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Lee C, Park YH, Cho B, Lee HA. A network-based approach to explore comorbidity patterns among community-dwelling older adults living alone. GeroScience 2024; 46:2253-2264. [PMID: 37924440 PMCID: PMC10828172 DOI: 10.1007/s11357-023-00987-z] [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: 07/12/2023] [Accepted: 10/14/2023] [Indexed: 11/06/2023] Open
Abstract
The detailed comorbidity patterns of community-dwelling older adults have not yet been explored. This study employed a network-based approach to investigate the comorbidity patterns of community-dwelling older adults living alone. The sample comprised a cross-sectional cohort of adults 65 or older living alone in a Korean city (n = 1041; mean age = 77.7 years, 77.6% women). A comorbidity network analysis that estimates networks aggregated from measures of significant co-occurrence between pairs of diseases was employed to investigate comorbid associations between 31 chronic conditions. A cluster detection algorithm was employed to identify specific clusters of comorbidities. The association strength was expressed as the observed-to-expected ratio (OER). As a result, fifteen diseases were interconnected within the network (OER > 1, p-value < .05). While hypertension had a high prevalence, osteoporosis was the most central disease, co-occurring with numerous other diseases. The strongest associations among comorbidities were found between thyroid disease and urinary incontinence, chronic otitis media and osteoporosis, gastric duodenal ulcer/gastritis and anemia, and depression and gastric duodenal ulcer/gastritis (OER > 1.85). Three distinct clusters were identified as follows: (a) cataracts, osteoporosis, chronic otitis media, osteoarthritis/rheumatism, low back pain/sciatica, urinary incontinence, post-accident sequelae, and thyroid diseases; (b) hyperlipidemia, diabetes mellitus, and hypertension; and (c) depression, skin disease, gastric duodenal ulcer/gastritis, and anemia. The results may prove valuable in guiding the early diagnosis, management, and treatment of comorbidities in older adults living alone.
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Affiliation(s)
- Chiyoung Lee
- School of Nursing & Health Studies, University of Washington Bothell, 18115 Campus Way NE, Bothell, WA, 98011, USA
| | - Yeon-Hwan Park
- College of Nursing, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- The Research Institute of Nursing Science, College of Nursing, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
| | - Belong Cho
- Department of Family Medicine, College of Medicine, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Health Promotion Center, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Hye Ah Lee
- Clinical Trial Center, Ewha Womans University Mokdong Hospital, 1071 Anyangcheon-Ro, Yangcheon-Gu, Seoul, 07985, Republic of Korea
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Romero Moreno G, Restocchi V, Fleuriot JD, Anand A, Mercer SW, Guthrie B. Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups. EBioMedicine 2024; 102:105081. [PMID: 38518656 PMCID: PMC10966445 DOI: 10.1016/j.ebiom.2024.105081] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/05/2024] [Accepted: 03/09/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. METHODS We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. FINDINGS Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. INTERPRETATION Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. FUNDING National Institute for Health and Care Research.
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Affiliation(s)
| | | | | | - Atul Anand
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Stewart W Mercer
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Bruce Guthrie
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
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3
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Strayer N, Vessels T, Choi K, Zhang S, Li Y, Sharber B, Hsi RS, Bejan CA, Bick AG, Balko JM, Johnson DB, Wheless LE, Wells QS, Shah R, Philips EJ, Self WH, Pulley JM, Wilkins CH, Chen Q, Hartert T, Savona MR, Shyr Y, Roden DM, Smoller JW, Ruderfer DM, Xu Y. Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems. medRxiv 2024:2024.03.28.24305045. [PMID: 38585743 PMCID: PMC10996752 DOI: 10.1101/2024.03.28.24305045] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administration raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combing data from multiple sources for online multimorbidity analysis. Findings Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest a similar structure of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation Our findings demonstrate the robustness of large-scale EHR data for studying complex disease interactions. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared etiology of diseases. The consistent core-periphery network structure offers a strategic approach to analyze disease clusters. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding VUMC Biostatistics Development Award, UL1 TR002243, R21DK127075, R01HL140074, P50GM115305, R01CA227481.
