Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission

Selecting phototrophic species of native biocrusts in arid and semi-arid regions

Background: Biological soil crusts (BSCs) that are able to produce sticky extracellular polymeric substances (EPS) play an important role in the formation of soil aggregates, thereby, reducing soil erosion. In this study, experiments were undertaken to identify biocrust species that produce EPS, in order to combat desertification in the Sejzi desert of Iran. Methods: A biocrust distribution map of Sejzi plain was prepared using Landsat 8 OLI images, then, various sampling points were selected. Some physicochemical parameters of samples from lichendominated and non-biocrusted areas were measured. The relationship between soil parameters and biocrusts presence was confirmed based on the Pearson’s correlation coefficient and principal component analysis (PCA) method. The type of chemical compounds in the soil content were determined via Fourier transform infrared spectroscopy (FTIR), including polysaccharides. To estimate the degradability of polysaccharides, each soil sample was placed under defined UV-B radiation for 24, 48, and 72 hours at three replications. Results: There was no significant correlation between moss and lichen species with the amount of EPS (%) values and various occurring cyanolichen species in three biocrusted soil samples, which included Collema coccophorum, Collema tenax, Peccania terricola, and Placidium squamulosum. It was speculated that these polysaccharides were produced by the photobiotic partners (microalgae or cyanobacteria) and secreted to the soil. Conclusion: According to the results, the cyanobacteria species of biocrusted samples might have high potential to combat desertification and soil stabilization in Sejzi desert. Keywords: Cyanobacteria, Fourier transform infrared, Lichens, Polysaccharides, Sejzi plain, Ira

Grazing intensity alters the plant diversity‐ecosystem carbon storage relationship in rangelands across topographic and climatic gradients

1. Plant diversity supports multiple ecosystem functions, including carbon sequestration. Recent shifts in plant diversity in rangelands due to increased grazing pressure and climate changes have the potential to impact the sequestration of carbon in arid to semi-humid regions worldwide. However, plant diversity, grazing intensity and carbon storage are also influenced by environmental factors such as nutrient availability, climate, and topography. The complexity of these interactions limits our ability to fully assess the impacts of grazing on biodiversity-ecosystem function (BEF) relationships. Read the free Plain Language Summary for this article on the Journal blog. 2. We assessed how grazing intensity modifies BEF relationships by determining the links between plant diversity and ecosystem carbon stocks (plant and soil carbon) across broad environmental gradients and different plant growth forms. To achieve this, we surveyed 1493 quadrats across 10 rangelands, covering an area of 23,756 ha in northern Iran. 3. We show that aboveground carbon stocks increased with plant diversity across topographic, climatic and soil fertility gradients. The relationship between aboveground carbon stocks and plant diversity was strongest for forbs, followed by shrubs and grasses. Soil carbon stocks increased strongly with soil fertility across sites, but aridity, grazing, plant diversity and topography were also important in explaining variation in soil carbon stocks. 4. Importantly, aboveground and soil carbon stocks declined at high grazing intensity, and grazing modified the relationship between plant diversity and carbon stocks regardless of differences in abiotic conditions across sites. 4. Our study demonstrates that relationships between plant diversity and ecosystem carbon stocks persist across gradients of aridity, topography, and soil fertility, but the relationships are modified by grazing intensity. Our findings suggest that potential losses in plant diversity under grazing intensification could reduce ecosystem carbon storage across wide areas of arid to semi-humid rangelands. We discuss the potential mechanisms underpinning rangeland BEF relationships to stimulate future research