Antimicrobial susceptibility of bacterial uropathogens in a South African regional hospital

Background: Urinary tract infections are common bacterial infections affecting millions worldwide. Although treatment options for urinary tract infections are well established, with ciprofloxacin long considered one of the antibiotics of choice, increasing antibiotic resistance may delay the initiation of appropriate therapy. While this increase in antimicrobial resistance has been demonstrated in multiple studies around the world, there is a dearth of information from developing countries. Objective: This study aimed to describe the antimicrobial susceptibility patterns of commonly isolated bacterial uropathogens in a South African hospital. Methods: Antimicrobial susceptibility data of isolates obtained from urine specimens at the RK Khan Hospital, a regional hospital in KwaZulu-Natal, South Africa, between January 2018 and December 2020 were retrieved from the hospital’s laboratory information system and analysed to determine the differences in resistance rates between the most frequently isolated bacterial uropathogens. Results: Of the 3048 bacterial urinary pathogens isolated between 2018 and 2020, Escherichia coli (1603; 53%) was the most common, followed by Klebsiella spp. (437; 14%). Both E. coli and Klebsiella spp. showed high rates of resistance to amoxicillin/clavulanic acid (29.8% and 42.3%) and ciprofloxacin (37.7% and 30.4%). Nitrofurantoin resistance was low among E. coli (6.2%) but high among Klebsiella spp. (61.3%). Conclusion: E. coli and Klebsiella spp. in this study were highly resistant to amoxicillin/clavulanic acid and ciprofloxacin, two of the frequently prescribed oral treatment options. What this study adds: This study highlights the importance of regular local antimicrobial resistance surveillance to inform appropriate empiric therapy

Effects of introducing Xpert MTB/RIF test on multi-drug resistant tuberculosis diagnosis in KwaZulu-Natal South Africa.

CAPRISA, 2014.Abstract available in pdf

Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal.

CAPRISA, 2015.Abstract available in pdf

Updating the approaches to define susceptibility and resistance to anti-tuberculosis agents: implications for diagnosis and treatment

11 páginas, 2 figuras, 1 tablaInappropriately high breakpoints have resulted in systematic false-susceptible AST results to anti-TB drugs. MIC, PK/PD and clinical outcome data should be combined when setting breakpoints to minimise the emergence and spread of antimicrobial resistance.I. Comas was supported by PID2019-104477RB-I00 from the Spanish Science Ministry and by ERC (CoG 101001038)Peer reviewe

Failure of BACTEC™ MGIT 960™ to detect <i>Mycobacterium tuberculosis </i> complex within a 42-day incubation period

For the optimal recovery of Mycobacterium tuberculosis from the BACTEC™ Mycobacterium Growth Indicator Tube 960™ system, an incubation period of 42–56 days is recommended by the manufacturer. Due to logistical reasons, it is common practice to follow an incubation period of 42 days. We undertook a retrospective study to document positive Mycobacterium Growth Indicator Tube cultures beyond the 42-day incubation period. In total, 98/110 (89%) were positive for M. tuberculosis complex. This alerted us to M. tuberculosis growth detection failure at 42 days

Evolution of extensively drug-resistant tuberculosis over four decades revealed by whole genome sequencing of Mycobacterium tuberculosis from KwaZulu-Natal, South Africa

The largest global outbreak of extensively drug-resistant (XDR) tuberculosis (TB) was identified in Tugela Ferry, KwaZulu-Natal (KZN), South Africa in 2005. The antecedents and timing of the emergence of drug resistance in this fatal epidemic XDR outbreak are unknown, and it is unclear whether drug resistance in this region continues to be driven by clonal spread or by the development of de novo resistance. A whole genome sequencing and drug susceptibility testing (DST) was performed on 337 clinical isolates of Mycobacterium tuberculosis (M.tb) collected in KZN from 2008 to 2013, in addition to three historical isolates, one of which was isolated during the Tugela Ferry outbreak. Using a variety of whole genome comparative approaches, 11 drug-resistant clones of M.tb circulating from 2008 to 2013 were identified, including a 50-member clone of XDR M.tb that was highly related to the Tugela Ferry XDR outbreak strain. It was calculated that the evolutionary trajectory from first-line drug resistance to XDR in this clone spanned more than four decades and began at the start of the antibiotic era. It was also observed that frequent de novo evolution of MDR and XDR was present, with 56 and 9 independent evolutions, respectively. Thus, ongoing amplification of drug-resistance in KwaZulu-Natal is driven by both clonal spread and de novo acquisition of resistance. In drug-resistant TB, isoniazid resistance was overwhelmingly the initial resistance mutation to be acquired, which would not be detected by current rapid molecular diagnostics that assess only rifampicin resistance

Molecular evolution and dating of drug resistance emergence within the Tugela Ferry XDR Clone.

