Dataset Information

Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository.

ABSTRACT: The extent to which pharmacogenomic-guided medication use has been adopted in various health systems is unclear. To assess the uptake of pharmacogenomic-guided medication use, we determined its frequency across our health system, which does not have a structured testing program. Using a multisite clinical data repository, we identified adult patients' first prescribed medications between January 2011 and December 2013 and investigated the frequency of germline and somatic pharmacogenomic testing, by the Pharmacogenomics Knowledgebase level of the US Food and Drug Administration label information. There were 268,262 medication orders for drugs with germline pharmacogenomic testing information in their drug labels. Pharmacogenomic testing was detected for 1.5% (129/8,718) of medication orders with recommended or required testing. Of the 3,817 medication orders associated with somatic pharmacogenomic testing information in their drug labels, 20% (372/1,819) of required tests were detected. The low rates of detectable pharmacogenomic testing suggest that structured testing programs are required to achieve the success of precision medicine.

SUBMITTER: Mathias PC

PROVIDER: S-EPMC5503815 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository.

Mathias P C PC Hendrix N N Wang W-J WJ Keyloun K K Khelifi M M Tarczy-Hornoch P P Devine B B

Clinical pharmacology and therapeutics 20170605 2

The extent to which pharmacogenomic-guided medication use has been adopted in various health systems is unclear. To assess the uptake of pharmacogenomic-guided medication use, we determined its frequency across our health system, which does not have a structured testing program. Using a multisite clinical data repository, we identified adult patients' first prescribed medications between January 2011 and December 2013 and investigated the frequency of germline and somatic pharmacogenomic testing ...[more]

PMID: 28073152

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Project description:Ninety-five percent of the population are estimated to carry at least one genetic variant that is discordant with at least one medication. Pharmacogenomic (PGx) testing has the potential to identify patients with genetic variants that puts them at risk of adverse drug reactions and sub-optimal therapy. Predicting a patient's response to medications could support the safe management of medications and reduce hospitalization. These benefits can only be realized if prescribing clinicians make the medication changes prompted by PGx test results. This review examines the current evidence on the impact PGx testing has on hospital admissions and whether it prompts medication changes. A systematic search was performed in three databases (Medline, CINAHL and EMBASE) to search all the relevant studies published up to the year 2020, comparing hospitalization rates and medication changes amongst PGx tested patients with patients receiving treatment-as-usual (TAU). Data extracted from full texts were narratively synthesized using a process model developed from the included studies, to derive themes associated to a suggested workflow for PGx-guided care and its expected benefit for medications optimization and hospitalization. A meta-analysis was undertaken on all the studies that report the number of PGx tested patients that had medication change(s) and the number of PGx tested patients that were hospitalized, compared to participants that received TAU. The search strategy identified 5 hospitalization themed studies and 5 medication change themed studies for analysis. The meta-analysis showed that medication changes occurred significantly more frequently in the PGx tested arm across 4 of 5 studies. Meta-analysis showed that all-cause hospitalization occurred significantly less frequently in the PGx tested arm than the TAU. The results show proof of concept for the use of PGx in prescribing that produces patient benefit. However, the review also highlights the opportunities and evidence gaps that are important when considering the introduction of PGx into health systems; namely patient involvement in PGx prescribing decisions, thus a better understanding of the perspective of patients and prescribers. We highlight the opportunities and evidence gaps that are important when considering the introduction of PGx into health systems.

Project description:Background: Given the increasing number and heterogeneity of data repositories, an improvement and harmonisation of practice within repositories for clinical trial data is urgently needed. The objective of the study was to develop and evaluate a demonstrator repository, using a widely used repository system (DSpace), and then explore its suitability for providing access to individual participant data (IPD) from clinical research. Methods: After a study of the available options, DSpace (version 6.3) was selected as the software for developing a demonstrator implementation of a repository for clinical trial data. In total, 19 quality criteria were defined, using previous work assessing clinical data repositories as a guide, and the demonstrator implementation was then assessed with respect to those criteria. Results: Generally, the performance of the DSpace demonstrator repository in supporting sensitive personal data such as that from clinical trials was strong, with 14 requirements demonstrated (74%), including the necessary support for metadata and identifiers. Two requirements could not be demonstrated (inability to incorporate de-identification tools in the submission workflow, lack of a self-attestation system) and three requirements were only partially demonstrated (ability to provide links to de-identification tools and requirements, incorporation of a data transfer agreement in system workflow, and capability to offer managed access through application on a case by case basis). Conclusions: Technically, the system was able to support most of the pre-defined requirements, though there are areas where support could be improved. Of course, in a productive repository, appropriate policies and procedures would be needed to direct the use of the available technical features. A technical evaluation should therefore be seen as indicating a system's potential, rather than being a definite assessment of its suitability. DSpace clearly has considerable potential in this context and appears a suitable base for further exploration of the issues around storing sensitive data.

Project description:To characterize radiation therapy patient breathing patterns based on measured external surrogate information.Breathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml∕s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume).There were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation with κ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients.Based on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.

Dataset Information

Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository.

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Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository.

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