Risk factors for developing acute kidney injury in older people with diabetes and community-acquired pneumonia: a population-based UK cohort study

The outcome of interest was AKI diagnosed in hospital, with the hospital episode start date within 28 days of the CAP diagnosis. This included patients who were diagnosed and treated with CAP in primary care; follow-up was not limited to hospitalisation for CAP but included any admission for AKI in the 28 days following CAP diagnosis. The primary definition of AKI was a record in the HES data coded with the specific International Classification of Diseases-10 (ICD-10) code N17 (acute renal failure). Patients with either ICD-10 codes N17 or N19 (unspecified kidney failure) were used in sensitivity analyses to capture additional patients with AKI who might have been coded with the less specific ICD-10 code.

Potential risk factors for AKI identified in previous studies were examined [19, 21, 33‐37]. These included older age, gender, body mass index (BMI), deprivation (IMD), smoking status and alcohol intake; nine co-morbidities: CKD, hypertension, congestive cardiac failure, ischemic heart disease, cerebrovascular disease, dementia, chronic lung disease, malignancies, connective tissue disease and three medications: the prescription of angiotensin converting enzyme inhibitors (ACEI) and/or angiotensin II receptor blockers (ARBs) prescribed within 6 months of CAP onset, and lifetime history of anti-diabetes prescriptions (oral hypoglycaemic drugs and/or insulin) prior to infection. Socio-economic status was ascertained using 2007 Office for National Statistics’ IMD, a composite area-level marker of deprivation categorised in quintiles (1, least deprived; to 5, most deprived) [38]. Records with missing individual level IMD were substituted with values for the relevant general practice. All other risk factors were defined using primary care records in CPRD. The most recent glycated haemoglobin level that was recorded at least 28 days prior to infection onset (to describe baseline glycaemic control independent of incipient CAP) was used. BMI was estimated using the weight recorded closest to the CAP onset date and categorised into underweight (<18.5 kg/m²), healthy weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²) and obese (≥30 kg/m²). Similarly, smoking status (non-, current or ex-smoker) and alcohol intake (non-, ex-, current up to 6 units/day and heavy >6 units/day) were ascertained using the last record prior to CAP onset and if this was unavailable then the earliest record after the infection onset were used. Diagnostic Read codes at any time prior to CAP onset date were used to identify the nine co-morbidities of interest, and prescription records at any time prior to CAP onset for medication history. Underlying renal function was ascertained from primary care records using either estimated glomerular filtration rate (eGFR, from laboratory reports) or presence of proteinuria without urinary tract infection (from laboratory reports or Read codes for proteinuria or proteinuric disease recorded by GPs), as detailed in our previous study [39]. In brief, eGFR was based on the latest serum creatinine result recorded prior to CAP onset (excluding results recorded within 28 days of CAP onset as these may reflect the effect of acute infection rather than pre-existing underlying renal function) and was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation adjusted for ethnicity [40]. It was classified under three categories: ≥60, 30–59 and ≤29 mL/min/1.73m² to maintain adequate numbers in each group. Proteinuria was identified by a positive urine protein test (without a concurrent urinary tract infection), or by any Read code recording proteinuric disease as previously described [39].

Individuals were followed up from the CAP diagnosis date (the index date) until the earliest of: 28 days after the CAP diagnosis, AKI diagnosis, patient’s death or transfer out of the practice, the last date at which the practice contributed data, the last HES linkage date, initiation of renal replacement therapy (recorded in either CPRD or HES) or 31/03/11.

Multivariable logistic regression was used to examine the association of explanatory variables with presence or absence of an AKI-code in the 28 days after CAP, using odds ratios (OR) and 95% confidence interval (CI). Individuals with complete covariate data were included in analyses.

Age, gender and study-period were considered a priori confounders as well as potential explanatory variables in their own right. The study period split into two categories, April 2004–September 2007 and October 2007–March 2011, to assess the impact of the Acute Kidney Injury Network (AKIN) criteria introduced in 2007 [41]. The ORs for each explanatory variable were first examined in a minimally adjusted model, adjusted for the three a priori confounders. Hypothesis testing was conducted using likelihood ratio test. Ordered categorical variables such as eGFR, BMI, smoking-status, alcohol-intake and the study-period were also examined for linear trend.

A conceptual framework describing the hypothesised hierarchy of risk factors for AKI was then developed (Fig. 1) to inform the building of a multivariable model to determine risk factors for AKI [19, 21, 33, 34, 36, 37]. Deprivation (IMD) was considered a distal determinant [42], that mediates its effect first via lifestyle factors (BMI, smoking and alcohol) and then via more proximate determinants [42]: hypertension and other co-morbidities, which themselves could act partly via medications (Fig. 1). These variables were added sequentially to the minimally adjusted model in the order of their hypothesised position in the causal pathway to AKI.

