Unveiling the dynamics of antimicrobial utilization and resistance in a large hospital network over five years: Insights from health record data analysis

Abstract

Antimicrobial Resistance (AMR) presents a pressing public health challenge globally which has been compounded by the COVID-19 pandemic. Elucidation of the impact of the pandemic on AMR evolution using population-level data that integrates clinical, laboratory and prescription data remains lacking. Data was extracted from the centralized electronic platform which captures the health records of 60,551 patients with a confirmed infection across the network of public healthcare facilities in Dubai, United Arab Emirates. For all inpatients and outpatients diagnosed with bacterial infection between 01/01/2017 and 31/05/2022, structured and unstructured Electronic Health Record data, microbiological laboratory data including antibiogram, molecular typing and COVID-19 testing information as well as antibiotic prescribing data were extracted curated and linked. Various analytical methods, including time-series analysis, natural language processing (NLP) and unsupervised clustering algorithms, were employed to investigate the trends of antimicrobial usage and resistance over time, assess the impact of prescription practices on resistance rates, and explore the effects of COVID-19 on antimicrobial usage and resistance. Our findings identified a significant impact of COVID-19 on antimicrobial prescription practices, with short-term and long-lasting over-prescription of these drugs. Resistance to antimicrobials increased the odds ratio of all mortality to an average of 2.18 (95% CI: 1.87-2.49) for the most commonly prescribed antimicrobials. Moreover, the effects of antimicrobial prescription practices on resistance were observed within one week of initiation. Significant trends in antimicrobial resistance, exhibiting fluctuations for various drugs and organisms, with an overall increasing trend in resistance levels, particularly post-COVID-19 were identified. This study provides a population-level insight into the evolution of AMR in the context of COVID-19 pandemic. The findings emphasize the impact of COVID-19 on the AMR crisis, which remained evident even two years after the onset of the pandemic. This underscores the necessity for enhanced antimicrobial stewardship to address the evolution of AMR.

Fig 1. Heatmap summarizing seasonal distribution of antimicrobial prescriptions for inpatients and outpatients for the pre- and post-covid era for the top 25 highly prescribed antimicrobials in inpatients and outpatients.

Each column has been normalized based on the maximum and minimum values.

Fig 2

A) The top ten most important terms extracted from the admission diagnosis notes for the mostly prescribed antimicrobials for inpatients. The highlighted terms are COVID related terms of <>(blue) and <>(red). Frequency refers the frequency of the prescription of the antimicrobials. The importance of the top-ranking terms, as measured by tf-idf, for the most frequently prescribed antimicrobials among B) inpatients and C) outpatients.

Fig 3. Major temporal trends in antimicrobial prescriptions.

A) The pairwise correlation between the temporal prescription trends for inpatient and out-patient antimicrobials. The colour intensity corresponds to the strength of correlation. B) The clustering of prescription trends with parametric modelling approach. C) The ensemble trends of antimicrobials for the groups. Each line is an average of the antimicrobial trends in each cluster. COVID-19 start corresponds to the month when the first case of SARS-CoV-2 was reported.

Fig 4

The rate of prescription of drugs across the inpatient A) The measured effect of COVID-19 incidence on Covid rate for Azithromycin. The upper panel shows the real values. The dotted line corresponds to mean value of the baseline case in which COVID-19 had not occurred. The dotted line in the bottom panel shows the mean inferred causal effect of COVID-19. The grey area corresponds to 95% confidence interval. B) The inferred causal effects for major antimicrobials that showed significant differences in prescription between patients with and without SARS-CoV-2. C) and outpatient settings D) for patients with and without a confirmed COVID-19 test. The error bars denote 95% confidence intervals.

Fig 5. The correlation between time series for antimicrobial weekly consumption rate and resistance level.

A) The blue and red curves correspond to the consumption rate and resistance level for macrolides in E. coli infections. X and Y axis were normalized based on the maximum and minimum values to allow comparison between the time series. B) The correlations between the timeseries as measured by the sample cross-correlation function (CCF). The negative and positive values for the regression refer to the time series lags or leads for the resistance level, respectively.

Fig 6

The odds of death across A) various groups of antimicrobials and organisms and B) the presence of major carbapenemase and ESBL genes. Only significant values are shown. The upper panels in A) and B) show the strength of coefficients from the survival analysis and logistic regression analysis after accounting for the impact of the confounders (see Methods). The dotted line in the bottom figure in A) and B) corresponds to odds ratio of one (no effect).

Fig 7

A) Major clusters in antimicrobial resistance across strains and for different drugs. B) Major trends over time the blue vertical line shows the start of COVID-19 C) the distribution of organisms and drugs in the clusters.