Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 04 2020
08 04 2020
Historique:
received:
17
06
2019
accepted:
13
02
2020
entrez:
10
4
2020
pubmed:
10
4
2020
medline:
26
11
2020
Statut:
epublish
Résumé
Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PC-based and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most death-averse 4-item equal-weight syndromes. In addition to chance alone, input symptoms' variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.
Identifiants
pubmed: 32269245
doi: 10.1038/s41598-020-60869-8
pii: 10.1038/s41598-020-60869-8
pmc: PMC7142157
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5357Références
Merriam, W. Merriam-Webster’s collegiate dictionary. (Merriam-Webster, 2004).
Venes, D. Taber’s cyclopedic medical dictionary. (FA Davis, 2017).
New Journal for Syndrome Identification. Radiology 106, 626–626, https://doi.org/10.1148/106.3.626 (1973).
Kendler, K. S., Munoz, R. A. & Murphy, G. The development of the Feighner criteria: a historical perspective. The American journal of psychiatry 167, 134–142, https://doi.org/10.1176/appi.ajp.2009.09081155 (2010).
doi: 10.1176/appi.ajp.2009.09081155
pubmed: 20008944
pmcid: 20008944
Feighner, J. P. et al. Diagnostic criteria for use in psychiatric research. Arch. Gen. Psychiatry 26, 57–63 (1972).
doi: 10.1001/archpsyc.1972.01750190059011
Teebi, A. S. Naming of a syndrome: The story of “Adam Wright” syndrome. American Journal of Medical Genetics Part A 125A, 329–330, https://doi.org/10.1002/ajmg.a.20460 (2004).
doi: 10.1002/ajmg.a.20460
pubmed: 14994249
pmcid: 14994249
Low, D. E. Toxic Shock Syndrome: Major Advances in Pathogenesis, But Not Treatment. Critical Care Clinics 29, 651–675, https://doi.org/10.1016/j.ccc.2013.03.012 (2013).
doi: 10.1016/j.ccc.2013.03.012
pubmed: 23830657
pmcid: 23830657
Aguilar-Salinas, C. A. et al. The metabolic syndrome: a concept in evolution. Gaceta medica de Mexico 140(Suppl 2), S41–48 (2004).
pubmed: 15641471
pmcid: 15641471
Sattar, N. et al. Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies. The Lancet 371, 1927–1935, https://doi.org/10.1016/S0140-6736(08)60602-9 (2008).
doi: 10.1016/S0140-6736(08)60602-9
Kahn, R. Metabolic syndrome-what is the clinical usefulness? The Lancet 371, 1892–1893, https://doi.org/10.1016/S0140-6736(08)60731-X (2008).
doi: 10.1016/S0140-6736(08)60731-X
Cigolle, C. T., Ofstedal, M. B., Tian, Z. & Blaum, C. S. Comparing models of frailty: the Health and Retirement Study. J. Am Geriatr. Soc. 57, 830–839, https://doi.org/10.1111/j.1532-5415.2009.02225.x (2009).
doi: 10.1111/j.1532-5415.2009.02225.x
pubmed: 19453306
pmcid: 19453306
Chao, Y.-S., Wu, H.-C., Wu, C.-J. & Chen, W.-C. Index or illusion: The case of frailty indices in the Health and Retirement Study. PLOS ONE 13, e0197859, https://doi.org/10.1371/journal.pone.0197859 (2018).
doi: 10.1371/journal.pone.0197859
pubmed: 30020923
pmcid: 30020923
Fried, L. P. et al. Frailty in Older Adults: Evidence for a Phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 56, M146–M157, https://doi.org/10.1093/gerona/56.3.M146 (2001).
doi: 10.1093/gerona/56.3.M146
Searle, S. D., Mitnitski, A., Gahbauer, E. A., Gill, T. M. & Rockwood, K. A standard procedure for creating a frailty index. BMC Geriatrics 8, 24, https://doi.org/10.1186/1471-2318-8-24 (2008).
doi: 10.1186/1471-2318-8-24
pubmed: 18826625
pmcid: 18826625
Rockwood, K., Andrew, M. & Mitnitski, A. A comparison of two approaches to measuring frailty in elderly people. J. Gerontol. A. Biol. Sci. Med. Sci. 62, 738–743 (2007).
doi: 10.1093/gerona/62.7.738
Rockwood, K. & Mitnitski, A. Frailty in relation to the accumulation of deficits. J. Gerontol. Biol. Sci. Med. Sci. 62A, https://doi.org/10.1093/gerona/62.7.722 (2007).
Rockwood, K., Abeysundera, M. J. & Mitnitski, A. How should we grade frailty in nursing home patients? J. Am. Med. Dir. Assoc. 8, https://doi.org/10.1016/j.jamda.2007.07.012 (2007).
Chao, Y.-S., McGolrick, D., Wu, C.-J., Wu, H.-C. & Chen, W.-C. A proposal for a self-rated frailty index and status for patient-oriented research. BMC Research Notes 12, 172, https://doi.org/10.1186/s13104-019-4206-3 (2019).
doi: 10.1186/s13104-019-4206-3
pubmed: 6434809
pmcid: 6434809
National Institute on Ageing. (National Institute on Ageing, Toronto, ON, 2018).
