TY - JOUR
T1 - Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation
T2 - Development and External Validation of Three Novel (Machine Learning) Models
AU - Wieringa, André
AU - Ewoldt, Tim M.J.
AU - Gangapersad, Ravish N.
AU - on behalf of the DOLPHIN Investigators
AU - Gijsen, Matthias
AU - Parolya, Nestor
AU - Kats, Chantal J.A.R.
AU - Spriet, Isabel
AU - Endeman, Henrik
AU - Haringman, Jasper J.
AU - van Hest, Reinier M.
AU - Koch, Birgit C.P.
AU - Abdulla, Alan
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/11/28
Y1 - 2023/11/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85180680134&partnerID=8YFLogxK
U2 - 10.3390/antibiotics12121674
DO - 10.3390/antibiotics12121674
M3 - Article
C2 - 38136709
AN - SCOPUS:85180680134
SN - 2079-6382
VL - 12
JO - Antibiotics
JF - Antibiotics
IS - 12
M1 - 1674
ER -