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Chowdhury SH, Chen LK, Hu P, Badjatia N, Podell JE. Group based trajectory modeling identifies distinct patterns of sympathetic hyperactivity following traumatic brain injury. RESEARCH SQUARE 2024:rs.3.rs-4803007. [PMID: 39281875 PMCID: PMC11398559 DOI: 10.21203/rs.3.rs-4803007/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Background Paroxysmal Sympathetic Hyperactivity (PSH) occurs with high prevalence among critically ill Traumatic Brain Injury (TBI) patients and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale (CFS) and a Diagnosis Likelihood Tool (DLT), intended to quantify the severity of sympathetically-mediated symptoms and likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM. Methods We performed an observational cohort study of 221 acute critically ill TBI patients for whom daily PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of CFS evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge GCS, ICU and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression. Results We identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, ICU length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and post-resuscitation motor GCS. Conclusions This study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve TBI patient outcomes.
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Jørgensen IF, Haue AD, Placido D, Hjaltelin JX, Brunak S. Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:251-276. [PMID: 39178424 DOI: 10.1146/annurev-biodatasci-110123-041001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.
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Affiliation(s)
- Isabella Friis Jørgensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Amalie Dahl Haue
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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Raschka T, Li Z, Gaßner H, Kohl Z, Jukic J, Marxreiter F, Fröhlich H. Unraveling progression subtypes in people with Huntington's disease. EPMA J 2024; 15:275-287. [PMID: 38841617 PMCID: PMC11148000 DOI: 10.1007/s13167-024-00368-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/09/2024] [Indexed: 06/07/2024]
Abstract
Background Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00368-2.
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Affiliation(s)
- Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Zexin Li
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Friedrich-Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Zacharias Kohl
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Jelena Jukic
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Center for Rare Diseases Erlangen (ZSEER), University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Center for Movement Disorders, Passauer Wolf, 93333 Bad Gögging, Germany
- Center for Rare Diseases Erlangen (ZSEER), University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Friedrich-Hirzebruch-Allee 6, 53115 Bonn, Germany
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Kempaiah P, Libertin CR, Chitale RA, Naeyma I, Pleqi V, Sheele JM, Iandiorio MJ, Hoogesteijn AL, Caulfield TR, Rivas AL. Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes. Biomedicines 2024; 12:871. [PMID: 38672225 PMCID: PMC11048687 DOI: 10.3390/biomedicines12040871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 03/14/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND METHODS Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. RESULTS While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.
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Affiliation(s)
- Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Rohit A. Chitale
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Islam Naeyma
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
| | - Vasili Pleqi
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Michelle J. Iandiorio
- Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA;
| | | | - Thomas R. Caulfield
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ariel L. Rivas
- Center for Global Health, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA
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Giannoula A, Comas M, Castells X, Estupiñán-Romero F, Bernal-Delgado E, Sanz F, Sala M. Exploring long-term breast cancer survivors' care trajectories using dynamic time warping-based unsupervised clustering. J Am Med Inform Assoc 2024; 31:820-831. [PMID: 38193340 PMCID: PMC10990519 DOI: 10.1093/jamia/ocad251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary. MATERIALS AND METHODS A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison. RESULTS The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions. DISCUSSION The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories. CONCLUSION The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans.
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Affiliation(s)
- Alexia Giannoula
- Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences (MELIS), Hospital del Mar Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
| | - Mercè Comas
- Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
| | - Xavier Castells
- Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
| | - Francisco Estupiñán-Romero
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
- Data Science for Health Services and Policy Research Group, Institute for Health Sciences (IACS), Zaragoza, Aragon, 50009, Spain
| | - Enrique Bernal-Delgado
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
- Data Science for Health Services and Policy Research Group, Institute for Health Sciences (IACS), Zaragoza, Aragon, 50009, Spain
| | - Ferran Sanz
- Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences (MELIS), Hospital del Mar Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Sala
- Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain
- RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain
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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] [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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Thierry B, Stanley K, Kestens Y, Winters M, Fuller D. Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research. Am J Epidemiol 2024; 193:180-192. [PMID: 37646642 DOI: 10.1093/aje/kwad176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 05/08/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023] Open
Abstract
In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.
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Taylor RA, Gilson A, Chi L, Haimovich AD, Crawford A, Brandt C, Magidson P, Lai JM, Levin S, Mecca AP, Hwang U. Dementia risk analysis using temporal event modeling on a large real-world dataset. Sci Rep 2023; 13:22618. [PMID: 38114545 PMCID: PMC10730574 DOI: 10.1038/s41598-023-49330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.
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Affiliation(s)
- R Andrew Taylor
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Section for Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
| | - Aidan Gilson
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ling Chi
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Adrian D Haimovich
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Anna Crawford
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Cynthia Brandt
- Section for Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Phillip Magidson
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - James M Lai
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Scott Levin
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Clinical Decision Support Solutions, Beckman Coulter, Brea, CA, USA
| | - Adam P Mecca
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Yale Alzheimer's Disease Research Center, New Haven, CT, USA
| | - Ula Hwang
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
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Morel JD, Morel JM, Alvarez L. Time warping between main epidemic time series in epidemiological surveillance. PLoS Comput Biol 2023; 19:e1011757. [PMID: 38150476 PMCID: PMC10775977 DOI: 10.1371/journal.pcbi.1011757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 01/09/2024] [Accepted: 12/12/2023] [Indexed: 12/29/2023] Open
Abstract
The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.
