A1 Refereed original research article in a scientific journal
Development of prediction model for alanine transaminase elevations during the first 6 months of conventional synthetic DMARD treatment
Authors: Kuusalo Laura, Venäläinen Mikko, Kirjala Heidi, Saranpää Sofia, Elo Laura L, Pirila Laura
Publisher: NATURE PORTFOLIO
Publication year: 2023
Journal: Scientific Reports
Journal name in source: SCIENTIFIC REPORTS
Journal acronym: SCI REP-UK
Article number: 12943
Volume: 13
Number of pages: 9
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-023-39694-2
Web address : https://doi.org/10.1038/s41598-023-39694-2
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/180857000
Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013-2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 x ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency.
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