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Affiliation(s)
- Nick Strayer
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tess Vessels
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Digital Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Karmel Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yajing Li
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brian Sharber
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ryan S Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cosmin A Bejan
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander G. Bick
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Justin M Balko
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Douglas B Johnson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lee E Wheless
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ravi Shah
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth J Philips
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Wesley H Self
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jill M Pulley
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Consuelo H Wilkins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qingxia Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tina Hartert
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael R Savona
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Douglas M Ruderfer
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Digital Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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Dervić E, Sorger J, Yang L, Leutner M, Kautzky A, Thurner S, Kautzky-Willer A, Klimek P. Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. NPJ Digit Med 2024; 7:56. [PMID: 38454004 PMCID: PMC10920888 DOI: 10.1038/s41746-024-01015-w] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/18/2024] [Indexed: 03/09/2024] Open
Abstract
We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.
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Affiliation(s)
- Elma Dervić
- Complexity Science Hub Vienna, Vienna, Austria
- Supply Chain Intelligence Institute Austria (ASCII), Vienna, Austria
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
| | | | | | - Michael Leutner
- Medical University of Vienna, Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Vienna, Austria
| | - Alexander Kautzky
- Medical University of Vienna, Department of Psychiatry and Psychotherapy, Vienna, Austria
| | - Stefan Thurner
- Complexity Science Hub Vienna, Vienna, Austria
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Santa Fe Institute, Santa Fe, NM, USA
| | - Alexandra Kautzky-Willer
- Medical University of Vienna, Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Vienna, Austria
- Gender Institute, Gars am Kamp, Austria
| | - Peter Klimek
- Complexity Science Hub Vienna, Vienna, Austria.
- Supply Chain Intelligence Institute Austria (ASCII), Vienna, Austria.
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
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Wang M, Liu G, Ni Z, Yang Q, Li X, Bi Z. Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes. BMC Med Inform Decis Mak 2024; 24:35. [PMID: 38310256 PMCID: PMC10837944 DOI: 10.1186/s12911-024-02435-0] [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: 07/19/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVE Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. METHODS We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged [Formula: see text] 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. RESULTS We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. CONCLUSION The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.
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Affiliation(s)
- Menglu Wang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Guangjian Liu
- Shenzhen Dymind Biotechnology Co., Ltd, Shenzhen, 518000, China
| | - Zhennan Ni
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Qianjun Yang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Xiaojun Li
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
| | - Zhisheng Bi
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China.
- Department of Emergency Medicine, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, China.
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Lee C, Wei S, McConnell ES, Tsumura H, Xue TM, Pan W. Comorbidity Patterns in Older Patients Undergoing Hip Fracture Surgery: A Comorbidity Network Analysis Study. Clin Nurs Res 2024; 33:70-80. [PMID: 37932937 DOI: 10.1177/10547738231209367] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Comorbidity network analysis (CNA) is a technique in which mathematical graphs encode correlations (edges) among diseases (nodes) inferred from the disease co-occurrence data of a patient group. The present study applied this network-based approach to identifying comorbidity patterns in older patients undergoing hip fracture surgery. This was a retrospective observational cohort study using electronic health records (EHR). EHR data were extracted from the one University Health System in the southeast United States. The cohort included patients aged 65 and above who had a first-time low-energy traumatic hip fracture treated surgically between October 1, 2015 and December 31, 2018 (n = 1,171). Comorbidity includes 17 diagnoses classified by the Charlson Comorbidity Index. The CNA investigated the comorbid associations among 17 diagnoses. The association strength was quantified using the observed-to-expected ratio (OER). Several network centrality measures were used to examine the importance of nodes, namely degree, strength, closeness, and betweenness centrality. A cluster detection algorithm was employed to determine specific clusters of comorbidities. Twelve diseases were significantly interconnected in the network (OER > 1, p-value < .05). The most robust associations were between metastatic carcinoma and mild liver disease, myocardial infarction and congestive heart failure, and hemi/paraplegia and cerebrovascular disease (OER > 2.5). Cerebrovascular disease, congestive heart failure, and myocardial infarction were identified as the central diseases that co-occurred with numerous other diseases. Two distinct clusters were noted, and the largest cluster comprised 10 diseases, primarily encompassing cardiometabolic and cognitive disorders. The results highlight specific patient comorbidities that could be used to guide clinical assessment, management, and targeted interventions that improve hip fracture outcomes in this patient group.
