Nursing · 2025
Leveraging electronic health records for detection of acute kidney injury in critical care units.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Arar Mays Mohammad, Khalil Amani A
Аннотация
PURPOSE: To assess the incidence of acute kidney injury (AKI) and identify associated risk factors using Kidney Disease Improving Global Outcomes (KDIGO) criteria through the analysis of electronic health record (EHR) data in ICUs.
METHODS: A retrospective observational study was conducted using EHR data from 563 adult ICU patients admitted to a large public hospital in Jordan between January and December 2019, analyzing demographic, clinical, and medication variables through univariate and multivariate logistic regression.
RESULTS: The incidence of AKI was 24.5%, with significant risk factors including advanced age, respiratory disorders, post-CPR status, electrolyte imbalances, and use of specific medications such as inotropes, vasopressors, mannitol, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and glycopeptide antibiotics.
CONCLUSION: This study, leveraging EHR data, identified key predictors of AKI in critically ill patients, highlighting the potential of using high-content analytic techniques on comprehensive datasets to improve early detection and prevention strategies in ICU settings.
METHODS: A retrospective observational study was conducted using EHR data from 563 adult ICU patients admitted to a large public hospital in Jordan between January and December 2019, analyzing demographic, clinical, and medication variables through univariate and multivariate logistic regression.
RESULTS: The incidence of AKI was 24.5%, with significant risk factors including advanced age, respiratory disorders, post-CPR status, electrolyte imbalances, and use of specific medications such as inotropes, vasopressors, mannitol, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and glycopeptide antibiotics.
CONCLUSION: This study, leveraging EHR data, identified key predictors of AKI in critically ill patients, highlighting the potential of using high-content analytic techniques on comprehensive datasets to improve early detection and prevention strategies in ICU settings.