ArticleInfection and drug resistance2026
Prediction of Meropenem Target Attainment in Critically Ill Patients Using Routine Clinical Data from a Single Center: A Machine Learning Approach.
Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Meropenem pharmacokinetics vary considerably among critically ill patients, resulting in uncertainty in target concentration attainment. This study aimed to develop a predictive model using real-world therapeutic drug monitoring (TDM) data to assess target attainment and provide a reference for individualized meropenem management. Methods: This single-center retrospective study included critically ill patients administered meropenem treatment and subjected to trough concentration monitoring from August 2023 to January 2026. Meropenem target attainment was defined as a trough concentration (Cmin) exceeding 8.0 mg/L. The analysis included 399 TDM samples from 333 patients, which were partitioned into training and test sets at an 8:2 ratio using a patient-level splitting approach. Following feature selection, eight machine learning algorithms were applied to build prediction models. Predictive performance was assessed primarily by the area under the receiver operating characteristic curve (AUC), with accuracy, sensitivity, specificity, F1 score, and calibration curves used as additional evaluation measures. SHapley Additive exPlanations (SHAP) analysis was then used to interpret the final model. Results: Ten features were ultimately selected, including age, CRRT, ECMO, Scr, ALP, PLT, P-LCR, APACHE II score, CrCl, and dose per body weight. In the independent test set, the random forest showed the best performance, with an AUC of 0.873 (95% CI: 0.779-0.946), followed by extreme gradient boosting with an AUC of 0.867 (95% CI: 0.763-0.942) and support vector machine with an AUC of 0.863 (95% CI: 0.771-0.935). SHAP analysis showed that CrCl, APACHE II score, and Scr were the most important predictors. Conclusion: Machine learning models developed using routine clinical variables showed good performance in predicting meropenem target attainment in critically ill patients and may help identify patients at high risk of target non-attainment, providing a reference for individualized meropenem management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.