Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Understanding how components of frailty change over time and how they can be modeled as time-dependent predictors of mortality could lead to better risk prediction in the dialysis population.
Methods: We measured frailty at baseline, 12 months, and 24 months among 727 patients receiving hemodialysis in Northern California and Atlanta. We examined the likelihood of meeting frailty components (weight loss, exhaustion, low physical activity, weak grip strength, and slow gait speed) as a function of time in logistic regression analysis and association of frailty components with mortality in time-updated multivariable Cox models.
Results: Physical activity and gait speed declined, exhaustion and grip strength did not change, and the odds of meeting the weight loss criterion declined with time. All five components were associated with higher mortality in multivariable analyses, but gait speed was the strongest individual predictor. All frailty components except physical inactivity were independently associated with mortality when all five components were included in the same model. The number of frailty components met was associated with mortality in a gradient that ranged from a hazard ratio of 2.73 for one component to 10.07 for five components met; the model including all five components was the best model based on Akaike information criterion.
Conclusions: Measurement of all frailty components was necessary for optimal mortality prediction, and the number of components met was strongly associated with mortality in this cohort.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6376100 | PMC |
http://dx.doi.org/10.1093/gerona/gly206 | DOI Listing |
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