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). https://doi.org/10.3310/hsdr04060
|Authors||Bedford, M., Stevens, P., Coulton, S., Billings, J., Farr, M., Wheeler, T., Kalli, M., Mottishaw, T. and Farmer, C.|
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;
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.
|Journal||Health Service Delivery Research|
|Journal citation||4 (6)|
|Publisher||National Institute of Health Research|
|Digital Object Identifier (DOI)||https://doi.org/10.3310/hsdr04060|
|23 Feb 2016|
|Publication process dates|
|Deposited||26 Jan 2017|
|Accepted author manuscript|
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