Maximizing Knowledge Extraction From Patient-Reported Outcome Data Using Exposure-Outcome Item Response Modeling Approach: Understanding Efavirenz-Induced Central Nervous System Toxicity.

Maximizing Knowledge Extraction From Patient-Reported Outcome Data Using Exposure-Outcome Item Response Modeling Approach: Understanding Efavirenz-Induced Central Nervous System Toxicity. J Clin Pharmacol. 2020 Feb 25;: Authors: Bisaso KS, Bisaso KR, Mukonzo JK, Ette EI Abstract This investigation was undertaken to maximally extract hidden knowledge from an efavirenz-based trial data set using an item response theory-based approach to exposure-outcome analysis. The aim was to understand the influence of efavirenz exposure on the underlying neuropsychiatric impairment in HIV/AIDS patients. Data from 196 individuals with 4136 neuropsychiatric impairment symptom observations at baseline and 2 and 12 weeks of 600-mg efavirenz-based therapy was analyzed. The 7 symptoms were categorized as sleep disorders (3), hallucinations (3), and cognitive impairment (1). A longitudinal item response theory model incorporating 3 latent variables based on the symptom categories and a linear disease progression model with a symptomatic drug effect was developed in NONMEM 7.4.1. The model adequately characterized the observed symptoms and revealed the hidden knowledge on the informativeness of symptoms in characterizing the underlying neuropsychiatric impairment. Informativeness, which was affected by underlying impairment severity and efavirenz therapy duration, varied among symptoms. Sleep disorders were the most efavirenz-sensitive symptom category. Vi...
Source: The Journal of Clinical Pharmacology - Category: Drugs & Pharmacology Authors: Tags: J Clin Pharmacol Source Type: research