IMPORTANCE Individuals with sickle cell disease (SCD) have reduced life expectancy; however, there are limited data available on lifetime income in patients with SCD. OBJECTIVE To estimate life expectancy, quality-adjusted life expectancy, and income differences between a US cohort of patients with SCD and an age-, sex-, and race/ethnicity-matched cohort without SCD. DESIGN, SETTING, AND PARTICIPANTS Cohort simulation modeling was used to (1) build a prevalent SCD cohort and a matched non-SCD cohort, (2) identify utility weights for quality-adjusted life expectancy, (3) calculate average expected annual personal income, and (4) model life expectancy, quality-adjusted life expectancy, and lifetime incomes for SCD and matched non-SCD cohorts. Data sources included the Centers for Disease Control and Prevention, National Newborn Screening Information System, and published literature. The target population was individuals with SCD, the time horizon was lifetime, and the perspective was societal. Model data were collected from

Denosumab is a cost-effective treatment option for the prevention of SREs in patients with advanced solid tumors and bone metastases compared to ZA. The overall value of denosumab is based on superior efficacy, favorable safety, and more efficient administration.

Based on a model, UC plus onabotulinumtoxinA improved disability, which translated into greater QALYs but also increased direct medical costs compared with UC alone; however, the resulting ICER can be considered cost-effective. Moreover, UC plus onabotulinumtoxinA can be cost-saving if reduction in caregiver burden was included. OnabotulinumtoxinA offers value for money in the management of ULPSS in Scotland.

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BackgroundIt is important to identify an easily defined subset of patients at increased risk of adverse clinical outcomes associated with mineral and bone disorder (MBD) biomarkers (parathyroid hormone, calcium and phosphate).MethodsObservational cohort study of 26 221 prevalent hemodialysis patients in Davita clinics as of 31 August 2005 and followed up until 31 December 2006 (16 months). Predictors were 12 possible definitions of ‘clinically important’ MBD based on all 3 biomarkers, and 18 alternative definitions based on only 1 or 2 biomarkers. Events were death alone and a composite of cardiovascular hospitalization or death. Excess events were calculated based on a multivariate Cox model using 5224 patients in target for all MBD biomarkers and applied to 20 997 patients out of target for at least one biomarker. Excess events attributable to MBD were estimated by subtracting the multivariate model-derived predicted number from the actual number. Outcomes were the proportion of excess events attributable to MBD captured by each definition (threshold ≥70%) and the reduction in the population size considered to have clinically important MBD (threshold ≥30%). The excess fraction was excess events divided by actual events.ResultsPatients with more biochemical markers out of target tended to be younger, black and have longer times since starting dialysis. The excess fraction associated with MBD ranged from ∼10 to 26% depending on the clinical endpoint and definition. The only definition to meet the thresholds required at least two of the three MBD biomarkers to be out of target (high or low). It captured 82% of excess composite endpoints and 74% of excess deaths and reduced the at-risk population by 46%.ConclusionsPatients with at least two of three MBD biomarkers out of target represent a subgroup of patients at elevated risk of adverse clinical events.

Background Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration. Methods There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to focus on clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance. The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model. Results The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses. Conclusions The GDM offers researchers a simpler process for transforming data, clear data provenance, and a path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in protocol implementation as part of a complete data pipeline for researchers. Electronic supplementary material The online version of this article (10.1186/s12911-019-0837-5) contains supplementary material, which is available to authorized users.

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Introduction High mortality rates in patients with chronic kidney disease‐mineral and bone disorder (CKD‐MBD) receiving maintenance hemodialysis are largely due to cardiovascular (CV) events. Methods We evaluated associations between MBD parameters, fibroblast growth factor 23 (FGF23) concentrations, and clinically adjudicated CV events from the Evaluation of Cinacalcet Hydrochloride Therapy to Lower Cardiovascular Events (EVOLVE) trial. Patients enrolled in EVOLVE, who had not experienced any study endpoints between randomization and week 20 with evaluable baseline and week 20 values for key laboratory parameters (parathyroid hormone, calcium, phosphate, and FGF23), were assessed. We used adjusted Cox proportional hazards regression models to estimate relative risk of outcomes (primary composite, all‐cause mortality, and CV events) based on FGF23 and MBD parameters. Laboratory values were modeled with linear terms and using natural cubic splines with two degrees of freedom. Findings For the primary endpoint, patients assessed (N = 2309) were followed up over a mean duration of 3.1 years, during which 1037 CV events (497 deaths, 540 nonfatal events) occurred. Adjusted models showed an association between FGF23 and the risk of CV events. Hazard ratio per log unit of FGF23 at week 20 was 1.09 [95% CI: 1.03–1.16], and the hazard ratio per log unit change in FGF23 from week 0 to week 20 was 1.09 [95% CI: 1.00–1.17]. Discussion Our data highlight FGF23 as an independent CV risk factor and potential biomarker and therapeutic target for patients with CKD‐MBD receiving maintenance hemodialysis.