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Affiliation(s)
- Jean-David Morel
- Laboratoire de Physiologie Intégrative et Systémique, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Michel Morel
- City University of Hong Kong, Department of Mathematics, Tat Chee Ave, Kowloon Tong, Hong Kong
| | - Luis Alvarez
- Departamento de Informática y Sistemas, Campus de Tafira, Universidad de Las Palmas de Gran Canaria, Spain
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Herzeel C, D’Hondt E, Vandeweerd V, Botermans W, Akand M, Van der Aa F, Wuyts R, Verachtert W. A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development. PLOS DIGITAL HEALTH 2023; 2:e0000384. [PMID: 37992021 PMCID: PMC10664923 DOI: 10.1371/journal.pdig.0000384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 09/22/2023] [Indexed: 11/24/2023]
Abstract
We present the Patient Trajectory Analysis Library (PTRA), a software package for explorative analysis of patient development. PTRA provides the tools for extracting statistically relevant trajectories from the medical event histories of a patient population. These trajectories can additionally be clustered for visual inspection and identifying key events in patient progression. The algorithms of PTRA are based on a statistical method developed previously by Jensen et al, but we contribute several modifications and extensions to enable the implementation of a practical tool. This includes a new clustering strategy, filter mechanisms for controlling analysis to specific cohorts and for controlling trajectory output, a parallel implementation that executes on a single server rather than a high-performance computing (HPC) cluster, etc. PTRA is furthermore open source and the code is organized as a framework so researchers can reuse it to analyze new data sets. We illustrate our tool by discussing trajectories extracted from the TriNetX Dataworks database for analyzing bladder cancer development. We show this experiment uncovers medically sound trajectories for bladder cancer.
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Affiliation(s)
| | | | - Valerie Vandeweerd
- Janssen Research & Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Wouter Botermans
- Janssen Research & Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Murat Akand
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Frank Van der Aa
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
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11
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Using Markov chains and temporal alignment to identify clinical patterns in Dementia. J Biomed Inform 2023; 140:104328. [PMID: 36924843 DOI: 10.1016/j.jbi.2023.104328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/16/2023]
Abstract
In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.
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12
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Fu M, Yan Y, Olde Loohuis LM, Chang TS. Defining the distance between diseases using SNOMED CT embeddings. J Biomed Inform 2023; 139:104307. [PMID: 36738869 DOI: 10.1016/j.jbi.2023.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/10/2022] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients and identifying common time courses of disease onset. Distance metrics developed in prior work focused on smaller, targeted disease sets. Distance metrics covering all diseases have not yet been defined, which limits the applications to a broader disease spectrum. Our current study defines disease distances for all disease pairs within the International Classification of Diseases, version 10 (ICD-10), the diagnostic classification system universally used in electronic health records. Our proposed distance is computed based on a biomedical ontology, SNOMED CT (Systemized Nomenclature of Medicine, Clinical Terms), which can also be viewed as a structured knowledge graph. We compared the knowledge graph-based metric to three other distance metrics based on the hierarchical structure of ICD, clinical comorbidity, and genetic correlation, to evaluate how each may capture similar or unique aspects of disease relationships. We show that our knowledge graph-based distance metric captures known phenotypic, clinical, and molecular characteristics at a finer granularity than the other three. With the continued growth of using electronic health records data for research, we believe that our distance metric will play an important role in subgrouping patients for precision health, and enabling individualized disease prevention and treatments.
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Affiliation(s)
- Mingzhou Fu
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA; Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Yu Yan
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Loes M Olde Loohuis
- Center for Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Timothy S Chang
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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13
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Roso-Llorach A, Vetrano DL, Trevisan C, Fernández S, Guisado-Clavero M, Carrasco-Ribelles LA, Fratiglioni L, Violán C, Calderón-Larrañaga A. 12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models. Aging (Albany NY) 2022; 14:9805-9817. [PMID: 36435509 PMCID: PMC9831736 DOI: 10.18632/aging.204395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns. METHODS Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up. FINDINGS Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability. INTERPRETATION Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern.
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Affiliation(s)
- Albert Roso-Llorach
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain,Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Davide L. Vetrano
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Caterina Trevisan
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Sergio Fernández
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain
| | - Marina Guisado-Clavero
- Unidad Docente Multiprofesional de Atención Familiar y Comunitaria Norte, Gerencia Asistencial Atención Primaria, Madrid Health Service, Madrid, Spain
| | - Lucía A. Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Laura Fratiglioni
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Concepción Violán
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain,Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitaria per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Mataró, Barcelona, Spain
| | - Amaia Calderón-Larrañaga
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
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14
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Koskinen M, Salmi JK, Loukola A, Mäkelä MJ, Sinisalo J, Carpén O, Renkonen R. Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates. Sci Rep 2022; 12:18492. [PMID: 36323789 PMCID: PMC9630271 DOI: 10.1038/s41598-022-23090-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/25/2022] [Indexed: 11/07/2022] Open
Abstract
The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.
