ArticleInternational journal of general medicine2015
Health behaviors and quality of life predictors for risk of hospitalization in an electronic health record-linked biobank.
Article in International journal of general medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Self-Rated Health in Middle Age and Risk of Hospitalizations and Death: Recurrent Event Analysis of the ARIC Study.Journal of general internal medicine · 2024Article
- Associations between change in physical activity and sedentary time and health-related quality of life in older english adults: the EPIC-Norfolk cohort study.Health and quality of life outcomes · 2023Article
- Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes.SSM - population health · 2022Article
- MIMIC-SBDH: A Dataset for Social and Behavioral Determinants of Health.Proceedings of machine learning research · 2021Article
- Area Deprivation Index and Cardiac Readmissions: Evaluating Risk-Prediction in an Electronic Health Record.Journal of the American Heart Association · 2021Observational
- No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study.Pharmacogenomics and personalized medicine · 2021Article
- Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile.BMJ open · 2019Article
- Health-Related Quality of Life of Economically Disadvantaged African American Older Adults: Age and Gender Differences.International journal of environmental research and public health · 2019Article
- Review
- Article
- Association of mitochondrial DNA copy number with self-rated health status.The application of clinical genetics · 2018Article
- Early characterization of an adult population at an insurer's point of entry as an opportunity to identify hospitalization risk.Revista CuidarteArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundHospital risk stratification models using electronic health records (EHRs) often use age and comorbid health burden. Our primary aim was to determine if quality of life or health behaviors captured in an EHR-linked biobank can predict future risk of hospitalization.
methodsParticipants in the Mayo Clinic Biobank completed self-administered questionnaires at enrollment that included quality of life and health behaviors. Participants enrolled as of December 31, 2010 were followed for one year to ascertain hospitalization. Data on comorbidities and hospitalization were derived from the Mayo Clinic EHR. Hazard ratios (HR) and 95% confidence interval (CI) were used, adjusted for age and sex. We used gradient boosting machines models to integrate multiple factors. Different models were compared using C-statistic.
resultsOf the 8,927 eligible Mayo Clinic Biobank participants, 834 (9.3%) were hospitalized. Self-perceived health status and alcohol use had the strongest associations with risk of hospitalization. Compared to participants with excellent self-perceived health, those reporting poor/fair health had higher risk of hospitalization (HR =3.66, 95% CI 2.74-4.88). Alcohol use was inversely associated with hospitalization (HR =0.57 95% CI 0.45-0.72). The gradient boosting machines model estimated self-perceived health as the most influential factor (relative influence =16%). The predictive ability of the model based on comorbidities was slightly higher than the one based on the self-perceived health (C-statistic =0.67 vs 0.65).
conclusionThis study demonstrates that self-perceived health may be an important piece of information to add to the EHR. It may be another method to determine hospitalization risk.
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