Population Pharmacokinetics and Pharmacodynamics of Caspofungin in Pediatric Patients▿†

We describe the pharmacokinetics (PKs) of caspofungin, an echinocandin antifungal, administered once daily as a 1-hour intravenous infusion in children and adolescents (ages, 3 months to 17 years), based on pooled data from four prospective pediatric studies. Caspofungin dosing was body-surface-area (BSA) based (50 mg/m2 daily after 70 mg/m2 on day 1). The area under the concentration-time curve from time zero to 24 h (AUC0–24), the concentration at the end of infusion (1 h after the start of infusion; C1), and the trough concentration (24 h after the start of infusion; C24) were obtained for 32 pediatric patients with invasive candidiasis, 10 with invasive aspergillosis, and 82 in the setting of empirical therapy with fever and neutropenia. Exposures were modestly higher (93 to 134% for C1, 45 to 78% for C24, ∼40% for AUC0–24) in pediatric patients than in adults receiving the standard 50-mg daily dose. The potential for covariates (age, gender, weight, race, renal status, serum albumin level, and disease state) to alter PKs was evaluated with a multiple-linear-regression model. Weight and disease state had statistically significant (P < 0.05) yet small effects on caspofungin PKs in pediatric patients. Concomitant use of dexamethasone (a cytochrome p450 inducer) was associated with a statistically significant reduction (44%) in C24 in a limited number of patients (n = 4). Odds ratios were estimated for the association between log-transformed PKs and treatment outcome or adverse events. No PK parameter or hybrid parameter (AUC/MIC, C1/MIC, and C24/MIC) was significantly correlated with treatment outcome or adverse events in the setting of similar response levels as adults, which suggests that the concentrations examined fall within the therapeutic window for caspofungin in pediatric patients. These results support a 50-mg/m2 daily dosing regimen (after a 70-mg/m2 loading dose) in children ages 3 months to 17 years

Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study

BACKGROUND: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.status: publishe