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Identifying on admission patients likely to develop acute kidney injury in hospital
Anastasios Argyropoulos1, Stuart Townley2, Paul M Upton3
1Centre for Implementation Science, Faculty of Health Sciences, University of Southampton, Southampton, SO17 1BJ, UK. a.argyropoulos@soton.ac.uk.
Insights
Early detection of Acute Kidney Injury (AKI) is crucial. Fuzzy logic and regression models accurately identify high-risk patients, aiding timely intervention to prevent severe outcomes.
Area of Science:
- Nephrology
- Medical Informatics
- Predictive Analytics
Background:
- Acute Kidney Injury (AKI) incidence and mortality are rising in the UK.
- AKI is associated with approximately 20% of hospital admissions.
- A third of hospitalized patients develop AKI, with 20% of cases being avoidable.
Purpose of the Study:
- To develop and validate predictive models for Acute Kidney Injury (AKI) risk.
- To identify patients at high risk of developing specific stages of AKI.
- To improve early risk detection and potentially reduce avoidable AKI cases in hospitals.
Main Methods:
- Utilized electronic health records from Royal Cornwall Hospitals Trust (2015).
- Developed Takagi-Sugeno Fuzzy Logic Systems (FLS) and multivariable logistic regression (MLR) models.
- Trained, tested, and validated models to predict AKI Stages I, II, and III within 7 days of admission.
Main Results:
- FLS and MLR models demonstrated varying accuracy, with Area Under the Curve (AUC) ranging from 0.70 to 0.95.
- Models for predicting AKI Stage 2/3 (FLS II, MLR II) and AKI Stage 3 (FLS III, MLR III) showed high accuracy (AUC 0.77 and 0.95, respectively).
- FLS I and MLR I performance for predicting any AKI Stage was comparable to existing models (AUC 0.70).
Conclusions:
- Fuzzy Logic Systems (FLS) and Multivariable Logistic Regression (MLR) models effectively identify high-risk patients for AKI Stages II/III.
- This study represents a novel quantification of AKI stage-specific risk for a diverse inpatient population.
- FLS and MLR models offer promising tools for early AKI detection and intervention in clinical settings.
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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