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Affiliation(s)
- Chiyoung Lee
- School of Nursing & Health Studies, University of Washington Bothell, Bothell, WA, USA
| | - Sijia Wei
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eleanor S McConnell
- Duke University School of Nursing, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
| | | | - Tingzhong Michelle Xue
- Duke University School of Nursing, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Wei Pan
- Duke University School of Nursing, Durham, NC, USA
- Duke University School of Medicine, Durham, NC, USA
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Ukolova E, Burcin B. Racial/Ethnic disparities in the chains of morbid events leading to death: network analysis of US multiple cause of death data. Biodemography Soc Biol 2023; 68:149-165. [PMID: 37899643 DOI: 10.1080/19485565.2023.2271841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Multiple-cause-of-death data have not yet been applied to the study of racial/ethnic differences in causal chains of events leading to death, nor they have been used to examine racial/ethnic disparities in cause-of-death certification. We use publicly available 2019 US death certificate data to reassemble chains of morbid events leading to death. From them, we construct and analyze directed multiple cause of death networks by race and sex of deaths aged 60+. Three perspectives to measure disparities are employed: (i) relative prevalence of cause-of-death-pairs, (ii) strength of associations between diseases, (iii) similarities in transition matrices. Non-Hispanic Blacks (NHB) had overall lower prevalence of cause of death pairs, Hispanics (HIS) were burdened more by alcohol-related mortality and Asian and Pacific Islanders (API) exceeded in transitions to cerebrovascular diseases. Lower similarity was observed in transitions to external causes of death, dementia and Alzheimer's disease, pulmonary heart diseases, interstitial respiratory diseases, and diseases of the liver. After excluding rare diseases, the similarity further decreased for ill-defined conditions, diabetes mellitus, other cardiovascular diseases, diseases of the pleura, and anemia. To sum up, races/ethnicities not only vary in structure and timing of death but they differ in morbid processes leading to death as well.
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Affiliation(s)
- Elizaveta Ukolova
- Department of Demography and Geodemography, Faculty of Science, Charles University, Prague, Czechia
| | - Boris Burcin
- Department of Demography and Geodemography, Faculty of Science, Charles University, Prague, Czechia
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Zhao B, Huepenbecker S, Zhu G, Rajan SS, Fujimoto K, Luo X. Comorbidity network analysis using graphical models for electronic health records. Front Big Data 2023; 6:846202. [PMID: 37663273 PMCID: PMC10470017 DOI: 10.3389/fdata.2023.846202] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Importance The comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality. Objective The main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation. Method This cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients. Results Out of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between "poisoning by psychotropic agents" and "accidental poisoning by tranquilizers" (logOR 8.16), and the most connected diagnosis was "disorders of fluid, electrolyte, and acid-base balance" (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, "diagnoses of mitral and aortic valve" and "other rheumatic heart disease" (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, "disorders of fluid, electrolyte, and acid-base balance" was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes. Conclusion and relevance Our graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.
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Affiliation(s)
- Bo Zhao
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Sarah Huepenbecker
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gen Zhu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Suja S. Rajan
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Kayo Fujimoto
- Department of Health Promotion and Behavioral Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
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9
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Zhang S, Strayer N, Vessels T, Choi K, Wang GW, Li Y, Bejan CA, Hsi RS, Bick AG, Velez Edwards DR, Savona MR, Philips EJ, Pulley J, Self WH, Hopkins WC, Roden DM, Smoller JW, Ruderfer DM, Xu Y. PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis. medRxiv 2023:2023.07.23.23293047. [PMID: 37547012 PMCID: PMC10402210 DOI: 10.1101/2023.07.23.23293047] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Motivation Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and implementation The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data availability statement The data underlying this article are available in the article and in its online web application or supplementary material.
