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: Detection of acute (pre-seroconversion) HIV infection (AHI), the phase of highest transmission risk, requires resource-intensive RNA- or antigen-based detection methods that can be infeasible for routine use. Risk score algorithms can improve the efficiency of AHI detection by identifying persons at highest risk of AHI for prioritized RNA/antigen testing, but prior algorithms have not considered geospatial information, potential differences by sex, or current antibody testing paradigms.
Methods: We used elastic net models to develop sex-stratified risk score algorithms in a case-control study of persons (136 with AHI, 250 without HIV) attending a sexually transmitted infections (STI) clinic in Lilongwe, Malawi from 2015 to 2019. We designed algorithms for varying clinical contexts according to three levels of data availability: 1) routine demographic and clinical information, 2) behavioral and occupational data obtainable through patient interview, and 3) geospatial variables requiring external datasets or field data collection. We calculated sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to assess model performance and developed a web application to support implementation.
Results: The highest-performing AHI risk score algorithm for men (AUC=0.74) contained five variables (condom use, body aches, fever, rash, genital sores/ulcers) from the first two levels of data availability. The highest-performing algorithm for women (AUC=0.81) contained fifteen variables from all three levels of data availability. A risk score cut-point of 0.26 had an AHI detection sensitivity of 93% and specificity of 27% for males, and a cut-point of 0.15 had 97% sensitivity and 44% specificity for females. Additional models are available in the web application.
Conclusion: Risk score algorithms can facilitate efficient AHI detection in STI clinic settings, creating opportunities for HIV transmission prevention interventions during this critical period of elevated transmission risk.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1097/QAI.0000000000003519 | DOI Listing |
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