The analysis aimed at identifying subject-specific characteristics (covariates) influencing exposure to daridorexant and quantification of covariate effects to determine clinical relevance. Data from 13 phase I, two phase II, and two phase III studies were pooled to develop a population pharmacokinetic model describing daridorexant concentration over time. Covariate effects were quantified based on model predictions. A two-compartment model with dose-dependent bioavailability, absorption lag time, linear absorption, and nonlinear elimination described the data best. Statistically significant covariates were food status on absorption (lag time and rate constant), time of drug administration (morning, bedtime) on absorption rate constant, lean body weight on central volume of distribution and elimination, fat mass on peripheral volume of distribution and intercompartmental drug transfer, and age and alkaline phosphatase on elimination. Age, lean body weight, fat mass, and alkaline phosphatase influence exposure (area under the curve, time of maximum concentration after dose administration, maximum plasma concentration, and next-morning concentration) to a limited extent, that is, less than 20% difference from a typical subject. Morning administration is not relevant for daridorexant use by insomnia patients. The food effect with simultaneous intake of a high-fat, high-calorie food is an extreme-case scenario unlikely to occur in clinical practice. Body composition, alkaline phosphatase, and age showed clinically negligible effects on exposure to daridorexant. Lean body weight and fat mass described the pharmacokinetics of daridorexant better than other body size descriptors (body weight, height, body mass index), suggesting a convenient physiological alternative to reduce the number of covariates in population pharmacokinetic models. The results indicate that differences between subjects do not require dose adjustments.
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http://dx.doi.org/10.1002/psp4.12877 | DOI Listing |
JPEN J Parenter Enteral Nutr
March 2025
Department of Dietitian Services, Abbotsford Regional Hospital, Abbotsford, British Columbia, Canada.
Background: Predictive equations often inaccurately estimate energy needs in critically ill patients. This study evaluated the level of agreement between resting energy expenditure using 12 and 25 kcal/kg as recommended by the 2021 American Society for Parenteral and Enteral Nutrition critical care guidelines for nutrition support and energy expenditure measured by indirect calorimetry in patients in the intensive care unit.
Methods: An agreement study was conducted on mechanically ventilated adults who had a documented measured energy expenditure within 10 days of intensive care unit admission.
J Arthroplasty
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Department of Orthopaedic Surgery, Northwell Orthopaedic Institute, Lenox Hill Hospital, New York, New York.
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View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
October 2024
Department of Pediatrics, Third Xiangya Hospital, Central South University, Changsha 410013.
Objectives: In the post-epidemic era, the prevalence of obesity among urban residents in China has risen sharply, with 34.8% of the population being overweight and 14.1% classified as obese.
View Article and Find Full Text PDFBMJ Open
March 2025
Clinical College of Nanjing Medical University, Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China
Objectives: Patients experience significant physical and psychological changes within the first 3 months post-surgery, yet few studies focus on patient experiences during the early postoperative period. This study aimed to explore the patient experiences and expectations for nursing follow-up during the home recovery period following metabolic and bariatric surgery.
Design: A qualitative descriptive study design was used.
BMJ Paediatr Open
March 2025
Biocruces Bizkaia Health Research Institute, Barakaldo, País Vasco, Spain.
Objective: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.
Methods: Anthropometric data in a cohort of 11 287 children were used to develop machine learning models to predict weight using height and the body mass index (BMI) quartile (as surrogate for body habitus (BH)). The models were later validated in an independent cohort of 780 children admitted to paediatric emergencies in two other hospitals.
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