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Affiliation(s)
- Siwei Zhang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | | | - Tess Vessels
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Karmel Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | | | - Yajing Li
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Cosmin A Bejan
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
| | - Ryan S Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Digna R Velez Edwards
- Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael R Savona
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth J Philips
- Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Jill Pulley
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wesley H Self
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wilkins Consuelo Hopkins
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
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10
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Sudhakar SK, Sridhar S, Char S, Pandya K, Mehta K. Prevalence of comorbidities post mild traumatic brain injuries: a traumatic brain injury model systems study. Front Hum Neurosci 2023; 17:1158483. [PMID: 37397857 PMCID: PMC10309649 DOI: 10.3389/fnhum.2023.1158483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 02/03/2023] [Accepted: 05/26/2023] [Indexed: 07/04/2023] Open
Abstract
Traumatic brain injury (TBI) is associated with an increased risk of long-lasting health-related complications. Survivors of brain trauma often experience comorbidities which could further dampen functional recovery and severely interfere with their day-to-day functioning after injury. Of the three TBI severity types, mild TBI constitutes a significant proportion of total TBI cases, yet a comprehensive study on medical and psychiatric complications experienced by mild TBI subjects at a particular time point is missing in the field. In this study, we aim to quantify the prevalence of psychiatric and medical comorbidities post mild TBI and understand how these comorbidities are influenced by demographic factors (age, and sex) through secondary analysis of patient data from the TBI Model Systems (TBIMS) national database. Utilizing self-reported information from National Health and Nutrition Examination Survey (NHANES), we have performed this analysis on subjects who received inpatient rehabilitation at 5 years post mild TBI. Our analysis revealed that psychiatric comorbidities (anxiety, depression, and post-traumatic stress disorder (PTSD)), chronic pain, and cardiovascular comorbidities were common among survivors with mild TBI. Furthermore, depression exhibits an increased prevalence in the younger compared to an older cohort of subjects whereas the prevalence of rheumatologic, ophthalmological, and cardiovascular comorbidities was higher in the older cohort. Lastly, female survivors of mild TBI demonstrated increased odds of developing PTSD compared to male subjects. The findings of this study would motivate additional analysis and research in the field and could have broader implications for the management of comorbidities after mild TBI.
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11
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do Valle IF, Ferolito B, Gerlovin H, Costa L, Demissie S, Linares F, Cohen J, Gagnon DR, Gaziano JM, Begoli E, Cho K, Barabási AL. Network-medicine framework for studying disease trajectories in U.S. veterans. Sci Rep 2022; 12:12018. [PMID: 35835798 DOI: 10.1038/s41598-022-15764-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.
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12
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Higa S, Nozawa K, Karasawa Y, Shirai C, Matsuyama S, Yamamoto Y, Laurent T, Asami Y. The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database. Drugs Real World Outcomes 2022; 9:463-476. [PMID: 35780274 PMCID: PMC9392665 DOI: 10.1007/s40801-022-00310-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2022] [Indexed: 11/29/2022] Open
Abstract
Background Reducing the considerable non-communicable disease (NCD) burden in the aging Japanese population depends on better understanding of the comorbid and temporal relationships between different NCDs. Objective We aimed to identify associations between NCDs and temporal patterns of NCDs in Japan using data from a large medical claims database. Methods The study used three-digit International Classification of Diseases, Tenth Revision codes for NCDs for employees and their dependents included in the MinaCare database, which covers the period since 2010. Associations between pairs of NCDs were assessed by calculating risk ratios. The calculated risk ratios were used to create a network of closely associated NCDs (risk ratio > 15, statistically significant) and to assess temporal patterns of NCD diagnoses (risk ratio ≥ 5). The Infomap algorithm was used to identify clusters of diseases for different sex and age strata. Results The analysis included 4,200,254 individuals (age < 65 years: 98%). Many of the temporal associations and patterns of the diseases of interest identified in this study were previously known. Regarding the diseases of interest, these associations can be classified as comorbidities, early manifestations initially diagnosed as something else, diseases attributable to or that cause the disease of interest, or caused by pharmacological treatment. International Classification of Diseases, Tenth Revision chapters that were most associated with other chapters included L Diseases of the skin and subcutaneous tissue. In the age-stratified and gender-stratified networks, clusters with the highest numbers of International Classification of Diseases, Tenth Revision codes included I Diseases of the circulatory system and F Mental and behavioral disorders. Conclusions Our findings reinforce established associations between NCDs and underline the importance of comprehensive NCD care. Supplementary Information The online version contains supplementary material available at 10.1007/s40801-022-00310-w.
