High Prevalence of Multidrug Resistant <i>Klebsiella</i> Species Isolated from the Yaounde University Teaching Hospital, Cameroon
Bibliographic record
Abstract
Background and Purpose: Klebsiella species are amongst the most common causes of a variety of community-acquired and hospital-acquired infections (HAI), characterized by high morbidity and mortality rates. Most infections caused by Klebsiella species are usually treated using antibiotics. The aim of this study was to determine the antimicrobial resistance profile of Klebsiella species isolated from in-patients and out-patients at the Yaounde University Teaching Hospital. The data generated will go a long way to improve on the choice of an adequate empiric antibiotic treatment for infections caused by Klebsiella species. Methodology: A cross-sectional descriptive study was carried out over a period of 6 months, spanning from February 2019 to July 2019 with a sample size of 37 isolates, obtained from 6 different clinical specimens. Identification of isolates was done using API 20E identification system (Biomerieux SA, Lyon, France). Susceptibility to antibiotics was tested as described by Kirby-Bauer in 1956. Inhibition diameters were interpreted according to recommendations from the European Committee on Antimicrobial Susceptibility Testing (EUCAST, 2019). Results and Conclusion: Among the 37 Klebsiella isolates identified, Klebsiella pneumoniae was the most prevalent species isolated with a percentage of 54.1%, followed by Klebsiella rhinoscleromatis 18.9%, Klebsiella ozaenae 16.2% and Klebsiella oxytoca, 10.8%. The resistance pattern of Klebsiella to amoxicillin, amoxicillin/clavulanate, tircacillin, tircacillin + clavulanic acid, piperacillin, piperacillin + tazobactam, cefalotin, cefuroxim, ceftazidime, cefotaxime, ceftriaxone, cefepime, imipenem, meropenem, aztreonam, amikacin, gentamicin, tobramycin, trimethoprim/ sulfamethoxazole, nalidixic acid, pipemidic acid, norfloxacin, ciprofloxacin, levofloxacin, ofloxacin, and moxifoxacin was as follows; 100%, 86.5%, 97.3%, 83.6%, 86.5%, 16.2%, 86.5%, 83.8%, 78.4%, 32.4%, 78.4%, 76.7%, 2.7%, 2.7%, 76.7%, 13.5%, 75.7%, 73.0%, 91.9%, 51.4%, 48.6%, 64.9%, 48.6%, 48.6%, 73.0% and 62.2% respectively. Multidrug resistance was observed in 94.6% of the Klebsiella isolates. Conclusion: This study shows that the level of multidrug resistance is high. The isolates expressed good sensitivity to carbapenems, piperacillin + tazobactam, amikacin and high resistance to all other antimicrobials tested. Therefore, antimicrobial susceptibility testing prior to prescriptions should be encouraged and sensitization of the population about consequences of inappropriate antibiotic treatment and auto medication should be enforced as a means to curb antimicrobial resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".