Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI

Abstract

Large observational databases linking kidney function and other routine patient health data are increasingly being used to study acute kidney injury (AKI). Routine health care data show an apparent rise in the incidence of population AKI and an increase in acute dialysis. Studies also report an excess in mortality and adverse renal outcomes after AKI, although with variation depending on AKI severity, baseline, definition of renal recovery, and the time point during follow-up. However, differences in data capture, AKI awareness, monitoring, recognition, and clinical practice make comparisons between health care settings and periods difficult. In this review, we describe the growing role of large databases in determining the incidence and prognosis of AKI and evaluating initiatives to improve the quality of care in AKI. Using examples, we illustrate this use of routinely collected health data and discuss the strengths, limitations, and implications for researchers and clinicians.

Keywords: Acute kidney injury; Big-data; Incidence; Prognosis; Quality improvement.

Figure 1

Methodologic challenges in AKI epidemiology. (A) Approaches to studying AKI using observational data and their advantages and disadvantages. (B) Bias that may arise because of convenience sampling of those admitted to hospital. In this scenario, of 1000 people in the population, 250 people had AKI (25% population incidence) including 93 who died (37% fatality). If only people above the threshold are observable, 113 people have observed AKI (11% estimated population incidence) including 80 observed deaths (71% fatality). If the admission threshold changes (eg, with a new policy), this would affect both the incidence and fatality of hospital AKI. Abbreviations: AKI, acute kidney injury; ICD, International Classification of Diseases.

Figure 2

Study of the incidence of AKI in the Grampian population 2001-2014. (A) Growth of Grampian population (red solid) and increase in the proportion of people receiving a blood test (blue dash). (B) Association between testing intensity and the incidence of new AKI presentations by day of the week 2001-2014. (C) Rates of KDIGO-AKI using creatinine change criteria (red solid and pink dot) and ICD-10 code-classified AKI (blue dash). (D) AKI incidence represented as a proportion of the tested population at risk. Abbreviations: AKI, acute kidney injury; ICD-10, International Classification of Diseases, Tenth Revision; KDIGO-AKI, Kidney Disease: Improving Global Outcomes.

Figure 3

Mortality rates and age- and sex-adjusted rate ratios by (A-D) baseline eGFR group and acute kidney injury (AKI; 1-3 denote severity stage). Abbreviations: AKI, acute kidney injury; ref, reference group; eGFR, estimated glomerular filtration rate. Note the log scale: each increment on the y axis represents a doubling of mortality rates.

Figure 4

Percentage of people undergoing surgery who developed postoperative AKI stages 1, 2, and 3: (A) following a gentamicin policy change among people undergoing orthopedic surgery (excluding NOF). (B) People undergoing surgery of an NOF fracture (for whom the policy change did not involve gentamicin). Abbreviations: AKI, acute kidney injury; NOF, non-neck of femur.