Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data
Groenhof, T Katrien J; Kofink, Daniel; Bots, Michiel L; Nathoe, Hendrik M; Hoefer, Imo E; Van Solinge, Wouter W; Lely, A Titia; Asselbergs, Folkert W; Haitjema, Saskia
(2020) JMIR medical informatics, volume 8, issue 4, pp. 1 - 12
(Article)
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
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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.
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Keywords: Cardiovascular risk management, LDL-c, Learning health care system, Routine clinical data, Health Informatics, Health Information Management
ISSN: 2291-9694
Publisher: JMIR Publications Inc.
Note: Funding Information: This project was financially supported in part by Sanofi. DK is currently a full-time employee of Sanofi-Aventis. The remaining authors declare no conflict of interest. Funding Information: WWVS, IEH, SH, and Mark de Groot are members of the UPOD study group. The following are members of the UCC-CVRM Study group: FWA, Department of Cardiology; GJ de Borst, Department of Vascular Surgery; MLB (chair), Julius Center for Health Sciences and Primary Care; S Dieleman, Division of Vital Functions (anesthesiology and intensive care); MH Emmelot, Department of Geriatrics; PA de Jong, Department of Radiology; ATL, Department of Obstetrics and Gynecology; IEH, Laboratory of Clinical Chemistry and Hematology; NP van der Kaaij, Department of Cardiothoracic Surgery; YM Ruigrok, Department of Neurology; MC Verhaar, Department of Nephrology and Hypertension; and FLJ Visseren, Department of Vascular Medicine, UMC Utrecht and Utrecht University. Publisher Copyright: © 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
(Peer reviewed)