Interpretative reading of the antibiogram:a semi-naïve Bayesian approach

AbstractBackgroundAn antibiogram (ABG) gives the results of in vitro susceptibility tests performed on a pathogen isolated from a culture of a sample taken from blood or other tissues. The institutional cross-ABG consists of the conditional probability of susceptibility for pairs of antimicrobials. This paper explores how interpretative reading of the isolate ABG can be used to replace and improve the prior probabilities stored in the institutional ABG. Probabilities were calculated by both a naïve and semi-naïve Bayesian approaches, both using the ABG for the given isolate and institutional ABGs and cross-ABGs.Methods and MaterialWe assessed an isolate database from an Israeli university hospital with ABGs from 3347 clinically significant blood isolates, where on average 19 antimicrobials were tested for susceptibility, out of 31 antimicrobials in regular use for patient treatment. For each of 14 pathogens or groups of pathogens in the database the average (prior) probability of susceptibility (also called the institutional ABG) and the institutional cross-ABG were calculated. For each isolate, the normalized Brier distance was used as a measure of the distance between susceptibility test results from the isolate ABG and respectively prior probabilities and posteriori probabilities of susceptibility. We used a 5-fold cross-validation to evaluate the performance of different approaches to predict posterior susceptibilities.ResultsThe normalized Brier distance between the prior probabilities and the susceptibility test results for all isolates in the database was reduced from 37.7% to 28.2% by the naïve Bayes method. The smallest normalized Brier distance of 25.3% was obtained with the semi-naïve min2max2 method, which uses the two smallest significant odds ratios and the two largest significant odds ratios expressing respectively cross-resistance and cross-susceptibility, calculated from the cross-ABG.ConclusionA practical method for predicting probability for antimicrobial susceptibility could be developed based on a semi-naïve Bayesian approach using statistical data on cross-susceptibilities and cross-resistances. The reduction in Brier distance from 37.7% to 25.3%, indicates a significant advantage to the proposed min2max2 method (p<10 99)

Estimating coverage of empiric treatment regimens for childhood bloodstream infection based on routine microbiological data

This research paper style thesis comprises six papers, each addressing a different aspect of the selection of empiric antibiotic regimens for the treatment of severe childhood infections, focussing on suspected bloodstream infection. Antibiotics are a means to effectively manage life-threatening bacterial infections, such as bloodstream infections. Recommendations for life-saving empiric antibiotic treatment for bloodstream infection are traditionally based on knowledge of the epidemiology of the targeted infection, and are strongly influenced by knowledge about antibiotic resistance in causative pathogens. The underlying assumption is that the in vitro phenomenon of antimicrobial resistance relates to a poor response to antibiotics in vivo. Bacteria causing bloodstream infection are increasingly found to be resistant to antibiotics and this can vary by region, hospital and patient group. It is therefore necessary to select and review best options for empiric treatment taking into account these trends. Details on the current approaches, data sources and the advantages and limitations of both are discussed in the first part of thesis (chapters 2-5). The methods for selecting optimal empiric treatment from microbiological data, including information on antimicrobial resistance, are poorly defined. It is unclear which approach is most informative clinically and which can still use microbiology data generated as part of routine care and utilized for surveillance. Importantly, empiric regimens must be based on knowledge of the bacteria associated with a specific infection syndrome including their relative frequency as well as their resistance patterns. The probability that a given regimen will cover the next clinically identified episode of the infection in question can then be derived as guidance for regimen selection. In the second part of the thesis, a specific method for constructing a weighted-incidence syndromic combination antibiogram (or WISCA) to estimate coverage is therefore developed and presented. The WISCA is derived from a Bayesian decision tree model, and has the advantages of explicitly combining relative incidence and resistance patterns for a given syndrome as well as accurately reflecting imprecision of coverage estimates. The Bayesian decision tree WISCA is used to investigate coverage of empiric antibiotic regimens at hospital level in Europe, including potential methods for dealing with heterogeneity between centres while still supporting data pooling to improve precision (Chapter 6). A further application is the estimation and comparison of coverage offered by recommended regimens for neonatal sepsis in Asian countries with data pooling at the level of country (Chapter 7). Finally, the potential influence of patient characteristics on selection of antibiotics of last resort (i.e. those with a broad therapeutic spectrum but likely to be strong drivers for the selection of antimicrobial resistance and therefore to be used only when necessary) was investigated (Chapter 8). This demonstrates that certain patients or infection episodes are more likely to be treated with last resort antibiotics than others, and would seem to indicate expected heterogeneity among neonates and children with bloodstream infection. The Bayesian WISCA provides a useful approach to pooling information to guide empiric therapy and could increase confidence in the selection of specific regimens. In presented analyses, it provides evidence for the continued use of narrow-spectrum regimens in certain contexts, and could be further developed to address data pooling and allow the integration of local resistance data with surveillance data for data-based modification of high-level treatment recommendations (Chapter 9). Further work should focus on promoting the uniform reporting of coverage (and WISCA) to enable robust meta-analysis of antimicrobial resistance data and address best methods for dealing with small sample sizes expected at hospital-level and for stratified coverage estimates

