Evidence map›Paper›PMID 29386204›Full record

ArticleCirculation2018

Hospital Variation in Adherence Rates to Secondary Prevention Medications and the Implications on Quality.

Robin Mathews, William Wang, Lisa A Kaltenbach, Laine Thomas, Rashmee U Shah, Murtuza Ali, Eric D Peterson, Tracy Y Wang

Abstract read
In one paragraph

Article in Circulation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
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

31 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Robin MathewsDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.) robin.mathews@duke.edu.
William WangDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.).
Lisa A KaltenbachDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.).
Laine ThomasDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.).
Rashmee U ShahUniversity of Utah School of Medicine, Salt Lake City (R.U.S.).
Murtuza AliLouisiana State University School of Medicine, New Orleans (M.A.).
Eric D PetersonDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.).
Tracy Y WangDuke Clinical Research Institute, Durham, NC (R.M., W.W., L.A.K., L.T., E.D.P., T.Y.W.).

Funding

Inter-disciplinary Program for Training and Mentoring in CER Methods and PracticeKM1CA156687 · NCI · DUKE UNIVERSITY · PI ABERNETHY, AMY P., DELONG, ELIZABETH R. · 2010 to 2010
$2.4M
Improving Cardiovascular Drug Safety With Automated Bleeding ClassificationK08HL136850 · NHLBI · UNIVERSITY OF UTAH · PI SHAH, RASHMEE U. · 2017 to 2020
$652k
NCI NIH HHS KM1 CA156687NHLBI NIH HHS K08 HL136850
6 · The paper itself

Abstract

backgroundMedication adherence is important to improve the long-term outcomes after acute myocardial infarction (MI). We hypothesized that there is significant variation among US hospitals in terms of medication adherence after MI, and that patients treated at hospitals with higher medication adherence after MI will have better long-term cardiovascular outcomes.

methodsWe identified 19 704 Medicare patients discharged after acute MI from 347 US hospitals participating in the ACTION Registry-GWTG (Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines) from January 2, 2007, to October 1, 2010. Using linked Medicare Part D prescription filling data, medication adherence was defined as proportion of days covered >80% within 90 days after discharge. Cox proportional hazards modeling was used to compare 2-year major adverse cardiovascular events among hospitals with high, moderate, and low 90-day medication adherence.

resultsBy 90 days after MI, overall rates of adherence to medications prescribed at discharge were 68% for β-blockers, 63% for statins, 64% for angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and 72% for thienopyridines. Adherence to these medications up to 90 days varied significantly among hospitals: β-blockers (proportion of days covered >80%; 59% to 75%), statins (55% to 69%), thienopyridines (64% to 77%), and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (57% to 69%). Compared with hospitals in the lowest quartile of 90-day composite medication adherence, hospitals with the highest adherence had lower unadjusted and adjusted 2-year major adverse cardiovascular event risk (27.5% versus 35.3%; adjusted hazard ratio, 0.88; 95% confidence interval, 0.80-0.96). High-adherence hospitals also had lower adjusted rates of death or readmission (hazard ratio, 0.90; 95% confidence interval, 0.85-0.96), whereas there was no difference in mortality after adjustment.

conclusionsUse of secondary prevention medications after discharge varies significantly among US hospitals and is inversely associated with 2-year outcomes. Hospitals may improve medication adherence after discharge and patient outcomes through better coordination of care between inpatient and outpatient settings.

Indexed as

Drug MonitoringSecondary PreventionAdrenergic beta-AntagonistsAgedAged, 80 and overCardiovascular DiseasesFemaleHospitalsHumansHydroxymethylglutaryl-CoA Reductase InhibitorsKaplan-Meier EstimateMaleMedicare Part DPatient DischargeProportional Hazards ModelsRegistriesAdrenergic beta-AntagonistsHydroxymethylglutaryl-CoA Reductase Inhibitorsmedical adherencemyocardial infarctionpatient outcomes

Identifiers

PMID29386204
PMCPMC5953793

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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.