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Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting.

ABSTRACT: In northern Australia, a region with limited access to healthcare and a substantial population living remotely, antibiotic resistance adds to the complexity of treating infections. Focussing on Escherichia coli urinary tract infections (UTIs) and Staphylococcus aureus skin & soft tissue infections (SSTIs) captured by a northern Australian antibiotic resistance surveillance system, we used logistic regression to investigate predictors of a subsequent resistant isolate during the same infection episode. We also investigated predictors of recurrent infection. Our analysis included 98,651 E. coli isolates and 121,755 S. aureus isolates from 70,851 patients between January 2007 and June 2020. Following an initially susceptible E. coli UTI, subsequent recovery of a cefazolin (8%) or ampicillin (13%) -resistant isolate during the same infection episode was more common than a ceftriaxone-resistant isolate (2%). For an initially susceptible S. aureus SSTI, subsequent recovery of a methicillin-resistant isolate (8%) was more common than a trimethoprim-sulfamethoxazole-resistant isolate (2%). For UTIs and SSTIs, prior infection with a resistant pathogen was a strong predictor of both recurrent infection and resistance in future infection episodes. This multi-centre study demonstrates an association between antibiotic resistance and an increased likelihood of recurrent infection. Particularly in remote areas, a patient's past antibiograms should guide current treatment choices since recurrent infection will most likely be at least as resistant as previous infection episodes. Using population-level surveillance data in this way can also help clinicians decide if they should switch antibiotics for patients with ongoing symptoms, while waiting for diagnostic results.

SUBMITTER: Cuningham W

PROVIDER: S-EPMC10825221 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting.

Cuningham Will W Perera Shalinie S Coulter Sonali S Wang Zhiqiang Z Tong Steven Y C SYC Wozniak Teresa M TM

Scientific reports 20240129 1

In northern Australia, a region with limited access to healthcare and a substantial population living remotely, antibiotic resistance adds to the complexity of treating infections. Focussing on Escherichia coli urinary tract infections (UTIs) and Staphylococcus aureus skin & soft tissue infections (SSTIs) captured by a northern Australian antibiotic resistance surveillance system, we used logistic regression to investigate predictors of a subsequent resistant isolate during the same infection ep ...[more]

PMID: 38287025

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Project description:Background: Across Southeast Asia, declining malaria incidence poses a challenge for healthcare providers, in how best to manage the vast majority of patients with febrile illnesses who have a negative malaria test. In rural regions, where the majority of the population reside, empirical treatment guidelines derived from central urban hospitals are often of limited relevance. In these settings, health workers with limited training deliver care, often without any laboratory diagnostic support. In this paper, we model the impact of point-of-care C-reactive protein testing to inform the decision to prescribe antibiotics and regional surveillance data to inform antibiotic selection, and then simulate the subsequent impact on mortality from febrile illnesses, rooted in the real-world context of rural Savannakhet province, southern Laos. Methods: Our model simulates 100 scenarios with varying quarterly incidence of six key pathogens known to be prevalent in rural Laos. In the simulations, community health workers either prescribe antibiotics in-line with current practice as documented in health facilities in rural Laos, or with the aid of the two interventions. We provide cost-effectiveness estimates for each strategy alone and then for an integrated approach using both interventions. Results: We find that each strategy is predicted to be highly cost-effective, and that the combined approach is predicted to result in the biggest reduction in mortality (averting a predicted 510 deaths per year in rural Savannakhet, a 28% reduction compared to standard practice) and is highly cost-effective, with an incremental cost-effectiveness ratio of just $66 per disability-adjusted life year averted. Conclusions: Substantial seasonal variation in the predicted optimal empirical antibiotic treatment for febrile illness highlights the benefits of up-to-date information on regional causes of fever. In this modelling analysis, an integrated system incorporating point-of-care host biomarker testing and regional surveillance data appears highly cost-effective, and may warrant piloting in a real-life setting.

Project description:ImportanceThere is a lack of studies comparing the intended and unintended consequences of prospective review and feedback (PRF) with computerized decision support systems (CDSS), especially in the longer term in antimicrobial stewardship.ObjectiveTo examine the outcomes associated with the sequential implementation of PRF and CDSS and changes to these interventions with long-term use of antibiotics for and incidence of multidrug resistant organisms (MDROs) and other unintended outcomes.Design, setting, and participantsThis cohort study used an interrupted time series with segmented regression analysis of data from January 2007 to December 2018. Data were extracted from the electronic medical records of patients admitted at a large university teaching hospital with high rates of antibiotic resistance in Singapore. Data were analyzed from June 2019 to June 2020.ExposuresPRF of piperacillin-tazobactam and carbapenems (intervention 1, April 2009), with the addition of hospital-wide CDSS (intervention 2, April 2011), and lifting of CDSS for half of the hospital wards for 6 months (intervention 3, March 2017).Main outcomes and measuresMonthly antimicrobial use was measured in defined daily doses (DDDs) per 1000 patient-days. The monthly incidence of MDROs was calculated as number of clinical isolates detected per 1000 inpatient-days over a 6-month period. Unintended outcomes examined included in-hospital mortality and age-adjusted length of stay (LOS).ResultsThe number of inpatients increased from 56 263 in 2007 to 63 572 in 2018. During the same period, the mean monthly patient days increased from 33 929 in 2007 to 45 603 in 2018, and the proportion of patients older than 65 years increased from 45.5% in 2007 to 56.6% in 2018. After intervention 1, there were 0.33 (95% CI, 0.18 to 0.48) more DDDs per 1000 patient-days per month of piperacillin-tazobactam and carbapenems and -11.05 (95% CI, -15.55 to -6.55) fewer DDDs per 1000 patient-days per month for other broad-spectrum antibiotics. After intervention 2, there were -0.22 (95% CI, -0.33 to -0.10) fewer DDDs per 1000 patient-days per month of piperacillin-tazobactam and carbapenems and -2.10 (95% CI, -3.13 to -1.07) fewer DDDs per 1000 patient-days per month for other broad-spectrum antibiotics. After intervention 3, use of piperacillin-tazobactam and carbapenem increased by 0.28 (95% CI, 0.02 to 0.55) DDDs per 1000 patient-days per month. After intervention 2, incidence of Clostridioides difficile decreased (estimate, -0.02 [95% CI, -0.03 to -0.01] cases per 1000 patient-days per month).Conclusions and relevanceIn this cohort study, concurrent PRF and CDSS were associated with limiting the use of piperacillin-tazobactam and carbapenems while reducing use of other antibiotics.

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Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting.

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Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting.

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