Comparison of COVID-19 versus influenza on the incidence, features, and recovery from acute kidney injury in hospitalized United States Veterans.

Acute kidney injury is a common complication in patients hospitalized with SARSCoV-2 (COVID-19), with prior studies implicating multiple potential mechanisms of injury. Although COVID-19 is often compared to other respiratory viral illnesses, few formal comparisons of these viruses on kidney health exist. In this retrospective cohort study, we compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons. A total of 3402 hospitalizations for COVID-19 and 3680 hospitalizations for influenza admitted between October 1, 2019 and May 31, 2020 across 127 Veterans Administration hospitals nationally were studied using the electronic medical record. Acute kidney injury occurred more frequently among those with COVID-19 compared to those with influenza (40.9% versus 29.4%, weighted analysis) and was more severe. Patients with COVID-19 were more likely to require mechanical ventilation and vasopressors and experienced higher mortality. Proteinuria and hematuria were frequent in both groups but more common in COVID-19. Recovery of kidney function was less common in patients with COVID-19 and acute kidney injury but was similar among survivors. Thus, findings from this study confirm that acute kidney injury is more common and severe among patients hospitalized with COVID-19 compared to influenza, a finding that may be driven largely by illness severity. Hence, the combined impact of these two illnesses on kidney health may be significant and have important implications for resource allocation.

Renin-angiotensin aldosterone inhibitor use at hospital discharge among patients with moderate to severe acute kidney injury and its association with recurrent acute kidney injury and mortality.

Recurrent episodes of acute kidney injury (AKI) are common among AKI survivors. Renin-angiotensin aldosterone inhibitors (RAASi) are often indicated for these patients but may increase the risk for recurrent AKI. Here, we examined whether RAASi associates with a higher risk for recurrent AKI and mortality among survivors of moderate to severe AKI in a retrospective cohort of Veterans who survived Stage II or III AKI. The primary exposure was RAASi at hospital discharge and the primary endpoint was recurrent AKI within 12 months. Cox proportional hazards models were fit on a propensity score-weighted cohort to compare time to recurrent AKI and mortality by RAASi exposure. Among 96,983 patients, 40% were on RAASi at discharge. Compared to patients who continued RAASi use, those discontinuing use experienced no difference in risk for recurrent AKI but had a significantly higher risk of mortality [hazard ratio 1.33 (95% confidence interval1.26-1.41)]. No differences in recurrent AKI risk was observed for non-users started or not on RAASi compared to prevalent users who continued RAASi. Subgroup analyses among those with diabetes, chronic kidney disease, heart failure, and malignancy were similar with exception of a modest reduction in recurrent AKI risk among RAASi discontinuers with chronic kidney disease. Thus, RAASi use among survivors of moderate to severe AKI was associated with little to no difference in risk for recurrent AKI but was associated with improved survival. Reinitiating or starting RAASi among patients with strong indications is warranted but should be balanced with individual overall risk for recurrent AKI and with adequate monitoring.

Considerations for advancing nephrology research and practice through natural language processing.

Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation.

Timing of Recovery From Moderate to Severe AKI and the Risk for Future Loss of Kidney Function.

RATIONALE & OBJECTIVE: The extent of recovery of kidney function following acute kidney injury (AKI) is known to be associated with future chronic kidney disease. Less is known about how the timing of recovery affects the rate of future loss of kidney function. STUDY DESIGN: We performed a retrospective cohort study examining the independent association between the timing of recovery from moderate to severe AKI and future loss of kidney function. SETTING & PARTICIPANTS: 47,903 adult US veterans with stage 2 or 3 AKI who recovered to within 120% of baseline creatinine level within 90 days of peak injury. EXPOSURE: The timing of recovery of kidney function from peak inpatient serum creatinine level grouped into 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days. OUTCOME: A sustained 40% decline in estimated glomerular filtration rate below that calculated from the last serum creatinine level available during the 90-day recovery period or kidney failure (2 outpatient estimated glomerular filtration rates<15mL/min/1.73m2, dialysis procedures > 90 days apart, kidney transplantation, or registry within the US Renal Data System). ANALYTICAL APPROACH: Time to the primary outcome was examined using multivariable Cox proportional hazards regression. RESULTS: Among 47,903 patients, 29,316 (61%), 10,360 (22%), 4,520 (9%), and 3,707 (8%) recovered within 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days, respectively. With a median follow-up of 42 months, unadjusted incidence rates for the kidney outcome were 2.01, 3.55, 3.86, and 3.68 events/100 person-years, respectively. Compared with 1 to 4 days, recovery within 5 to 10, 11 to 30, and 31 to 90 days was associated with increased rates of the primary outcome: adjusted HRs were 1.33 (95% CI, 1.24-1.43), 1.41 (95% CI, 1.28-1.54), and 1.58 (95% CI, 1.43-1.75), respectively. LIMITATIONS: Predominately male population, residual confounding, and inability to make causal inferences because of the retrospective observational study design. CONCLUSIONS: The timing of recovery provides an added dimension to AKI phenotyping and prognostic information regarding the future occurrence of loss of kidney function. Studies to identify effective interventions on the timing of recovery from AKI are warranted.

