0000000001084854

AUTHOR

J. P. Jensen

showing 2 related works from this author

Factors controlling hydrochemical and trophic state variables in 86 shallow lakes in Europe

2003

In order to disentangle the causes of variations in water chemistry among European shallow lakes, we performed standardised sampling programs in 86 lakes along a latitudinal gradient from southern Spain to northern Sweden. Lakes with an area of 0.1 to 27 000 ha and mean depth of 0.4–5.6 m located in low to high altitudes were investigated within the EC project ECOFRAME 1–4 times during June–October 2000–2001. Several variables like conductivity, alkalinity, abundance of submerged plants, concentrations of suspended solids, total nitrogen and phosphorus were latitude-dependent decreasing from south to north. Secchi depth, concentrations of total nitrogen, total phosphorus, suspended solids, …

ecological statusChlorophyll aSuspended solidsEcologyPhosphorusLimnologychemistry.chemical_elementlatitudeAquatic ScienceSeasonalitymedicine.diseasehydrochemistryLatitudechemistry.chemical_compoundchemistryEuropean shallow lakesAbundance (ecology)ddc:570medicineEnvironmental sciencePhysical geographyTrophic level
researchProduct

Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU: a prospective cohort…

2021

Abstract Background The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. Methods We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for…

MaleShort term mortalityCritical Care and Intensive Care MedicineCohort Studies0302 clinical medicinekwetsbaarheidMedicine and Health Sciences80 and overMedicine610 Medicine & healthProspective cohort studyCorrelation of Data11 Medical and Health SciencesAged 80 and overOUTCOMESIntensive care unitsFrailtyVIP1Aged&nbspMedical emergencies. Critical care. Intensive care. First aidScale (social sciences)Femaleprospectief onderzoekLife Sciences & BiomedicineCRITICALLY-ILL PATIENTS 80 and overStudy groupsmedicine.medical_specialtyAnestesi och intensivvård80 jaar en ouder610 Medicine & healthINTENSIVE-CAREBED AVAILABILITYNO03 medical and health sciencesCritical Care MedicineIntensive caresterfteGeneral & Internal MedicineHumansIntensive care units; Aged; 80 and over; Frailty; Prospective studies; MortalityIn patientddc:610Aged 80 and over; Frailty; Intensive care units; Mortality; Prospective studies; Aged 80 and over; Cohort Studies; Correlation of Data; Female; Frailty; Humans; Intensive Care Units; Logistic Models; Male; Mortality; Prospective StudiesMortalityAgedScience & TechnologyAnesthesiology and Intensive Carebusiness.industryRC86-88.9Research030208 emergency & critical care medicineADULTSAged 80 and over; Frailty; Intensive care units; Mortality; Prospective studies;Emergency & Critical Care MedicineLogistic Models030228 respiratory systemintensivecareafdelingenAged 80 and overCritical illnessEmergency medicineVIP2 study group&nbspCRITICAL ILLNESSbusinessProspective studies
researchProduct