Search Results (4)

Detection and timely intervention of acute kidney injury (AKI) is a major challenge worldwide. Electronic alerts for AKI may improve process- and patient-related endpoints. The present study evaluated the efficacy of an AKI electronic alert system and care bundle. This is a two-arm, prospective, cluster-randomized, controlled trial enrolling patients with AKI (KDIGO criteria) and cardiac diseases. Patients were randomly assigned to a routine care group or intervention group (DRKS-IDDRKS00017751). Two hundred patients (age 79 years, 46% female) were enrolled, with 100 patients in each group. The primary endpoint did not differ between patients in the routine care group 0.5 (−7.6–10.8) mL/min/1.73 m² versus patients in the intervention group 1.0 (−13.5–15.1) mL/min/1.73 m², p = 0.527. Proportions of patients in both study groups with hyperkalemia, pulmonary edema, and renal acidosis were comparable. The stop of antihypertensive medication during hypotensive periods was more frequent in patients in the intervention group compared to patients in the control group, p = 0.029. The AKI diagnosis and text module for AKI in the discharge letter were more frequently documented in patients in the intervention group (40%/48% vs. 25%/34%, p = 0.034; p = 0.044, respectively). Continued intake of RAAS inhibitors and the presence of a cardiac device were independently associated with a less pronounced decrease in eGFR from admission to the lowest value. In this RCT, electronic alerts for AKI and a care bundle improved process- but not patient-related endpoints. Full article

Background: Acute kidney injury (AKI) is a syndrome with heterogeneous causes and mechanisms. An early warning system (EWS) for AKI was created to reduce the incidence and improve outcomes. However, the benefits of AKI-EWS remain debatable. Methods: We launched a project to design and create AKI-EWS for inpatients in our institute. Incidence of AKI and its outcome before and after the implementation of AKI-EWS were collected for analysis. Results: We enlisted a stakeholder map before creating AKI-EWS. We then started an action plan for this initiative. The diagnosis was automatic and based on the definition of Kidney Disease: Improving Global Outcomes (KDIGO). The differential diagnosis of causes of AKI was also automatic. Users are to adjust the threshold of detection. After the implementation of this AKI-EWS, the incidence of AKI fell. The proportion of AKI > 4% was reduced significantly (47.7% and 41.6%, p = 0.010) in patients with serum creatinine measured. The proportion of AKI > 0.9% also dropped significantly (51.67% and 35.94%, p = 0.024) in all inpatients. Trends of AKI outcomes also showed improvement. The loading of consultation of nephrologists decreased by 15.5%. Conclusions: Through well-designed AKI-EWS, the incidence of AKI dropped, showing improved outcomes. The factors affecting benefits from AKI-EWS included high-risk identification (individual threshold detection), timely and automatic diagnosis, real-time alerting on electronic health information systems, fast self-diagnosing of the cause of AKI, and coverage of all inpatients. Full article

Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction. Full article

Acute kidney injury (AKI) is a common finding in kidney donors and recipients. AKI in kidney donor, which increases the risk of delayed graft function (DGF), may not by itself jeopardize the short- and long-term outcome of transplantation. However, some forms of AKI may induce graft rejection, fibrosis, and eventually graft dysfunction. Therefore, various strategies have been proposed to identify conditions at highest risk of AKI-induced DGF, that can be treated by targeting the donor, the recipient, or even the graft itself with the use of perfusion machines. AKI that occurs early post-transplant after a period of initial recovery of graft function may reflect serious and often occult systemic complications that may require prompt intervention to prevent graft loss. AKI that develops long after transplantation is often related to nephrotoxic drug reactions. In symptomatic patients, AKI is usually associated with various systemic medical complications and could represent a risk of mortality. Electronic systems have been developed to alert transplant physicians that AKI has occurred in a transplant recipient during long-term outpatient follow-up. Herein, we will review most recent understandings of pathophysiology, diagnosis, therapeutic approach, and short- and long-term consequences of AKI occurring in both the donor and in the kidney transplant recipient. Full article