Expert Consensus on a List of Inappropriate Prescribing after Prescription Review in Pediatric Units in Abidjan, Côte d'Ivoire.

2021 
Introduction Inappropriate prescribing (IP) includes inappropriate prescription and omission of prescription. IP can adversely affect the quality of health care in pediatric units. A list of IP taking into account frequently encountered drug-related problems (DRPs) can be useful to optimize prescriptions in pediatrics. The aim of this study was to validate by expert consensus a list of IP after a prescription review in pediatric units in Abidjan. Materials and Methods A list of IPs was developed from a prescription review of inpatients and outpatients aged 1 month to 15 years and followed in pediatric units at teaching hospitals of Abidjan during 16 months. A two-round Delphi method was used to validate a qualitative list of IPs by experts according to their level of agreement on a six-point Likert scale of 0-5 (0, no opinion; 5, strongly agree). Only propositions obtaining the agreement (rating 4 or 5) of >70% of experts who gave a non-zero rating for the first round and 80% for the second round were retained. Results A qualitative list of 54 IPs was drawn up from 267 DRPs detected after prescription review of 4,992 prescription lines for 881 patients. Our panel comprised 22 pediatricians (96%) and one clinical pharmacist (4%). Mean agreement ratings were 4.43/5 (95% CI 4.39-4.48) and 4.6/5 (95% CI 4.56-4.64), respectively, during the first Delphi round and the second (p<0.001). At the end of the first round, all items submitted (54) were retained, including 13 items that had been reworded. In the second round, 20 experts participated and two IPs (4%) were not retained for the final list. This list comprised 52 IPs (44 inappropriate prescriptions and eight omissions of prescription). Conclusion The list of IP validated in this study should help in the detection of DRPs and optimize prescriptions in pediatric units in Cote d'Ivoire.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    37
    References
    0
    Citations
    NaN
    KQI
    []