Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models

Author:

Wieringa André123ORCID,Ewoldt Tim M. J.124ORCID,Gangapersad Ravish N.12ORCID,Gijsen Matthias56ORCID,Parolya Nestor7,Kats Chantal J. A. R.8ORCID,Spriet Isabel56,Endeman Henrik4,Haringman Jasper J.9ORCID,van Hest Reinier M.10ORCID,Koch Birgit C. P.12,Abdulla Alan12ORCID

Affiliation:

1. Department of Hospital Pharmacy, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands

2. Rotterdam Clinical Pharmacometrics Group, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands

3. Department of Clinical Pharmacy, Isala Hospital, Dr. van Heesweg 2, 8025 AB Zwolle, The Netherlands

4. Department of Intensive Care, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands

5. Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium

6. Pharmacy Department, UZ Leuven, 3000 Leuven, Belgium

7. Delft Institute of Applied Mathematics, Mekelweg 4, 2628 CD Delft, The Netherlands

8. Department of Hospital Pharmacy, Haaglanden Medical Center, Lijnbaan 32, 2512 VA The Hague, The Netherlands

9. Department of Intensive Care, Isala Hospital, Dr. van Heesweg 2, 8025 AB Zwolle, The Netherlands

10. Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

Abstract

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.

Publisher

MDPI AG

Subject

Pharmacology (medical),Infectious Diseases,Microbiology (medical),General Pharmacology, Toxicology and Pharmaceutics,Biochemistry,Microbiology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3