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
Objective: Acute coronary syndromes (ACS) are a diagnostic challenge for Emergency Medicine (EM) clinicians. To help clinicians assess patients with non-ST-elevation ACS (NSTEACS), clinical decision aids have been developed, combining clinical history, cardiac troponin and the electrocardiograph (ECG). These models ask the clinician to subjectively assess the ECG variable, introducing reliability issues. We set out to derive an ECG model that would provide an objective measure for ischaemia using non-ST-elevation myocardial infarction (NSTEMI) as the primary outcome.
Methods: We derived an ECG model in a retrospective Emergency Department cohort using logistic regression with a primary outcome of NSTEMI. All patients presented with signs or symptoms suggestive of an ACS. The model was validated in a multi-centre prospective Emergency Department cohort.
Results: Derivation included 1246 patients, 156 (12.5%) had the primary outcome; validation included 1139 patients, 170 (14.9%) had the primary outcome. Derivation demonstrated Sn 25.6% (95% CI 19.0-33.2), Sp 96.3% (95% CI 95.0-97.4), PPV 50.0% (95% CI 40.0-60.0) and NPV 90.1% (95% CI 89.2-90.9). Validation demonstrated Sn 23.5% (95% CI 17.4% to 30.6%), Sp 95.2% (95% CI 93.6% to 96.4%), PPV 46.0% (95% CI 36.6% to 55.7%) and NPV 87.6% (95% CI 86.7% to 88.5%).
Conclusion: We have derived and validated an ECG model that is highly specific for NSTEMI and may be suitable for integration into existing clinical decision aids.
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Source |
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http://dx.doi.org/10.1016/j.ajem.2022.04.016 | DOI Listing |
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