found that indirect land-use effects, i.e. those mediated by biodiversity loss and by changes to functional composition, were as strong as direct effects on average. Their strength varied with land-use objectives and regional context. Biodiversity loss explained indirect effects in a region of intermediate productivity and was most damaging when land-use objectives favoured supporting and cultural services. In contrast, functional composition shifts, towards fast-growing plant species, strongly increased provisioning services in more inherently unproductive grasslands.