A decision support system for antibiotic prescription based on local cumulative antibiograms

Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study

BACKGROUND

Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs).

METHODS

Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released.

CONCLUSIONS

Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.

Spanish Antibiogram Committee (COESANT) recommendations for cumulative antibiogram reports

The Spanish Antibiogram Committee (Comité Español del Antibiograma, COESANT) presents in this document a series of recommendations intending to unify how cumulative antibiogram reports must be made in Clinical Microbiology Spanish laboratories. This article is based on the information included in the Clinical Microbiology Procedure No. 51, «Preparation of cumulative reports on antimicrobial susceptibility» of the Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), published in 2014. The recommendations also include the modifications in the definition of clinical interpretive categories recently published by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in 2019. Its final objective is to establish a homogeneous way of preparing these summaries to compare results from different centers or aggregate the information from these in order to carry out an adequate local or even national surveillance regarding the evolution of antimicrobial susceptibility.

The role of the microbiology laboratory in the diagnosis of multidrug-resistant Gram-negative bacilli infections. The importance of the determination of resistance mechanisms

Early diagnosis and treatment has an important impact on the morbidity and mortality of infections caused by multidrug-resistant bacteria. Multidrug-resistant gram-negative bacilli (MR-GNB) constitute the main current threat in hospitals and especially in intensive care units (ICU). The role of the microbiology laboratory is essential in providing a rapid and effective response. This review updates the microbiology laboratory procedures for the rapid detection of BGN-MR and its resistance determinants. The role of the laboratory in the surveillance and control of outbreaks caused by these bacteria, including typing techniques, is also studied. The importance of providing standardized resistance maps that allow knowing the epidemiological situation of the different units is emphasized. Finally, the importance of effective communication systems for the transmission of results and decision making in the management of patients infected by BGN-MR is reviewed.

Management of Children Admitted to Hospitals across Bangladesh with Suspected or Confirmed COVID-19 and the Implications for the Future: A Nationwide Cross-Sectional Study

There is an increasing focus on researching children admitted to hospital with new variants of COVID-19, combined with concerns with hyperinflammatory syndromes and the overuse of antimicrobials. Paediatric guidelines have been produced in Bangladesh to improve their care. Consequently, the objective is to document the management of children with COVID-19 among 24 hospitals in Bangladesh. Key outcome measures included the percentage prescribed different antimicrobials, adherence to paediatric guidelines and mortality rates using purposely developed report forms. The majority of 146 admitted children were aged 5 years or under (62.3%) and were boys (58.9%). Reasons for admission included fever, respiratory distress and coughing; 86.3% were prescribed antibiotics, typically parenterally, on the WHO ‘Watch’ list, and empirically (98.4%). There were no differences in antibiotic use whether hospitals followed paediatric guidance or not. There was no prescribing of antimalarials and limited prescribing of antivirals (5.5% of children) and antiparasitic medicines (0.7%). The majority of children (92.5%) made a full recovery. It was encouraging to see the low hospitalisation rates and limited use of antimalarials, antivirals and antiparasitic medicines. However, the high empiric use of antibiotics, alongside limited switching to oral formulations, is a concern that can be addressed by instigating the appropriate programmes.

Making Sense of Patient-Derived iPSCs, Transdifferentiated Neurons, Olfactory Neuronal Cells, and Cerebral Organoids as Models for Psychiatric Disorders

The improvement of experimental models for disorders requires a constant approximation towards the dysregulated tissue. In psychiatry, where an impairment of neuronal structure and function is assumed to play a major role in disease mechanisms and symptom development, this approximation is an ongoing process implicating various fields. These include genetic, animal, and post-mortem studies. To test hypotheses generated through these studies, in vitro models using non-neuronal cells such as fibroblasts and lymphocytes have been developed. For brain network disorders, cells with neuronal signatures would, however, represent a more adequate tissue. Considering the limited accessibility of brain tissue, research has thus turned towards neurons generated from induced pluripotent stem cells as well as directly induced neurons, cerebral organoids, and olfactory neuroepithelium. Regarding the increasing importance and amount of research using these neuronal cells, this review aims to provide an overview of all these models to make sense of the current literature. The development of each model system and its use as a model for the various psychiatric disorder categories will be laid out. Also, advantages and limitations of each model will be discussed, including a reflection on implications and future perspectives.

Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach

BACKGROUND

To evaluate the ability of Weighted-Incidence Syndromic Combination Antibiograms (WISCA) to inform the selection of empirical antibiotic regimens for suspected paediatric community-acquired urinary tract infections.

METHODS

Data were collected from outpatients (< 15 years) accessing the emergency rooms of Padua University-Hospital and Mestre Dell' Angelo-Hospital (Venice) between January 1st, 2016, and December 31st, 2018. WISCAs were developed by estimating the coverage of eight regimens using a Bayesian hierarchical model adjusted for age, sex, and previous antibiotic treatment or renal/urological comorbidities.

RESULTS

385 of 620 urine culture requests were included in the model analysis. The most frequently observed bacterium was E. coli (85% and 87%, Centre A and B). No centre effect on coverage estimates was found, and data were successfully pooled together. Coverage ranged from 77.8% (Co-trimoxazole) to 97.6% (Carbapenems). Complex cases and males had significantly lower odds of being covered by a regimen than non-complex cases and females (odds ratio (OR) 0.49 [95% HDI, 0.38-0.65], and OR: 0.73 [95% HDIs, 0.56-0.96] respectively). Children aged 3-5 years had lower odds of being covered by a regimen than other age groups, except for neonates.

CONCLUSIONS

The developed WISCAs provide highly informative estimates on coverage patterns overcoming the limitation of combination antibiograms and expanding the framework of previous Bayesian WISCA algorithm.

A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy

BACKGROUND

The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice.

METHODS

We proposed a methodology for decision making that uses a multiple criteria decision analysis (MCDA) to support the clinician in the selection of an efficient combined empiric therapy. The MCDA includes a multi-objective constrained optimization model whose criteria are the maximum efficacy of therapy, maximum activity, the minimum activity overlapping, the minimum use of restricted antibiotics, the minimum toxicity of antibiotics and the activity against the most prevalent and virulent bacteria. The decision process can be defined in 4 steps: (1) selection of clinical situation of interest, (2) definition of local optimization criteria, (3) definition of constraints for reducing combinations, (4) manual sorting of solutions according to patient's clinical conditions, and (5) selection of a combination.

EXPERIMENTS AND RESULTS

In order to show the application of the methodology to a clinical case, we carried out experiments with antibiotic susceptibility tests in blood samples taken during a five years period at a university hospital. The validation of the results consists of a manual review of the combinations and experiments carried out by an expert physician that has explained the most relevant solutions proposed according to current clinical knowledge and their use.

CONCLUSION

We show that with the decision process proposed, the physician is able to select the best combined therapy according to different criteria such as maximum efficacy, activity and minimum toxicity. A method for the recommendation of combined antibiotic therapy developed on the basis of a multi-objective optimization model may assist the physicians in the search for alternatives to the use of broad-spectrum antibiotics or restricted antibiotics for empirical therapy. The decision proposed can be easily reproduced for any local epidemiology and any different clinical settings.

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions

OBJECTIVE

Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS).

METHOD

Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries.

RESULTS

We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.