Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 1 de 1
Filter
Add more filters










Database
Language
Publication year range
1.
Chest ; 160(4): 1211-1221, 2021 10.
Article in English | MEDLINE | ID: mdl-33905680

ABSTRACT

BACKGROUND: The benefits of early antibiotics for sepsis have recently been questioned. Evidence for this mainly comes from observational studies. The only randomized trial on this subject, the Prehospital Antibiotics Against Sepsis (PHANTASi) trial, did not find significant mortality benefits from early antibiotics. That subgroups of patients benefit from this practice is still plausible, given the heterogeneous nature of sepsis. RESEARCH QUESTION: Do subgroups of sepsis patients experience 28-day mortality benefits from early administration of antibiotics in a prehospital setting? And what key traits drive these benefits? STUDY DESIGN AND METHODS: We used machine learning to conduct exploratory partitioning cluster analysis to identify possible subgroups of sepsis patients who may benefit from early antibiotics. We further tested the influence of several traits within these subgroups, using a logistic regression model. RESULTS: We found a significant interaction between age and benefits of early antibiotics (P = .03). When we adjusted for this interaction and several other confounders, there was a significant benefit of early antibiotic treatment (OR, 0.07; 95% CI, 0.01-0.79; P = .03). INTERPRETATION: An interaction between age and benefits of early antibiotics for sepsis has not been reported before. When validated, it can have major implications for clinical practice. This new insight into benefits of early antibiotic treatment for younger sepsis patients may enable more effective care.


Subject(s)
Anti-Bacterial Agents/therapeutic use , Emergency Medical Services , Mortality , Sepsis/drug therapy , Time-to-Treatment , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Ambulances , Body Temperature , Cluster Analysis , Early Medical Intervention , Emergency Service, Hospital , Female , Humans , Logistic Models , Machine Learning , Male , Middle Aged , Multivariate Analysis , Young Adult
SELECTION OF CITATIONS
SEARCH DETAIL
...