Population Pharmacokinetics Analysis and Dosing Simulations Of Meropenem in Critically Ill Patients with Pulmonary Infection

Lan, Jinhua; Wu, Zheng; Wang, Xipei; Wang, Yifan; Yao, Fen; Zhao, Boxin; Wang, Yirong; Chen, Jingchun; Chen, Chunbo

doi:10.1016/j.xphs.2022.01.015

Cited by 15 publications

(7 citation statements)

References 44 publications

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“…Length of stay in ICU was also associated with the increased risk of pulmonary infections in our cohort, which is in agreement with the study by Lan et al 19 Prolonged bedrest leads to the limited lung expansion and accumulation of respiratory secretions, providing good conditions for pathogen colonization and causing lung infections. 19 …”

Section: Discussionsupporting

confidence: 93%

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Dai,

Zhong,

Chen

et al. 2024

Pak J Med Sci

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Objective: To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model. Methods: Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve. Results: A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value. Conclusions: Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value. doi: https://doi.org/10.12669/pjms.40.6.9307 How to cite this: Dai W, Zhong T, Chen F, Shen M, Zhu L. Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients. Pak J Med Sci. 2024;40(6):1129-1134. doi: https://doi.org/10.12669/pjms.40.6.9307 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionsupporting

confidence: 93%

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Dai,

Zhong,

Chen

et al. 2024

Pak J Med Sci

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“…In this study, we describe our experience of using meropenem in a continuous infusion in geriatric trauma patients with sepsis. This is because several population pharmacokinetics studies of meropenem in adults indicate that meropenem must be administered by a prolonged infusion in order to achieve microbiological success and subsequent clinical cure [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Usage of Meropenem Continuous Infusion for Treatment of Infectious Complications in Orthopedic Elderly Patients with Anemia: A Case Series

Konkayev,

Kadralinova,

Azimova

et al. 2024

Medicina

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Background and Objectives: The prolonged infusion of meropenem is recommended by guidelines for the treatment of sepsis. However, studies provide controversial data on the advantages of prolonged infusions over intermittent ones. In our opinion, this can be related to age, which possibly distorts the final data, as older people have age-related characteristics. In our study, we analyzed the ventilatory status, laboratory tests and vital signs of the patient and carried out microbiological cultures. Materials and Methods: This was a prospective single-center case series investigation conducted from June 2022 to June 2023. The objective of this study was to evaluate the effectiveness of continuous infusion in elderly patients with severe infectious complications after orthopedic interventions. The primary endpoints were 28-day survival and the emergence of new multidrug-resistant strains. Secondary endpoints were long-term mortality and length of stay in the ICU. Results: Three patients (median age 65, 100% female) received a continuous infusion of meropenem. Two patients were alive at hospital discharge, and one patient died on the 105th day of hospitalization. Multi-resistant bacteria were observed in one patient. Conclusions: The use of a continuous meropenem infusion in the complex treatment of purulent-septic complications in elderly patients with periprosthetic infection and anemia probably led to clinical improvement in these case reports. However, the emergence of new pan-resistant strains and overall mortality using this infusion technique remains unclear. Further, high-quality RCTs for the elderly are needed.

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“…The concentration-time profile of meropenem was predicted for each patient based on a previously published meropenem population PK model in conjunction with the patient's dosing history and specific covariates, because meropenem concentrations were not available from the patients. In total, five two-compartment population PK models of meropenem in patients in the ICU were identified in literature, [26][27][28][29][30] and the PK model developed by Ehmann et al was selected as the model most representative of the study population due to its inclusion of multiple covariate relationships based on a wide distribution of covariate values, as well as similar population characteristics as the study patients. 29 Briefly, the population PK model had been built upon densely sampled meropenem serum concentrations (1376 observations) in 41 non-CRRT critically ill patients (mostly patients with sepsis) receiving standard dosing of meropenem (1000 mg as 30-min i.v.…”

Section: Pharmacokinetic Model Derived Pk/pd Indexmentioning

confidence: 99%

Multistate modeling for survival analysis in critically ill patients treated with meropenem

Peng,

Minichmayr,

Liu

et al. 2023

CPT Pharmacom & Syst Pharma

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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, CLCRCG (creatinine clearance calculated by Cockcroft–Gault equation), fT>MIC (time that unbound concentrations exceed the minimum inhibitory concentration), and microbiology‐related measures. The probabilities to transit to other states from ongoing meropenem treatment increased over time. A 10 mL/min decrease in CLCRCG was found to elevate the hazard of transitioning from states of ongoing meropenem treatment and antibiotic treatment termination to the death state by 18%. The attainment of 100% fT>MIC significantly increased the transition rate from ongoing meropenem treatment to antibiotic treatment termination (by 9.7%), and was associated with improved survival outcome. The multistate model prospectively assessed predictors of death and can serve as a useful tool for survival analysis in different infection scenarios, particularly when competing risks are present.

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Population Pharmacokinetics Analysis and Dosing Simulations Of Meropenem in Critically Ill Patients with Pulmonary Infection

Cited by 15 publications

References 44 publications

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Usage of Meropenem Continuous Infusion for Treatment of Infectious Complications in Orthopedic Elderly Patients with Anemia: A Case Series

Multistate modeling for survival analysis in critically ill patients treated with meropenem

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