Safety, Effectiveness, Quality of Life, Costs, and Efficiency of the Home Setup of the Hybrid Closed-Loop System in People With Type 1 Diabetes: Application of a Value-Based Diabetes Management Model
Safety, Effectiveness, Quality of Life, Costs, and Efficiency of the Home Setup of the Hybrid Closed-Loop System in People With Type 1 Diabetes: Application of a Value-Based Diabetes Management Model
The goal of this observational study is to evaluate a new home-based setup and care model for an advanced hybrid closed-loop insulin pump system (Tandem with Control-IQ). The study will look at the safety, effectiveness, costs, and impact on quality of life in adults with type 1 diabetes.
The main questions it aims to answer are:
Participants will:
Background and Rationale: The standard model for initiating Advanced Hybrid Closed-Loop (AHCL) systems in Spain is primarily hospital-centric. This model consumes significant healthcare resources and can lead to variability in access or delays in treatment indication. The Region of Murcia currently has a higher hospitalization rate for Type 1 Diabetes (T1D) than the national average, suggesting room for improvement in care organization. This project proposes an alternative Value-Based Healthcare (VBHC) model that shifts the initiation of the Tandem Control-IQ system to the patient's home, supported by structured online education, continuous telemonitoring, and shared clinical follow-up between the hospital and a specialized external nursing team.
Study Pathway and Procedures: Eligible patients will first undergo online technical training on the AHCL system provided by a specialized nurse. Subsequently, the nurse will perform the physical setup of the system at the patient's home, configuring the device according to the medical parameters (e.g., basal rates, sensitivity factors, carbohydrate ratios) prescribed by the patient's endocrinologist. Sampling Method: Eligible participants will be selected using consecutive participant sampling to minimize selection bias.
Throughout the 12-month follow-up, patients will be telemonitored every 14 days. The external nursing team will review glycemic metrics to identify automated clinical alerts. High alerts (defined as Time in Range [TIR] <=50% or Time Below Range [TBR] >=8%) and Medium alerts will trigger a telephone intervention. Technical issues will be resolved directly by the specialized nurses, while persistent clinical issues will trigger a protocolized referral back to the hospital's endocrinology team. Scheduled data collection for clinical indicators, questionnaires, and resource consumption will occur at baseline, and at 1, 3, 6, and 12 months.
Healthcare Professionals Sub-study: A parallel evaluation will involve the participating doctors and nurses (approximately 9 physicians and 3 nurses). They will complete specific, anonymized questionnaires halfway through the study to assess their acceptance of the model, perceived experience, and the estimated time saved in hospital consultations and avoided emergency visits due to the home-based model.
Data Management and Quality Assurance: Data will be prospectively collected using an electronic Case Report Form (e-CRD) hosted on a secure, centralized cloud clinical platform (ReseaArch). The system is designed with filters and restrictions to minimize data entry errors, flag out-of-range values, and detect inconsistencies.
Plan for missing data: Missing data is expected to be minimal, as continuous glucose monitoring records are automatically generated and extracted from the technology used. Furthermore, the database will be reviewed at least monthly by the research team to verify the enrollment pace, ensure the completeness of the entered data, and address any missing questionnaire responses.
Sample Size Assessment: The study aims to enroll 80 patients across three participating hospitals over a 1-year recruitment period. This sample size allows for the estimation of proportions with a 95% confidence interval (CI) margin of error of approximately +/- 11% under maximum indeterminacy (p=q=0.5). For continuous variables (e.g., TIR), assuming a standard deviation of 10, the precision of the mean would be 2.18, providing sufficient statistical power (alpha=0.05; beta=0.2) to detect relevant differences of 6.3 units between patient subgroups.
Statistical Analysis Plan: Descriptive statistics will be used to summarize baseline characteristics and outcome variables. Continuous variables will be evaluated for normal distribution using the Kolmogorov-Smirnov test and reported as means and standard deviations (SD), or medians and interquartile ranges (IQR) if not normally distributed. Categorical variables will be expressed as frequencies and percentages. Comparisons between groups (e.g., by gender or educational level) will be performed using Student's t-test or the Mann-Whitney U test for continuous variables, and the Chi-square or Fisher's exact test for categorical variables. Multivariate analyses, including multiple linear or logistic regression models, will be conducted as appropriate to adjust for potential confounders. Statistical significance will be set at a two-tailed p-value <0.05.
Economic Evaluation: An economic evaluation will be conducted from the perspective of the Healthcare System, considering only direct medical costs. Unit costs will be assigned based on the official healthcare tariffs of the Murcian Health Service (SMS). Since the study lacks a comparator group, outcomes will be expressed using cost-outcome and cost-utility indicators, such as cost per unit of Time in Range (TIR) and Quality-Adjusted Life Years (QALYs) accumulated over 12 months. QALYs will be calculated using the EQ-5D-5L index scores based on the validated Spanish tariffs. Any estimation of "gains" (e.g., QALYs gained or TIR gained) will be conducted explicitly as an exploratory analysis, using a before-after counterfactual compared against the patient's own baseline state. To manage the underlying uncertainty in costs and health outcomes, a deterministic sensitivity analysis will be performed by constructing three scenarios (baseline, most favorable, and least favorable) using the 95% confidence intervals of the variables.
Inclusion Criteria:
Exclusion Criteria:
maria.martinez-mateos@airliquide.com+34 690 283 723
carla.yagodiez@airliquide.com