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Yoo J, Hur J, Yoo J, Jurivich D, Lee KJ. A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health. GeroScience 2024; 46:3387-3403. [PMID: 38302843 PMCID: PMC11009216 DOI: 10.1007/s11357-024-01079-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
Accurate prediction of biological age can inform public health measures to extend healthy lifespans and reduce chronic conditions. Multiple theoretical models and methods have been developed; however, their applicability and accuracy are still not extensive. Here, we report Differential Aging and Health Index (DAnHI), a novel measure of age deviation, developed using physical and serum biomarkers from four million individuals in Korea's National Health Screening Program. Participants were grouped into aging statuses (< 26 vs. ≥ 26, < 27 vs. ≥ 27, …, < 75 vs. ≥ 75 years) as response variables in a binary logistic regression model with thirteen biomarkers as independent variables. DAnHI for each individual was calculated as the weighted mean of their relative probabilities of being classified into each older age status, based on model ages ranging from 26 to 75. DAnHI in our large study population showed a steady increase with the increase in age and was positively associated with death after adjusting for chronological age. However, the effect size of DAnHI on the risk of death varied according to the age group and sex. The hazard ratio was highest in the 50-59-year age group and then decreased as the individuals aged. This study demonstrates that routine health check-up biomarkers can be integrated into a quantitative measure for predicting aging-related health status and death via appropriate statistical models and methodology. Our DAnHI-based results suggest that the same level of aging-related health status does not indicate the same degree of risk for death.
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Affiliation(s)
- Jinho Yoo
- YooJin BioSoft, 24, Jeongbalsan-Ro Ilsandong-Gu, Goyang-Si Gyeonggi-Do, 10402, Korea
| | - Junguk Hur
- Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Jintae Yoo
- YooJin BioSoft, 24, Jeongbalsan-Ro Ilsandong-Gu, Goyang-Si Gyeonggi-Do, 10402, Korea
| | - Donald Jurivich
- Department of Geriatrics, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Kyung Ju Lee
- Department of Women's Rehabilitation, National Rehabilitation Center, 58, Samgaksan-Ro, Gangbuk-Gu, Seoul, 01022, Korea.
- Institute for Occupational & Environmental Health, Korea University, Seoul, 02841, Korea.
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Sottile PD, Albers D, DeWitt PE, Russell S, Stroh JN, Kao DP, Adrian B, Levine ME, Mooney R, Larchick L, Kutner JS, Wynia MK, Glasheen JJ, Bennett TD. Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study. J Am Med Inform Assoc 2021; 28:2354-2365. [PMID: 33973011 PMCID: PMC8136054 DOI: 10.1093/jamia/ocab100] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/19/2021] [Accepted: 05/06/2021] [Indexed: 11/24/2022] Open
Abstract
Objective To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. Materials and Methods We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. Results The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. Discussion Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. Conclusion We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.
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Affiliation(s)
- Peter D Sottile
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - David Albers
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Peter E DeWitt
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Seth Russell
- Data Science to Patient Value Initiative, University of Colorado School of Medicine, Aurora, CO, USA
| | - J N Stroh
- Department of Bioengineering, University of Colorado-Denver College of Engineering, Design, and Computing, Denver, CO, USA
| | - David P Kao
- Divisions of Cardiology and Bioinformatics/Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Bonnie Adrian
- UCHealth Clinical Informatics and University of Colorado College of Nursing, Aurora, CO, USA
| | - Matthew E Levine
- Department of Computational and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | | | | | - Jean S Kutner
- Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Chief Medical Officer, University of Colorado Hospital/UCHealth, Aurora, CO, USA
| | - Matthew K Wynia
- Center for Bioethics and Humanities, University of Colorado and Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Jeffrey J Glasheen
- Division of Hospital Medicine, Department of Medicine, University of Colorado School of Medicine and Chief Quality Officer, UCHealth, Aurora, CO, USA
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA.,Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
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Wang Z, Wang B, Zhou Y, Li D, Yin Y. Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction. J Biomed Inform 2019; 101:103340. [PMID: 31756495 DOI: 10.1016/j.jbi.2019.103340] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 06/14/2019] [Accepted: 11/10/2019] [Indexed: 11/16/2022]
Abstract
Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by short-term (in-hospital) and long-term (6-12 month) mortality. Accurate prediction of HF mortality plays a critical role in evaluating early treatment effects. However, due to the lack of a simple and effective prediction model, mortality prediction of HF is difficult, resulting in a low rate of control. To handle this issue, we propose a Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. In our method, feature selection by calculating the F-value of each feature is first performed to identify the crucial clinical features. Then, different weights are assigned to each empirical kernel space according to the centered kernel alignment criterion. To make use of the discriminant information of samples, neighbor discriminant constraint is finally integrated into multiple empirical kernel learning framework. Extensive experiments were performed on a real clinical dataset containing 10, 198 in-patients records collected from Shanghai Shuguang Hospital in March 2009 and April 2016. Experimental results demonstrate that our proposed WMEKL-NDC method achieves a highly competitive performance for HF mortality prediction of in-hospital, 30-day and 1-year. Compared with the state-of-the-art multiple kernel learning and baseline algorithms, our proposed WMEKL-NDC is more accurate on mortality prediction Moreover, top 10 crucial clinical features are identified together with their meanings, which are very useful to assist clinicians in the treatment of HF disease.
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Affiliation(s)
- Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Bolu Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yangming Zhou
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yichao Yin
- Shanghai Shuguang Hospital, Shanghai 200021, China
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Gultepe E, Green JP, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2013; 21:315-25. [PMID: 23959843 DOI: 10.1136/amiajnl-2013-001815] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. MATERIALS AND METHODS Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. RESULTS An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. DISCUSSION This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. CONCLUSIONS Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.
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Affiliation(s)
- Eren Gultepe
- Department of Biomedical Engineering, University of California, Davis, California, USA
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