Being cautious about data sparsity and collinearity, the number of variables included in the final model was restricted to ensure at least ten outcomes per parameter estimate [43]. If data sparsity was encountered the decision for inclusion in the final model was based on the magnitude of the effect estimates in the minimally adjusted model and P-values of <0.2. Collinearity was assessed by comparing the log standard errors of the coefficients in the multivariable models.

An interaction of a priori interest, namely between age and renal functional reserve was examined by fitting the final multivariable model first with interaction terms between age and eGFR and then between age and proteinuria. Age was re-categorised as a binary variable to examine effect modification (65–79 years and ≥80 years) and models with and without interaction terms were compared using the likelihood ratio tests.

Three sensitivity analyses were conducted: (1) an analysis including individuals who were excluded from the primary analysis because of missing data, dropping selected variables with missing data; (2) repeating analyses after categorising AKI using both ICD-10 N19 and N17 codes, to capture non-specific coding for AKI; and (3) repeating the final multivariable analyses for only those individuals who were hospitalised within 28 days (for any reason) of CAP diagnosis to minimise ascertainment bias of AKI.

In the analysis adjusted for age, gender and study period (Table 2 column 2), there was little evidence of an association between most demographic and lifestyle factors (age, deprivation, smoking and alcohol intake) and AKI, except for increased odds of AKI amongst males and the most obese individuals (BMI ≥30 kg/m²). More AKI diagnoses were observed in the second half of study period. Amongst the nine co-morbidities examined as risk factors, the odds for AKI in patients with hypertension was 1.6 times higher compared to those without hypertension. There was also very strong evidence for an association with underlying renal function: there was a linear trend in increasing odds of AKI with decreasing eGFR, with those with eGFR ≤ 29 at more than five times the odds of AKI, and those with proteinuria at 1.7 times the odds of AKI. In contrast, there was some evidence for reduced odds for AKI among those with connective tissue disease and cancer. For medication, higher odds of AKI were observed amongst patients taking either ACEI/ARB, and among those prescribed insulin. Due to concerns about data-sparsity, IMD (the least strongly associated variable in this analysis) was dropped from further models.

The multivariable models were built sequentially as described in the Methods. After adjusting for lifestyle and socio-demographic factors (Table 4 in Appendix) and then for hypertension, and then other co-morbidities (Model 4: Table 2, column 4), the increased odds associated with hypertension persisted and the linear increased odds of AKI with reducing eGFR were still observed. However, the previously observed strong evidence of higher odds of AKI with proteinuria was reduced (P = 0.07) and the association of high BMI with AKI seen in the previous model disappeared.

In the final multivariable analysis (Model 5: Table 2 column 6), ACEI/ARB users had 60% increased odds of AKI after adjusting for the other co-morbidities and lifestyle factors. The increased risk of AKI with insulin use remained. The previously observed association of AKI with BMI, hypertension and proteinuria was attenuated after adjusting for these additional mediating variables. There was evidence of lower odds of AKI amongst patients with ischemic heart disease.

Table 3 shows the age-specific ORs for the association of AKI with eGFR and proteinuria as markers of CKD. There was strong evidence (P_interaction = 0.007) of an interaction between age and eGFR after adjusting for other risk factors for AKI, with a more pronounced effect of eGFR < 30 mL/min/1.73m² on AKI amongst those aged ≥80 years (adjusted-OR 5.51 95% CI 3.28–9.27) compared to the younger group (adjusted-OR 2.90 95%CI 1.46–5.77). In the older individuals (aged ≥80 years) the graded increase in odds of AKI with decreasing eGFR remained but this graded association was not evident in the younger age-group for whom any eGFR < 60 ml/min/1.73m² was associated with an approximately three fold increase in AKI risk (Table 3).

In contrast, there was little evidence (P_interaction = 0.34) that the effect of proteinuria on AKI varied by age.

After excluding the four variables with missing data that had less strong evidence of an association with AKI (smoking status, alcohol intake, BMI and HbA1C) there were 4067 individuals included in the analysis. The results from this model were similar to those of the main model (Table 5 in Appendix).

After widening the definition of AKI to include the non-specific N19 code, the number of patients who developed AKI increased from 298 (coded only as ICD-10 N17) to 373 (coded either ICD-10 N17 or N19). The lower odds associated with connective tissue diseases was no longer apparent in this model, but no other appreciable changes were evident (Table 6 in Appendix).

Amongst those hospitalised the overall risk of developing AKI was 9.8% (N = 298). Results after restricting analyses to those who were hospitalised were very similar to those in the entire study population (Table 7 in Appendix).