[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651551":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":12,"centralContacts":24,"locations":12,"responsibleParty":30,"collaborators":12,"id":34,"slug":35,"hasResults":36,"nctId":37,"briefTitle":38,"officialTitle":39,"acronym":12,"eligibilityCriteria":40,"healthyVolunteers":36,"sex":41,"minAge":42,"maxAge":12,"enrollmentInfo":43,"targetDuration":12,"studyType":46,"phases":47,"briefSummary":49,"conditions":50,"keywords":54,"overallStatus":60,"whyStopped":12,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":12},{"fullName":5,"class":6},"Instituto de Investigación Sanitaria Gregorio Marañón","OTHER",[8,13],{"label":9,"type":10,"description":11,"interventionNames":12},"Pre-interventional","NO_INTERVENTION","Hospital standard practices for diagnosis and\u002For treatment applied",null,{"label":14,"type":15,"description":16,"interventionNames":17},"Interventional","EXPERIMENTAL","Hospital standard practices + use of a CDSS tool",[18],"Other: Machine Learning Decision Support System",[20],{"type":6,"name":21,"description":22,"armGroupLabels":23,"otherNames":12},"Machine Learning Decision Support System","iAST® (Pragmatech AI Solutions) is a medical device designed to assist the antibiotic prescription, currently approved by the European Medicines Agency. It used complex algorithms to accurately predict the most likely recommended antibiotics for providing coverage for specific aerobic bacteria before definitive microbiological results, bacterial identification and antibiotic susceptibility testing, were known",[14],[25],{"name":26,"role":27,"phone":28,"phoneExt":12,"email":29},"Sofía De la Villa","CONTACT","34912868453","sofiadela.villa@salud.madrid.org",{"type":31,"investigatorFullName":32,"investigatorTitle":33,"investigatorAffiliation":5,"oldNameTitle":12,"oldOrganization":12},"PRINCIPAL_INVESTIGATOR","Sofia De La Villa","Principal Investigator","100651551","clinical-impact-of-a-machine-learning-decision-support-system-for-empirical-antibiotic-therapy-100651551",false,"NCT07762378","Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy","Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy: A Prospective Quasi-Experimental Study","Inclusion Criteria:\n\n* Adult patients (aged ≥18 years)\n* Admitted to the Nephrology, Oncology or ICU wards\n* Diagnosis of sepsis, pneumonia and\u002For UTI\n* Empirical antibiotics prescribed\n\nExclusion Criteria:\n\n* informed consent obtained \\> 48 hours since the infection onset\n* beta-lactam allergy\n* infection syndrome other than sepsis, pneumonia or UTI\n* confirmed no-bacterial infection\n* death within the first 48 hours of inclusion or imminent risk of death at time of the inclusion\n* pregnancy and\u002For breastfeeding\n* inclusion in a clinical trial of antimicrobial treatment","ALL","18 Years",{"count":44,"type":45},486,"ESTIMATED","INTERVENTIONAL",[48],"NA","The goal of this quasi-experimental study is to analyze if a Machine Learning Clinical Decision Support System can improve the empirical antibiotic treatment in patients with pneumonia, urinary tract infection and \u002F or sepsis.\n\nThe main questions it aims to answer are:\n\n* Primary outcome: clinical success defined as clinical cure (resolution of all signs and symptoms related to infection); no complications until day 30 (recurrence, or development of adverse events- AEs-); no new acquisition of MDROs; and survival at day 30.\n* Secondary outcomes: a subgroup analysis of the primary outcome according to the department participants, infectious syndrome, severity of the infection assessed by the SOFA score, and in microbiological confirmed infections. In microbiological confirmed infections, desirability of Outcome Ranking (DOOR) for the Management of Antimicrobial Therapy (MAT) according to the beta-lactam classification\n\nResearchers will compare a pre-intervention group with a post-intervention to see if improve in the DOOR MAT score\n\nParticipants in the post-intervention group will:\n\n• Received empirical antibiotic therapy prescribed by their treating physicians according to the machine-learning recommendations",[51,52,53],"Pneumonia - Bacterial","Urinary Tract Infection Bacterial","Bloodstream Infection",[55,56,57,58,59],"antibiotics","empiric therapy","clinical decision support system","multidrug resistant microorganism","antimicrobial stewardship","NOT_YET_RECRUITING","2026-08-08",{"date":63,"type":64},"2026-08-13","ACTUAL",{"date":66,"type":45},"2026-09",{"date":68,"type":45},"2027-09",{"name":5,"class":6}]