Near Future Perspective of ESBL-Producing Klebsiella pneumoniae Strains Using Mathematical Modeling

Abstract

While antibiotic resistance is becoming increasingly serious today, there is almost no doubt that more challenging times await us in the future. Resistant microorganisms have increased in the past decades leading to limited treatment options, along with higher morbidity and mortality. Klebsiella pneumoniae is one of the significant microorganisms causing major public health problems by acquiring resistance to antibiotics and acting as an opportunistic pathogen of healthcare-associated infections. The production of extended spectrum beta-lactamases (ESBL) is one of the resistance mechanisms of K. pneumoniae against antibiotics. In this study, the future clinical situation of ESBL-producing K. pneumoniae was investigated in order to reflect the future scenarios to understand the severity of the issue and to determine critical points to prevent the spread of the ESBL-producing strain. For evaluation purposes, SIS-type mathematical modeling was used with retrospective medical data from the period from 2016 to 2019. Stability of the disease-free equilibrium and basic reproduction ratios were calculated. Numerical simulation of the SIS model was conducted to describe the dynamics of non-ESBL and ESBL-producing K. pneumoniae. In order to determine the most impactful parameter on the basic reproduction ratio, sensitivity analysis was performed. A study on mathematical modeling using data on ESBL-producing K. pneumoniae strains has not previously been performed in any health institution in Northern Cyprus, and the efficiency of the parameters determining the spread of the relevant strains has not been investigated. Through this study, the spread of ESBL-producing K. pneumoniae in a hospital environment was evaluated using a different approach. According to the study, in approximately seventy months, ESBL-producing K. pneumoniae strains will exceed non-ESBL K. pneumoniae strains. As a result, the analyses showed that hospital admissions and people infected with non-ESBL or ESBL-producing K. pneumoniae have the highest rate of spreading the infections. In addition, it was observed that the use of antibiotics plays a major role in the spread of ESBL-producing K. pneumoniae compared to other risk factors such as in-hospital transmissions. As a matter of course, recoveries from Klebsiella infections were seen to be the most effective way of limiting the spread. It can be concluded from the results that although the use of antibiotics is one of the most effective factors in the development of the increasing ESBL-producing K. pneumoniae,

A Markov chain model to investigate the spread of antibiotic‐resistant bacteria in hospitals

“…To examine the implications of the emergence and spread of antibiotic resistance, mathematical modeling (Niewiadomska et al 7 ) provides a platform for in silico experiments that improve our ability to determine the quantitative effects of the transmission process and potential control measures. Most of the existing models follow a deterministic approach, mostly based on the use of ordinary differential equations, on either within-host (Techitnutsarut and Chamchod 8 ) or between-host (Bagkur et al, 9 D'Agata et al, 10 Lipsitch et al 11 ) frameworks; for a novel work formulating a two-level population model, we refer the reader to the paper by Webb et al 12 An excellent summary on antibiotic-resistance modeling is the review of Spicknall et al, 13 where the peer-reviewed literature on between-host resistance modeling-in particular, papers published from 1993 to 2011-is categorized by classifying each paper's model structure into up to six categories based on the underlying inherent assumptions. In the probabilistic setting, Seigal et al 14 introduce a transmission model-which uses the negative binomial distribution-, present a statistical hypothesis test that calculates the significance of resistance trends occurring in a hospital, and apply the method to each of 16 antibiotics in a case study of spectrum 𝛽-lactamases samples collected from patients at a community hospital over a 2.5-year period.…”

“…provides a platform for in silico experiments that improve our ability to determine the quantitative effects of the transmission process and potential control measures. Most of the existing models follow a deterministic approach, mostly based on the use of ordinary differential equations, on either within‐host (Techitnutsarut and Chamchod 8 ) or between‐host (Bagkur et al., 9 D'Agata et al., 10 Lipsitch et al 11 12 .…”