Mio Sakuma1, David W Bates, Takeshi Morimoto. 1. Center for General Internal Medicine and Emergency Care, Kinki University School of Medicine, Osaka-sayama, Japan.
Abstract
PURPOSE: Adverse drug events (ADEs) are common health problems worldwide. Developing a prediction rule to identify patients at high risk for ADEs to prevent or ameliorate ADEs could be one attractive strategy. METHODS: The Japan Adverse Drug Events (JADE) study is a prospective cohort study including 3459 participants. We randomly divided the JADE study cohort into the derivation and the validation sets, using an automated random digit generator. We calculated the probabilities of ADE in each patient in the validation set after applying the prediction rule developed in the derivation set. The actual incidence and area under the receiver operating characteristic curve (AUC) in the validation set were compared with those in the derivation set to evaluate the prognostic ability of our developed prediction rule. RESULTS: The developed prediction rule included eight independent risk factors. Each patient in the validation set was classified into three categories of risk for the ADEs according to the probability of ADEs calculated by the developed prediction rule. Eight percent (137/1730) of patients in the validation set fell into the high-risk group, and 35% of this group (48/137) had at least one ADE. The AUC in the validation set was 0.63 (95%CI 0.60-0.66), and the performance to discriminate the probability of ADE was similar (p = 0.08) compared with that in the derivation set. CONCLUSIONS: This prediction rule had the modest predictive ability and could help physicians and other healthcare professionals to make an estimation of patients at high risk for ADEs.
RCT Entities:
PURPOSE: Adverse drug events (ADEs) are common health problems worldwide. Developing a prediction rule to identify patients at high risk for ADEs to prevent or ameliorate ADEs could be one attractive strategy. METHODS: The Japan Adverse Drug Events (JADE) study is a prospective cohort study including 3459 participants. We randomly divided the JADE study cohort into the derivation and the validation sets, using an automated random digit generator. We calculated the probabilities of ADE in each patient in the validation set after applying the prediction rule developed in the derivation set. The actual incidence and area under the receiver operating characteristic curve (AUC) in the validation set were compared with those in the derivation set to evaluate the prognostic ability of our developed prediction rule. RESULTS: The developed prediction rule included eight independent risk factors. Each patient in the validation set was classified into three categories of risk for the ADEs according to the probability of ADEs calculated by the developed prediction rule. Eight percent (137/1730) of patients in the validation set fell into the high-risk group, and 35% of this group (48/137) had at least one ADE. The AUC in the validation set was 0.63 (95%CI 0.60-0.66), and the performance to discriminate the probability of ADE was similar (p = 0.08) compared with that in the derivation set. CONCLUSIONS: This prediction rule had the modest predictive ability and could help physicians and other healthcare professionals to make an estimation of patients at high risk for ADEs.
Authors: Dorthe Krogsgaard Bonnerup; Marianne Lisby; Eva Aggerholm Sædder; Birgitte Brock; Tania Truelshøj; Charlotte Arp Sørensen; Anita Gorm Pedersen; Lars Peter Nielsen Journal: Ther Adv Drug Saf Date: 2020-09-20
Authors: Lea Jung-Poppe; Hagen Fabian Nicolaus; Anna Roggenhofer; Anna Altenbuchner; Harald Dormann; Barbara Pfistermeister; Renke Maas Journal: J Clin Med Date: 2022-09-01 Impact factor: 4.964