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Identifying on admission patients likely to develop acute kidney injury in hospital

Identifying on admission patients likely to develop acute kidney injury in hospital
Identifying on admission patients likely to develop acute kidney injury in hospital
Background
The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.

Methods
Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation.

Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).

Results
Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98).

Conclusions
FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.
1471-2369
Argyropoulos, Anastasios
ec3a2bdc-92b6-49a8-b80c-9dd3c3073c11
Townley, Stuart
648056b5-391a-4e2a-9fa0-990d2925c590
Upton, Paul
0a0cf813-7ed8-4d82-b225-7d2b2239374a
Dickinson, Stephen
0ee209af-25a1-4d98-a772-929068078a42
Pollard, Adam
55e19627-1fce-4b4c-accb-56252fe36863
Argyropoulos, Anastasios
ec3a2bdc-92b6-49a8-b80c-9dd3c3073c11
Townley, Stuart
648056b5-391a-4e2a-9fa0-990d2925c590
Upton, Paul
0a0cf813-7ed8-4d82-b225-7d2b2239374a
Dickinson, Stephen
0ee209af-25a1-4d98-a772-929068078a42
Pollard, Adam
55e19627-1fce-4b4c-accb-56252fe36863

Argyropoulos, Anastasios, Townley, Stuart, Upton, Paul, Dickinson, Stephen and Pollard, Adam (2019) Identifying on admission patients likely to develop acute kidney injury in hospital. BMC Nephrology, 20 (56). (doi:10.1186/s12882-019-1237-x).

Record type: Article

Abstract

Background
The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.

Methods
Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation.

Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).

Results
Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98).

Conclusions
FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.

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s12882-019-1237-x
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More information

Accepted/In Press date: 29 January 2019
e-pub ahead of print date: 14 February 2019

Identifiers

Local EPrints ID: 428803
URI: https://eprints.soton.ac.uk/id/eprint/428803
ISSN: 1471-2369
PURE UUID: 992e0529-dccc-4289-91d2-b489a6eae67b

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Date deposited: 08 Mar 2019 17:30
Last modified: 13 Mar 2019 17:33

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