Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study

Bedford, M., Stevens, P., Coulton, S., Billings, J., Farr, M., Wheeler, T., Kalli, M., Mottishaw, T. and Farmer, C. (2016) Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study. Health Service Delivery Research, 4 (6). ISSN 2050-4349.

[img]
Preview
PDF
AKI paper.pdf - Accepted Version

Download (2MB) | Preview

Abstract

Background: Acute kidney injury (AKI) is a common clinical problem with significant morbidity and mortality. All hospitalised patients are at risk. AKI is often preventable and reversible; however, the 2009 National Confidential Enquiry into Patient Outcome and Death highlighted systematic failings of identification and management, and recommended risk assessment of all emergency admissions.

Objectives: To develop three predictive models to stratify the risk of (1) AKI on arrival in hospital;
(2) developing AKI during admission; and (3) worsening AKI if already present; and also to (4) develop a clinical algorithm for patients admitted to hospital and explore effective methods of delivery of this information at the point of care.

Study design: Quantitative methodology (1) to formulate predictive risk models and (2) to validate the models in both our population and a second population. Qualitative methodology to plan clinical decision support system (CDSS) development and effective integration into clinical care.

Data analysis: Quantitative – both traditional and Bayesian regression methods were used. Traditional methods were performed using ordinal logistic regression with univariable analyses to inform the development of multivariable analyses. Backwards selection was used to retain only statistically significant variables in the final models. The models were validated using actual and predicted probabilities, an area under the receiver operating characteristic (AUROC) curve analysis and the Hosmer–Lemeshow test. Qualitative – content analysis was employed.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
R Medicine > RB Pathology
Divisions: Faculty of Social and Applied Sciences > The Business School
Depositing User: Maria Kalli
Date Deposited: 26 Jan 2017 13:53
Last Modified: 27 Jan 2017 04:40
URI: https://create.canterbury.ac.uk/id/eprint/15481

Actions (login required)

Update Item (CReaTE staff only) Update Item (CReaTE staff only)

Downloads

Downloads per month over past year

View more statistics

Share

Connect with us

Last edited: 29/06/2016 12:23:00