<p>Midpoint rooted maximum-likelihood phylogeny of 107 <i>M</i>. <i>tuberculosis</i> isolates of the LAM4 spoligotype. The gray shaded box identifies the Tugela Ferry XDR Clone. KZN605, the historical XDR strain collected in Tugela Ferry during the outbreak, is a member of this clone. Two additional historical isolates, KZN1435 and KZN4207, are not members of the Tugela Ferry XDR Clone. Each evolutionary gain of a drug resistance mutation was assigned to its position on the phylogenetic tree by parsimony (colored circles). A–E traces the stepwise order of drug resistance acquisition in the Tugela Ferry XDR Clone and estimates the year when each mutation was gained. Gray bars indicate the 95% highest posterior density (HPD) intervals. (A) <i>katG</i> S315T (isoniazid); <i>gidB</i> 130 bp deletion (streptomycin); 1957 (95% HPD: 1937–1971); (B) <i>inhA</i> promoter -8 (isoniazid and ethionamide); 1964 (95% HPD: 1948–1976); (C) <i>embB</i> M306V (ethambutol); 1967 (95% HPD: 1950–1978); (D) <i>rpoB</i> L452P (rifampicin); <i>pncA</i> 1bp insertion (pyrazinamide); 1984 (95% HPD: 1974–1992); and (E) <i>rpoB</i> D435G (rifampicin); <i>rrs</i> 1400 (kanamycin); <i>gyrA</i> A90V (ofloxacin); 1995 (95% HPD: 1988–1999). The accumulation of individual drug-resistant mutations within a strain is denoted to the right of the phylogenetic tree. The dates of drug discovery are displayed at the bottom of the figure [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001880#pmed.1001880.ref053" target="_blank">53</a>]. Four additional LAM4 strains on a distant branch were not included in this figure because of size constraints. Bootstrap values are provided for lettered nodes, and bootstrap values for all nodes are shown in S5 Fig.</p

Isoniazid resistance is the first step towards drug resistance.

<p>Acquisition of <i>katG</i> S315 mutations precedes all other resistance mutations, including rifampicin, in all instances in which the order of acquisition can be disambiguated. For the 214 strains with genotypic resistance to two or more MDR or XDR defining drugs, and in which the order of acquisition of these mutations could be disambiguated, we quantified the number of evolutions in which resistance to one drug was gained before resistance to a second drug. Isoniazid resistance was divided into mutations conferred by the <i>katG</i>S315 codon versus “Other INH” mutations (defined as loss-of-function mutations in <i>katG</i> that do not involve codon 315 or mutations in the <i>inhA</i> promoter). Reported numbers represent the number of independent evolutionary events (not the number of strains) in which the drug resistance indicated by the row labeled “first resistance” was acquired before the drug resistance indicated by the column labeled “second resistance.” The background color is shaded to indicate the fraction of unambiguous evolutionary events in which the “first resistance” was acquired before the “second resistance” for that given drug pair.</p

Diverse strains contribute to drug resistance in KwaZulu-Natal.

<p>(A) Midpoint rooted maximum-likelihood phylogeny of 340 <i>M</i>. <i>tuberculosis</i> isolates. Four of the seven known <i>M</i>. <i>tuberculosis</i> lineages were identified: CAS (<i>Lin1</i>), Beijing (<i>Lin2</i>), EAI (<i>Lin 3</i>), and Euro-American (<i>Lin4</i>). Digital spoligotyping identified 17 unique spoligotypes in the dataset; spoligotypes are shown on this figure if they are represented by three or more strains. Corresponding spoligotypes and phenotypes are reported for all strains in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001880#pmed.1001880.s009" target="_blank">S4 Table</a>. Phenotypic XDR, MDR, poly- and monodrug resistance (labeled “Drug-resistant other”), and pansusceptible strains are indicated by colored tick marks at the tip of each leaf node. (B) Histogram of pairwise SNP distances between strains. The number of pairs within each SNP distance range is plotted. The peaks correspond to the distance between major lineages. The peak at the far left of the figure corresponds to the distance between pairs of strains within a clone.</p

Demographic characteristics of participants and phenotypic drug susceptibility of strains.

<p>* HIV-positive and HIV-negative individuals were compared using Fisher’s exact test for categorical variables and nonparametric Mann-Whitney test for continuous variables.</p><p>^† Data in this row are from the XDR data, not the total dataset.</p><p>Results having a <i>p</i>-value < 0.05 were considered statistically significant.</p><p>Data are <i>n</i> (%) or mean ± standard deviation (SD).</p