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Affiliation(s)
- Miika Koskinen
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland
| | - Jani K. Salmi
- grid.15485.3d0000 0000 9950 5666Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland
| | - Anu Loukola
- grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland
| | - Mika J. Mäkelä
- grid.15485.3d0000 0000 9950 5666Division of Allergology, Skin and Allergy Hospital, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Juha Sinisalo
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.7737.40000 0004 0410 2071Heart and Lung Center, Helsinki University Hospital, and Helsinki University, Helsinki, Finland
| | - Olli Carpén
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland
| | - Risto Renkonen
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland
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15
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Nagamine T, Gillette B, Kahoun J, Burghaus R, Lippert J, Saxena M. Data-driven identification of heart failure disease states and progression pathways using electronic health records. Sci Rep 2022; 12:17871. [PMID: 36284167 PMCID: PMC9596465 DOI: 10.1038/s41598-022-22398-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 10/13/2022] [Indexed: 01/20/2023] Open
Abstract
Heart failure (HF) is a leading cause of morbidity, healthcare costs, and mortality. Guideline based segmentation of HF into distinct subtypes is coarse and unlikely to reflect the heterogeneity of etiologies and disease trajectories of patients. While analyses of electronic health records show promise in expanding our understanding of complex syndromes like HF in an evidence-driven way, limitations in data quality have presented challenges for large-scale EHR-based insight generation and decision-making. We present a hypothesis-free approach to generating real-world characteristics and progression patterns of HF. Patient disease state snapshots are extracted from the complaints mentioned in unstructured clinical notes. Typical disease states are generated by clustering and characterized in terms of their distinguishing features, temporal relationships, and risk of important clinical events. Our analysis generates a comprehensive "disease phenome" of real-world patients computed from large, noisy, secondary-use EHR datasets created in a routine clinical setting.
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Affiliation(s)
| | - Brian Gillette
- Department of Surgery, NYU Langone Long Island, Mineola, NY, USA
- Department of Foundations of Medicine, NYU Long Island School of Medicine, Mineola, NY, USA
| | - John Kahoun
- Droice Research, New York, NY, USA
- Clinical Informatics, CityMD, New York, NY, USA
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16
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Libertin CR, Kempaiah P, Gupta Y, Fair JM, van Regenmortel MHV, Antoniades A, Rivas AL, Hoogesteijn AL. Data structuring may prevent ambiguity and improve personalized medical prognosis. Mol Aspects Med 2022; 91:101142. [PMID: 36116999 DOI: 10.1016/j.mam.2022.101142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 01/17/2023]
Abstract
Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.
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Affiliation(s)
- Claudia R Libertin
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Yash Gupta
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Jeanne M Fair
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Marc H V van Regenmortel
- School of Biotechnology, Centre National de la Recherche Scientifique (CNRS), University of Strasbourg, France
| | | | - Ariel L Rivas
- Center for Global Health-Division of Infectious Diseases, School of Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Almira L Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, Yucatán, Mexico
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17
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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] [Abstract] [Grants] [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.
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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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18
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Network-medicine framework for studying disease trajectories in U.S. veterans. Sci Rep 2022; 12:12018. [PMID: 35835798 PMCID: PMC9283486 DOI: 10.1038/s41598-022-15764-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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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19
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Feelisch M, Cortese-Krott MM, Santolini J, Wootton SA, Jackson AA. Systems redox biology in health and disease. EXCLI JOURNAL 2022; 21:623-646. [PMID: 35721574 PMCID: PMC9203981 DOI: 10.17179/excli2022-4793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/16/2022] [Indexed: 12/31/2022]
Abstract
Living organisms need to be able to cope with environmental challenges and other stressors and mount adequate responses that are as varied as the spectrum of those challenges. Understanding how the multi-layered biological stress responses become integrated across and between different levels of organization within an organism can provide a different perspective on the nature and inter-relationship of complex systems in health and disease. We here compare two concepts which have been very influential in stress research: Selye's 'General Adaptation Syndrome' and Sies's 'Oxidative Stress' paradigm. We show that both can be embraced within a more general framework of 'change and response'. The 'Reactive Species Interactome' allows each of these to be considered as distinct but complementary aspects of the same system, representative of roles at different levels of organization within a functional hierarchy. The versatile chemistry of sulfur - exemplified by hydrogen sulfide, glutathione and proteinous cysteine thiols - enriched by its interactions with reactive oxygen, nitrogen and sulfur species, would seem to sit at the heart of the 'Redox Code' and underpin the ability of complex organisms to cope with stress.
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Affiliation(s)
- Martin Feelisch
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton and NIHR Biomedical Research Center, University Hospital Southampton, NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK
| | - Miriam M Cortese-Krott
- Myocardial Infarction Research Laboratory, Department of Cardiology, Pulmonology and Angiology, Medical Faculty, Heinrich Heine University of Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Jérôme Santolini
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette Cedex, France
| | - Stephen A Wootton
- Institute of Human Nutrition, University of Southampton and University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK
| | - Alan A Jackson
- Institute of Human Nutrition, University of Southampton and University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK
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20
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Yang H, Pawitan Y, Fang F, Czene K, Ye W. Biomarkers and Disease Trajectories Influencing Women's Health: Results from the UK Biobank Cohort. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:184-193. [PMID: 35578620 PMCID: PMC9096057 DOI: 10.1007/s43657-022-00054-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 02/02/2023]
Abstract
Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00054-1.
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Affiliation(s)
- Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public Health and Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Xue Yuan Road 1, University Town, Fuzhou, 350122 China ,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Weimin Ye
- Department of Epidemiology and Health Statistics, School of Public Health and Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Xue Yuan Road 1, University Town, Fuzhou, 350122 China ,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
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21
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Künnapuu K, Ioannou S, Ligi K, Kolde R, Laur S, Vilo J, Rijnbeek PR, Reisberg S. Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. JAMIA Open 2022; 5:ooac021. [PMID: 35571357 PMCID: PMC9097714 DOI: 10.1093/jamiaopen/ooac021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 03/05/2022] [Indexed: 11/14/2022] Open
Abstract
Objective To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions For the first time, there is a complete software package for detecting disease trajectories in health data.