Kidney Disease Awareness and Knowledge among Survivors ofAcute Kidney Injury.

BACKGROUND: Acute kidney injury (AKI) survivors are at risk for chronic kidney disease, recurrent AKI, and cardiovascular disease. The transition from hospital to ambulatory care is an opportunity to reduce these sequelae by launching self-care plans through effective patient education. How well AKI survivors are informationally prepared to apply kidney-specific self-care is unknown. The purpose of this study was to identify awareness and disease-specific knowledge among AKI survivors. METHODS: We performed a cross-sectional survey of AKI-related awareness and knowledge in 137 patients with Kidney Disease Improving Global Outcomes Stage II or III AKI near the time of hospital discharge. Patients were asked (1) "Did you experience AKI while in the hospital?" and (2) "Do you have a problem with your kidney health?" Objective knowledge of AKI was evaluated with a 15-item adapted version of the validated Kidney Knowledge Survey that included topics such as common causes, risk factors, and how AKI is diagnosed. RESULTS: Median age was 54 (interquartile range 43-63) and 81% were white. Eighty percent of patients were unaware that they had experienced AKI and 53% were both unaware they had experienced AKI or had a "problem with their kidneys." Multivariable logistic regression identified being male and lack of nephrology consult as predictors of unawareness with ORs of 3.92 (95% CI 1.48-10.33) and 5.10 (95% CI 1.98-13.13), respectively. Less than 50% recognized nonsteroidal anti-inflammatory drugs, contrast, or phosphate-based cathartics as risk factors for AKI. Two-thirds of patients did not agree that they knew a lot about AKI and more than 80% desired more information. CONCLUSIONS: Most patients with moderate to severe AKI are unaware of their condition, lack understanding of risk factors for recurrent AKI, and desire more information. Patient-centered communication to optimize awareness, understanding, and care will require coordinated educational strategies throughout the continuum of AKI care.

Acute kidney injury is a risk factor for subsequent proteinuria.

Acute kidney injury (AKI) is associated with subsequent chronic kidney disease (CKD), but the mechanism is unclear. To clarify this, we examined the association of AKI and new-onset or worsening proteinuria during the 12 months following hospitalization in a national retrospective cohort of United States Veterans hospitalized between 2004-2012. Patients with and without AKI were matched using baseline demographics, comorbidities, proteinuria, estimated glomerular filtration rate, blood pressure, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker (ACEI/ARB) use, and inpatient exposures linked to AKI. The distribution of proteinuria over one year post-discharge in the matched cohort was compared using inverse probability sampling weights. Subgroup analyses were based on diabetes, pre-admission ACEI/ARB use, and AKI severity. Among the 90,614 matched AKI and non-AKI pairs, the median estimated glomerular filtration rate was 62 mL/min/1.73m2. The prevalence of diabetes and hypertension were 48% and 78%, respectively. The odds of having one plus or greater dipstick proteinuria was significantly higher during each month of follow-up in patients with AKI than in patients without AKI (odds ratio range 1.20-1.39). Odds were higher in patients with Stage II or III AKI (odds ratios 1.32-1.81) than in Stage I AKI (odds ratios 1.18-1.32), using non-AKI as the reference group. Results were consistent regardless of diabetes status or baseline ACEI/ARB use. Thus, AKI is a risk factor for incident or worsening proteinuria, suggesting a possible mechanism linking AKI and future CKD. The type of proteinuria, physiology, and clinical significance warrant further study as a potentially modifiable risk factor in the pathway from AKI to CKD.

