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10.1371/journal.pone.0187825 http://scihub22266oqcxt.onion/10.1371/journal.pone.0187825 C5690585!5690585!29145407     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29145407&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (11): ä Nephropedia Template TP {{tp|p=29145407|t=2017. Predicting mortality and hospitalization of older adults by the multimorbidity frailty index |pdf=|usr=}}{{29145407}} gab.com Text Predicting mortality and hospitalization of older adults by the multimorbidity frailty index JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29145407 #FOAvip Background: Existing operational definitions of frailty are personnel-costly and time-consuming, resulting in estimates with a small sample size that cannot be generalized to the population level. The objectives were to develop a multimorbidity frailty index using Taiwan?s claim database, and to understand its ability to predict adverse event. Methods: This is a retrospective cohort study. Subjects aged 65 to 100 years who have full National Health Insurance coverage in 2005 were included. We constructed the multimorbidity frailty index using cumulative deficit approach and categorized study population according to the multimorbidity frailty index quartiles: fit, mild frailty, moderate frailty and severe frailty. The multimorbidity frailty index included deficits from outpatient and inpatient diagnosis. Associations with all-cause mortality, unplanned hospitalization and intensive care unit admission were assessed using Kaplan-Meier curves and Cox regression analyses. Results: The multimorbidity frailty index incorporated 32 deficits, with mean multimorbidity frailty index score of 0.052 (standard deviation = 0.060) among 86,133 subjects included. Compared to subjects in fit category, subjects with severe frailty were associated with a 5.0-fold (adjusted hazard ratio, aHR 4.97; 95% confidence interval, 95% CI 4.49?5.50) increased risk of death at 1 year after adjusting for age and gender. Subjects with moderate frailty or mild frailty was associated with 3.1- (adjusted HR 3.08; 95% CI 2.80?3.39) or 1.9- (adjusted HR 1.86; 95% CI 1.71?2.01) folds increased risk, respectively.4.49?5.50). The risk trend of unplanned hospitalization and intensive care unit admission is similar among the study population. Besides, the association between the frailty categories and all three outcomes was slightly stronger among women. Conclusion: The multimorbidity frailty index was highly associated with all-cause mortality, unplanned hospitalization and ICU admission. It could serve as an efficient tool for stratifying older adults into different risk groups for planning care management programs. Twit Text FOAVip Predicting mortality and hospitalization of older adults by the multimorbidity frailty index ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29145407 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29145407 #FOAvip English Wikipedia <ref>{{Cite pmid|29145407}}</ref> Predicting mortality and hospitalization of older adults by the multimorbidity frailty index #MMPMID29145407 Wen YC; Chen LK; Hsiao FY PLoS One 2017[]; 12 (11): ä PMID29145407show ga Background: Existing operational definitions of frailty are personnel-costly and time-consuming, resulting in estimates with a small sample size that cannot be generalized to the population level. The objectives were to develop a multimorbidity frailty index using Taiwan?s claim database, and to understand its ability to predict adverse event. Methods: This is a retrospective cohort study. Subjects aged 65 to 100 years who have full National Health Insurance coverage in 2005 were included. We constructed the multimorbidity frailty index using cumulative deficit approach and categorized study population according to the multimorbidity frailty index quartiles: fit, mild frailty, moderate frailty and severe frailty. The multimorbidity frailty index included deficits from outpatient and inpatient diagnosis. Associations with all-cause mortality, unplanned hospitalization and intensive care unit admission were assessed using Kaplan-Meier curves and Cox regression analyses. Results: The multimorbidity frailty index incorporated 32 deficits, with mean multimorbidity frailty index score of 0.052 (standard deviation = 0.060) among 86,133 subjects included. Compared to subjects in fit category, subjects with severe frailty were associated with a 5.0-fold (adjusted hazard ratio, aHR 4.97; 95% confidence interval, 95% CI 4.49?5.50) increased risk of death at 1 year after adjusting for age and gender. Subjects with moderate frailty or mild frailty was associated with 3.1- (adjusted HR 3.08; 95% CI 2.80?3.39) or 1.9- (adjusted HR 1.86; 95% CI 1.71?2.01) folds increased risk, respectively.4.49?5.50). The risk trend of unplanned hospitalization and intensive care unit admission is similar among the study population. Besides, the association between the frailty categories and all three outcomes was slightly stronger among women. Conclusion: The multimorbidity frailty index was highly associated with all-cause mortality, unplanned hospitalization and ICU admission. It could serve as an efficient tool for stratifying older adults into different risk groups for planning care management programs. äPredicting mortality and hospitalization of older adults by the multim(epmc).pdf DeepDyve Pubget Overpricing