Cardiometabolic disease, comorbidities and risk of death: findings using data from large-scale electronic health records

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Canoy ◽  
M Zottoli ◽  
J Tran ◽  
R Ramakrishnan ◽  
A Hasseine ◽  
...  

Abstract Background Myocardial infarction (MI), stroke and diabetes are separately associated with increased risk of mortality but it is uncertain if their combined effects are proportional, amplified or less than the expected risk of each disease individually. In addition, patients with these conditions tend to also have other long-term comorbidities. How the relationship between cardiometabolic disease and risk of death is modified by the presence of comorbidity is unclear. Purpose We investigated the separate and combined effects of MI, stroke and diabetes on all-cause mortality, and examined the impact of comorbidity on these associations. Methods We selected a patient cohort of 2,007,731 (51% women) aged ≥16 years at registration with their general practice, using large-scale UK primary care electronic health records that were linked to the national death registry. We identified patients with a recorded diagnosis of MI, stroke, diabetes or none before 2005 (baseline), and classified the patient cohort into mutually exclusive categories of their baseline disease status. For each group, we also extracted information on another major 53 long-term conditions prior to baseline. The cohort was followed until death, deregistration from the practice or censored at the end of study (31 Dec 2014). We used Cox regression, and tested for departure from additivity and multiplicativity to assess interaction. Results At baseline, the mean age of the cohort was 51 (SD=18) years and 7% (N=145,910) had a cardiometabolic disease. Over an average follow-up of 7 (SD=3) years, 270,036 died (mean age of death=79 years). After adjusting for baseline age and sex, the hazard ratio (HR) (95% confidence interval [CI]), relative to those without cardiometabolic disease, were as follows: diabetes=1.53 (1.51 to 1.55), MI=1.54 (1.51 to 1.56), stroke=1.87 (1.84 to 1.90), diabetes and MI=2.16 (2.09 to 2.23), MI and stroke=2.39 (2.28 to 2.49), diabetes and stroke=2.56 (2.47 to 2.65), and all three=3.17 (2.95 to 3.41). After adjusting for the 53 comorbidities, the HR (95% CI) were attenuated: diabetes=1.37 (1.35 to 1.39), MI=1.25 (1.23 to 1.27), stroke=1.49 (1.46 to 1.52), diabetes and MI=1.60 (1.55 to 1.65), MI and stroke=1.52 (1.45 to 1.59), diabetes and stroke=1.91 (1.84 to 1.98), and all three=1.77 (1.64 to 1.91). The results did not materially changed with adjustment for smoking and deprivation level. Test for interaction revealed some minor synergistic effects when cardiometabolic disease co-occurred but excess risks were lower than expected for two combined vs individual disease effects; no significant interaction was seen for all three vs individual disease effects. Conclusion MI, stroke and diabetes are associated with excess mortality, which was partly due to associated chronic conditions. We found no evidence that the co-occurrence of these three conditions contribute to a higher excess mortality than expected from each of them separately. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): NIHR Oxford Biomedical Research Centre; Oxford Martin School, University of Oxford

2006 ◽  
Vol 45 (03) ◽  
pp. 240-245 ◽  
Author(s):  
A. Shabo

Summary Objectives: This paper pursues the challenge of sustaining lifetime electronic health records (EHRs) based on a comprehensive socio-economic-medico-legal model. The notion of a lifetime EHR extends the emerging concept of a longitudinal and cross-institutional EHR and is invaluable information for increasing patient safety and quality of care. Methods: The challenge is how to compile and sustain a coherent EHR across the lifetime of an individual. Several existing and hypothetical models are described, analyzed and compared in an attempt to suggest a preferred approach. Results: The vision is that lifetime EHRs should be sustained by new players in the healthcare arena, who will function as independent health record banks (IHRBs). Multiple competing IHRBs would be established and regulated following preemptive legislation. They should be neither owned by healthcare providers nor by health insurer/payers or government agencies. The new legislation should also stipulate that the records located in these banks be considered the medico-legal copies of an individual’s records, and that healthcare providers no longer serve as the legal record keepers. Conclusions: The proposed model is not centered on any of the current players in the field; instead, it is focussed on the objective service of sustaining individual EHRs, much like financial banks maintain and manage financial assets. This revolutionary structure provides two main benefits: 1) Healthcare organizations will be able to cut the costs of long-term record keeping, and 2) healthcare providers will be able to provide better care based on the availability of a lifelong EHR of their new patients.


Author(s):  
Milica Milutinovic ◽  
Bart De Decker

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.


2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.


2021 ◽  
Author(s):  
Sergiusz Wesolowski ◽  
Gordon Howard Lemmon ◽  
Edgar J Hernandez ◽  
Alex Ryan Henrie ◽  
Thomas A Miller ◽  
...  

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.


2017 ◽  
Vol 5 (3) ◽  
pp. e35 ◽  
Author(s):  
Clemens Scott Kruse ◽  
Michael Mileski ◽  
Alekhya Ganta Vijaykumar ◽  
Sneha Vishnampet Viswanathan ◽  
Ujwala Suskandla ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document