Validation of Risk Prediction Models to Inform Clinical Decisions After Acute Kidney Injury

Abstract

Rationale & objective: There is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.

Study design: External validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.

Setting & participants: Validation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.

Predictors: KDIGO-based measures of AKI severity and comorbidities specified in the original models.

Outcomes: Death or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.

Analytical approach: Decision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).

Results: 26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.

Limitations: Both original models overstated risks, indicating a need for regular updating.

Conclusions: Follow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.

Keywords: CKD progression; CKD surveillance; acute kidney injury (AKI); chronic kidney disease (CKD); death; follow-up care; hospital readmission; model-guided decisions; mortality; net benefit; post-AKI care; post-discharge monitoring; risk prediction.

Figure 1

Calibration of (A) the reported Aberdeen readmissions model, and (B) after recalibration, and of (C) the reported Alberta chronic kidney disease glomerular filtration rate categories 4 and 5 model, and (D) after recalibration. The dashed line represents the line of perfect agreement; circles represent tenths of increasing predicted risk.

Figure 2

Decision curve analysis for the net benefit of the (A) Aberdeen readmissions model and (B) Alberta CKD G4-G5 model compared with alternative decision strategies. Net benefit represents the trade-off between true positives and false positives, with false positives weighted by the threshold probability from 0 (no penalization for false positives) to 1 (infinite penalization for false positives). At a threshold of 0.1, 1 true positive would be balanced by 9 false positives. Abbreviations: AKI, acute kidney injury; CKD G4-G5, chronic kidney disease glomerular filtration rate categories 4 and 5; GFR, glomerular filtration rate.

Figure 3

Care processes over the first (A) 30 and (B) 90 days after discharge, following hospital admission with AKI. The figure shows the flow of people from hospital discharge through care events including the accident and emergency department (A&E); primary care general practice (GP); outpatient specialty clinics (outpatient), and hospital readmission or death. Dashed lines represent cohort entry following discharge and exit after 30 and 90 days have elapsed, respectively. Directed arrows are the most common paths between events, weighted by number of people. Numbers beside the arrows between boxes represent the movement of people, and the median days (d) and hours (hrs) between events underneath.