Electronic Medical Record-Based Predictive Model for Acute Kidney Injury in an Acute Care Hospital

Patients with acute kidney injury (AKI) are at risk for increased morbidity and mortality. Lack of specific treatment has meant that efforts have focused on early diagnosis and timely treatment. Advanced algorithms for clinical assistance including AKI prediction models have potential to provide accurate risk estimates. In this project, we aim to provide a clinical decision supporting system (CDSS) based on a self-learning predictive model for AKI in patients of an acute care hospital. Data of all in-patient episodes in adults admitted will be analysed using "data mining" techniques to build a prediction model. The subsequent machine-learning process including two algorithms for data stream and concept drift will refine the predictive ability of the model. Simulation studies on the model will be used to quantify the expected impact of several scenarios of change in factors that influence AKI incidence. The proposed dynamic CDSS will apply to future in-hospital AKI surveillance in clinical practice

Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery—development and validation of a risk score and effect of acute kidney injury on survival:observational cohort study

Funding: This study was funded by Tenovus Tayside, Chief Scientist Office, Scotland and a travelling fellowship from the Royal College of Physicians and Surgeons of Glasgow. The funders had no role in the study design; collection, analysis, and interpretation of the data; writing of the report; or the decision to submit the article for publication. The researchers are independent of the funders.Non peer reviewedPublisher PD

Continued monitoring of acute kidney injury survivors might not be necessary in those regaining an estimated glomerular filtration rate > 60 mL/min at 1 year

Background. Severe acute kidney injury (AKI) among hospitalized patients often necessitates initiation of short-term dialysis. Little is known about the long-term outcome of those who recover to normal renal function. The aim of this study was to determine the long-term renal outcome of patients experiencing AKI requiring dialysis secondary to hypoperfusion injury and/or sepsis who recovered to apparently normal renal function. Methods. All adult patients with AKI requiring dialysis in our centre between 1 January 1980 and 31 December 2010 were identified. We included patients who had estimated glomerular filtration rate (eGFR) >60 mL/min/1.73 m2 12 months or later after the episode of AKI. Patients were followed up until 3 March 2015. The primary outcome was time to chronic kidney disease (CKD) (defined as eGFR persistently <60 mL/min/1.73 m2) from first dialysis for AKI. Results. Among 2922 patients with a single episode of dialysis-requiring AKI, 396 patients met the study inclusion criteria. The mean age was 49.8 (standard deviation 16.5) years and median follow-up was 7.9 [interquartile range (IQR) 4.8–12.7] years. Thirty-five (8.8%) of the patients ultimately developed CKD after a median of 5.3 (IQR 2.8–8.0) years from first dialysis for AKI giving an incidence rate of 1 per 100 person-years. Increasing age, diabetes and vascular disease were associated with higher risk of progression to CKD [adjusted hazard ratios (95% confidence interval): 1.06 (1.03, 1.09), 3.05 (1.41, 6.57) and 3.56 (1.80, 7.03), respectively]. Conclusions. Recovery from AKI necessitating in-hospital dialysis was associated with a very low risk of progression to CKD. Most of the patients who progressed to CKD had concurrent medical conditions meriting monitoring of renal function. Therefore, it seems unlikely that regular follow-up of renal function is beneficial in patients who recover to eGFR >60 mL/min/1.73 m2 by 12 months after an episode of AKI