Wieringa André, Ewoldt Tim M J, Gangapersad Ravish N, Gijsen Matthias, Parolya Nestor, Kats Chantal J A R, Spriet Isabel, Endeman Henrik, Haringman Jasper J, van Hest Reinier M, Koch Birgit C P, Abdulla Alan
Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands.
Rotterdam Clinical Pharmacometrics Group, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands.
Antibiotics (Basel). 2023 Nov 28;12(12):1674. doi: 10.3390/antibiotics12121674.
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.
在重症监护病房（ICU），感染相关死亡率很高。尽管在感染治疗中充分的抗生素治疗至关重要，但在高达45%的ICU患者中会出现β-内酰胺类药物未达目标浓度的情况，这与临床治疗成功的可能性较低有关。为了优化抗生素治疗，我们旨在建立ICU患者β-内酰胺类药物未达目标浓度的预测模型。纳入了来自两项多中心研究的患者，这些患者接受静脉间歇β-内酰胺类抗生素治疗，并在抗生素开始使用后的12 - 36小时内采集血样。使用随机森林（RF）、逻辑回归（LR）和朴素贝叶斯（NB）模型，基于376例患者建立并验证了β-内酰胺类药物未达目标浓度模型。对150例ICU患者进行了外部验证。我们通过在默认阈值概率为0.20时测量区分度、校准度和净效益来评估模型性能。发现年龄、性别、血清肌酐和β-内酰胺类抗生素类型可预测β-内酰胺类药物未达目标浓度。在外部验证中，RF、LR和NB模型分别证实具有良好的区分度，曲线下面积分别为0.79 [95%可信区间0.72 - 0.86]、0.80 [95%可信区间0.73 - 0.87]和0.75 [95%可信区间0.67 - 0.82]，并且RF和LR模型具有净效益。我们建立了ICU患者抗生素开始使用后12 - 36小时内β-内酰胺类药物未达目标浓度的预测模型。这些可在线获取的模型使用易于获得的患者变量，有助于优化抗生素治疗。在测试的三个模型中，RF和LR模型表现最佳。