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Affiliation(s)
| | - Solomon Ioannou
- Department of Medical Informatics, Erasmus University Medical
Center, Rotterdam, the Netherlands
| | - Kadri Ligi
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Sven Laur
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Jaak Vilo
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
- Quretec, Tartu, Estonia
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical
Center, Rotterdam, the Netherlands
| | - Sulev Reisberg
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
- Quretec, Tartu, Estonia
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22
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Jeong E, Osmundson S, Gao C, Edwards DRV, Malin B, Chen Y. Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106397. [PMID: 34530389 PMCID: PMC8551018 DOI: 10.1016/j.cmpb.2021.106397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acute and chronic diseases on forecasting NE, which could assist clinicians in choosing the best course of action to prevent NE or reduce its severity and complications. In this study, we aimed to engineer features based on acute and chronic diseases and assess the differences of the impact of acute and chronic diseases on NE prediction using machine learning models. MATERIALS AND METHODS We used ten years of electronic health records of mothers from a large academic medical center to develop three types of features: chronic disease, recurrence of an acute disease, and temporal relationships between acute diseases. Two types of NE prediction models, based on acute and chronic diseases, respectively, were trained with feature selection. We further compared the prediction performance of the models with two state-of-the-art NE forecasting models. The machine learning models ranked the three types of engineered features based on their contributions to the NE prediction. RESULTS The NE model trained on acute disease features showed significantly higher AUC than the model relying on chronic disease features (AUC difference: 0.161, p-value < 0.001). The NE model trained on both acute and chronic disease features achieved the highest average AUC (0.889), with a significant improvement over the best existing model (0.854) with p = 0.0129. Recurrence of "known or suspected fetal abnormality affecting management of mother (655)" was assigned the highest weights in predicting NE. CONCLUSIONS Machine learning models based on the three types of engineered features significantly improve NE prediction. Our results specifically suggest that acute disease-associated features play a more important role in predicting NE.
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Affiliation(s)
- Eugene Jeong
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah Osmundson
- Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Cheng Gao
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Digna R Velez Edwards
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bradley Malin
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, United States; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - You Chen
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.
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23
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Booij MM, van Noorden MS, van Vliet IM, Ottenheim NR, van der Wee NJA, Van Hemert AM, Giltay EJ. Dynamic time warp analysis of individual symptom trajectories in depressed patients treated with electroconvulsive therapy. J Affect Disord 2021; 293:435-443. [PMID: 34252687 DOI: 10.1016/j.jad.2021.06.068] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/20/2021] [Accepted: 06/27/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) effectively improves severity scores of depression, its effects on its individual symptoms has scarcely been studied. We aimed to study which depressive symptom trajectories dynamically cluster together in individuals as well as groups of patients during ECT using Dynamic Time Warp (DTW) analysis. METHODS We analysed the standardized weekly scores on the 25-item abbreviated version of the Comprehensive Psychopathological Rating Scale (CPRS) in depressed patients before and during their first six weeks of ECT treatment. DTW analysis was used to analyse the (dis)similarity of time series of items scores at the patient level (300 'DTW distances' per patient) as well as on the group level. Hierarchical cluster, network, and Distatis analyses yielded symptom dimensions. RESULTS We included 133 patients, 64.7% female, with an average age of 60.4 years (SD 15.1). Individual DTW distance matrices and networks revealed marked differences in hierarchical and network clusters among patients. Based on cluster analyses of the aggregated matrices, four symptom clusters emerged. In patients who reached remission, the average DTW distance between their symptoms was significantly smaller than non-remitters, reflecting denser symptom networks in remitters than non-remitters (p=0.04). LIMITATIONS The assessments were done only weekly during the first six weeks of ECT treatment. The use of individual items of the abbreviated CPRS may have led to measurement error as well as floor and ceiling effects. CONCLUSION DTW offers an efficient new approach to analyse symptom trajectories within individuals as well as groups of patients, aiding personalized medicine of psychopathology.
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Affiliation(s)
- Marijke M Booij
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | | | - Irene M van Vliet
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | | | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | - Albert M Van Hemert
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center (LUMC), the Netherlands.
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24
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Pinaire J, Aze J, Bringay S, Poncelet P, Genolini C, Landais P. Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years. Health Informatics J 2021; 27:14604582211033020. [PMID: 34474603 DOI: 10.1177/14604582211033020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009-2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause. We used these patterns to characterize hospital healthcare flows in a visualization tool. We clustered these trajectories with kmlShape to identify time gap and tariff profiles. ACS hospital healthcare flows have three key categories: Angina pectoris, Myocardial Infarction or Ischemia. Elderly flows were more complex. Time gap profiles showed that readmissions were closer together as time goes by. Tariff profiles were different according to age and initial event. Our approach might be applied to monitoring other chronic diseases. Further work is needed to integrate these results into a medical decision-making tool.
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Affiliation(s)
- Jessica Pinaire
- UPRES EA 2415, Clinical Research University Institute, France.,LIRMM, UMR 5506, Montpellier University, France
| | - Jérôme Aze
- LIRMM, UMR 5506, Montpellier University, France
| | - Sandra Bringay
- AMIS, Paul Valéry University, France.,LIRMM, UMR 5506, Montpellier University, France
| | | | - Christophe Genolini
- CeRSM (EA 2931), Paris Nanterre University, France.,Zébrys - ENAC (bâtiment Védrines), France
| | - Paul Landais
- UPRES EA 2415, Clinical Research University Institute, France
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25
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Doornenbal BM, Bakx R. Self-rated health trajectories: A dynamic time warp analysis. Prev Med Rep 2021; 24:101510. [PMID: 34430192 PMCID: PMC8371205 DOI: 10.1016/j.pmedr.2021.101510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 11/18/2022] Open
Abstract
Self-rated health (SRH), individuals’ overall perception of their health, is a key predictor of health events. To target disease prevention efforts, it is important to understand how SRH develops over time. The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping, a time series comparison technique initially developed for speech recognition. Revealing prototypic SRH trajectories can help direct disease prevention efforts towards trajectories that are more likely to result in adverse health events. Based on data from a Dutch representative sample of 2,154 individuals, our dynamic time warp analysis suggests that Dutch individuals do not typically show a steady growth or decline in SRH. Instead, we identified four relatively stable SRH trajectories that differed in average SRH. One of these trajectories is a path of consistent low SRH.
