Bacterial Spectrum, Isolation Sites and Susceptibility Patterns of Pathogens in Adult Febrile Neutropenic Cancer Patients at a Specialist Hospital in Saudi Arabia
Bibliographic record
Abstract
Background: Knowing local spectrum and sensitivity for bacterial isolates causing febrile neutropenia is important as starting an appropriate empirical antibiotic therapy is considered a medical emergency in these high-risk patients. Methods: A retrospective study of a total of 106 microbiologically febrile episodes in hospitalized adult neutropenic cancer patients, who were admitted from May 2009 to May 2013, at King Fahad Specialist Hospital, Dammam, Saudi Arabia, was conducted. Results: Among 106 microbiologically documented febrile neutropenic episodes, the majority of malignancies were solid tumors accounting for 53.8% (57/106) and hematological malignancies accounted for 46.23% (49/106). The most common malignancies were non-Hodgkin's lymphoma 19.81% (21/106) followed by acute myeloid leukemia 15.09% (16/106), then colorectal cancer 13.21% (14/106), pancreatic cancer and acute lymphoblastic leukemia accounting for 5.66% (6/106) each, multiple myeloma 4.72% (5/106), gall bladder cancer 3.77% (4/106), and lung cancer 2.83% (3/106). A total of 138 bacterial isolates were identified. The overall prevalence of gram-negative bacteria was 65.94% (91/138) and for gram-positive bacteria was 34.06% (47/138). The most common bacterial isolation sites were blood 33.32% (46 isolates), urine 29.71% (41 isolates), wound 19.55% (27 isolates), body fluids 9.41% (13 isolates) and sputum 7.96% (11 isolates). The most predominant pathogens were Escherichia coli 30.43 (42/138), Klebsiella pneumonia 14.49% (20/138), Staphylococcus aureus 13.04% (18/138), Sptreptococcus spp. 7.25% (10/138), Pseudomonas spp. 7.25% (10/138), Enterococcus spp. 5.80% (8/138), Staphylococcus spp. 4.35% (6/138), Corynebacterium spp. 3.62% (5/138), Enterobacter spp. 3.62% (5/138), Acinobacter spp. 2.90% (4/138), Serratia marcescens 2.17% (3/138), Proteus mirabilis 1.45% (2). Aeromonas hydrophylia, Citrobacter freundii, Providencia stuartii, Sphingomonas paucimobilis and Stenotropomonas multipholia contributed to 0.72% with one isolate each. For gram-negative Escherichia coli and Klebsiella pneumonia , the extended-spectrum beta-lactamases producers (ESBLs) rates were 38% and 22.22% respectively. For Pseudomonas aerugenosa imipenem-cilastatin resistance rate was 18.84%. For gram-positive bacteria, methicillin-resistant Staphylococcus aureus (MRSA) rate was 28.62%. The vancomycin-resistant Enterococci (VRE) rate was 1.18%. Conclusion: Gram-negative bacteria were more prevalent as a cause of infection in adult cancer patients with febrile neutropenia at our institution, with Escherichia coli and Klebsiella pneumonia with high ESBLs rates being the most common pathogens. Blood stream infections followed by urinary tract infections were the most common sites of infection. The use of initial antibiotic therapy in febrile neutropenic episodes should be based on local bacterial spectrum and susceptibility/sensitivity patterns to prevent treatment failure with increased morbidity and mortality. World J Oncol. 2014;5(5-6):196-203 doi: http://dx.doi.org/10.14740/wjon850w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".