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Algorithm for the evaluation of therapeutic failure reports--proposal and pilot analysis
Claudia Patricia Vaca González1, Roxana Patricia De las salas Martínez, José Julián López Gutiérrez
1Pharmacy Department, Faculty of Sciences, Universidad Nacional de Colombia, Bogotá, Colombia. cpvacag@unal.edu.co
Purpose:
To propose and validate an algorithm to analyze the reports of suspected therapeutic failure (TF) received in pharmacovigilance programs.
Methods:
A Delphi consensus method with a group of 12 international experts was used to identify the different causes that prompt TF and to propose an algorithm to analyze reports of suspected lack of efficacy of medicines. A pilot analysis of 50 reports was the basis to evaluate the interrater and intrarater validity of the algorithm.
Results:
A 10-question algorithm was proposed. The evaluation of 50 reports of suspected TF showed that only 8% could be actually attributed to a manufacturing quality problem, whereas the real reason underlying the alleged TF was the inappropriate use of the prescribed drug in 28%. Minimum information to attribute the cause to a TF was lacking in 31% of these reports. The interrater reliability was "moderate" (kappa coefficient = 0.55), and the intrarater reliability ranged from 0.732 to 0.908 ("substantial" to "almost perfect").
Conclusions:
The proposed TF algorithm is a valid, reliable, and reproducible analysis tool that can help to disentangle the frequent and complex reports of suspected TF.
Insights
A new algorithm helps analyze suspected therapeutic failure (TF) reports in pharmacovigilance. It found inappropriate drug use, not manufacturing issues, as a key cause of alleged TF.
Area of Science:
- Pharmacovigilance
- Drug Safety
- Clinical Pharmacology
Background:
- Therapeutic failure (TF) reports in pharmacovigilance are complex and frequent.
- Accurate analysis of TF reports is crucial for drug safety monitoring.
Purpose of the Study:
- To develop and validate an algorithm for analyzing suspected therapeutic failure (TF) reports.
- To differentiate causes of alleged TF in pharmacovigilance data.
Main Methods:
- A Delphi consensus method involving 12 international experts.
- Development of a 10-question algorithm to analyze TF reports.
- Pilot analysis of 50 reports to assess interrater and intrarater reliability.
Main Results:
- Inappropriate drug use accounted for 28% of alleged TF.
- Manufacturing quality problems were attributed to only 8% of TF.
- 31% of reports lacked sufficient information for TF cause attribution.
- Moderate interrater reliability (kappa=0.55) and substantial to almost perfect intrarater reliability (0.732-0.908).
Conclusions:
- The proposed TF algorithm is a valid, reliable, and reproducible tool.
- The algorithm aids in clarifying complex TF reports.
- It highlights the importance of appropriate drug use in therapeutic outcomes.
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