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Affiliation(s)
- Shingo Higa
- Viatris Pharmaceuticals Japan Inc., Tokyo, Japan.
| | | | | | | | | | | | | | - Yuko Asami
- Viatris Pharmaceuticals Japan Inc., Tokyo, Japan
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13
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Kim HJ, Shin SY, Jeong SH. Nature and Extent of Physical Comorbidities Among Korean Patients With Mental Illnesses: Pairwise and Network Analysis Based on Health Insurance Claims Data. Psychiatry Investig 2022; 19:488-499. [PMID: 35753688 PMCID: PMC9233950 DOI: 10.30773/pi.2022.0068] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/29/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis. METHODS A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity. RESULTS In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks. CONCLUSION Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.
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Affiliation(s)
- Ho Joon Kim
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - Sam Yi Shin
- Department of Psychiatry, The Healer's Hospital, Busan, Republic of Korea
| | - Seong Hoon Jeong
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
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14
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Wang T, Bendayan R, Msosa Y, Pritchard M, Roberts A, Stewart R, Dobson R. Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach. J Biomed Inform 2022; 127:104010. [PMID: 35151869 PMCID: PMC8894882 DOI: 10.1016/j.jbi.2022.104010] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/30/2021] [Accepted: 01/30/2022] [Indexed: 11/25/2022]
Abstract
Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.
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Affiliation(s)
- Tao Wang
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom.
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Yamiko Msosa
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Megan Pritchard
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Robert Stewart
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Department of Psychological Medicine, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Institute of Health Informatics, University College London, Euston Road, London NW1 2DA, United Kingdom; Health Data Research UK London, University College London, Euston Road, London NW1 2DA, United Kingdom
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15
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Chen J, Mittendorfer-Rutz E, Berg L, Norredam M, Sijbrandij M, Klimek P. Associations between Multimorbidity Patterns and Subsequent Labor Market Marginalization among Refugees and Swedish-Born Young Adults-A Nationwide Registered-Based Cohort Study. J Pers Med 2021; 11:jpm11121305. [PMID: 34945776 PMCID: PMC8705997 DOI: 10.3390/jpm11121305] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Young refugees are at increased risk of labor market marginalization (LMM). We sought to examine whether the association of multimorbidity patterns and LMM differs in refugee youth compared to Swedish-born youth and identify the diagnostic groups driving this association. Methodology: We analyzed 249,245 individuals between 20–25 years, on 31 December 2011, from a combined Swedish registry. Refugees were matched 1:5 to Swedish-born youth. A multimorbidity score was computed from a network of disease co-occurrences in 2009–2011. LMM was defined as disability pension (DP) or >180 days of unemployment during 2012–2016. Relative risks (RR) of LMM were calculated for 114 diagnostic groups (2009–2011). The odds of LMM as a function of multimorbidity score were estimated using logistic regression. Results: 2841 (1.1%) individuals received DP and 16,323 (6.5%) experienced >180 annual days of unemployment during follow-up. Refugee youth had a marginally higher risk of DP (OR (95% CI): 1.59 (1.52, 1.67)) depending on their multimorbidity score compared to Swedish-born youth (OR (95% CI): 1.51 (1.48, 1.54)); no differences were found for unemployment (OR (95% CI): 1.15 (1.12, 1.17), 1.12 (1.10, 1.14), respectively). Diabetes mellitus and influenza/pneumonia elevated RR of DP in refugees (RRs (95% CI) 2.4 (1.02, 5.6) and 1.75 (0.88, 3.45), respectively); most diagnostic groups were associated with a higher risk for unemployment in refugees. Conclusion: Multimorbidity related similarly to LMM in refugees and Swedish-born youth, but different diagnoses drove these associations. Targeted prevention, screening, and early intervention strategies towards specific diagnoses may effectively reduce LMM in young adult refugees.
