ArticleCPT: pharmacometrics & systems pharmacology2024
Multistate modeling for survival analysis in critically ill patients treated with meropenem.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Integration of individual preclinical and clinical anti-infective PKPD data to predict clinical study outcomes.Clinical and translational science · 2024Trial
- A stakeholder-inclusive conceptual framework for modeling routinely collected health data for therapeutic decision-making illustrated by means of multistate models.BMC medical research methodology · 2026Article
- A mixture cure modeling framework for survival analysis of hospitalized COVID-19 patients: implications for hospital resource management.BMC medical informatics and decision making · 2026Article
- Sampling from covariate distribution may not always be necessary in PK/PD simulations: illustrative examples with antibiotics.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Population Pharmacokinetics of Prolonged Infusion for Meropenem: Tailoring Dosing Recommendations for Chinese Critically Ill Patients on Continuous Renal Replacement Therapy with Consideration for Renal Function.Drug design, development and therapy · 2025Article
- Multistate modeling for survival analysis in critically ill patients treated with meropenem.CPT: pharmacometrics & systems pharmacology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Appropriate antibiotic dosing to ensure early and sufficient target attainment is crucial for improving clinical outcome in critically ill patients. Parametric survival analysis is a preferred modeling method to quantify time-varying antibiotic exposure - response effects, whereas bias may be introduced in hazard functions and survival functions when competing events occur. This study investigated predictors of in-hospital mortality in critically ill patients treated with meropenem by pharmacometric multistate modeling. A multistate model comprising five states (ongoing meropenem treatment, other antibiotic treatment, antibiotic treatment termination, discharge, and death) was developed to capture the transitions in a cohort of 577 critically ill patients treated with meropenem. Various factors were investigated as potential predictors of the transitions, including patient demographics, creatinine clearance calculated by Cockcroft-Gault equation (CLCR
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Registered trials
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