Novel Method for Early Prediction of Clinically Significant Drug-Drug Interactions with a Machine Learning Algorithm Based on Risk Matrix Analysis in the NICU.
Nadir YalçınMerve KaşıkcıHasan Tolga ÇelikKarel AllegaertKutay DemirkanŞule YiğitMurat YurdakökPublished in: Journal of clinical medicine (2022)
Aims: Evidence for drug-drug interactions (DDIs) that may cause age-dependent differences in the incidence and severity of adverse drug reactions (ADRs) in newborns is sparse. We aimed to develop machine learning (ML) algorithms that predict DDI presence by integrating each DDI, which is objectively evaluated with the scales in a risk matrix (probability + severity). Methods: This double-center, prospective randomized cohort study included neonates admitted to the neonatal intensive care unit in a tertiary referral hospital during the 17-month study period. Drugs were classified by the Anatomical Therapeutic Chemical (ATC) classification and assessed for potential and clinically relevant DDIs to risk analyses with the Drug Interaction Probability Scale (DIPS, causal probability) and the Lexicomp ® DDI (severity) database. Results: A total of 412 neonates (median (interquartile range) gestational age of 37 (4) weeks) were included with 32,925 patient days, 131 different medications, and 11,908 medication orders. Overall, at least one potential DDI was observed in 125 (30.4%) of the patients (2.6 potential DDI/patient). A total of 38 of these 125 patients had clinically relevant DDIs causing adverse drug reactions (2.0 clinical DDI/patient). The vast majority of these DDIs (90.66%) were assessed to be at moderate risk. The performance of the ML algorithms that predicts of the presence of relevant DDI was as follows: accuracy 0.944 (95% CI 0.888-0.972), sensitivity 0.892 (95% CI 0.769-0.962), F1 score 0.904, and AUC 0.929 (95% CI 0.874-0.983). Conclusions: In clinical practice, it is expected that optimization in treatment can be achieved with the implementation of this high-performance web tool, created to predict DDIs before they occur with a newborn-centered approach.
Keyphrases
- adverse drug
- machine learning
- gestational age
- end stage renal disease
- deep learning
- artificial intelligence
- chronic kidney disease
- newly diagnosed
- electronic health record
- big data
- drug induced
- emergency department
- ejection fraction
- healthcare
- primary care
- clinical practice
- preterm infants
- case report
- prognostic factors
- low birth weight
- preterm birth
- pregnant women
- physical activity
- patient reported outcomes
- risk assessment
- randomized controlled trial
- birth weight
- weight gain
- human health
- high intensity
- quality improvement
- body mass index
- smoking cessation