Piperacillin-Tazobactam Adverse Event Analysis: Clinical Cases and FAERS Data

A research document combining a clinical case series with a pharmacovigilance disproportionality analysis for the antibiotic piperacillin-tazobactam. The study reports three patient cases and analyzes data from the FDA Adverse Event Reporting System (FAERS) using four quantitative methods. The document was authored by Jing Lu and last updated on 2026-05-21.

Use Cases

• Assessing the risk signal for thrombocytopenia associated with piperacillin-tazobactam based on FAERS disproportionality analysis
• Reviewing detailed clinical case evidence of piperacillin-tazobactam-associated anemia and thrombocytopenia
• Comparing results from different pharmacovigilance signal detection methods (ROR, PRR, BCPNN, MGPS)
• Informing clinical monitoring guidelines for complete blood counts during piperacillin-tazobactam treatment

Strengths

• Integrates primary clinical case observations with large-scale, public pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS)
• Employs four established quantitative methods for disproportionality analysis, including Bayesian and frequentist approaches
• Reports a specific and significant risk signal for thrombocytopenia (ROR = 5.26, 95% CI 4.81–5.74)

Limitations

• The dataset is a single 79.1 KB DOCX file; its scope is limited to the described analysis and does not contain the raw FAERS data.
• Column-level documentation for any underlying data is absent; field semantics must be inferred from the document text.
• The row count for any underlying tabular data is unknown, which may limit suitability assessment for large-scale ML tasks.

Provenance

Source

FDA Adverse Event Reporting System (FAERS) and a clinical case series.

Collection Method

Disproportionality analysis using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS).

Freshness

Last updated 2026-05-21 05:33:56; freshness should be verified.