Chao, Y.-S. & Wu, C.-J. Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011. PLoS ONE 12, e0183997, https://doi.org/10.1371/journal.pone.0183997 (2017).
doi: 10.1371/journal.pone.0183997
pubmed: 28886057
pmcid: 28886057
Easterbrook, P. J., Gopalan, R., Berlin, J. A. & Matthews, D. R. Publication bias in clinical research. The Lancet 337, 867–872 (1991).
doi: 10.1016/0140-6736(91)90201-Y
Chao, Y.-S., Wu, H.-C., Wu, C.-J. & Chen, W.-C. Principal Component Approximation and Interpretation in Health Survey and Biobank Data. Frontiers in Digital Humanities 5, https://doi.org/10.3389/fdigh.2018.00011 (2018).
Chao, Y.-S., Wu, H.-C., Wu, C.-J. & Chen, W.-C. Index or illusions: the case of frailty indices in the Health and Retirement Study (Centre de recherche du centre hospitalier de l’Université de Montréal, 2017).
Strawbridge, W. J., Shema, S. J., Balfour, J. L., Higby, H. R. & Kaplan, G. A. Antecedents of frailty over three decades in an older cohort. J. Gerontol. B. Psychol. Sci. Soc. Sci. 53, S9–16 (1998).
doi: 10.1093/geronb/53B.1.S9
Mitnitski, A. & Rockwood, K. Transitions in cognitive test scores over 5 and 10 years in elderly people: evidence for a model of age-related deficit accumulation. BMC Geriatr 8, https://doi.org/10.1186/1471-2318-8-3 (2008).
Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D. & Anderson, G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 59, M255–M263 (2004).
doi: 10.1093/gerona/59.3.M255
Vetrano, D. L. et al. Frailty and multimorbidity: a systematic review and meta-analysis. The Journals of Gerontology: Series A, gly110 (2018).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. (Springer New York, 2009).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: with Applications in R. (Springer New York, 2013).
Park, S. H. & Gordon, M. E. Publication records and tenure decisions in the field of strategic management. Strategic Management Journal 17, 109–128 (1996).
doi: 10.1002/(SICI)1097-0266(199602)17:2<109::AID-SMJ796>3.0.CO;2-P
Cooper, A. The use of online strategies and social media for research dissemination in education. education policy analysis archives 22, 88 (2014).
Osterrieder, A. The value and use of social media as communication tool in the plant sciences. Plant methods 9, 26 (2013).
doi: 10.1186/1746-4811-9-26
(ed Produced and distributed by the University of Michigan with funding from the National Institute on Aging (grant number NIA U01AG009740)). (Ann Arbor, MI, 2016).
Juster, F. T. & Suzman, R. An Overview of the Health and Retirement Study. The Journal of Human Resources 30, S7–S56, https://doi.org/10.2307/146277 (1995).
doi: 10.2307/146277
RAND Corporation. RAND HRS Data Files, supported by NIA and SSA, http://www.rand.org/labor/aging/dataprod/hrs-data.html (2016).
Everitt, B. S. & Hothorn, T. In A Handbook of Statistical Analyses Using R Ch. 13, 215-226 (Chapman and Hall/CRC, 2006).
He, R., Hu, B., Yuan, X. & Zheng, W.-S. Principal component analysis based on non-parametric maximum entropy. Neurocomputing 73, 1840–1852, https://doi.org/10.1016/j.neucom.2009.12.032 (2010).
doi: 10.1016/j.neucom.2009.12.032
Jolliffe, I. T. Principal Component Analysis. (Springer New York, 2013).
Oja, H. Multivariate Nonparametric Methods with R: An approach based on spatial signs and ranks. (Springer New York, 2010).
Lumley, T. In Complex Surveys 1–15 (John Wiley & Sons, Inc., 2010).
Lumley, T. Analysis of Complex Survey Samples. Journal of Statistical Software 9, 19, https://doi.org/10.18637/jss.v009.i08 (2004).
doi: 10.18637/jss.v009.i08
Chao, Y. S., Wu, H. C., Wu, C. J. & Chen, W. C. Stages of Biological Development across Age: An Analysis of Canadian Health Measure Survey 2007–2011. Front Public Health 5, https://doi.org/10.3389/fpubh.2017.00355 , eCollection 2017. (2018).
Chao, Y. S., Wu, H. T. & Wu, C. J. Feasibility of Classifying Life Stages and Searching for the Determinants: Results from the Medical Expenditure Panel Survey 1996–2011. Front Public Health 5:247, https://doi.org/10.3389/fpubh.2017.00247 . eCollection 2017 (2017).
Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (Springer, 2001).
Moon, J. R., Glymour, M. M., Subramanian, S. V., Avendaño, M. & Kawachi, I. Transition to retirement and risk of cardiovascular disease: Prospective analysis of the US Health and Retirement Study. Social science & medicine (1982) 75, 526–530, https://doi.org/10.1016/j.socscimed.2012.04.004 (2012).
doi: 10.1016/j.socscimed.2012.04.004
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2016).
RStudio: Integrated Development for R (RStudio, Inc., Boston, MA, 2016).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57, 289–300 (1995).