Predicting the need for a reduced drug dose, at first prescription

Adrien Coulet (1, 2) , Nigam H. Shah (1) , Maxime Wack (3) , Mohammad Chawki (3) , Nicolas Jay (3, 2) , Michel Dumontier (1, 4)

1 BMIR - Stanford Center for BioMedical Informatics Research
2 ORPAILLEUR - Knowledge representation, reasonning
3 Service d'Evaluation et Information Médicales [CHRU Nancy]
4 Institute of Data Science [Maastricht]

Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients’ prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford’s clinical data warehouse to construct cohorts of patients that either did or did not need a dose change. After feature selection, we trained models to predict the patients who will (or will not) require a dose change after being prescribed one of 34 drugs across 23 drug classes. Overall, we can predict (AUC ≥ 0.70–0.95) a dose reduction for 23 drugs and 22 drug classes. Several of these drugs are associated with clinical guidelines that recommend dose reduction exclusively in the case of adverse reaction. For these cases, a reduction in dosage may be considered as a surrogate for an adverse reaction, which our system could indirectly help predict and prevent. Our study illustrates the role machine learning may take in providing guidance in setting the starting dose for drugs associated with response variability.

Domains: Databases [cs.DB]; Artificial Intelligence [cs.AI]; Bioinformatics [q-bio.QM]; Machine Learning [cs.LG]

Adrien Coulet, Nigam H. Shah, Maxime Wack, Mohammad Chawki, Nicolas Jay, et al.. Predicting the need for a reduced drug dose, at first prescription. Scientific Reports, 2018, 8 (1), ⟨10.1038/s41598-018-33980-0⟩. ⟨hal-01901566⟩