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Affiliation(s)
- Brian M. Doornenbal
- Leiden University Medical Center, the Netherlands
- Salut., the Netherlands
- Corresponding author at: Jansbuitensingel 7, 6811 AA Arnhem, the Netherlands.
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26
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Larvin H, Kang J, Aggarwal VR, Pavitt S, Wu J. Multimorbid disease trajectories for people with periodontitis. J Clin Periodontol 2021; 48:1587-1596. [PMID: 34409647 DOI: 10.1111/jcpe.13536] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/28/2021] [Accepted: 08/10/2021] [Indexed: 11/29/2022]
Abstract
AIM Periodontitis is a multifactorial condition linked to increased risk of systemic diseases. This study aimed to identify disease trajectories of people with periodontitis using the process mining technique as a heuristic approach. MATERIALS AND METHODS A total of 188,863 participants from the UK Biobank cohort were included. Self-reported oral health indicators (bleeding gums, painful gums, loose teeth) were surrogates for periodontitis at baseline. Systemic disease diagnoses and dates formed the process mining event log. Relative risk (RR) of systemic diseases, disease trajectories, and Cox proportional hazard ratio models for mortality were compared to age- and sex-matched controls who did not report a history of periodontitis. RESULTS Participants with loose teeth had shorter median time to most systemic diseases, and crude RR was increased for several diseases including cardiovascular disease (crude RR: 1.15, 95% CI: 1.03-1.28), hypertension (crude RR: 1.14, 95% CI: 1.05-1.24), and depression (crude RR: 1.33, 95% CI: 1.09-1.61). Participants with loose teeth had increased RR for 20 disease trajectories, though these were not significant after adjustments. Participants with bleeding/painful gums had similar disease trajectories as those of matched controls. CONCLUSIONS Self-reported periodontitis may be associated with early and frequent multimorbidity development, though further evidence is required to confirm this hypothesis. People with periodontitis should be informed of the risks of disease progression and be targeted in prevention initiatives.
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Affiliation(s)
| | - Jing Kang
- Oral Biology, School of Dentistry, University of Leeds, Leeds, UK
| | | | - Sue Pavitt
- School of Dentistry, University of Leeds, Leeds, UK
| | - Jianhua Wu
- School of Dentistry, University of Leeds, Leeds, UK.,Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
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27
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Strauss MJ, Niederkrotenthaler T, Thurner S, Kautzky-Willer A, Klimek P. Data-driven identification of complex disease phenotypes. J R Soc Interface 2021; 18:20201040. [PMID: 34314651 PMCID: PMC8315834 DOI: 10.1098/rsif.2020.1040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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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28
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Lee C, Rashbass J, van der Schaar M. Outcome-Oriented Deep Temporal Phenotyping of Disease Progression. IEEE Trans Biomed Eng 2021; 68:2423-2434. [PMID: 33259292 DOI: 10.1109/tbme.2020.3041815] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.
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29
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Jansana A, Poblador-Plou B, Gimeno-Miguel A, Lanzuela M, Prados-Torres A, Domingo L, Comas M, Sanz-Cuesta T, Del Cura-Gonzalez I, Ibañez B, Abizanda M, Duarte-Salles T, Padilla-Ruiz M, Redondo M, Castells X, Sala M. Multimorbidity clusters among long-term breast cancer survivors in Spain: Results of the SURBCAN study. Int J Cancer 2021; 149:1755-1767. [PMID: 34255861 DOI: 10.1002/ijc.33736] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 11/07/2022]
Abstract
The disease management of long-term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long-term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow-up. Five multimorbidity clusters were identified: C1-unspecific (29.9%), C2-metabolic and neurodegenerative (28.3%), C3-anxiety and fractures (9.7%), C4-musculoskeletal and cardiovascular (9.6%) and C5-thyroid disorders (5.3%). All clusters except C5-thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52-2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long-term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.
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Affiliation(s)
- Anna Jansana
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,European Higher Education Area Doctoral Program in Methodology of Biomedical Research and Public Health in Department of Pediatrics, Obstetrics and Gynecology, Preventive Medicine and Public Health, Universitat Autónoma de Barcelona (UAB), Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Beatriz Poblador-Plou
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Antonio Gimeno-Miguel
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Manuela Lanzuela
- Radiotherapy Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Alexandra Prados-Torres
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Laia Domingo
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Comas
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Teresa Sanz-Cuesta
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Madrid Health Service, Primary Care Research Unit, Calle San Martín de Porres, Madrid, Spain
| | - Isabel Del Cura-Gonzalez
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Madrid Health Service, Primary Care Research Unit, Calle San Martín de Porres, Madrid, Spain
| | - Berta Ibañez
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Navarrabiomed-Complejo Hospitalario de Navarra-Universidad Pública de Navarra, IdiSNA, Pamplona, Spain
| | - Mercè Abizanda
- Department of Organization and Communication, Parc Sanitari Pere Virgili, Barcelona, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Padilla-Ruiz
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Maximino Redondo
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Xavier Castells
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Autonomous University of Barcelona (UAB), Barcelona, Spain
| | - Maria Sala
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
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30
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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31
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Giannoula A, Centeno E, Mayer MA, Sanz F, Furlong LI. A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities. Bioinformatics 2021; 37:1435-1443. [PMID: 33185649 DOI: 10.1093/bioinformatics/btaa964] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 09/16/2020] [Accepted: 11/03/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. RESULTS In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. AVAILABILITY AND IMPLEMENTATION https://gitlab.com/agiannoula/diseasetrajectories. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexia Giannoula
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Emilio Centeno
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Miguel-Angel Mayer
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
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32
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Carrasco-Ribelles LA, Pardo-Mas JR, Tortajada S, Sáez C, Valdivieso B, García-Gómez JM. Predicting morbidity by local similarities in multi-scale patient trajectories. J Biomed Inform 2021; 120:103837. [PMID: 34119690 DOI: 10.1016/j.jbi.2021.103837] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 03/01/2021] [Accepted: 06/06/2021] [Indexed: 11/18/2022]
Abstract
Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.
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Affiliation(s)
- Lucía A Carrasco-Ribelles
- Biomedical Data Science Lab (BDSLAB), Instituto de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
| | - Jose Ramón Pardo-Mas
- Biomedical Data Science Lab (BDSLAB), Instituto de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Salvador Tortajada
- Instituto de Física Corpuscular (IFIC), Universitat de València, Consejo Superior de Investigaciones Científicas (CSIC), 46980 Paterna, Spain
| | - Carlos Sáez
- Biomedical Data Science Lab (BDSLAB), Instituto de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Bernardo Valdivieso
- Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 10, 46026 Valencia, Spain
| | - Juan M García-Gómez
- Biomedical Data Science Lab (BDSLAB), Instituto de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
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Gilson AS, Chartash D, Chang D, Hawk K, D'Onofrio G, Haimovich AD, Fiellin DA, Taylor RA. Analysis of Health Trajectories Leading to Adverse Opioid-Related Events. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2021; 2021:248-256. [PMID: 34457139 PMCID: PMC8378649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this article, we aim to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, we discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.
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Affiliation(s)
| | - David Chartash
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT
| | - David Chang
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT
| | - Kathryn Hawk
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT
| | - Gail D'Onofrio
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT
- Department of Medicine, Yale School of Medicine, New Haven, CT
| | - Adrian D Haimovich
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT
| | - David A Fiellin
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT
- Yale School of Public Health, New Haven, CT
- Department of Medicine, Yale School of Medicine, New Haven, CT
| | - R Andrew Taylor
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT
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Younan M, Elhoseny M, Ali AEMA, Houssein EH. Data Reduction Model for Balancing Indexing and Securing Resources in the Internet-of-Things Applications. IEEE INTERNET OF THINGS JOURNAL 2021; 8:5953-5972. [DOI: 10.1109/jiot.2020.3035248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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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] [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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Bonomi L, Fan L, Jiang X. Noise-tolerant similarity search in temporal medical data. J Biomed Inform 2020; 113:103667. [PMID: 33359112 DOI: 10.1016/j.jbi.2020.103667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 01/12/2023]
Abstract
Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate the design of preemptive interventions. For example, specific temporal patterns could be used to recognize low-prevalence diseases, which are often under-diagnosed. However, searching these patterns in temporal medical data is challenging, as the data are often noisy, complex, and large in scale. In this work, we propose an effective and efficient solution to search for patients who exhibit conditions that resemble the input query. In our solution, we propose a similarity notion based on the Longest Common Subsequence (LCSS), which is used to measure the similarity between the query and the patient's temporal medical data and to ensure robustness against noise in the data. Our solution adopts locality sensitive hashing techniques to address the high dimensionality of medical data, by embedding the recorded clinical events (e.g., medications and diagnosis codes) into compact signatures. To perform pattern search in large EHR datasets, we propose a filtering approach based on tandem patterns, which effectively identifies candidate matches while discarding irrelevant data. The evaluations conducted using a real-world dataset demonstrate that our solution is highly accurate while significantly accelerating the similarity search.
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Affiliation(s)
- Luca Bonomi
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, United States of America.
| | - Liyue Fan
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, United States of America.
| | - Xiaoqian Jiang
- UTHealth School of Biomedical Informatics, Houston, United States of America.
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Dagliati A, Plant D, Nair N, Jani M, Amico B, Peek N, Morgan AW, Isaacs J, Wilson AG, Hyrich KL, Geifman N, Barton A. Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs. Arthritis Rheumatol 2020; 72:1632-1642. [PMID: 32475078 DOI: 10.1002/art.41379] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 05/21/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28. METHODS Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each. RESULTS Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity. CONCLUSION Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.
