PubMed HealthSearch

PubMed · 42704487

Ibuprofen versus acetaminophen for acute mild-to-moderate pain management in pediatric populations: a systematic review and meta-analysis of their efficacy.

Abstract

UNLABELLED: Ibuprofen and acetaminophen are the most widely used analgesics in pediatric practice for the management of acute mild-to-moderate pain. Despite their widespread use, the comparative analgesic efficacy of these two agents in children remains a subject of ongoing debate, with existing evidence largely derived from heterogeneous clinical settings and small individual trials. Therefore, this study aimed to systematically review and meta-analyze randomized controlled trials comparing the analgesic efficacy of ibuprofen versus acetaminophen in pediatric populations with acute mild-to-moderate pain. A systematic literature search was conducted up to May 2026 in PubMed, Scopus, and Web of Science. The review was conducted and reported in accordance with the PRISMA-Children and Adolescents (PRISMA-C) 2026 reporting guideline. Eligible studies were randomized controlled trials comparing ibuprofen with acetaminophen in children and adolescents (defined as individuals aged 0 to&#x2009;<&#x2009;18&#xa0;years) with acute pain, reporting at least one extractable efficacy outcome. Continuous outcomes were synthesized as standardized mean differences (Hedges' g) using random-effects models; dichotomous outcomes were pooled as risk ratios (RRs) with 95% confidence intervals. Risk of bias was assessed using the Cochrane RoB 2 tool and certainty of evidence was evaluated using the GRADE framework. Eight randomized controlled trials enrolling 1325 participants were included. Three pediatric trials contributed to the primary continuous pain outcome meta-analysis (n&#x2009;=&#x2009;196 analyzable participants), yielding a pooled SMD of&#x2009;-&#x2009;0.28 (95% CI&#x2009;-&#x2009;0.57 to 0.00; p&#x2009;=&#x2009;0.052; I2&#x2009;=&#x2009;0%), indicating a small effect favoring ibuprofen that did not reach conventional statistical significance. Given the small number of contributing studies (k&#x2009;=&#x2009;3), the I2 statistic should be interpreted with caution as it has limited power to detect heterogeneity in this context. For the dichotomous pain freedom outcome (2 trials, n&#x2009;=&#x2009;114), no significant difference was observed (pooled RR 1.03, 95% CI 0.53-1.99; p&#x2009;=&#x2009;0.93; I2&#x2009;=&#x2009;0%). A prespecified sensitivity analysis including an adult soft-tissue injury trial attenuated the pooled effect toward the null (SMD&#x2009;-&#x2009;0.15, 95% CI&#x2009;-&#x2009;0.38 to 0.09; p&#x2009;=&#x2009;0.23; I2&#x2009;=&#x2009;36.6%). Narrative synthesis of additional studies generally demonstrated comparable analgesic efficacy between the two agents across postoperative and outpatient pediatric settings. The overall certainty of evidence was rated as low for both primary outcomes, primarily due to imprecision and indirectness. CONCLUSION: Current evidence from randomized controlled trials does not demonstrate a superiority of ibuprofen over acetaminophen for acute mild-to-moderate pain management in children. Both agents appear to provide clinically meaningful analgesia across heterogeneous pediatric pain settings. The clinical choice between agents should be guided by individual patient factors, including contraindications to NSAIDs, the inflammatory nature of the pain etiology, and patient-specific characteristics. The low certainty of evidence underscores the need for adequately powered, methodologically rigorous trials to definitively establish the comparative efficacy of these two analgesics in the pediatric population. WHAT IS KNOWN: &#x2022; Ibuprofen and acetaminophen are the two most widely used non-opioid analgesics for acute mild-to-moderate pain in children, and both are recommended as first-line agents by major international guidelines. &#x2022; Prior meta-analyses in mixed pediatric-adult populations have suggested a modest analgesic advantage of ibuprofen over acetaminophen, but pediatric-specific evidence has remained limited and methodologically heterogeneous. WHAT IS NEW: &#x2022; This systematic review and meta-analysis, restricted to randomized controlled trials in pediatric populations, found that ibuprofen showed a small effect favoring pain reduction compared with acetaminophen (SMD&#x2009;-&#x2009;0.28, p&#x2009;=&#x2009;0.052), although this did not reach conventional statistical significance. &#x2022; The analgesic advantage of ibuprofen may be more pronounced in pain etiologies with a significant inflammatory component (e.g., fractures). At the same time, both agents appear broadly equivalent in most other acute pediatric pain settings, supporting individualized analgesic selection based on clinical context and patient-specific factors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gian Luigi Marseglia, Paola Giovanna Marchisio, Gregorio Paolo Milani, Michele Miraglia Del Giudice, Irene Schiavetti, Giorgio Ciprandi. 2026-09-07. Ibuprofen versus acetaminophen for acute mild-to-moderate pain management in pediatric populations: a systematic review and meta-analysis of their efficacy.. https://doi.org/10.1007/s00431-026-07383-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans