The Association of Chemotherapy-Induced Neutropenia with the Risk of Febrile Neutropenia and Infections: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION Chemotherapy-induced neutropenia (CIN) is a primary dose-limiting toxicity of myelosuppressive cancer therapy. It significantly increases a patient's susceptibility to febrile neutropenia (FN), an oncologic emergency associated with severe infections, substantial morbidity, and mortality. This systematic review synthesizes the evidence linking CIN to the risk of FN and subsequent infectious complications. METHODS A systematic search of PubMed, EMBASE, and the Cochrane Library was conducted for studies published from January 2010 onwards, following PRISMA guidelines. Observational studies and clinical trials involving cancer patients receiving chemotherapy and reporting on neutropenic complications were included. Data on over 15 distinct clinical, microbiological, and treatment-related outcomes were extracted. The methodological quality of included studies was appraised using the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale. RESULTS Twenty studies, encompassing prospective and retrospective cohorts, cross-sectional analyses, and validation studies, met the inclusion criteria. The evidence confirms that CIN is a direct precursor to FN, with an incidence of up to 16.8% per chemotherapy course, and a peak risk during the first treatment cycle. FN frequently necessitates hospitalization, with a mean duration of approximately 8 days, and is associated with significant in-hospital mortality rates ranging from 2.6% to over 25% in high-risk, culture-positive cohorts. Bloodstream infections are the most common severe documented complication. The microbiological landscape is evolving, with a resurgence of Gram-negative pathogens, including multidrug-resistant organisms, challenging standard empiric antibiotic regimens. Neutropenic events directly impact oncologic care, leading to frequent chemotherapy dose reductions and delays, which can compromise treatment efficacy. DISCUSSION The synthesized evidence underscores the profound clinical and economic burden of neutropenic complications. A critical gap exists between guideline recommendations for risk stratification and the poor predictive performance of existing clinical tools like the MASCC and CISNE scores. An episode of FN serves not only as an acute event but also as a sentinel marker of patient vulnerability, predicting a significantly increased long-term risk of subsequent infections. CONCLUSION CIN is unequivocally associated with an elevated risk of FN and life-threatening infections. This relationship drives significant morbidity, mortality, and disruptions to cancer treatment. Future efforts must prioritize the development of dynamic, data-driven risk prediction models and surveillance-informed antimicrobial stewardship to optimize patient management and improve oncologic outcomes.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".