首页|Research and Innovation Department Reports Findings in Antibiotics (Retrospectiv e validation study of a machine learning-based software for empirical and organi sm-targeted antibiotic therapy selection)

Research and Innovation Department Reports Findings in Antibiotics (Retrospectiv e validation study of a machine learning-based software for empirical and organi sm-targeted antibiotic therapy selection)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Antibiotics is the subject of a report. According to news reporting originating in Oviedo, Spain, by NewsRx journalists, research stated, “Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-base d software offering empirical and organism-targeted antibiotic recommendations, was assessed.” The news reporters obtained a quote from the research from Research and Innovati on Department, “The study was conducted in a 12-hospital Spanish institution. Af ter model fine-tuning with 27,531 historical antibiograms, 325 consecutive patie nts with acute infections were selected for retrospective validation. The primar y endpoint was comparing each of the top three of iAST’s antibiotic recommendati ons’ success rates (confirmed by antibiogram results) with the antibiotic prescr ibed by the physicians. Secondary endpoints included examining the same hypothes is within specific study population subgroups and assessing antibiotic stewardsh ip by comparing the percentage of antibiotics recommended that belonged to diffe rent World Health Organization AWaRe groups within each arm of the study. All of iAST first three recommendations were non-inferior to doctor prescription in th e primary endpoint analysis population as well as the secondary endpoint. The ov erall success rate of doctors’ empirical treatment was 68.93 %, whil e that of the first three iAST options was 91.06% (<0.001), 90.63% (<0.001), and 91.06% ( <001), respectively. For organism-targeted therapy, the d octor’s overall success rate was 84.16%, and that of the first thre e ranked iAST options was 97.83% (<0.001), 9 4.09% (<0.001), and 91.30% ( <0.001), respectively. In empirical therapy, compared to physician prescriptions , iAST demonstrated a greater propensity to recommend access antibiotics, fewer watch antibiotics, and higher reserve antibiotics. In organism-targeted therapy, iAST advised a higher utilization of access antibiotics. The present study demo nstrates iAST accuracy in predicting antibiotic susceptibility, showcasing its p otential to promote effective antibiotic stewardship.”

OviedoSpainEuropeAntibacterialsA ntibioticsAntimicrobialsCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine LearningSoftwareTherapy

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Sep.9)