Evidence map›Paper›PMID 32238333›Full record

ArticleJMIR medical informatics2020

Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data.

T Katrien J Groenhof, Daniel Kofink, Michiel L Bots, Hendrik M Nathoe, Imo E Hoefer, Wouter W Van Solinge, A Titia Lely, Folkert W Asselbergs, Saskia Haitjema

Abstract read
In one paragraph

Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
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

9 authors.

T Katrien J Groenhof *Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-7315-3409
Daniel Kofink *Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-0223-8608
Michiel L BotsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0003-2871-9810
Hendrik M NathoeDivision of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-8211-8197
Imo E HoeferLaboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-8379-7814
Wouter W Van SolingeLaboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0003-2867-2581
A Titia LelyDepartment of Obstetrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0001-6356-7707
Folkert W AsselbergsDivision of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-1692-8669
Saskia HaitjemaLaboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID https://orcid.org/0000-0001-5465-4868

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDirect feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice.

objectiveThis study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center.

methodsWe extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement.

resultsBetween February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management.

conclusionsRoutine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback.

Indexed as

cardiovascular risk managementLDL-clearning health care systemroutine clinical data

Identifiers

PMID32238333
PMCPMC7163416

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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.