Capturing Global, Predicting Local for Controlling Antimicrobial Resistance: a retrospective multivariable analysis

BackgroundAntimicrobial resistance (AMR) is a complex multifactorial outcome of health, socio-economic and geopolitical factors. Therefore, tailored solutions for mitigation strategies could be more effective in dealing with this challenge. Knowledge-synthesis and actionable models learned upon large datasets are critical in order to diffuse the risk of entering into a post-antimicrobial era. ObjectiveThis work is focused on learning Global determinants of AMR and predicting the susceptibility of antibiotics at the isolate level (Local) for WHO (world health organization) declared critically important pathogens Pseudomonas aeruginosa, Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, Enterobacter cloacae, Staphylococcus aureus. MethodsIn this study, we used longitudinal data (2004-2017) of AMR having 633820 isolates from 70 Middle and High-income countries. We integrated AMR data with the Global burden of disease (GBD), Governance (WGI), and Finance data sets in order to find the unbiased and actionable determinants of AMR. We chose a Bayesian Decision Network (BDN) approach within the causal modeling framework to quantify determinants of AMR. Finally Integrating Bayesian networks with classical machine learning approaches lead to effective modeling of the level of AMR. ResultsFrom MAR (Multiple Antibiotic Resistance) scores, we found that developing countries are at higher risk of AMR compared to developed countries, for all the critically important pathogens. Also, Principal Components Analysis(PCA) revealed that governance, finance, and disease burden variables have a strong association with AMR. We further quantified the impact of determinants in a probabilistic way and observed that health system access and government effectiveness are strong actionable factors in reducing AMR, which was in turn confirmed by what-if analysis. Finally, our supervised machine learning models have shown decent performance, with the highest on Staphylococcus aureus. For Staphylococcus aureus, our model predicted susceptibility to Ceftaroline and Oxacillin with the highest AUROC, 0.94(with SE of 0.01%) and 0.89(with SE of 0.002%) respectively.