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Affiliation(s)
- Jiaying Chen
- Section for Science of Complex Systems, CeMSIIS Medical University of Vienna, 1090 Vienna, Austria;
- Complexity Science Hub Vienna, 1090 Vienna, Austria
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden;
| | - Ellenor Mittendorfer-Rutz
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden;
| | - Lisa Berg
- Department of Public Health Sciences, Stockholm University, 10691 Stockholm, Sweden;
- Centre for Health Equity Studies, Stockholm University/Karolinska Institutet, 10691 Stockholm, Sweden
| | - Marie Norredam
- Danish Research Centre for Migration, Ethnicity, and Health (MESU), Section for Health Services Research, Department of Public Health, University of Copenhagen, 1014 Copenhagen, Denmark;
- Section of Immigrant Medicine, Department of Infectious Diseases, University Hospital Hvidovre, 2650 Hvidovre, Denmark
| | - Marit Sijbrandij
- Department of Clinical, Neuro- and Developmental Psychology and WHO Collaborating Centre for Research and Dissemination of Psychological Interventions, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands;
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS Medical University of Vienna, 1090 Vienna, Austria;
- Complexity Science Hub Vienna, 1090 Vienna, Austria
- Correspondence:
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16
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Abstract
Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from network science, machine learning and computational phenotyping to assess interactions between two or more diseases in a transparent way across the full diagnostic spectrum. We demonstrate that health states of hospitalized patients can be better characterized by including higher-order features capturing interactions between more than two diseases. We identify a meaningful set of higher-order diagnosis features that account for synergistic disease interactions in a population-wide (N = 9 M) medical claims dataset. We construct a generalized disease network where (higher-order) diagnosis features are linked if they predict similar diagnoses across the whole diagnostic spectrum. The fact that specific diagnoses are generally represented multiple times in the network allows for the identification of putatively different disease phenotypes that may reflect different disease aetiologies. At the example of obesity, we demonstrate the purely data-driven detection of two complex phenotypes of obesity. As indicated by a matched comparison between patients having these phenotypes, we show that these phenotypes show specific characteristics of what has been controversially discussed in the medical literature as metabolically healthy and unhealthy obesity, respectively. The findings also suggest that metabolically healthy patients show some progression towards more unhealthy obesity over time, a finding that is consistent with longitudinal studies indicating a transient nature of metabolically healthy obesity. The disease network is available for exploration at https://disease.network/.
