Clinical and microbiological evaluation of temocillin for bloodstream infections with Enterobacterales: a Belgian single-centre retrospective study.

Background: Expanding the use of temocillin could be an important weapon in the fight against antimicrobial resistance. However, EUCAST defined clinical breakpoints for a limited number of species and only for urinary tract infections (UTI), including urosepsis but excluding severe sepsis and septic shock. Moreover, a dosage of 2 g q8h is advised in most cases. Objectives: Evaluation of temocillin use for the treatment of bacteraemia, correlating clinical and microbiological outcomes with infection site, infection severity, temocillin dosage, Enterobacterales species and MIC. Patients and methods: All adult patients with blood cultures positive for temocillin-susceptible Enterobacterales and treated with temocillin for ≥72 h from June 2018 until June 2021 were considered for inclusion. The primary outcome was clinical success, defined as resolution of infection signs, no relapse of the same infection and no antibiotic switch due to insufficient clinical improvement. The secondary outcome was microbiological success. Results: In total, 182 episodes were included [140 UTI versus 42 non-UTI, 171 Escherichia coli, Klebsiella species (except Klebsiella aerogenes) and Proteus mirabilis (EKPs) versus 11 non-EKPs]. Clinical and microbiological failure were low (8% and 3%, respectively). No difference in outcome was observed for dosages of 2 g q12h versus 2 g q8h, either for EKP versus non-EKP isolates or MIC values ≤8 versus 16 mg/L. Considering only bacteraemia episodes of UTI origin, using the 16 mg/L breakpoint, there was no difference in success rate between regimens of 2 g q12h and 2 g q8h. Conclusions: Temocillin 2 g q12h can be successfully used for the treatment of systemic UTI. Prospective studies are needed to assess outcomes and evaluate non-inferiority compared with other broad-spectrum antibiotics in non-UTI infections, including bacteraemia.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores. METHODS: A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality. RESULTS: A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82). CONCLUSION: Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.