0000000000409361

AUTHOR

Rita Murri

showing 2 related works from this author

Italian young doctors’ knowledge, attitudes and practices on antibiotic use and resistance: A national cross-sectional survey

2020

Abstract Objectives Antimicrobial resistance (AMR) is one of the major health issues worldwide. Clinicians should play a central role to fight AMR, and medical training is a pivotal issue to combat it; therefore, assessing levels of knowledge, attitudes and practices among young doctors is essential for future antimicrobial stewardship (AMS) programmes. Methods A nationwide, cross-sectional, multicentre survey was conducted in Italy. A descriptive analysis of knowledge and attitudes was performed, along with a univariate and multivariate analysis of their determinants. Results Overall, 1179 young doctors accessed the survey and 1055 (89.5%) completed all sections. Regarding the knowledge se…

0301 basic medicineMicrobiology (medical)Health Knowledge Attitudes Practicemedicine.medical_specialtyMultivariate analysisCross-sectional study030106 microbiologyImmunologySpecialtyResistance (psychoanalysis)Antimicrobial stewardshipMultidrug resistanceAntimicrobial resistanceMicrobiology03 medical and health sciences0302 clinical medicinePhysiciansSurvey Antimicrobial resistance Antimicrobial stewardship Multidrug resistance Knowledge Attitudes and practices KAPHumansImmunology and AllergyAntimicrobial stewardshipMedicine030212 general & internal medicineSurveyCurriculumDescriptive statisticsAttitudes and practicesbusiness.industryQR1-502Anti-Bacterial AgentsCross-Sectional StudiesKnowledgeItalyFamily medicinebusinessInclusion (education)Journal of Global Antimicrobial Resistance
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Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings f…

2020

Background and aims There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. Methods and results Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a…

MaleEpidemiologyEndocrinology Diabetes and MetabolismMedicine (miscellaneous)030204 cardiovascular system & hematologycomputer.software_genreMachine Learning0302 clinical medicineRetrospective StudieRisk FactorsCardiovascular DiseaseEpidemiology80 and overMedicineAge FactorViralHospital MortalityBetacoronavirus Hospital MortalityYoung adultAged 80 and overNutrition and DieteticsCOVID-19; Epidemiology; In-hospital mortality; Risk factorsMortality rateHazard ratioAge FactorsMiddle AgedIn-hospital mortalityC-Reactive ProteinCardiovascular DiseasesFemaleSurvival AnalysiCardiology and Cardiovascular MedicineCoronavirus InfectionsHumanGlomerular Filtration RateAdultmedicine.medical_specialtyAdolescentPneumonia Viral030209 endocrinology & metabolismSettore MED/17 - MALATTIE INFETTIVEMachine learningCOVID-19; Epidemiology; In-hospital mortality; Risk factors; Adolescent; Adult; Age Factors; Aged; Aged 80 and over; C-Reactive Protein; COVID-19; Cardiovascular Diseases; Coronavirus Infections; Female; Glomerular Filtration Rate; Humans; Male; Middle Aged; Pandemics; Pneumonia Viral; Retrospective Studies; Risk Factors; SARS-CoV-2; Survival Analysis; Young Adult; Betacoronavirus; Hospital Mortality; Machine LearningArticle03 medical and health sciencesBetacoronavirusYoung AdultHumansRisk factorPandemicsSurvival analysisAgedRetrospective StudiesPandemicBetacoronavirubusiness.industryCoronavirus InfectionSARS-CoV-2Risk FactorCOVID-19Retrospective cohort studyPneumoniaSurvival AnalysisConfidence intervalRisk factorsArtificial intelligencebusinesscomputerNutrition, metabolism, and cardiovascular diseases : NMCD
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