Austin J Nephrol Hypertens. 2021; 8(2): 1095.
Helman SR1, Stewart PM2, Siddiqui T3,4, Fink JC1,4 and Weiner S1*
1Department of Medicine, University of Maryland School of Medicine, USA
2Pharmaceutical Research Computing, University of Maryland School of Pharmacy, USA
3Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, USA
4Division of Nephrology, University of Maryland School of Medicine, USA
*Corresponding author: Weiner S, Department of Medicine, University of Maryland School of Medicine, 419 West Redwood Street, Suite 620, Baltimore, MD 21201, USA
Received: June 18, 2021; Accepted: July 15, 2021; Published: July 22, 2021
Objective: The impact of Nonsteroidal Anti-Inflammatory Drugs (NSAID) and iodine-based contrast exposures on developing Chronic Kidney Disease (CKD) is controversial. We examined the association of these exposures with the development of CKD in a Veteran population.
Methods: A retrospective case-control study of 154,448 veterans from the Veterans Affairs (VA) Corporate Data Warehouse (CDW) database between 2005 and 2014 was conducted to assess the association between incident stage 3 CKD with Acute Kidney Injury (AKI), NSAID use, iodine-based contrast exposures, and comorbid conditions. Stepwise logistic regression was used to determine multivariable adjusted Odds Ratios (OR).
Results: The mean age was 59 (SD±13), and the median eGFR was 84 (IQR: 73, 96). AKI was associated with increased odds of CKD (inpatient: OR=3.76, 95% CI: 3.44, 4.11; outpatient: OR=4.73, 95% CI: 4.09, 5.46) and demonstrated escalated odds with >1 episode (inpatient: OR=5.72, 95% CI: 4.71, 6.95; outpatient: OR=8.36, 95% CI: 6.32, 11.06). Months of NSAID prescriptions was associated with CKD, with ORs at >0-6 months, >6-12 months, and >12 months of 1.27 (95% CI: 1.23, 1.32), 1.54 (95% CI: 1.46, 1.63), and 1.69 (95% CI: 1.62, 1.77) respectively. Iodine-based contrast exposure was associated with increased odds of CKD, with ORs for 1-2 Computed Tomography (CT) scans, ≥3CT scans, and left heart catheterization of 1.29 (95% CI: 1.24, 1.35), 1.29 (95% CI: 1.20, 1.28), and 1.38 (95% CI: 1.17, 1.63) respectively.
Conclusion: AKI events, NSAID use, and iodine-based contrast exposures are associated with increased odds for developing stage 3 CKD in veterans.
Keywords: Chronic kidney disease; Acute kidney injury; Iodine-based contrast; Nonsteroidal anti-inflammatory drugs
AKI: Acute Kidney Injury; CAD: Coronary Artery Disease; CDW: Corporate Data Warehouse; CHF: Congestive Heart Failure; CKD: Chronic Kidney Disease; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration; CT: Computed Tomography; DM: Diabetes Mellitus; eGFR: Estimated Glomerular Filtration Rates; ESKD: End-Stage Kidney Disease; HTN: Hypertension; LHC: Left Heart Catheterizations; NSAID: Nonsteroidal Anti-Inflammatory Drugs; VA: Veterans Affairs
Chronic Kidney Disease (CKD) affects up to 15% of the United States population and is responsible for significant morbidity, mortality, and health care costs [1-3]. Patients with other chronic medical conditions, such as hypertension, diabetes, and cardiovascular disease are at increased risk for developing CKD and progressive disease compared to the general population [4-15]. The compounded impact of these chronic medical conditions creates a need to optimize how we manage patients at risk for CKD to minimize morbidity and mortality.