Acute Kidney Injury and Risk of Incident Heart Failure Among US Veterans.

BACKGROUND: Acute kidney injury (AKI) is common and associated with poor outcomes. Heart failure is a leading cause of cardiovascular disease among patients with chronic kidney disease. The relationship between AKI and heart failure remains unknown and may identify a novel mechanistic link between kidney and cardiovascular disease. STUDY DESIGN: Observational study. SETTING & PARTICIPANTS: We studied a national cohort of 300,868 hospitalized US veterans (2004-2011) without a history of heart failure. PREDICTOR: AKI was the predictor and was defined as a 0.3-mg/dL or 50% increase in serum creatinine concentration from baseline to the peak hospital value. Patients with and without AKI were matched (1:1) on 28 in- and outpatient covariates using optimal Mahalanobis distance matching. OUTCOMES: Incident heart failure was defined as 1 or more hospitalization or 2 or more outpatient visits with a diagnosis of heart failure within 2 years through 2013. RESULTS: There were 150,434 matched pairs in the study. Patients with and without AKI during the index hospitalization were well matched, with a median preadmission estimated glomerular filtration rate of 69mL/min/1.73m2. The overall incidence rate of heart failure was 27.8 (95% CI, 19.3-39.9) per 1,000 person-years. The incidence rate was higher in those with compared with those without AKI: 30.8 (95% CI, 21.8-43.5) and 24.9 (95% CI, 16.9-36.5) per 1,000 person-years, respectively. In multivariable models, AKI was associated with 23% increased risk for incident heart failure (HR, 1.23; 95% CI, 1.19-1.27). LIMITATIONS: Study population was primarily men, reflecting patients seen at Veterans Affairs hospitals. CONCLUSIONS: AKI is an independent risk factor for incident heart failure. Future studies to identify underlying mechanisms and modifiable risk factors are needed.

Development of an automated phenotyping algorithm for hepatorenal syndrome.

OBJECTIVE: Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS: A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS: The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION: This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

Predictors of Recurrent AKI.

Recurrent AKI is common among patients after hospitalized AKI and is associated with progressive CKD. In this study, we identified clinical risk factors for recurrent AKI present during index AKI hospitalizations that occurred between 2003 and 2010 using a regional Veterans Administration database in the United States. AKI was defined as a 0.3 mg/dl or 50% increase from a baseline creatinine measure. The primary outcome was hospitalization with recurrent AKI within 12 months of discharge from the index hospitalization. Time to recurrent AKI was examined using Cox regression analysis, and sensitivity analyses were performed using a competing risk approach. Among 11,683 qualifying AKI hospitalizations, 2954 patients (25%) were hospitalized with recurrent AKI within 12 months of discharge. Median time to recurrent AKI within 12 months was 64 (interquartile range 19-167) days. In addition to known demographic and comorbid risk factors for AKI, patients with longer AKI duration and those whose discharge diagnosis at index AKI hospitalization included congestive heart failure (primary diagnosis), decompensated advanced liver disease, cancer with or without chemotherapy, acute coronary syndrome, or volume depletion, were at highest risk for being hospitalized with recurrent AKI. Risk factors identified were similar when a competing risk model for death was applied. In conclusion, several inpatient conditions associated with AKI may increase the risk for recurrent AKI. These findings should facilitate risk stratification, guide appropriate patient referral after AKI, and help generate potential risk reduction strategies. Efforts to identify modifiable factors to prevent recurrent AKI in these patients are warranted.