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Affiliation(s)
- Arianna Dagliati
- Centre for Health Informatics and Manchester Molecular Pathology Innovation Centre, University of Manchester, Manchester, UK
| | - Darren Plant
- Versus Arthritis Centre for Genetics and Genomics, University of Manchester, NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, and Manchester Academic Health Science Centre, Manchester, UK
| | - Nisha Nair
- Versus Arthritis Centre for Genetics and Genomics, University of Manchester, Manchester, UK
| | - Meghna Jani
- Versus Arthritis Centre for Epidemiology, University of Manchester, Manchester, UK
| | | | - Niels Peek
- Centre for Health Informatics, University of Manchester, NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, and Manchester Academic Health Science Centre, Manchester, UK
| | - Ann W Morgan
- University of Leeds School of Medicine, NIHR Leeds Biomedical Research Centre, and Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - John Isaacs
- Translational and Clinical Research Institute, Newcastle University, and Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Anthony G Wilson
- Centre for Arthritis Research, Conway Institute, University College Dublin, Dublin, Ireland
| | - Kimme L Hyrich
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, and Versus Arthritis Centre for Epidemiology, University of Manchester, Manchester, UK
| | - Nophar Geifman
- Centre for Health Informatics and Manchester Molecular Pathology Innovation Centre, University of Manchester, Manchester, UK
| | - Anne Barton
- Versus Arthritis Centre for Genetics and Genomics, University of Manchester, NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, and Manchester Academic Health Science Centre, Manchester, UK
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Hassaine A, Salimi-Khorshidi G, Canoy D, Rahimi K. Untangling the complexity of multimorbidity with machine learning. Mech Ageing Dev 2020; 190:111325. [PMID: 32768443 PMCID: PMC7493712 DOI: 10.1016/j.mad.2020.111325] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/20/2022]
Abstract
The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.
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Affiliation(s)
- Abdelaali Hassaine
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Dexter Canoy
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
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Vlietstra WJ, Vos R, van den Akker M, van Mulligen EM, Kors JA. Identifying disease trajectories with predicate information from a knowledge graph. J Biomed Semantics 2020; 11:9. [PMID: 32819419 PMCID: PMC7439632 DOI: 10.1186/s13326-020-00228-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 08/12/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often diagnosed in patients in specific temporal sequences, which are referred to as disease trajectories. Here, we determine whether a sequence of two diseases forms a trajectory by leveraging the predicate information from paths between (disease) proteins in a knowledge graph. Furthermore, we determine the added value of directional information of predicates for this task. To do so, we create four feature sets, based on two methods for representing indirect paths, and both with and without directional information of predicates (i.e., which protein is considered subject and which object). The added value of the directional information of predicates is quantified by comparing the classification performance of the feature sets that include or exclude it. RESULTS Our method achieved a maximum area under the ROC curve of 89.8% and 74.5% when evaluated with two different reference sets. Use of directional information of predicates significantly improved performance by 6.5 and 2.0 percentage points respectively. CONCLUSIONS Our work demonstrates that predicates between proteins can be used to identify disease trajectories. Using the directional information of predicates significantly improved performance over not using this information.
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Affiliation(s)
- Wytze J. Vlietstra
- Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Rein Vos
- Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
- Department of Methodology & Statistics, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
| | - Marjan van den Akker
- Institute of General Practice, Johann Wolfgang Goethe University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
- Department of Family Medicine, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
| | - Erik M. van Mulligen
- Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
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Xu Z, Wang F, Adekkanattu P, Bose B, Vekaria V, Brandt P, Jiang G, Kiefer RC, Luo Y, Pacheco JA, Rasmussen LV, Xu J, Alexopoulos G, Pathak J. Subphenotyping depression using machine learning and electronic health records. Learn Health Syst 2020; 4:e10241. [PMID: 33083540 PMCID: PMC7556423 DOI: 10.1002/lrh2.10241] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/08/2020] [Accepted: 07/15/2020] [Indexed: 12/19/2022] Open
Abstract
Objective To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Materials and Methods Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. Results Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype. Conclusion Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.
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Affiliation(s)
- Zhenxing Xu
- Weill Cornell Medicine New York New York USA
| | - Fei Wang
- Weill Cornell Medicine New York New York USA
| | | | | | | | | | | | | | - Yuan Luo
- Northwestern University Chicago Illinois USA
| | | | | | - Jie Xu
- Weill Cornell Medicine New York New York USA
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Syed S, Baghal A, Prior F, Zozus M, Al-Shukri S, Syeda HB, Garza M, Begum S, Gates K, Syed M, Sexton KW. Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models. Healthc Inform Res 2020; 26:193-200. [PMID: 32819037 PMCID: PMC7438698 DOI: 10.4258/hir.2020.26.3.193] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 04/17/2020] [Indexed: 01/02/2023] Open
Abstract
Objectives The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs). Methods A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs. Results At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate. Conclusions TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.
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Affiliation(s)
- Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Ahmad Baghal
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Meredith Zozus
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Shaymaa Al-Shukri
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Hafsa Bareen Syeda
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Kim Gates
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Kevin W Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.,Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, USA.,Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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Dot T, Quijoux F, Oudre L, Vienne-Jumeau A, Moreau A, Vidal PP, Ricard D. Non-Linear Template-Based Approach for the Study of Locomotion. SENSORS 2020; 20:s20071939. [PMID: 32235667 PMCID: PMC7180476 DOI: 10.3390/s20071939] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/17/2020] [Accepted: 03/26/2020] [Indexed: 12/25/2022]
Abstract
The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.