Innovations in rapid Mycoplasma bovis diagnostics with MALDI-TOF MS and nanopore sequencing

Mycoplasma bovis is a leading, primary cause of pneumonia, arthritis, otitis and mastitis in cattle, resulting in impaired animal welfare and huge economic losses in all cattle sectors worldwide. This small bacterium lost its cell wall and several physiological mechanisms through evolution, whereupon it acquired inherent resistance against many conventional antimicrobials (e.g. penicillines, cephalosporines, sulfonamides, ..). Next to this natural resistance, M. bovis may acquire resistance against other antimicrobials as well. Currently, isolation and identification of M. bovis by culture takes 1-2 weeks, and subsequent antimicrobial susceptibility testing is currently not performed in routine diagnostics. No standard protocol is available and the lack of clinical breakpoints limits the translation of in vitro results to clinical outcome predictions of antimicrobial treatment. At a higher price, faster identification is possible with PCR (2 days). Although diagnostic accuracy of PCR is expected to be higher than culture, scientific information on this matter is limited. To be able to control M. bovis and start appropriate antimicrobial treatment immediately, there is a great need for rapid and reliable diagnostic tools for this pathogen. However, next to control, prevention of M. bovis spreading into/within the herd is also very important. How M. bovis is exactly transmitted, and whether there are specific M. bovis strains associated with antimicrobial resistance or sectors, has not been elucidated yet. Key factors for successful control and prevention are the formulation of specific biosecurity protocols and guidelines targeted to M. bovis. To achieve this, a rapid diagnosis of infected or carrier animals and better insights into the spread of M. bovis between herds, sectors, and countries are needed. Therefore, the general aim of this thesis was to develop new Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and nanopore sequencing based diagnostic methods for rapid identification, strain typing, and antimicrobial susceptibility testing of M. bovis, and to apply those methods on Belgian field samples, gaining better insight into the epidemiology of M. bovis. In the general introduction (Chapter 1) a literature overview is provided, presenting current state-of-the-art on disease course, risk factors and treatment of M. bovis. Subsequently, the many different diagnostic techniques available for identification, strain typing, and susceptibility testing are described. Next to the existing techniques, more innovative techniques, such as MALDI-TOF MS and nanopore sequencing and their potential as rapid diagnostic methods are explained. In the first experimental study different methods were explored to identify M. bovis cultures grown on solid medium with MALDI-TOF MS (Chapter 3.1). The most straight-forward method, being the direct transfer method, is broadly applied for most bacteria, but faced several problems for M. bovis identification. In this study, these problems were better identified and it was shown that medium-related peaks (mostly obtained from horse serum and colistin) can result in false positive Mycoplasma alkalescens and Mycoplasma arginini identification. Unfortunately, it was not possible to obtain a more reliable direct transfer protocol. Therefore, in Chapter 3.2 the identification of M. bovis with MALDI-TOF MS from liquid medium was further explored and optimized. Here it was shown that identification was possible within 24 hours after inoculation of one colony from a solid medium into liquid medium. Supplementing pleuropneumonia-like organism broth (PPLO-broth) with pyruvate prolonged the possibility of M. bovis identification to at least 120 hours after inoculation. Also, supplementation with antimicrobials prevented overgrowth with other bacteria, and did not influence the identification score. Although with the previous two methods, a step towards more rapid identification of M. bovis was set, prior isolation of M. bovis from any sample is still necessary and could easily take 5-10 days. Therefore, methods to identify M. bovis directly from bronchoalveolar lavage fluid (BALf) with MALDI-TOF MS (Chapter 3.3) and nanopore sequencing (Chapter 3.4) were developed and validated in a Bayesian latent class model on 104 and 100 BALf from calves, respectively. It was possible to identify M. bovis with MALDI-TOF MS within 2-3 days with a sensitivity and specificity of 86.6% (CI95%: 69.4-97.6%) and 86.4% (76.1-93.8%), respectively. While sensitivity and specificity of nanopore sequencing were 77.4% (58.6-92.3%) and 97.3% (91.1-99.7%), respectively. Also when 5 BALf were pooled, both methods were still reliable, and therefore very cost-effective possibilities. In addition, the in Belgium widely used selective-indicative agar method based on lipase-activity, which was never validated before using a large number of field samples, showed a sensitivity of 70.5% (52.1-87.1%) and specificity of 93.9% (85.9-98.4). All three methods are useful in routine