High prevalence of non-steroidal anti-inflammatory drug use among acute kidney injury survivors in the southern community cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used and have been linked to acute kidney injury (AKI), chronic kidney disease (CKD) and cardiovascular disease (CVD). Patients who survive an AKI episode are at risk for future adverse kidney and cardiovascular outcomes. The objective of our study was to examine the prevalence and predictors of NSAID use among AKI survivors. METHODS: The Southern Community Cohort Study is a prospective study of low-income adults aged 40-79 in the southeastern US. Through linkage with Centers for Medicare and Medicaid Services, 826 participants with an AKI diagnosis (ICD-9 584.5-584.9) at any age prior to cohort enrollment were identified. At baseline, data were collected on regular use of prescription and over-the-counter NSAIDs, as well as demographic, medical and other characteristics. Additional comorbidities were ascertained via linkage with CMS or the US Renal Data System. RESULTS: One hundred fifty-four AKI survivors (19%) reported regular NSAID use at cohort enrollment (52 prescription, 81 OTC, 21 both) and the percentage of NSAID users did not vary by time since AKI event. Over 58% of users were taking NSAIDS regularly both before and after their AKI event. Hypertension (83%), arthritis (71%), heart failure (44%), CKD (36%) and diabetes (35%) were prevalent among NSAID users. In a multivariable model, history of arthritis (OR: 3.00; 95% CI: 1.92, 4.68) and acetaminophen use (OR: 2.43; 95% CI: 1.50, 3.93) were significantly associated with NSAID use, while prevalent CKD (OR: 0.63; 95% CI: 0.41, 0.98) and diabetes (OR: 0.44; 95% CI: 0.29, 0.69) were significantly inversely associated. CONCLUSIONS: NSAID use among AKI survivors is common and highlights the need to understand physician and patient decision-making around NSAIDs and to develop effective strategies to reduce NSAID use in this vulnerable population.

Urinary L-FABP predicts poor outcomes in critically ill patients with early acute kidney injury.

Biomarker studies for early detection of acute kidney injury (AKI) have been limited by nonselective testing and uncertainties in using small changes in serum creatinine as a reference standard. Here we examine the ability of urine L-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), and kidney injury molecule-1 (KIM-1) to predict injury progression, dialysis, or death within 7 days in critically ill adults with early AKI. Of 152 patients with known baseline creatinine examined, 36 experienced the composite outcome. Urine L-FABP demonstrated an area under the receiver-operating characteristic curve (AUC-ROC) of 0.79 (95% confidence interval 0.70-0.86), which improved to 0.82 (95% confidence interval 0.75-0.90) when added to the clinical model (AUC-ROC of 0.74). Urine NGAL, IL-18, and KIM-1 had AUC-ROCs of 0.65, 0.64, and 0.62, respectively, but did not significantly improve discrimination of the clinical model. The category-free net reclassification index improved with urine L-FABP (total net reclassification index for nonevents 31.0%) and urine NGAL (total net reclassification index for events 33.3%). However, only urine L-FABP significantly improved the integrated discrimination index. Thus, modest early changes in serum creatinine can help target biomarker measurement for determining prognosis with urine L-FABP, providing independent and additive prognostic information when combined with clinical predictors.

Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods.

BACKGROUND: Despite its high prevalence and clinical impact, research on peripheral artery disease (PAD) remains limited due to poor accuracy of billing codes. Ankle-brachial index (ABI) and toe-brachial index can be used to identify PAD patients with high accuracy within electronic health records. METHODS: We developed a novel natural language processing (NLP) algorithm for extracting ABI and toe-brachial index values and laterality (right or left) from ABI reports. A random sample of 800 reports from 94 Veterans Affairs facilities during 2015 to 2017 was selected and annotated by clinical experts. We trained the NLP system using random forest models and optimized it through sequential iterations of 10-fold cross-validation and error analysis on 600 test reports and evaluated its final performance on a separate set of 200 reports. We also assessed the accuracy of NLP-extracted ABI and toe-brachial index values for identifying patients with PAD in a separate cohort undergoing ABI testing. RESULTS: The NLP system had an overall precision (positive predictive value) of 0.85, recall (sensitivity) of 0.93, and F1 measure (accuracy) of 0.89 to correctly identify ABI/toe-brachial index values and laterality. Among 261 patients with ABI testing (49% PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1%, and specificity of 93.1% to identify PAD when compared with a structured chart review. The above findings were consistent in a range of sensitivity analysis. CONCLUSIONS: We successfully developed and validated an NLP system for identifying patients with PAD within the Veterans Affairs electronic health record. Our findings have broad implications for PAD research and quality improvement.

Clinician collaboration to improve clinical decision support: the Clickbusters initiative.

OBJECTIVE: We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. MATERIALS AND METHODS: We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. RESULTS: Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. CONCLUSIONS: At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.

Objective: Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes. Materials and Methods: Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets. Results: The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency. Conclusion: Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

Risk of Hypoglycemia Following Hospital Discharge in Patients With Diabetes and Acute Kidney Injury.