Kidney-associated safety events due to medical care are common and put patients with comorbidities at risk for incident CKD or disease progression [16-18]. Several studies have demonstrated the effects of individual categories of kidney-associated safety events on CKD. Acute kidney injury (AKI) is intimately tied to CKD  and shown to be independently associated with both incident CKD and progression to End-Stage Kidney Disease (ESKD) [20-22]. In addition to AKI, use of nephrotoxic agents, including Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) and iodine-based contrast, may impact kidney function and contribute to incident CKD risk. NSAIDs inhibit prostanoid synthesis, which is believed to interfere in both renal and cardiovascular function . Another possible mechanism of NSAIDs’ effect on renal function is via AKI, but AKI events secondary to NSAIDs are relatively rare in patients with higher Estimated Glomerular Filtration Rates (eGFRs) [24,25]. Iodine-based contrast can directly affect the kidney through toxic effects on tubular cells and indirectly by affecting kidney hemodynamics . The role contrast agents play in directly causing AKI and CKD is still disputed, and may be affected by the indication for and location of dye administration , with arterial infusion possibly carrying a higher risk of kidney damage than venous infusion . Like NSAIDs, AKI secondary to contrast use is now considered relatively rare in patients with higher eGFRs [26,28]. In those with existing CKD, contrast exposure has been associated with nephropathy and progression to ESKD in small studies [29,30]. Less is known about the association of contrast exposures with incident CKD.
How these common kidney-associated safety events affect the development of CKD is unknown. We conducted a case-control study and developed a model to assess associations of AKI events, NSAID use, and iodine-based contrast exposure with incident stage 3 CKD.
Data from the nationwide Corporate Data Warehouse (CDW) database was used to conduct a retrospective case-control study (Figure 1). 374,395 patients in the CDW database had a baseline eGFR measurement of >60mL/min/1.73m² between June 1, 2005 to December 31, 2008. Patients’ labs were followed through December 31, 2014. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate eGFR. White race was used for 18% of patients with missing race data since 80% of veterans on average are white. 243,215 patients with at least 2 eGFR measurements >60mL/min/1.73m² and no eGFR measurements <60mL/min/1.73m² were available as controls to identify a strict cohort of stable kidney function above stage 3 CKD. 45,347 possible cases were identified and had a minimum of 180 days before their first eGFR <60mL/min/1.73m² and two eGFR measures in the stage 3 CKD range that were 90 days apart. The study was approved by the University of Maryland, Baltimore Institutional Review Board and the Baltimore VA Research and Development Committee.
Figure 1: Selection of patients with incident stage 3 chronic kidney disease (cases) and without incident stage 3 chronic kidney disease (controls).
Cases and controls were matched by time of follow-up with follow-up time defined as the time between the baseline eGFR and either the last available eGFR for controls or the first eGFR of <60mL/min/1.73m² for cases. Follow-up times were within 90 days of each other. Cases were assigned to three controls. After available matching, the final cohort included 38,612 cases and 115,836 controls for analysis (Figure 1).
The main exposures included in the analysis were NSAID use, iodine-based contrast exposure, and inpatient and outpatient AKI episodes. Only exposures that occurred during the follow-up time were collected for analysis. Exposure to NSAIDs was determined by the number and months of oral, NSAIDs prescribed at the VA. All intravenous Computed Tomography (CT) studies with contrast, non-cardiac angiograms, and Left Heart Catheterizations (LHC) were used to determine exposure to contrast. All outpatient and inpatient encounters that included the ICD9 codes of 584.x were used to determine AKI events. Baseline comorbidities of Congestive Heart Failure (CHF), Coronary Artery Disease (CAD), hypertension, diabetes, lymphoma, solid organ cancer, and metastatic cancer were determined by ICD9 codes if they occurred between 180 days preand post- the first eGFR measurement (Supplementary Table 1). Demographics of age, gender, and race were collected as available.
Mean Follow-Up (years±SD)
56 (48, 63)
63 (57, 73)
Baseline eGFR (mL/min/1.73m2)
88 (78, 99)
72 (65, 81)
Baseline Creatinine (mg/dL±SD)
Coronary Artery Disease
Congestive Heart Failure
Solid Organ Cancer
Left Heart Catheterization
Months of NSAID Prescriptions
Data are presented as n (%) unless otherwise indicated. SD = standard deviation; IQR = interquartile range; eGFR = estimated glomerular filtration rate; AKI = acute kidney injury; NSAID = nonsteroidal anti-inflammatory drug.
Table 1: Baseline Participant Characteristics and Exposures.
Odds Ratio (95% CI)
3.10 (2.96, 3.25)
4.97 (4.75, 5.19)
8.61 (8.23, 9.01)
0.52 (0.49, 0.54)
Baseline eGFR (mL/min/1.73m2)
2.76 (2.66, 2.86)
11.69 (11.29, 12.10)
Coronary Artery Disease
2.35 (2.27, 2.43)
Congestive Heart Failure
2.67 (2.50, 2.86)
2.49 (2.42, 2.56)
2.75 (2.68, 2.82)
1.39 (1.18, 1.64)
Solid Organ Cancer
1.51 (1.44, 1.56)
0.88 (0.74, 1.03)
1.36 (1.13, 1.63)
1.39 (1.01, 1.91)
1.19 (1.15, 1.24)
1.17 (1.11, 1.24)
Left Heart Catheterization
2.01 (1.79, 2.37)
4.56 (4.23, 4.90)
8.16 (6.95, 9.58)
7.64 (6.78, 8.60)
13.35 (10.47, 17.03)
0.81 (0.78, 0.85)
0.82 (0.79, 0.85)
0.91 (0.87, 0.96)
1.02 (0.98, 1.05)
Months of NSAID Prescriptions
0.81 (0.79, 0.84)
0.95 (0.91, 0.998)
1.08 (1.04, 1.12)
Odds ratios from univariate analysis are indicated with 95% Confidence Intervals (CI). Ref: Reference category; eGFR: estimated Glomerular Filtration Rate; AKI: Acute Kidney Injury; NSAID: Nonsteroidal Anti-Inflammatory Drug.
Table 2: Univariate Odds Ratios of Baseline Characteristics and Exposures.
Baseline characteristics and exposures to kidney-related safety events were compared by the mean and standard deviation for normally distributed continuous variables, medians and IQR for continuous variables that were not normally distributed, and/or were divided into categories along with other categorical variables with comparison of percentages. Age was divided into quintiles and eGFR was divided into clinically meaningful categories. Univariate analysis was performed with OR calculations for each risk factor. Stepwise logistic regression with an inclusion p-value of 0.2 and remaining value of 0.1 was conducted. The resulting model was controlled for VA region and follow-up time. Effect modification was evaluated between age and all other covariates, and eGFR with contrast and NSAID exposures. Discrimination was evaluated with a c-statistic and calibration was evaluated with a calibration plot divided into 100 groups. All analyses were performed using SAS Enterprise Guide 7.15 HF8 (SAS Institute, Inc.).
38,612 cases were matched to 115,836 controls by follow-up time, creating comparable mean follow-up times between groups (Table 1). 92% of controls and 96% of cases were male. 61%, 20% and 18% of controls were white, black and race unknown, respectively, versus 63%, 17% and 19% of cases. All regions of the United States were represented in both groups, with the largest proportion of participants coming from the South. Cases were older (63 IQR: 57, 73 versus 56 IQR: 48, 63) and had lower baseline eGFR (71.8mL/min/1.73m2 IQR: 65.3, 81.4 versus 88.1 mL/min/1.73m² IQR: 77.9, 99.1). Cases were more likely to not receive NSAID prescriptions (56% versus 53%), but when they did receive prescriptions, they were more likely to receive more than 5 prescriptions (18% versus 17%) or more than 12 months of prescriptions (13% versus 12%).
In univariate analyses (Table 2), baseline hypertension, diabetes, CAD, CHF, lymphoma and solid tumors were associated with an increased odds of incident CKD. Iodine-based contrast exposures and inpatient and outpatient AKI events were associated with an increased odds of incident CKD. Six months or less of NSAID prescriptions and 5 or fewer prescriptions showed a protective association to incident CKD, while more than 12 months of NSAID prescriptions had an increased odds of incident CKD.
A stepwise multivariate logistic regression model was developed to assess the influence of comorbidities and kidney-related safety events on incident stage 3 CKD (Table 3). The model was adjusted for follow-up time and region of patient care. Lower baseline eGFR was associated with increased odds of incident stage 3 CKD. Age interacted with hypertension, diabetes and CHF, with younger ages showing increased odds of incident CKD in the presence of each comorbidity (Figure 2). This effect modification was not present for CAD.
Adjusted Odds Ratio (95% CI)
2.98 (2.65, 3.36)
2.60 (2.43, 2.79)
2.15 (2.03, 2.27)
1.48 (1.41, 1.56)
2.37 (2.16, 2.59)
1.91 (1.81, 2.03)
1.81 (1.72, 1.91)
1.58 (1.51, 1.66)
2.81 (1.90, 4.15)
1.71 (1.41, 2.07)
1.63 (1.41, 1.89)
1.35 (1.21, 1.52)
1.15 (1.10, 1.19)
Baseline eGFR (mL/min/1.73m2)
2.54 (2.44, 2.64)
10.54 (10.13, 10.96)
1.29 (1.24, 1.35)
1.29 (1.20, 1.28)
Left Heart Catheterization
1.38 (1.17, 1.63)
3.76 (3.44, 4.11)
5.72 (4.71, 6.95)
4.73 (4.09, 5.46)
8.36 (6.32, 11.06)
Months of NSAID Prescriptions
1.27 (1.23, 1.32)
1.54 (1.46, 1.63)
1.69 (1.62, 1.77)
Model was adjusted for region and follow-up time. Multivariate adjusted odds ratios with 95% Confidence Interval (95% CI) are indicated. Ref: Reference category; AKI: Acute Kidney Injury; eGFR: estimated Glomerular Filtration
Table 3: Multivariate Logistic Regression Model.
Figure 2: Effect modification of age (years) on comorbidities and stage 3 chronic kidney disease risk. Error bars denote 95% confidence intervals for each odds ratio. DM: Diabetes Mellitus; HTN: Hypertension; CHF: Congestive Heart Failure.
Among kidney-related safety events assessed in the multivariate analysis, inpatient and outpatient AKI showed the strongest correlation with incident stage 3 CKD and demonstrated a significant dose response (Table 3). LHC and intravenous contrast CT scans were both associated with increased odds of incident stage 3 CKD. CT scans did not show a dose response with similar effect sizes between 1-2 scans versus ≥3. Non-cardiac angiograms were not found to be significantly associated with CKD in the multivariate analysis. To avoid collinearity, only months rather than number of NSAID prescriptions was included in the multivariate analysis. The odds of incident stage 3 CKD increased with an increasing number of months of NSAID prescriptions. Interaction was not observed between eGFR and CT scan, LHC or months of NSAID prescriptions.
The model demonstrated good discrimination for distinguishing cases of incident CKD with a c-statistic of 0.82 (Figure 3). The calibration plot demonstrated good predictability up to a probability of 70%, after which the model over predicted the probability of incident CKD compared to observed (Figure 4).
Figure 3: Receiver operating characteristic curve for stage 3 chronic kidney disease model applied to study participants. Model was adjusted for follow-up time and region, and includes age, baseline eGFR, hypertension, diabetes, congestive heart failure, coronary artery disease, inpatient AKI, outpatient AKI, CT scans, left heart catheterizations, and months of non-steroidal antiinflammatory drug prescriptions. AUC: Area under Curve.
Figure 4: Calibration plot. Predicted probability of incident stage 3 CKD using our model was compared to the observed probability of incident stage 3 CKD. Calibration was evaluated with the cohort divided into 100 groups.
The presence of multiple comorbidities along with exposures to safety events can impact the development and progression of CKD. In our case-control study conducted in a large VA population, we found multiple comorbidities, intravenous contrast exposures; NSAID use and AKI events were associated with the development of incident stage 3 CKD. Our study uniquely included a large sample size and regional distribution of participants and evaluated multiple safety exposures simultaneously.
Several generalized prediction models have been developed to understand risks for developing incident CKD, many of which also identify older age, lower baseline eGFR, hypertension, diabetes, CHF, and CAD as risk factors for stage 3 CKD development [4-15]. Hanratty et al. studied 10,420 patients with hypertension in Denver, Colorado. Patients with lower baseline eGFR, older age, diabetes, and vascular disease (composite of CAD, peripheral vascular disease and cerebral vascular disease) had increased risks of incident CKD. CHF was not found to be associated, however only 37 patients had a history of CHF . A study of general community patients from the Atherosclerosis Risk in Communities Study and Cardiovascular Health Study did find CHF associated with CKD at a risk level similar to our findings for our middle-aged groups with an OR of 1.7 in their best-fitted model . They also found age, female gender, diabetes, hypertension, PVD, anemia, and low high-density lipoprotein levels associated with incident CKD. In the mainly white population of the Framingham study, increased age, lower baseline eGFR, diabetes and smoking were associated with incident CKD . This same population was used to build a prediction model of incident CKD at 10 years that included eGFR, age, diabetes, hypertension and albuminuria . There have also been multiple prediction models in Asian populations that have identified additional risk factors of waist circumference, Body Mass Index (BMI), systolic and diastolic blood pressures, education, C-reactive protein, triglycerides, glucose, and uric acid in various models [7,10,12].
Effect modification was not evaluated in the North American studies. Our study showed an interaction between hypertension, diabetes, CHF and age, with stronger associations between CKD and each comorbidity at a younger age. This likely reflects a survivor bias for younger patients with comorbidities, but also identifies a higher risk population for CKD that may need more intensive monitoring of kidney function.
In addition to more traditional risk factors, Hao et al.’s study evaluating the use of machine learning methods to predict incident CKD at 1-year in a Maine population identified risk factors such as number of outpatient visits and radiology tests and different medications, which suggests the possible importance of patient care events in understanding kidney function decline .
Inpatient AKI events are recognized contributors to CKD development, as shown in other VA studies [20-22,33,34]. A VA cohort comprised of veterans with diabetes who developed AKI during hospitalization was at increased risk for stage 4 CKD development, with each AKI episode approximately doubling the risk . A general VA inpatient study of AKI severity and recovery time conducted by Heung et al. found that mild cases of AKI demonstrated a Hazard Ratio (HR) of 1.43 for the development of CKD within 1 year of admission . Our study is unique in that we assessed both inpatient and outpatient AKI episodes, with outpatient AKI demonstrating a stronger association with developing stage 3 CKD. We did not, however, determine the severity of AKI events, and our control group would be limited to less severe episodes of AKI since the group is defined by all eGFR values remaining >60mL/min/1.73m². This may account for the large associations found in our study.
The role of NSAIDs in kidney function decline is still debated. Several studies in healthy populations from the Nurses’ Health Study and Physicians’ Health Study have not found an association [35-38]. These studies determined NSAID use via questionnaires, which are subject to recall bias . Studies also vary significantly in how they quantify NSAID exposure, with some quantifying by dosage and others quantifying by varying metrics of duration [35-38]. A study based in the United Kingdom that incorporated >2 NSAID prescriptions in the preceding 6 months into a model for risk of moderate-severe CKD development noted an increased risk due to NSAID use, with an HR of 1.3 . In a young active military cohort, patients who were prescribed ≥7 doses per month had a 20% increased risk of CKD, but <7 NSAID doses was not associated with an increased risk . Most similar to our findings is the dose response of NSAID exposure in a large hypertensive Taiwanese cohort that found an HR 1.18 for 1 to 89 days of NSAID prescriptions and an HR of 1.32 for ≥90 days of NSAIDs . Other studies have demonstrated that certain NSAIDs, such as ketorolac, oxicams, rofecoxib, ibuprofen, and indomethacin, have increased risk for kidney damage [25,43]. Since we did not separate NSAID prescriptions by type, we were unable to assess the effect of specific NSAIDs.
The effect of iodine-based contrast exposure on kidney function is another heavily debated topic and has focused mainly on AKI events following exposures. Decreased eGFR is the only reproducible risk factor for the development of intravenous contrast-induced AKI, which are events directly related to contrast effects on the kidney . Patients with an eGFR greater than 60mL/min/1.73m² have a risk approaching 0% for these events, yet may still experience a risk of 5% for contrast-associated AKI events, which are changes in kidney function of any cause around the timing of contrast exposure [26,28,44]. This reflects a growing concern that AKI events may be due to other patient-related factors at the time of contrast exposure, putting into question the direct nephrotoxicity of contrast in patients with better kidney function. A large meta-analysis did not find an increased risk for either AKI or ESKD after exposure to CT contrast compared to CT without contrast . Hinson et al. performed a propensity score matched analysis of emergency room patients who did and did not receive CT contrast and did not find an association between contrast exposure and CKD diagnosis 6 months after exposure . This study only evaluated one contrast exposure, and 6 months may not have been enough time to develop CKD after exposure. We evaluated multiple intravenous contrast exposures over a longer time and found a 30% increased odds of incident CKD with contrast exposure. The lack of dose response between 1-2 intravenous contrast exposures and ≥3 contrast exposures warrants more evaluation since the lack of a dose response is less supportive of a true correlation. We also did not directly determine if CT studies were associated with the AKI events, however, as AKI events are rare at higher eGFR values, the effect of contrast in this context would likely be reflective of more subtle changes to kidney function.
Arterial contrast exposures have mainly been studied in patients undergoing LHC and incur higher risks of AKI compared to intravenous contrast. Secondary analysis of a randomized control trial of patients comparing LHC to CT angiography for the evaluation of atypical angina revealed 13.2% of patients undergoing LHC experienced AKI versus 5.6% of patients undergoing CT angiography . Patients that experienced AKI after LHC were found to be at higher risk of CKD and kidney decline [27,47]. It is still uncertain how much of these effects are due to the contrast itself versus other clinical factors surrounding the need for LHC. This concern may reflect our finding that non-cardiac angiograms were not found to be associated with incident CKD in our multivariate analysis, and more research is needed to understand the possible role of contrast from non-cardiac angiograms in kidney function decline.
Our study has several limitations. Since this was a retrospective analysis, causal association between kidney-related safety events and incident stage 3 CKD cannot be determined. There may also be additional confounding from variables that were not evaluated in this analysis such as BMI, smoking and proteinuria. Due to missing race data, there may be misclassification of CKD cases and race could not be evaluated in the final model. Analysis with available race data did not significantly alter model estimates; however, black race carried increased odds of incident CKD in the analysis of participants with race data (Supplementary Table 2). Our conclusions are also limited mainly to men due to the small number of female veterans in this VA cohort. We were also unable to assess the influence of events either occurring prior to the study period or occurring outside the VA that may affect CKD risk. Additionally, the use of ICD-9 codes to identify exposures and comorbidities likely resulted in an underestimation of events and cannot assess the severity of comorbidities. In previous analyses, billing codes for AKI have demonstrated low sensitivity and high specificity, with a tendency to under-report AKI events and typically identify higher severity AKI events . Along similar lines, use of NSAID prescription data does not necessarily indicate total NSAIDs taken by the patient since we could not determine adherence to prescriptions or use of over the counter and external prescriptions. Lastly, we did not validate our findings externally and our findings may not be applicable to patients outside of the VA system.
CKD is a common condition with many factors that influence its development and progression. Our study demonstrates associations of multiple safety events with incident CKD concurrently, including AKI events, NSAIDs use and contrast exposures. More research is needed to understand the role of preventable safety events in developing CKD and how to best manage exposures to these events to prevent kidney function decline. Preventative efforts may be most beneficial in younger patients with CHF, diabetes, and hypertension.
Funding was provided by Pharmaceutical Research Computing, University of Maryland School of Pharmacy, USA.
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Citation: Helman SR, Stewart PM, Siddiqui T, Fink JC and Weiner S. Association of Kidney-Related Safety Events with Incident Chronic Kidney Disease in Veterans. Austin J Nephrol Hypertens. 2021; 8(2): 1095.