laboratories, depending on the diagnostic needs of the applicant. Currently the prevalence of M. bovis in the Belgian dairy and beef sector is estimated at 30%, whereas 100% of the veal calf herds tested positive. Together with the high antimicrobial use in the veal sector, the question has been raised whether there is a possible reservoir of multi-resistant and sector-specific M. bovis strains in this sector, as previously shown for other respiratory bacteria. To better understand the molecular epidemiology and genetic relatedness of different M. bovis isolates, the full genome of 100 Belgian M. bovis isolates collected from dairy, beef and veal herds was obtained using nanopore sequencing (Chapter 4). A single nucleotide polymorphism (SNP) analysis was performed and the phylogenetic tree showed five separate genomic clusters of M. bovis isolates and one outlier circulating in Belgium between 2014 and 2019. No sector-specific isolates and no association with spatial location in Belgium were identified. At world-scale, the Belgian M. bovis isolates clustered together with European, American and Israeli strains. These results contribute to emphasizing the importance of purchase protocols and biosecurity to prevent M. bovis from entering the country or herd. In Chapter 5.1, antimicrobial susceptibility testing of 141 M. bovis isolates retrieved from Belgian dairy, beef and veal calf herds was performed with broth microdilution. Minimum inhibitory concentration values were used to establish the epidemiological cut-off (ECOFF) with visual and statistical methods to distinguish the population in wild type M. bovis and those with acquired antimicrobial resistance (non-wild type). The results showed high percentages of acquired resistance for macrolides (tilmicosin, tylosin, and gamithromycin), but no acquired resistance for tetracyclines (oxytetracycline, doxycycline). Only little acquired resistance was observed for florfenicol, gentamicin, and tiamulin, while there was limited acquired resistance to enrofloxacin. Only M. bovis isolates from beef cattle or the third genomic cluster had a significantly higher change to have acquired resistance against gamithromycin than those collected from other sectors or genomic clusters. These results support the current national formulary for respiratory disease associated with M. bovis, recommending florfenicol as first choice, and oxytetracycline and macrolides as second choice. Possibly, a small remark for gamithromycin is needed, as higher risk for acquired resistance for this antimicrobial was seen in beef cattle. In vitro susceptibility testing results should be interpreted carefully, as the association with in vivo efficacy has not confirmed yet, due to the lack of clinical breakpoints. Finally, in Chapter 5.2, upgraded genomes derived from Chapter 4 and the susceptibility data from Chapter 5.1 were combined to compare genotype and phenotype antimicrobial susceptibility of M. bovis isolates. A genome wide association study to reveal genetic markers for antimicrobial resistance in M. bovis and verifying the ECOFF values obtained by the previously used different methods was executed. Many point mutations were associated with antimicrobial resistance against the critically important antibiotics of the macrolide (A2058G in the 23S rRNA gene, Gln83His in the L22 protein) and fluoroquinolone classes. For enrofloxacin the combination of different mutations in the GyrA and ParC gene showed the step-wise acquired resistance. Also previously described mutations for tilmicosin (G478A mutation in 23S rRNA alleles), and new markers for gentamicin (A1408G and G1488A in 16S rRNA) were identified. The visual estimation of de ECOFF showed to be a reliable method, although statistical methods can help when step-wise resistance results in difficult to interpret “tailing”. Even when phenotypical resistance is not yet obtained, in case of first-step mutations it should be discouraged to use fluoroquinolones as antimicrobial therapy, as selection pressure will eventually result in phenotypical resistance as well. In the general discussion (Chapter 6), the innovations in M. bovis diagnostics achieved with this thesis are discussed. In the second part, practical recommendations for diagnostics in M. bovis outbreak management, purchase policy, and eradication or herd status certificates are proposed. In this thesis new methods to identify, strain type, and access the antimicrobial susceptibility of M. bovis were developed. When rapid identification of M. bovis with MALDI-TOF MS (Chapter 3.3) is followed by the determination of antimicrobial resistance with nanopore sequencing (Chapter 5.2) it is now possible to obtain identification, strain typing and an antibiogram for critically important antibiotics within 3-5 days. This is a major step towards better control of M. bovis in clinical outbreaks and prevent herd infection when purchasing animals. Together with these new methods, also substantial epidemiological information came to light, showing the importance of a more national approach for the prevention of introducing M. bovis into the herd and country