@article {Chao2020, title = {Composite diagnostic criteria are problematic for linking potentially distinct populations: the case of frailty}, journal = {Scientific Reports}, volume = {10}, year = {2020}, note = {cited By 0}, type = {Article}, abstract = {{Composite diagnostic criteria are common in frailty research. We worry distinct populations may be linked to each other due to complicated criteria. We aim to investigate whether distinct populations might be considered similar based on frailty diagnostic criteria. The Functional Domains Model for frailty diagnosis included four domains: physical, nutritive, cognitive and sensory functioning. Health and Retirement Study participants with two or more deficiencies in the domains were diagnosed frail. The survival distributions were analyzed using discrete-time survival analysis. The distributions of the demographic characteristics and survival across the groups diagnosed with frailty were significantly different (p < 0.05). A deficiency in cognitive functioning was associated with the worst survival pattern compared with a deficiency in the other domains (adjusted p < 0.05). The associations of the domains with mortality were cumulative without interactions. Cognitive functioning had the largest effect size for mortality prediction (Odds ratios}, keywords = {Frail Elderly, Frailty Phenotype, Residence Characteristics}, issn = {20452322}, doi = {10.1038/s41598-020-58782-1}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079339704\&doi=10.1038\%2fs41598-020-58782-1\&partnerID=40\&md5=ba7c890ffb416ce5b17f819b2c21936a}, author = {Yi-Sheng Chao and Chao-Jung Wu and Hsing-Chien Wu and Hui-Ting Hsu and Tsao, L.-C. and Cheng, Y.-P. and Lai, Y.-C. and Wei-Chih Chen} } @article {10701, title = {Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes}, journal = {Scientific Reports}, volume = {10}, year = {2020}, type = {Journal}, abstract = {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{\textquoteright} variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.}, keywords = {Epidemiology, Geriatrics}, isbn = {2045-2322}, doi = {10.1038/s41598-020-60869-8}, author = {Yi-Sheng Chao and Chao-Jung Wu and Hsing-Chien Wu and Hui-Ting Hsu and Tsao, Lien-Cheng and Cheng, Yen-Po and Lai, Yi-Chun and Wei-Chih Chen} }