Clinical Epidemiology (Feb 2022)
Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction
Abstract
Benjamin Skov Kaas-Hansen,1– 3 Cristina Leal Rodríguez,2 Davide Placido,2 Hans-Christian Thorsen-Meyer,2,4 Anna Pors Nielsen,2 Nicolas Dérian,5 Søren Brunak,2 Stig Ejdrup Andersen1 1Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark; 2NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark; 3Section for Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; 4Department of Intensive Care Medicine, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark; 5Data and Development Support, Region Zealand, Sorø, DenmarkCorrespondence: Benjamin Skov Kaas-Hansen, Clinical Pharmacology Unit, Zealand University Hospital, Munkesoevej 18, Roskilde, 4000, Denmark, Tel +45 60 19 68 02, Email [email protected]: Dosing of renally cleared drugs in patients with kidney failure often deviates from clinical guidelines, so we sought to elicit predictors of receiving inappropriate doses of renal risk drugs.Patients and methods: We combined data from the Danish National Patient Register and in-hospital data on drug administrations and estimated glomerular filtration rates for admissions between 1 October 2009 and 1 June 2016, from a pool of about 2.6 million persons. We trained artificial neural network and linear logistic ridge regression models to predict the risk of five outcomes (> 0, ≥ 1, ≥ 2, ≥ 3 and ≥ 5 inappropriate doses daily) with index set 24 hours after admission. We used time-series validation for evaluating discrimination, calibration, clinical utility and explanations.Results: Of 52,451 admissions included, 42,250 (81%) were used for model development. The median age was 77 years; 50% of admissions were of women. ≥ 5 drugs were used between admission start and index in 23,124 admissions (44%); the most common drug classes were analgesics, systemic antibacterials, diuretics, antithrombotics, and antacids. The neural network models had better discriminative power (all AUROCs between 0.77 and 0.81) and were better calibrated than their linear counterparts. The main prediction drivers were use of anti-inflammatory, antidiabetic and anti-Parkinson’s drugs as well as having a diagnosis of chronic kidney failure. Sex and age affected predictions but slightly.Conclusion: Our models can flag patients at high risk of receiving at least one inappropriate dose daily in a controlled in-silico setting. A prospective clinical study may confirm that this holds in real-life settings and translates into benefits in hard endpoints.Keywords: predictive modelling, kidney failure, machine learning, risk markers, inappropriate drug dosing, renal risk drugs