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Affiliation(s)
- Tristan Dot
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Flavien Quijoux
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- ORPEA Group, F-92813 Puteaux, France
| | - Laurent Oudre
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- Correspondence: ; Tel.: +33-1-49-40-40-63
| | - Aliénor Vienne-Jumeau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Albane Moreau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
| | - Pierre-Paul Vidal
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Hangzhou Dianzi University, Hangzhou C-310005, China
| | - Damien Ricard
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Ecole de Santé des Armées, F-75005 Paris, France
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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: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [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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Huang M, Shah ND, Yao L. Evaluating global and local sequence alignment methods for comparing patient medical records. BMC Med Inform Decis Mak 2019; 19:263. [PMID: 31856819 PMCID: PMC6921442 DOI: 10.1186/s12911-019-0965-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background Sequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of similarity. For Electronic Health Records (EHR) data, sequence alignment helps to identify patients of similar disease trajectory for more relevant and precise prognosis, diagnosis and treatment of patients. Methods We tested two cutting-edge global sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA), together with their local modifications, DTW for Local alignment (DTWL) and Smith-Waterman algorithm (SWA), for aligning patient medical records. We also used 4 sets of synthetic patient medical records generated from a large real-world EHR database as gold standard data, to objectively evaluate these sequence alignment algorithms. Results For global sequence alignments, 47 out of 80 DTW alignments and 11 out of 80 NWA alignments had superior similarity scores than reference alignments while the rest 33 DTW alignments and 69 NWA alignments had the same similarity scores as reference alignments. Forty-six out of 80 DTW alignments had better similarity scores than NWA alignments with the rest 34 cases having the equal similarity scores from both algorithms. For local sequence alignments, 70 out of 80 DTWL alignments and 68 out of 80 SWA alignments had larger coverage and higher similarity scores than reference alignments while the rest DTWL alignments and SWA alignments received the same coverage and similarity scores as reference alignments. Six out of 80 DTWL alignments showed larger coverage and higher similarity scores than SWA alignments. Thirty DTWL alignments had the equal coverage but better similarity scores than SWA. DTWL and SWA received the equal coverage and similarity scores for the rest 44 cases. Conclusions DTW, NWA, DTWL and SWA outperformed the reference alignments. DTW (or DTWL) seems to align better than NWA (or SWA) by inserting new daily events and identifying more similarities between patient medical records. The evaluation results could provide valuable information on the strengths and weakness of these sequence alignment methods for future development of sequence alignment methods and patient similarity-based studies.
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Affiliation(s)
- Ming Huang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Nilay D Shah
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Lixia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
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Yang H, Pawitan Y, He W, Eriksson L, Holowko N, Hall P, Czene K. Disease trajectories and mortality among women diagnosed with breast cancer. Breast Cancer Res 2019; 21:95. [PMID: 31420051 PMCID: PMC6698019 DOI: 10.1186/s13058-019-1181-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 07/22/2019] [Indexed: 12/31/2022] Open
Abstract
Purpose Breast cancer is a common disease with a relatively good prognosis. Therefore, understanding the spectrum of diseases and mortality among breast cancer patients is important, though currently incomplete. We systematically examined the incidence and mortality of all diseases following a breast cancer diagnosis, as well as the sequential association of disease occurrences (trajectories). Methods In this national cohort study, 57,501 breast cancer patients (2001–2011) were compared to 564,703 matched women from the general Swedish population and followed until 2012. The matching criteria included year of birth, county of residence, and socioeconomic status. Based on information from the Swedish Patient and Cause of Death Registries, hazard ratios (HR) were estimated for disease incidence and mortality. Conditional logistic regression models were used to identify disease trajectories among breast cancer patients. Results Among 225 diseases, 45 had HRs > 1.5 and p < 0.0002 when comparing breast cancer patients with the general population. Diseases with highest HRs included lymphedema, radiodermatitis, and neutropenia, which are side effects of surgery, radiotherapy, and chemotherapy. Other than breast cancer, the only significantly increased cause of death was other solid cancers (HR = 1.16, 95% CI = 1.08–1.24). Two main groups of disease trajectories were identified, which suggest menopausal disorders as indicators for other solid cancers, and both neutropenia and dorsalgia as diseases and symptoms preceding death due to breast cancer. Conclusions While an increased incidence of other diseases was found among breast cancer patients, increased mortality was only due to other solid cancers. Preventing death due to breast cancer should be a priority to prolong life in breast cancer patients, but closer surveillance of other solid cancers is also needed. Electronic supplementary material The online version of this article (10.1186/s13058-019-1181-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Haomin Yang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.,Department of Oncology Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
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Capobianco E. Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology. J Clin Med 2019; 8:jcm8050664. [PMID: 31083565 PMCID: PMC6572295 DOI: 10.3390/jcm8050664] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/06/2019] [Accepted: 05/08/2019] [Indexed: 01/24/2023] Open
Abstract
Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes.
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Affiliation(s)
- Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL 33146, USA.
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Abstract
The article analyzes Bernoulli's binary sequences in the representation of empirical events about water usage and continuous expenditure systems. The main purpose is to identify among variables that constitute water resources consumption at public schools, the link between consumption and expenditures oscillations. It was obtained a theoretical model of how oscillations patterns are originated and how time lengths have an important role over expenditures oscillations ergodicity and non-ergodicity.
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Tényi Á, Vela E, Cano I, Cleries M, Monterde D, Gomez-Cabrero D, Roca J. Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective. BMJ Open Respir Res 2018; 5:e000302. [PMID: 29955364 PMCID: PMC6018856 DOI: 10.1136/bmjresp-2018-000302] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/22/2018] [Indexed: 02/06/2023] Open
Abstract
Introduction Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on healthcare. Identification of cost-effective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways explaining co-occurrence of diseases. Methods The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset. Results The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it. Conclusion The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients.
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Affiliation(s)
- Ákos Tényi
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Emili Vela
- Unitat d'Informació i Coneixement, Servei Catala de la Salut de la Generalitat de Catalunya, Barcelona, Catalunya, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Montserrat Cleries
- Unitat d'Informació i Coneixement, Servei Catala de la Salut de la Generalitat de Catalunya, Barcelona, Catalunya, Spain
| | - David Monterde
- Serveis Centrals, Institut Català de la Salut, Barcelona, Spain
| | - David Gomez-Cabrero
- Mucosal and Salivary Biology Division, King's College London Dental Institute, London, UK.,Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital and Science for Life Laboratory, Stockholm, Sweden.,Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
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