ArticleJournal of advanced research2026
Pharmacokinetic modelling during long-term anesthesia: minimizing the gap.
Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial.NPJ digital medicine · 2026Article
- Modeling drug retention as memory effects in obese patients using fractional and augmented models.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSurgical procedures exceeding six hours and intensive care for traumatic injuries often require prolonged general anesthesia, commonly maintained via induced coma. Similar protocols are used for COVID-19 patients under mechanical ventilation. These conditions pose challenges such as tissue drug accumulation and overdose risk. Extended supine or prone positioning alters tissue volumes based on their physical properties. Accurate anesthesia management depends on patient-specific models that characterize pharmacokinetics (PK, drug distribution) and pharmacodynamics (PD, drug effects) using compartmental representations. However, comorbidities such as obesity are typically overlooked in existing PK models.
objectivesThis study augments PK models to investigate the impact of obesity on drug distribution and clearance by incorporating the risk of drug trapping in adipose tissue as a nonlinear function of body mass index (BMI). A theoretical framework links BMI with tissue porosity and permeability, introducing a "trap" compartment to model delayed clearance in obese patients.
methodsThe model is validated using in vitro impedance measurements and numerical simulations. Fat tissue properties were characterized via Cole-Cole fractional-order models, with parameters identified using a genetic algorithm. The nonlinear BMI-trapping relationship was estimated using the trust region method. Simulations covered both open-loop (single-bolus) and closed-loop (continuous infusion) scenarios using model predictive control(MPC).
resultsImpedance measurements and identified Cole-Cole fractional-order models confirmed volume-dependent properties of fat tissue. Simulations using clinical data demonstrated delayed drug clearance in high-BMI patients, which support the proposed theoretical background. Open-loop simulations showed prolonged drug retention, while closed-loop control using MPC maintained anesthetic depth with reduced total drug input. Differences in BIS nadir and total drug usage were observed across BMI and age groups, supporting the model's clinical applicability.
conclusionThe augmented model with MPC minimizes drug trapping and reduces total drug use, lowering overdose risk and supporting safer anesthesia management.
Indexed as
Identifiers
What Socratic 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 Socratic graph.