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Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users.

Darcy E Ellis¹, Rebecca A Hubbard¹, Allison W Willis^1,2

¹Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Pharmacoepidemiology and Drug Safety

|December 9, 2021

Summary

LASSO modeling better predicts neurological side effects from fluoroquinolones than random forest. This finding is crucial for identifying patients at risk of central and peripheral nervous system dysfunction.

Keywords:

fluoroquinolones logistic models neurologic manifestations pharmacoepidemiology regression analysis supervised machine learning

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Area of Science:

Pharmacovigilance and Pharmacoepidemiology
Computational Statistics and Machine Learning
Clinical Risk Prediction Modeling

Background:

Fluoroquinolones are linked to central (CNS) and peripheral (PNS) nervous system symptoms.
Predicting these neurological risks is clinically significant.
The comparative performance of LASSO and random forest for this prediction is unclear.

Purpose of the Study:

To compare LASSO and random forest models for predicting neurological dysfunction in fluoroquinolone users.

Main Methods:

Developed and validated risk prediction models using insurance claims data.
Included adult fluoroquinolone users, assessing CNS and PNS dysfunction outcomes.
Utilized demographic, comorbidity, medication, and healthcare utilization predictors.
Evaluated model accuracy and calibration using AUC, calibration curves, and Brier scores.

Main Results:

LASSO demonstrated superior performance for CNS dysfunction (AUC 0.81) compared to random forest (AUC 0.80).
LASSO also outperformed random forest for PNS dysfunction (AUC 0.75 vs. 0.73).
LASSO models exhibited better calibration, evidenced by lower Brier scores for both CNS and PNS dysfunction.

Conclusions:

LASSO is a more effective method than random forest for predicting CNS and PNS dysfunction in fluoroquinolone users.
LASSO is recommended for modeling in modest-sized cohorts with primarily binary predictors.
These findings support improved clinical risk stratification for fluoroquinolone-associated neurological adverse events.