
Improving the assessment of adverse drug reactions using the Naranjo Algorithm in daily practice: The Japan Adverse Drug Events Study
Author(s) -
Murayama Hiroki,
Sakuma Mio,
Takahashi Yuri,
Morimoto Takeshi
Publication year - 2018
Publication title -
pharmacology research and perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.975
H-Index - 27
ISSN - 2052-1707
DOI - 10.1002/prp2.373
Subject(s) - medicine , clinical practice , adverse effect , adverse drug reaction , pharmacovigilance , drug , algorithm , drug reaction , prospective cohort study , categorization , pharmacology , family medicine , artificial intelligence , computer science
It is difficult to determine adverse drug reactions ( ADR s) in daily complicated clinical practice in which many kinds of drugs are prescribed. We evaluated how well the Naranjo Algorithm ( NA ) categorized ADR s among suspected ADR s. The Japan Adverse Drug Events ( JADE ) study was a prospective cohort study of 3459 inpatients. After all suspected ADR s were reported from research assistants, a single physician reviewer independently assigned an NA score to each. After all NA score of suspected ADR s were scored, two physician reviewers discussed and determined ADR s based on the literature. We investigated the sensitivity and specificity of NA and each component to categorize ADR s among suspected ADR s. A total of 1579 suspected ADR s were reported in 962 patients. Physician reviewers determined 997 ADR s. The percentage of ADR s was 94% if the total NA score reached 5. The modified NA consisted of 5 components that showed high classification abilities; its area under the curve ( AUC ) was 0.92 for categorizing ADR s, the same as the original. When we set the total NA score cut‐off value to 5, specificity was 0.95 and sensitivity was 0.59. When we reclassified NA components as binary variables, the specificity increased to 0.98 with a cut‐off value of 4 and yielded an AUC of 0.93. In conclusion, we showed that both NA and modified NA could categorize ADR s among suspected ADR s with a high likelihood in daily clinical practice.