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Filename: drivers/Session_files_driver.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: strpos
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Filename: helpers/my_audit_helper.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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Introduction: It is mentioned that the acute renal angina index (aRAI), a new concept, can be used in emergency departments to calculate and accurately predict the risk of developing acute kidney injury (AKI). The aims of the study included: to evaluate the predictive performance of the aRAI (AKI risk classification tool) in predicting AKI in the pediatric emergency department.
Method: Patients who met the criteria for systemic inflammatory response syndrome were examined. AKI was defined with creatinine N1.5× baseline 24-72 hours after hospitalization. aRAI and original RAI scores were calculated for patients and were shown as renal angina positive (RA+) above a population-derived threshold. The performance of aRAI in predicting AKI compared to changes in creatinine and original RAI was evaluated.
Results: In total, 241 eligible subjects were enrolled. The median age of the patients was 17 months (min-max 1-192). AKI developed in 60 (24.8%) of the patients. According to the aRAI, 76 (31.5%) of 241 patients were RA(+). The aRAI had an NPV of 1.00 and an AUC of 0.948 (0.914-0.983) for the prediction of AKI. Sensitivity was 95% for the aRAI as compared to 48% for an elevation in SCr noted to be at least two times greater than the baseline while in the PED and 61% for original RAI.
Conclusions: The aRAI is easily computable, does not depend on complex computational or derivation methods, and is universally accessible. We confirm and extend the findings of previous study reporting the performance of RAI for early prediction of AKI.
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http://dx.doi.org/10.1080/17843286.2022.2031667 | DOI Listing |
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