The effect of combining antibiotics on resistance: A systematic review and meta-analysis.

When and under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics. We searched CENTRAL, EMBASE and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to November 24th, 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. A patient was considered to have acquired resistance if, at the follow-up culture, a resistant bacterium was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials' risk of bias was assessed with the Cochrane tool. 42 trials were eligible and 29, including 5054 patients, were qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio (OR) for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68-2.25), with substantial between-study heterogeneity (I2 =77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions. The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall, is compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance.

Modifiable and nonmodifiable risk factors for non-ventilator-associated hospital-acquired pneumonia identified in a retrospective cohort study.

OBJECTIVES: Hospital-acquired pneumonia in nonventilated patients (nvHAP) belongs to the most common healthcare-associated infections. This study aimed to investigate risk factors for nvHAP in patients outside the intensive care unit, focusing on modifiable risk factors. METHODS: All inpatients admitted to an academic teaching hospital in Switzerland between 2017 and 2018 were included. nvHAP was defined according to European Centre for Disease Prevention and Control criteria. Patient days during and after ICU stay were excluded. Candidate risk factors-both constant and time varying-were included in uni- and multivariable Cox proportional hazards models. The decay ratio and the characteristic time of influence of hazard ratios (HRs) was estimated by adopting a linear decay in the Cox model. RESULTS: A total of 66 001 hospitalizations with 314 (0.48%) nvHAP and 471 401 patient days were included. Median age was 57 years (interquartile range: 38 to 71 years) and 32 253 (48.9%) patients were male. Among nonmodifiable risk factors, age (adjusted HR (aHR) 2.66 for age ≥60 years, 95% CI 1.59 to 4.45) and male sex (aHR 1.71, 95% CI 1.34 to 2.18) were independently associated with nvHAP. Time-varying exposures showing strongest independent association with nvHAP were tube feeding (aHR 3.24, 95% CI 2.17 to 4.83), impaired consciousness (aHR 2.32, 95% CI 1.63 to 3.31), and severely impaired activity and mobility (aHR 2.06, 95% CI 1.50 to 2.84). The association with nvHAP decayed within 7.1 to 13.2 days after these exposures ended. DISCUSSION: The risk for nvHAP varies with time, depending on the patient's medical condition and medical interventions. Several risk factors for nvHAP represent potential targets for specific prevention measures.

Identifying the drivers of multidrug-resistant Klebsiella pneumoniae at a European level.

Beta-lactam- and in particular carbapenem-resistant Enterobacteriaceae represent a major public health threat. Despite strong variation of resistance across geographical settings, there is limited understanding of the underlying drivers. To assess these drivers, we developed a transmission model of cephalosporin- and carbapenem-resistant Klebsiella pneumoniae. The model is parameterized using antibiotic consumption and demographic data from eleven European countries and fitted to the resistance rates for Klebsiella pneumoniae for these settings. The impact of potential drivers of resistance is then assessed in counterfactual analyses. Based on reported consumption data, the model could simultaneously fit the prevalence of extended-spectrum beta-lactamase-producing and carbapenem-resistant Klebsiella pneumoniae (ESBL and CRK) across eleven European countries over eleven years. The fit could explain the large between-country variability of resistance in terms of consumption patterns and fitted differences in hospital transmission rates. Based on this fit, a counterfactual analysis found that reducing nosocomial transmission and antibiotic consumption in the hospital had the strongest impact on ESBL and CRK prevalence. Antibiotic consumption in the community also affected ESBL prevalence but its relative impact was weaker than inpatient consumption. Finally, we used the model to estimate a moderate fitness cost of CRK and ESBL at the population level. This work highlights the disproportionate role of antibiotic consumption in the hospital and of nosocomial transmission for resistance in gram-negative bacteria at a European level. This indicates that infection control and antibiotic stewardship measures should play a major role in limiting resistance even at the national or regional level.

Assessing the potential impact of transmission during prolonged viral shedding on the effect of lockdown relaxation on COVID-19.

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19.