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Registry-based trials have emerged as a potentially cost-saving study methodology. Early estimates of cost savings, however, conflated the benefits associated with registry utilisation and those associated with other aspects of pragmatic trial designs, which might not all be as broadly applicable. In this study, we sought to build a practical tool that investigators could use across disciplines to estimate the ranges of potential cost differences associated with implementing registry-based trials versus standard clinical trials.
We built simulation Markov models to compare unique costs associated with data acquisition, cleaning, and linkage under a registry-based trial design versus a standard clinical trial. We conducted one-way, two-way, and probabilistic sensitivity analyses, varying study characteristics over broad ranges, to determine thresholds at which investigators might optimally select each trial design.
Registry-based trials were more cost effective than standard clinical trials 98.6% of the time. Data-related cost savings ranged from $4300 to $600,000 with variation in study characteristics. Cost differences were most reactive to the number of patients in a study, the number of data elements per patient available in a registry, and the speed with which research coordinators could manually abstract data. Registry incorporation resulted in cost savings when as few as 3768 independent data elements were available and when manual data abstraction took as little as 3.4 seconds per data field.
Registries offer important resources for investigators. When available, their broad incorporation may help the scientific community reduce the costs of clinical investigation. We offer here a practical tool for investigators to assess potential costs savings.
The goal of pharmacological treatment is a desired response, known as the target effect (e.g. bispectral index of 50). An understanding of the concentration–response relationship (i.e. pharmacodynamics (PD)) can be used to predict the target concentration (e.g. propofol 4 mg/L) required to achieve this target effect in a typical individual . Pharmacokinetic (PK) knowledge (e.g. clearance, volume) then determines the dose that will achieve the target concentration. Each individual, however, is somewhat different and there is variability associated with all parameters used in PK and PD equations (known as models). Covariate information (e.g. weight, age, pathology, drug interactions, pharmacogenomics) can be used to help predict the dose in a specific patient. The Holy Grail of clinical pharmacology is prediction of drug PK and PD in the individual patient (Fig. 4.1) and this requires knowledge of the covariates that contribute to variability .
Recent years have seen an exponential increase in the variety of healthcare data captured across numerous sources. However, mechanisms to leverage these data sources to support scientific investigation have remained limited. In 2013 the Pediatric Heart Network (PHN), funded by the National Heart, Lung, and Blood Institute, developed the Integrated CARdiac Data and Outcomes (iCARD) Collaborative with the goals of leveraging available data sources to aid in efficiently planning and conducting PHN studies; supporting integration of PHN data with other sources to foster novel research otherwise not possible; and mentoring young investigators in these areas. This review describes lessons learned through the development of iCARD, initial efforts and scientific output, challenges, and future directions. This information can aid in the use and optimisation of data integration methodologies across other research networks and organisations.
We evaluated whether a diagnostic stewardship initiative consisting of ASP preauthorization paired with education could reduce false-positive hospital-onset (HO) Clostridioides difficile infection (CDI).
Single center, quasi-experimental study.
Tertiary academic medical center in Chicago, Illinois.
Adult inpatients were included in the intervention if they were admitted between October 1, 2016, and April 30, 2018, and were eligible for C. difficile preauthorization review. Patients admitted to the stem cell transplant (SCT) unit were not included in the intervention and were therefore considered a contemporaneous noninterventional control group.
The intervention consisted of requiring prescriber attestation that diarrhea has met CDI clinical criteria, ASP preauthorization, and verbal clinician feedback. Data were compared 33 months before and 19 months after implementation. Facility-wide HO-CDI incidence rates (IR) per 10,000 patient days (PD) and standardized infection ratios (SIR) were extracted from hospital infection prevention reports.
During the entire 52 month period, the mean facility-wide HO-CDI-IR was 7.8 per 10,000 PD and the SIR was 0.9 overall. The mean ± SD HO-CDI-IR (8.5 ± 2.0 vs 6.5 ± 2.3; P < .001) and SIR (0.97 ± 0.23 vs 0.78 ± 0.26; P = .015) decreased from baseline during the intervention. Segmented regression models identified significant decreases in HO-CDI-IR (Pstep = .06; Ptrend = .008) and SIR (Pstep = .1; Ptrend = .017) trends concurrent with decreases in oral vancomycin (Pstep < .001; Ptrend < .001). HO-CDI-IR within a noninterventional control unit did not change (Pstep = .125; Ptrend = .115).
A multidisciplinary, multifaceted intervention leveraging clinician education and feedback reduced the HO-CDI-IR and the SIR in select populations. Institutions may consider interventions like ours to reduce false-positive C. difficile NAAT tests.