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: Recent research has focused on the relationship between heart failure (HF) symptom clusters and outcomes, including mortality, hospitalization, functional status, and quality of life. No known studies to date have explored the role of physical HF symptom clusters and delays in seeking treatment.
Objectives: Describe physical symptom clusters in a population of HF patients and determine if a specific cluster is predictive of delay in seeking treatment for HF symptoms.
Method: We analyzed combined data from two studies ( n = 406) collected during acute HF hospitalization. The Heart Failure Somatic Awareness Scale quantified physical HF symptoms. Delay, measured in days, was collected from the medical record and confirmed by interview. Hierarchical agglomerative clustering techniques determined physical HF symptom clusters. Hierarchical multiple regression analysis was computed to explore predictors of delay.
Results: Participants were primarily White, male sex older adults. Three physical HF symptom clusters were identified: discordant, edema-related, and dyspnea-related symptoms. Hierarchical multiple regression analysis revealed in Step 1 that age was a significant predictor of delay.
Discussion: Our findings provide valuable insight into the role of physical symptom clusters on delay in persons with HF. Through agglomerative hierarchical clustering techniques, we found three physical HF symptom clusters that were then used to determine differences in cluster membership by demographic and clinical variables. Significant age differences were noted by cluster membership with youngest older adults in a discordant symptom cluster.
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Source |
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http://dx.doi.org/10.1097/NNR.0000000000000755 | DOI Listing |
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