Graduate Program of Pharmaceutical Science, Health Science Center, Faculdade de Farmácia, Centro de Ciências da Saúde, Universidade Federal Do Rio Grande Do Norte UFRN, Av. General Gustavo Cordeiro de Farias, Petrópolis, Natal, RN, 59012-570, Brazil.
Pharmacy Department, Health Science Center, Federal University of Rio Grande Do Norte, Natal, Brazil.
Int J Clin Pharm. 2023 Aug;45(4):1007-1013. doi: 10.1007/s11096-023-01595-9. Epub 2023 May 22.
Algorithms for causality assessment of adverse drug reactions (ADRs) in a neonatal intensive care unit (NICU) are important in the management of adverse events, however, it is inconclusive which tool best suits pharmacovigilance in neonates.
To compare the performance of the algorithms of Du and Naranjo in determining causality in cases of ADRs in neonates in a NICU.
This observational and prospective study was conducted in a NICU of a Brazilian maternity school between January 2019 and December 2020. Independently, three clinical pharmacists used the algorithms of Naranjo and Du in 79 cases of ADRs in 57 neonates. The algorithms were evaluated for inter-rater and inter-tool agreement using Cohen's kappa coefficient (k).
The Du algorithm showed greater ability to identify definite ADRs (≈ 60%), but had low reproducibility (overall k = 0.108; 95% CI 0.064-0.149). In contrast, the Naranjo algorithm showed a lower proportion of definite ADRs (< 4%), but had good reproducibility (overall k = 0.402; 95% CI 0.379-0.429). The tools showed no significant correlation regarding ADR causality classification (overall k = - 0.031; 95% CI - 0.049 to 0.065).
Although the Du algorithm has a lower reproducibility compared to the Naranjo, this tool showed good sensitivity for classifying ADRs as definite, proving to be a more suitable tool for neonatal clinical routine.
在新生儿重症监护病房(NICU)中评估药物不良反应(ADR)因果关系的算法对于不良事件的管理非常重要,然而,哪种工具最适合新生儿的药物警戒尚无定论。
比较杜和纳兰霍算法在确定 NICU 中新生儿 ADR 因果关系中的表现。
本观察性前瞻性研究于 2019 年 1 月至 2020 年 12 月在巴西一所妇产学校的 NICU 进行。三位临床药师独立使用纳兰霍和杜的算法,对 57 名新生儿的 79 例 ADR 进行评估。使用 Cohen's kappa 系数(k)评估算法的评分者间和工具间一致性。
杜算法在识别确定性 ADR(≈60%)方面表现出更强的能力,但重复性较低(总体 k=0.108;95%CI 0.064-0.149)。相比之下,纳兰霍算法显示出较低比例的确定性 ADR(<4%),但具有良好的可重复性(总体 k=0.402;95%CI 0.379-0.429)。两种工具在 ADR 因果关系分类方面没有显著相关性(总体 k=−0.031;95%CI−0.049 至 0.065)。
尽管与纳兰霍算法相比,杜算法的可重复性较低,但该工具在将 ADR 分类为确定性方面具有较高的灵敏度,证明其更适合新生儿临床常规。