OBJECTIVE: Hypoglycemia is common in patients with diabetes. The risk of hypoglycemia after acute kidney injury (AKI) is not well defined. The purpose of this study was to compare the risk for postdischarge hypoglycemia among hospitalized patients with diabetes who do and do not experience AKI. RESEARCH DESIGN AND METHODS: We performed a propensity-matched analysis of patients with diabetes, with and without AKI, using a retrospective national cohort of veterans hospitalized between 2004 and 2012. AKI was defined as a 0.3 mg/dL or 50% increase in serum creatinine from baseline to peak serum creatinine during hospitalization. Hypoglycemia was defined as hospital admission or an emergency department visit for hypoglycemia or as an outpatient blood glucose <60 mg/dL. Time to incident hypoglycemia within 90 days postdischarge was examined using Cox proportional hazards models. Prespecified subgroup analyses by renal recovery, baseline chronic kidney disease, preadmission drug regimen, and HbA1c were performed. RESULTS: We identified 65,151 propensity score-matched pairs with and without AKI. The incidence of hypoglycemia was 29.6 (95% CI 28.9-30.4) and 23.5 (95% CI 22.9-24.2) per 100 person-years for patients with and without AKI, respectively. After adjustment, AKI was associated with a 27% increased risk of hypoglycemia (hazard ratio [HR] 1.27 [95% CI 1.22-1.33]). For patients with full recovery, the HR was 1.18 (95% CI 1.12-1.25); for partial recovery, the HR was 1.30 (95% CI 1.23-1.37); and for no recovery, the HR was 1.48 (95% CI 1.36-1.60) compared with patients without AKI. Across all antidiabetes drug regimens, patients with AKI experienced hypoglycemia more frequently than patients without AKI, though the incidence of hypoglycemia was highest among insulin users, followed by glyburide and glipizide users, respectively. CONCLUSIONS: AKI is a risk factor for hypoglycemia in the postdischarge period. Studies to identify risk-reduction strategies in this population are warranted.

Delayed Consequences of Acute Kidney Injury.

Acute kidney injury (AKI) is an increasingly common complication of hospitalization and acute illness. Experimental data indicate that AKI may cause permanent kidney damage through tubulointerstitial fibrosis and progressive nephron loss, while also lowering the threshold for subsequent injury. Furthermore, preclinical data suggest that AKI may also cause distant organ dysfunction. The extension of these findings to human studies suggests long-term consequences of AKI including, but not limited to recurrent AKI, progressive kidney disease, elevated blood pressure, cardiovascular events, and mortality. As the number of AKI survivors increases, the need to better understand the mechanisms driving these processes becomes paramount. Optimizing care for AKI survivors will require understanding the short- and long-term risks associated with AKI, identifying patients at highest risk for poor outcomes, and testing interventions that target modifiable risk factors. In this review, we examine the literature describing the association between AKI and long-term outcomes and highlight opportunities for further research and potential intervention.

Laboratory test surveillance following acute kidney injury.

BACKGROUND: Patients with hospitalized acute kidney injury (AKI) are at increased risk for accelerated loss of kidney function, morbidity, and mortality. We sought to inform efforts at improving post-AKI outcomes by describing the receipt of renal-specific laboratory test surveillance among a large high-risk cohort. METHODS: We acquired clinical data from the Electronic health record (EHR) of 5 Veterans Affairs (VA) hospitals to identify patients hospitalized with AKI from January 1st, 2002 to December 31st, 2009, and followed these patients for 1 year or until death, enrollment in palliative care, or improvement in renal function to estimated GFR (eGFR) ≥ 60 L/min/1.73 m(2). Using demographic data, administrative codes, and laboratory test data, we evaluated the receipt and timing of outpatient testing for serum concentrations of creatinine and any as well as quantitative proteinuria recommended for CKD risk stratification. Additionally, we reported the rate of phosphorus and parathyroid hormone (PTH) monitoring recommended for chronic kidney disease (CKD) patients. RESULTS: A total of 10,955 patients admitted with AKI were discharged with an eGFR<60 mL/min/1.73 m2. During outpatient follow-up at 90 and 365 days, respectively, creatinine was measured on 69% and 85% of patients, quantitative proteinuria was measured on 6% and 12% of patients, PTH or phosphorus was measured on 10% and 15% of patients. CONCLUSIONS: Measurement of creatinine was common among all patients following AKI. However, patients with AKI were infrequently monitored with assessments of quantitative proteinuria or mineral metabolism disorder, even for patients with baseline kidney disease.