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Affiliation(s)
- Markus J Strauss
- Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Wien, Austria
| | - Thomas Niederkrotenthaler
- Unit Suicide Research and Mental Health Promotion, Department of Social and Preventive Medicine, Center for Public Health, Medical University of Vienna, Kinderspitalgasse 15, 1090 Wien, Austria
| | - Stefan Thurner
- Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Wien, Austria.,Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, 1090 Wien, Austria.,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 85701, USA
| | - Alexandra Kautzky-Willer
- Department of Endocrinology and Metabolism, Internal Medicine III, Medical University of Vienna, Spitalgasse 23, 1090 Wien, Austria
| | - Peter Klimek
- Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Wien, Austria.,Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, 1090 Wien, Austria
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17
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Correia Y, Scheel J, Gupta S, Wang K. Placental mitochondrial function as a driver of angiogenesis and placental dysfunction. Biol Chem 2021; 402:887-909. [PMID: 34218539 DOI: 10.1515/hsz-2021-0121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
The placenta is a highly vascularized and complex foetal organ that performs various tasks, crucial to a healthy pregnancy. Its dysfunction leads to complications such as stillbirth, preeclampsia, and intrauterine growth restriction. The specific cause of placental dysfunction remains unknown. Recently, the role of mitochondrial function and mitochondrial adaptations in the context of angiogenesis and placental dysfunction is getting more attention. The required energy for placental remodelling, nutrient transport, hormone synthesis, and the reactive oxygen species leads to oxidative stress, stemming from mitochondria. Mitochondria adapt to environmental changes and have been shown to adjust their oxygen and nutrient use to best support placental angiogenesis and foetal development. Angiogenesis is the process by which blood vessels form and is essential for the delivery of nutrients to the body. This process is regulated by different factors, pro-angiogenic factors and anti-angiogenic factors, such as sFlt-1. Increased circulating sFlt-1 levels have been linked to different preeclamptic phenotypes. One of many effects of increased sFlt-1 levels, is the dysregulation of mitochondrial function. This review covers mitochondrial adaptations during placentation, the importance of the anti-angiogenic factor sFlt-1in placental dysfunction and its role in the dysregulation of mitochondrial function.
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Affiliation(s)
- Yolanda Correia
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, BirminghamB4 7ET, UK
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051Rostock, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051Rostock, Germany
| | - Keqing Wang
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, BirminghamB4 7ET, UK
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18
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Lu H, Uddin S, Hajati F, Moni MA, Khushi M. A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. APPL INTELL 2022; 52:2411-22. [DOI: 10.1007/s10489-021-02533-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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19
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Lee HA, Park H. Comorbidity network analysis related to obesity in middle-aged and older adults: findings from Korean population-based survey data. Epidemiol Health 2021; 43:e2021018. [PMID: 33677857 PMCID: PMC8060529 DOI: 10.4178/epih.e2021018] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/05/2021] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES We conducted a comorbidity network analysis using data from the seventh Korea National Health and Nutrition Examination Survey to systematically quantify obesity-related comorbidities. METHODS The study included 11,712 subjects aged 45 to 80 (5,075 male and 6,637 female). A prevalent disease was defined as a specific disease for which a subject had been diagnosed by a doctor and was being treated. Comorbidity network analysis was performed for diseases with a prevalence of 1% or more, including overweight and obesity. We estimated the observed-to-expected ratio of all possible disease pairs with comorbidity strength and visualized the network of obesity-related comorbidities. RESULTS In subjects over 45 years old, 37.3% of people had a body mass index over 25.0 kg/m2. The most common prevalent disease was hypertension (42.3%), followed by dyslipidemia (17.4%) and diabetes (17.0%). Overweight and obese subjects were 2.1 times (95% confidence interval, 1.9 to 2.3) more likely to have a comorbidity (i.e., 2 or more diseases) than normal-weight subjects. Metabolic diseases such as hypertension, dyslipidemia, diabetes, and osteoarthritis were directly associated with overweight and obesity. The probability of coexistence for each of those 4 diseases was 1.3 times higher than expected. In addition, hypertension and dyslipidemia frequently coexisted in overweight and obese female along with other diseases. In obese male, dyslipidemia and diabetes were the major diseases in the comorbidity network. CONCLUSIONS Our results provide evidence justifying the management of metabolic components in obese individuals. In addition, our results will help prioritize interventions for comorbidity reduction as a public health goal.
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Affiliation(s)
- Hye Ah Lee
- Clinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Hyesook Park
- Department of Preventive Medicine, Ewha Womans University College of Medicine, Seoul, Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Korea
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20
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Haug N, Sorger J, Gisinger T, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients. Front Physiol 2021; 11:612604. [PMID: 33469431 PMCID: PMC7813935 DOI: 10.3389/fphys.2020.612604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.
