Comparative Efficacy and Safety of Paracetamol, Ibuprofen, and Mefenamic Acid for Fever Management in Young Children: A Randomized Open-Label Tria

Abstract

Fever is a common symptom in childhood illnesses. This study aimed to compare the safety and efficacy of paracetamol, ibuprofen, and mefenamic acid in reducing fever in children. A randomized, open-label design was employed, with children aged 6 months to 12 years diagnosed with fever (axillary temperature ≥38.0°C) enrolled. Participants were randomly assigned to receive either paracetamol, ibuprofen, or mefenamic acid at standard pediatric doses. The primary outcome was the time to achieve afebrile status (axillary temperature ❤8.0°C) within 4 hours. Secondary outcomes included the proportion of children achieving afebrile status at specific time points, change in temperature over time, and adverse events.

Introduction

Fever is a common physiological response to infection or inflammation in children. While not necessarily harmful itself, fever can cause discomfort and distress for children and their caregivers. Antipyretic medications like paracetamol, ibuprofen, and mefenamic acid are widely used to reduce fever and improve comfort. However, limited research directly compares the efficacy and safety of these medications in children.

This study aimed to investigate the effectiveness of paracetamol, ibuprofen, and mefenamic acid in reducing fever in children and to assess their tolerability.

Methods

Study Design: A randomized, open-label clinical trial.

Participants: Children aged 6 months to 12 years diagnosed with fever (axillary temperature ≥38.0°C) presenting to the emergency department or pediatric clinic were eligible for enrollment. Exclusion criteria included known allergy to any study medication, chronic medical conditions, or recent use of other antipyretic medications.

Randomization: Participants were randomly assigned to one of three groups using a computer-generated randomization sequence in a 1:1:1 ratio. Allocation was concealed from investigators and participants until enrollment.

Interventions:

• Group 1: Paracetamol, administered orally at a standard pediatric dose based on age and weight.
• Group 2: Ibuprofen, administered orally at a standard pediatric dose based on age and weight.
• Group 3: Mefenamic Acid, administered orally at a standard pediatric dose based on age and weight.

Outcomes:

• Primary Outcome: Time to achieve afebrile status (axillary temperature ❤8.0°C) within 4 hours.
• Secondary Outcomes:
• Proportion of children achieving afebrile status at 1 hour, 2 hours, and 3 hours post-dose.
• Change in body temperature over time.
• Occurrence and severity of adverse events.

Data Collection: Baseline data including demographics, medical history, and vital signs were collected. Temperature measurements were recorded at baseline, and then every 30 minutes for 4 hours post-dose. Parents were instructed to report any adverse events experienced by their child.

Data Analysis:

Data will be analyzed using appropriate statistical methods. Time to achieve afebrile status will be compared between groups using Kaplan-Meier curves and log-rank test. The proportion of children achieving afebrile status at specific time points will be compared using Chi-square test. Changes in temperature will be analyzed using repeated-measures ANOVA. Adverse events will be reported descriptively.

Python Code for Exploratory Analysis (Example):

import pandas as pd
import matplotlib.pyplot as plt

# Load data from a CSV file (replace with your data source)
data = pd.read_csv("febrile_children_data.csv")

# Define descriptive variable names
data.columns = ["treatment_group", "baseline_temp", "temp_1h", "temp_2h", "temp_3h", "temp_4h"]

# Calculate temperature change for each time point
data["temp_change_1h"] = data["baseline_temp"] - data["temp_1h"]
data["temp_change_2h"] = data["baseline_temp"] - data["temp_2h"]
data["temp_change_3h"] = data["baseline_temp"] - data["temp_3h"]
data["temp_change_4h"] = data["baseline_temp"] - data["temp_4h"]

# Explore temperature changes by treatment group
mean_temp_change = data.groupby("treatment_group")["temp_change_4h"].mean()
mean_temp_change.plot(kind="bar")
plt.title("Mean Temperature Change by Treatment Group (4 hours)")
plt.ylabel("Temperature Change (°C)")
plt.show()

# Explore time to achieve afebrile status (additional code needed)
# ... (implement survival analysis using Kaplan-Meier curves)

# Analyze adverse events (additional code needed)
# ... (calculate frequencies and summary statistics)
  

• Data Loading: Load data from a CSV file using pd.read_csv for reusability.
• Variable Names: Use more descriptive variable names for clarity.
• Temperature Change: Calculate temperature change for each time point for easier analysis.
• Grouping and Plotting: Group data by treatment group and plot mean temperature change using pandas and matplotlib.
• Modular Code: Separate functionalities like calculating temperature change and plotting for better organization.
• Additional Analysis: Include comments highlighting sections for implementing survival analysis and adverse event analysis using appropriate libraries.

Conclusion

This randomized open-label study compared the safety and efficacy of paracetamol, ibuprofen, and mefenamic acid in reducing fever in children aged 6 months to 12 years.

Key findings (replace with actual results from your data analysis):

• Analyze the data to determine which medication group achieved afebrile status fastest (primary outcome).
• Compare the proportions of children achieving afebrile status at specific time points (1, 2, and 3 hours) across groups.
• Analyze the change in temperature over time for each treatment group.
• Summarize the frequency and severity of adverse events reported in each group.

Based on the findings (replace with your analysis):

• If one medication group achieved afebrile status significantly faster, conclude which medication is most effective for rapid fever reduction.
• Discuss if any medication demonstrated a consistently greater proportion of children achieving afebrile status at specific time points.
• Analyze the temperature change curves to see if any medication group showed a more sustained reduction in fever.
• Report if any treatment group had a significantly higher incidence of adverse events compared to others.

Limitations:

• Open-label design could introduce bias.
• Relatively short follow-up period might not capture long-term effects.

Future Directions:

• Conduct a double-blind placebo-controlled trial for more robust efficacy and safety assessment.
• Investigate the long-term effects of these medications on children’s health.
• Explore the cost-effectiveness of each medication in managing childhood fever.

Overall, this study provides valuable insights into the comparative effectiveness and safety of paracetamol, ibuprofen, and mefenamic acid for managing fever in children. The findings can inform clinical decision-making and guide further research in this area.

Remember to replace the bracketed sections with your actual results and analysis to create a comprehensive conclusion for your research article.