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

doi:10.2196/16400

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

JMIR Med Inform. 2020 Apr 2;8(4):e16400. doi: 10.2196/16400.

Authors

Affiliations

¹ Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
² Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
³ Laboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
⁴ Department of Obstetrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
⁵ Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
⁶ Health Data Research UK, Institute of Health Informatics, University College London, London, United Kingdom.

^# Contributed equally.

PMID: 32238333
PMCID: PMC7163416
DOI: 10.2196/16400

Abstract

Background: Direct 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.

Objective: This 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.

Methods: We 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.

Results: Between 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.

Conclusions: Routine 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.

Keywords: LDL-c; cardiovascular risk management; learning health care system; routine clinical 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. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.04.2020.