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1.
Sci Transl Med ; 12(568)2020 11 04.
Artigo em Inglês | MEDLINE | ID: mdl-33148625

RESUMO

Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations.


Assuntos
Gestão de Antimicrobianos , Infecções Urinárias , Algoritmos , Antibacterianos/uso terapêutico , Resistência Microbiana a Medicamentos , Feminino , Humanos , Pacientes Ambulatoriais , Infecções Urinárias/tratamento farmacológico
2.
Sci Transl Med ; 11(483)2019 03 13.
Artigo em Inglês | MEDLINE | ID: mdl-30867322

RESUMO

Multigram drug depot systems for extended drug release could transform our capacity to effectively treat patients across a myriad of diseases. For example, tuberculosis (TB) requires multimonth courses of daily multigram doses for treatment. To address the challenge of prolonged dosing for regimens requiring multigram drug dosing, we developed a gastric resident system delivered through the nasogastric route that was capable of safely encapsulating and releasing grams of antibiotics over a period of weeks. Initial preclinical safety and drug release were demonstrated in a swine model with a panel of TB antibiotics. We anticipate multiple applications in the field of infectious diseases, as well as for other indications where multigram depots could impart meaningful benefits to patients, helping maximize adherence to their medication.


Assuntos
Antituberculosos/uso terapêutico , Sistemas de Liberação de Medicamentos , Estômago/efeitos dos fármacos , Tuberculose/tratamento farmacológico , Animais , Antibacterianos/uso terapêutico , Antituberculosos/farmacologia , Preparações de Ação Retardada , Relação Dose-Resposta a Droga , Doxiciclina/uso terapêutico , Sistemas de Liberação de Medicamentos/economia , Liberação Controlada de Fármacos , Humanos , Suínos
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