Comparison of Cockcroft-Gault and Modification of Diet in Renal Disease Formulas as Predictors of Cardiovascular Outcomes in Patients With Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention

We prospectively assessed the value of estimated glomerular filtration rate (eGFR) by the Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault (C-G) equations in predicting inhospital adverse outcomes after primary coronary intervention for acute ST-segment elevation myocardial infarction. We classified 647 patients into 3 categories according to eGFR, 90 mL/min/1.73 m(2). The eGFRC-G classified 17 patients in the >90 mL/min/1.73 m(2) subgroup and 6 and 11 patients in the 60 to 90 and 90 mL/min/1.73 m(2) (P = .01 and P = .01, respectively); the eGFR(MDRD) was not predictive. Although the MDRD equation more accurately estimates GFR in certain populations, the CG formula may be a better predictor of adverse events

Comparison of Cockcroft-Gault and Modification of Diet in Renal Disease Formulas as Predictors of Cardiovascular Outcomes in Patients With Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention

We prospectively assessed the value of estimated glomerular filtration rate (eGFR) by the Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault (C-G) equations in predicting inhospital adverse outcomes after primary coronary intervention for acute ST-segment elevation myocardial infarction. We classified 647 patients into 3 categories according to eGFR, 90 mL/min/1.73 m(2). The eGFRC-G classified 17 patients in the >90 mL/min/1.73 m(2) subgroup and 6 and 11 patients in the 60 to 90 and 90 mL/min/1.73 m(2) (P = .01 and P = .01, respectively); the eGFR(MDRD) was not predictive. Although the MDRD equation more accurately estimates GFR in certain populations, the CG formula may be a better predictor of adverse events

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities