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Sop Acute Kidney Injury (SAKI): Predictive models in the management of acute kidney injury

Bedford, Michael (2016) Sop Acute Kidney Injury (SAKI): Predictive models in the management of acute kidney injury. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:62611)


The overarching aim of this PhD thesis is to develop methods, which will ultimately improve the management of patients with acute kidney injury (AKI). Over the years one inherent problem in both diagnosing AKI clinically and reviewing and comparing studies published in the literature has been the numerous definitions used to define AKI. 87 With now accepted definitions of AKI, the first question raised was to determine the true impact of AKI, in terms of incidence and outcomes, for both the patient (morbidity and mortality) and the healthcare economy.

Firstly, potential risk factors were explored. Three time points were also defined where significant clinical decision making takes place and at which points the use of risk models would have greatest impact on clinical care and patient management. These were the point of admission to hospital to guide renal function testing and inform admission planning, and secondly, at 24 hours after admission, often on the post-take ward round to highlight patients who are likely to develop new or worsening AKI if already present, in the first 72 hours of hospital admission so that appropriate management decisions can be made on the ward round.

The study population included hospital admissions to the three acute hospitals of East Kent Hospitals University NHS Foundation Trust (EKHUFT) in 2011, excluding maternity and elective admissions. For validation in a second population the study included hospital admissions to Medway NHS Foundation Trust.

The study developed and assessed traditional methods to provide risk models for the prediction of new or worsening AKI in patients presenting to hospital and in their management within the first 24 hours of admission. Ordinal logistic regression with uni-variable analyses were used to inform the development of multi-variable analyses. Backward selection was used to retain only statistically significant variables in the final models. The models were validated using actual and predicted probabilities, Area Under the Receiver Operating Characteristic (AUROC) curve analysis and the Hosmer Lemeshow test.

The analysis identified key variables which predict AKI both at admission and 72 hours post admission. Validation demonstrated area under ROC of 0.75 and 0.68 respectively. Predicting worsening AKI during admission was unsuccessful.

The work reported here has demonstrated the significant morbidity and mortality both long and short term of patients who experience acute kidney injury managed in hospital and has developed methods of alerting the presence of AKI to the point of care in real-time to ensure efficient intervention with an aim to improve these outcomes. Qualitative work has also highlighted the complexity regarding the implementation and delivery of alerting systems to the clinical front line. The work reported in this thesis has also demonstrated that routinely available data can be used to highlight patients at risk of acute kidney injury both at the point of admission to hospital and following admission.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Farmer, Chris
Thesis advisor: Coulton, Simon
Uncontrolled keywords: Acute Kidney Injury, Risk, Prediction, Intervention, Alert, Incidence, Outcomes, Communication, Point of Care, Real-Time, Socialisation.
Divisions: Faculties > Social Sciences > School of Social Policy Sociology and Social Research
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 11 Aug 2017 09:12 UTC
Last Modified: 06 Feb 2020 04:16 UTC
Resource URI: (The current URI for this page, for reference purposes)
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