The development of a scoring and ranking strategy for a patient-tailored adverse drug reaction prediction in polypharmacy

Andrei Valeanu,Cristian Damian,Cristina Daniela Marineci,Simona Negres

doi:10.1038/s41598-020-66611-8

Andrei Valeanu, Cristian Damian + Show 2 more

Open Access

https://doi.org/10.1038/s41598-020-66611-8

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Abstract

Only few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy. The study aimed to develop a patient-tailored ADR web application, considering characteristics from 734 drugs and relevant patient related factors. The application was designed in Python using a scoring and ranking system based on frequency and severity, computed for each ADR and expressed through an online platform. A neural networks algorithm was used for predicting the severity of ADRs. The application inputs are: age, gender, drugs, relevant pathologies. The outputs are: an overall severity profile (hospitalization and mortality risk), a stratified risk on specific ADR groups and a sorted list of the most important ADRs depending on frequency and severity. The Severity prediction model validation resulted in 79.7–85.1% Area Under the Receiver Operating Characteristic Curve Score, which lies in the good cut-off of 75–90%. The program offers a complex view regarding the ADR profile of a given patient and could be used by the physician and clinical pharmacist during patient safety monitoring, for a coherent therapy choice or medication adjustment, due to the good therapy coverage and the inclusion of relevant patient comorbidities.

Highlights

Few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy
A prediction of the ADRs which are likely to appear for a certain patient considered pre-clinical data and spontaneous reports and was described by Ngufor and Wojtusiak[12], which took into account the specific drug information and known ADRs from DrugBank and Side Effect Resource Database (SIDER), as well as drug-event signals and demographics from Food and Drug Administration Adverse Event Reporting System (FAERS) and MedEffect
The application was based on three databases (SIDER, FAERS and Medical Dictionary for Regulatory Activities (MedDRA)) and a Frequency Score and Severity Score were devised: the first based on the ADR incidence and the second – on the probability of a severe event linked to the analyzed ADR

Summary

Introduction

Few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy. A prediction of the ADRs which are likely to appear for a certain patient considered pre-clinical data and spontaneous reports and was described by Ngufor and Wojtusiak[12], which took into account the specific drug information and known ADRs from DrugBank and Side Effect Resource Database (SIDER), as well as drug-event signals and demographics from Food and Drug Administration Adverse Event Reporting System (FAERS) and MedEffect. In terms of personalized tools, the American College of Cardiology launched an online application measuring Statin intolerance, taking into account specific patient and drug information. It provides a very complex analysis in terms of musculoskeletal ADRs for patients receiving statin therapy[13]. Other works mainly focused on ADR severity ranking[15], while other recent publications estimated the hospitalization risk due to ADRs in elderly patients[16]

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