T. Katrien J. Groenhof, Saskia Haitjema, A. Titia Lely, Diederick E. Grobbee, Folkert W. Asselbergs, Michiel L. Bots
Abstract
Since 2015 we organized a uniform, structured collection of a fixed set of cardiovascular risk factors according the (inter)national guidelines on cardiovascular risk management. We evaluated the current state of a developing cardiovascular towards learning healthcare system–the Utrecht Cardiovascular Cohort Cardiovascular Risk Management (UCC-CVRM)—and its potential effect on guideline adherence in cardiovascular risk management. We conducted a before-after study comparing data from patients included in UCC-CVRM (2015–2018) and patients treated in our center before UCC-CVRM (2013–2015) who would have been eligible for UCC-CVRM using the Utrecht Patient Oriented Database (UPOD). Proportions of cardiovascular risk factor measurement before and after UCC-CVRM initiation were compared, as were proportions of patients that required (change of) blood pressure, lipid, or blood glucose lowering treatment.
Introduction
Healthcare is challenged by a growth in patient numbers. Furthermore, these patients are of higher age, suffer from multiple diseases–including a higher portion of chronic diseases-, and use more medications simultaneously [1]. Traditionally, evidence that forms the basis of care is derived from trials and cohorts. Yet, insights from trials are not always compatible with routine care. This science to care gap can in part be explained by differences in patient characteristics due to strict selection criteria or differences in care setting [2]. For example women are underrepresented in cardiovascular studies and have shown to receive substandard care in terms of risk management [3,4]. Altogether, the science to care gap results in modest use of evidence based findings in clinical practice, increased off label treatment, and non-adherence to guidelines. In return, the potential of routine care data is not captured fully, potentially resulting in a loss of valuable information on the course of disease and treatment in real life. This sparked the interest for a learning healthcare system (LHS).
Materials and methods
We conducted a before-after study comparing data from patients included in UCC-CVRM and patients treated in the period before UCC-CVRM initiation who would have been eligible for UCC-CVRM from UPOD. We used data from patients visiting outpatient clinics who provided a written informed consent for UCC-CVRM from January 2016 up to December 31th 2018, and UPOD patients from January 1, 2013 to December 31, 2015. UPOD patients were matched to UCC-CVRM patients 1:4 based on age, sex, department of referral and inclusion in UCC-CVRM and diagnosis at inclusion. Diagnosis at inclusion was defined as the diagnosis treatment code registered closest to date of inclusion in UCC-CVRM. For UPOD, a theoretical inclusion date was defined by taking the time difference between inclusion and diagnosis for the UCC-CVRM patient date and applying this difference to the date of diagnose code for the matched UPOD patient. Throughout the manuscript, we will refer to the UPOD population as the “before UCC-CVRM” patients.
Results
Patient selection and characteristics
Up to December 31st 2018, 1904 out-patient clinic patients were included in UCC-CVRM. We could match these patients to 7195 patients in UPOD (1:3.8 match). Baseline characteristics of both populations are described in S1 Table. Prevalence of current smoking was higher before UCC-CVRM (23%) versus after UCC-CVRM initiation (12%). Other risk factors showed similar distributions before and after in UCC-CVRM initiation.
Discussion
UCC-CVRM has shown to promote optimal care, to provide feedback on the quality of care, and facilitate research. Now, sufficient quality and amount of data is available to support automated feedback processes and decision support, for these computerized decision support systems (CDSS) require complete input data [11]. A CDSS was developed to aid guideline adherent cardiovascular risk management [24]. With this tool, clinicians and patients are provided with an overview of their cardiovascular risk factors, a 10-years risk prediction for cardiovascular events, and guideline adherent suggestions for therapy [24]. CDSS can also be used for automated feedback on extreme values via alerts. On a higher level, CDSS for care evaluations, and benchmarking between clinicians or even hospitals. Formal cost-effectiveness evaluations regarding CDSS use for cardiovascular risk management are yet to be conducted.
Conclusion
In a collaborative effort by care and research professionals, the UCC-CVRM has set a benchmark for a cardiovascular LHS. Systematic registration of the cardiovascular risk profile in all vascular patients referred for evaluation impacts tremendously on guideline adherence and risk of missing patients with elevated risk factors levels with an indication for treatment. An LHS approach provides insight into of quality of care within an more inclusive cross-section of cardiovascular population, ultimately leading to improved primary and secondary prevention of cardiovascular disease.
Acknowledgments
Members of the UPOD study group: Wouter van Solinge, Imo Hoefer, Saskia Haitjema, Mark de Groot. Members of the Utrecht Cardiovascular Cohort- CardioVascular Risk Management (UCC- CVRM) Study group: F.W. Asselbergs, Department of Cardiology; G.J. de Borst, Department of Vascular Surgery; M.L. Bots (chair),Julius Center for Health Sciences and Primary Care; S. Dieleman, Division of Vital Functions (anesthesiology and intensive care); M.H. Emmelot, Department of Geriatrics; P.A. de Jong, Department of Radiology; A.T. Lely, Department of Obstetrics/Gynecology; I.E. Hoefer, Laboratory of Clinical Chemistry and Hematology; N.P. van der Kaaij, Department of Cardiothoracic Surgery; Y.M. Ruigrok, Department of Neurology; M.C. Verhaar, Department of Nephrology & Hypertension, F.L.J. Visseren, Department of Vascular Medicine, University Medical Center Utrecht and Utrecht University.
Citation: Groenhof TKJ, Haitjema S, Lely AT, Grobbee DE, Asselbergs FW, Bots ML, et al. (2023) Optimizing cardiovascular risk assessment and registration in a developing cardiovascular learning health care system: Women benefit most. PLOS Digit Health 2(2): e0000190. https://doi.org/10.1371/journal.pdig.0000190
Editor: Leo Anthony Celi, Massachusetts Institute of Technology, UNITED STATES
Received: August 12, 2022; Accepted: December 30, 2022; Published: February 8, 2023
Copyright: © 2023 Groenhof et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data are based on information from confidential electronic health records. As such data cannot be made publicly available. Requests for use of the data can be directed to the UCC-CVRM project office (email: [email protected]). The request will be taken to the steering committee of UCC-CVRM and discussed further taking Dutch privacy and legal regulations into account. This approach has been detailed in the rationale and design publication of the UCC initiative. Eur J prev Cardiol 2017 by Asselbergs FW et al. (PMID: 28128643).
Funding: The UCC-CVRM is primarily financed by the UMC Utrecht as it is care as usual. UCC-CVRM as a whole, and via MLB as chair of the UCC-CVRM steering committee, was partly supported by a grant from the Netherlands Organization for Health Research and Development (#8480-34001) to develop feedback procedures, not for salary. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