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Affiliation(s)
- Nils Haug
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
| | | | - Teresa Gisinger
- Department of Medicine III, Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | | | - Alexandra Kautzky-Willer
- Department of Medicine III, Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria.,Gender Institute, Gars am Kamp, Austria
| | - Stefan Thurner
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria.,IIASA, Laxenburg, Austria.,Santa Fe Institute, Santa Fe, NM, United States
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria.,Complexity Science Hub Vienna, Vienna, Austria
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21
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Li X, Liu G, Chen W, Bi Z, Liang H. Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes. BMC Med Inform Decis Mak 2020; 20:268. [PMID: 33069223 PMCID: PMC7568351 DOI: 10.1186/s12911-020-01282-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 10/05/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism. METHODS We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children's Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search. RESULTS We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported. CONCLUSION We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.
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Affiliation(s)
- Xiaojun Li
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China
| | - Guangjian Liu
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China
| | - Wenxiong Chen
- Department of Neurology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China
| | - Zhisheng Bi
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Huiying Liang
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
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Díaz-Santiago E, Jabato FM, Rojano E, Seoane P, Pazos F, Perkins JR, Ranea JAG. Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases. PLoS Genet 2020; 16:e1009054. [PMID: 33001999 PMCID: PMC7553355 DOI: 10.1371/journal.pgen.1009054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 10/13/2020] [Accepted: 08/16/2020] [Indexed: 12/15/2022] Open
Abstract
Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub. Although rare diseases each affect a small number of people, taken together they affect millions. Better diagnosis and understanding of the underlying mechanisms are needed. By combining phenotypic data for many rare disease patients, we can build clusters of comorbid phenotypes that tend to co-occur together. By using genomic information, we can supplement these clusters and look for related genes and functional systems, such as pathways and molecular mechanisms. We applied such an approach to thousands of rare disease patients from the DECIPHER resources. We were able to detect hundreds of pairs of comorbid phenotypes, and use them to build tens of phenotype clusters. By mapping genes to these phenotypes, based on data from the same patients, we were able to detect related genes and functional systems, such as genes mapping to the 22q11 genomic region underlying a cluster of phenotypes related to DiGeorge syndrome. To ensure that these clusters made sensible predictions, results were validated using literature co-mention, overlap with known disease and semantic similarity measures. These comorbidity patterns, along with their underlying molecular systems, can give important insights into disease mechanisms, moreover they can be used to direct differential-diagnosis of rare disease patients.
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Affiliation(s)
- Elena Díaz-Santiago
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
| | - Fernando M. Jabato
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
| | - Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
| | - Pedro Seoane
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
- CIBER de Enfermedades Raras (CIBERER), ISCIII, Madrid, Spain
| | | | - James R. Perkins
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
- CIBER de Enfermedades Raras (CIBERER), ISCIII, Madrid, Spain
- The Biomedical Research Institute of Malaga (IBIMA), Malaga, Spain
- * E-mail:
| | - Juan A. G. Ranea
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
- CIBER de Enfermedades Raras (CIBERER), ISCIII, Madrid, Spain
- The Biomedical Research Institute of Malaga (IBIMA), Malaga, Spain
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Haug N, Deischinger C, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. High-risk multimorbidity patterns on the road to cardiovascular mortality. BMC Med 2020; 18:44. [PMID: 32151252 PMCID: PMC7063814 DOI: 10.1186/s12916-020-1508-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 02/03/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.
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Affiliation(s)
- Nina Haug
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria.,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria
| | - Carola Deischinger
- Gender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria
| | - Michael Gyimesi
- Gesundheit Österreich GmbH, Stubenring 6, Vienna, A-1010, Austria
| | - Alexandra Kautzky-Willer
- Gender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria
| | - Stefan Thurner
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria.,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria.,IIASA, Schloßplatz 1, Laxenburg, A-2361, Austria.,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, 85701, NM, USA
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, A-1090, Austria. .,Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna, A-1080, Austria.
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