Evidence map›Paper›PMID 28957369›Full record

ArticlePloS one2017

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

Timothy P Ryan, Ryan D Morrison, Jeffrey J Sutherland, Stephen B Milne, Kendall A Ryan, J Scott Daniels, Anita Misra-Hebert, J Kevin Hicks, Eric Vogan, Kathryn Teng and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.2field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 30 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Timothy P RyanSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.ORCID http://orcid.org/0000-0001-6025-801X
Ryan D MorrisonSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.
Jeffrey J SutherlandSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.
Stephen B MilneSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.
Kendall A RyanSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.
J Scott DanielsSano Laboratories, Sano Informed Prescribing, Franklin, Tennessee, United States of America.
Anita Misra-HebertDepartment of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America.
J Kevin HicksMedicines Department, Cleveland Clinic, Cleveland, Ohio, United States of America.
Eric VoganReporting and Analytics, Cleveland Clinic, Cleveland, Ohio, United States of America.
Kathryn TengMedicines Department, Cleveland Clinic, Cleveland, Ohio, United States of America.
Thomas M DalyRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
Cleveland Clinic · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPoor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy. METHODS AND

findingsWe validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.

conclusionsThis is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Indexed as

Drug MonitoringCohort StudiesDrug PrescriptionsElectronic Health RecordsFemaleHumansMaleMedical RecordsMiddle Aged

Identifiers

PMID28957369
PMCPMC5619774
OpenAlexW2758012210

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.