Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models
André Wieringa,
Tim M. J. Ewoldt,
Ravish N. Gangapersad,
Matthias Gijsen,
Nestor Parolya,
Chantal J. A. R. Kats,
Isabel Spriet,
Henrik Endeman,
Jasper J. Haringman,
Reinier M. van Hest,
Birgit C. P. Koch,
Alan Abdulla
Affiliations
André Wieringa
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
Tim M. J. Ewoldt
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
Ravish N. Gangapersad
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
Matthias Gijsen
Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium
Nestor Parolya
Delft Institute of Applied Mathematics, Mekelweg 4, 2628 CD Delft, The Netherlands
Chantal J. A. R. Kats
Department of Hospital Pharmacy, Haaglanden Medical Center, Lijnbaan 32, 2512 VA The Hague, The Netherlands
Isabel Spriet
Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium
Henrik Endeman
Department of Intensive Care, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
Jasper J. Haringman
Department of Intensive Care, Isala Hospital, Dr. van Heesweg 2, 8025 AB Zwolle, The Netherlands
Reinier M. van Hest
Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
Birgit C. P. Koch
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
Alan Abdulla
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
In the intensive care unit (ICU), infection-related mortality is high. Although adequate antibiotic treatment is essential in infections, beta-lactam target non-attainment occurs in up to 45% of ICU patients, which is associated with a lower likelihood of clinical success. To optimize antibiotic treatment, we aimed to develop beta-lactam target non-attainment prediction models in ICU patients. Patients from two multicenter studies were included, with intravenous intermittent beta-lactam antibiotics administered and blood samples drawn within 12–36 h after antibiotic initiation. Beta-lactam target non-attainment models were developed and validated using random forest (RF), logistic regression (LR), and naïve Bayes (NB) models from 376 patients. External validation was performed on 150 ICU patients. We assessed performance by measuring discrimination, calibration, and net benefit at the default threshold probability of 0.20. Age, sex, serum creatinine, and type of beta-lactam antibiotic were found to be predictive of beta-lactam target non-attainment. In the external validation, the RF, LR, and NB models confirmed good discrimination with an area under the curve of 0.79 [95% CI 0.72–0.86], 0.80 [95% CI 0.73–0.87], and 0.75 [95% CI 0.67–0.82], respectively, and net benefit in the RF and LR models. We developed prediction models for beta-lactam target non-attainment within 12–36 h after antibiotic initiation in ICU patients. These online-accessible models use readily available patient variables and help optimize antibiotic treatment. The RF and LR models showed the best performance among the three models tested.
