Recognition of CKD after the introduction of automated reporting of estimated GFR in the Veterans Health Administration

Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model

Rationale & Objective

Risk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ.

Study Design

Predictive model development and external validation using observational electronic health record data.

Setting & Participants

Patients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease.

New Predictors & Established Predictors

Established predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI.

Outcomes

A ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI.

Analytical Approach

Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention.

Results

Outpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807).

Limitations

Only surviving patients with measured creatinine levels during a baseline period and outcome period were included.

Conclusions

The outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.

Frequency of PPAR-γ, FTO and ABCC8 genetic variation in Pakistani cardiovascular smokers

Smoking is considered as one of the major reasons behind genetic variations in cardiometabolic disorders. However, effect of nicotine via smoking on Pakistani population still needs to be elucidated. This study was aimed to investigate genetic variation among PPAR-γ, FTO, and ABCC8 genes in cardiometabolic patients along with their biochemical parameters. A total of 472 CVD patients were enrolled in this study and divided into three groups; n = 144 for PPAR-γ (C/G) variation and n = 164 in each group to investigate FTO (T/A) and ABCC8 (G/T) variation, respectively. Polymorphisms within groups were identified by using Tetra and/or Tri ARM-PCR. This study showed positive association among genetic polymorphisms in PPAR-γ, FTO, and ABCC8 groups with altered metabolic parameters in CVD patients. Findings showed that smoking is major contributory factor for genetic polymorphism that was strongly associated with elevated blood glucose and serum TGs accompanying PPAR-γ, FTO, and ABCC8 genetic polymorphism in 25%, 24%, and 20% in smokers and 11%, 10%, and 5% in non-smoker CVD patients, respectively. However, highest polymorphism occurred in PPAR-γ both in smokers and non-smoker CVD patients that show that smoking-mediated gene polymorphism might be a contributory factor in provoking CVD risk approximately twice in smokers as compared to that in non-smoker CVD patients.

Lab-based and diagnosis-based chronic kidney disease recognition and staging concordance

BACKGROUND

Chronic kidney disease (CKD) is often under-recognized and poorly documented via diagnoses, but the extent of under-recognition is not well understood among Medicare beneficiaries. The current study used claims-based diagnosis and lab data to examine patient factors associated with clinically recognized CKD and CKD stage concordance between claims- and lab-based sources in a cohort of Medicare beneficiaries.

METHODS

In a cohort of fee-for-service (FFS) beneficiaries with CKD based on 2011 labs, we examined the proportion with clinically recognized CKD via diagnoses and factors associated with clinical recognition in logistic regression. In the subset of beneficiaries with CKD stage identified from both labs and diagnoses, we examined concordance in CKD stage from both sources, and factors independently associated with CKD stage concordance in logistic regression.

RESULTS

Among the subset of 206,036 beneficiaries with lab-based CKD, only 11.8% (n = 24,286) had clinically recognized CKD via diagnoses. Clinical recognition was more likely for beneficiaries who had higher CKD stages, were non-elderly, were Hispanic or non-Hispanic Black, lived in core metropolitan areas, had multiple chronic conditions or outpatient visits in 2010, or saw a nephrologist. In the subset of 18,749 beneficiaries with CKD stage identified from both labs and diagnoses, 70.0% had concordant CKD stage, which was more likely if beneficiaries were older adults, male, lived in micropolitan areas instead of non-core areas, or saw a nephrologist.

CONCLUSIONS

There is significant under-diagnosis of CKD in Medicare FFS beneficiaries, which can be addressed with the availability of lab results.

Comparison of automated and retrospectively calculated estimated glomerular filtration rate in electronic health record data

BACKGROUND

Estimated glomerular filtration rate (eGFR) is the clinical standard for assessing kidney function and staging chronic kidney disease. Automated reporting of eGFR using the Modification of Diet in Renal Disease (MDRD) study equation was first implemented within the Department of Veterans Affairs (VA) in 2007 with staggered adoption across laboratories. When automated eGFR are not used or unavailable, values are retrospectively calculated using clinical and demographic data that are currently available in the electronic health record (EHR). Due to the dynamic nature of EHRs, current data may not always match past data. Whether and to what extent the practice of re-calculating eGFR on retrospective data differs from using the automated values is unknown.

METHODS

We assessed clinical data for patients enrolled in VA who had their first automated eGFR lab in 2013.We extracted the eGFR value, the corresponding serum creatinine value, and patient race, gender, and date of birth from the EHR. The MDRD equation was applied to retrospectively calculate eGFR. Stage of chronic kidney disease (CKD) was defined using both eGFR values. We used Bland-Altman plots and percent agreement to assess the difference between the automated and calculated values. We developed an algorithm to select the most parsimonious parameter set to explain the difference in values and used chart review on a small subsample of patients to determine if one approach more accurately describes the patient at the time of eGFR measurement.

RESULTS

We evaluated eGFR data pairs from 266,084 patients. Approximately 33.0% (n = 86,747) of eGFR values differed between automated and retrospectively calculated methods. The majority of discordant pairs were classified as the same CKD stage (n = 74,542, 85.93%). The Bland-Altman plot showed differences in the data pairs were centered near zero (mean difference: 0.8 mL/min/1.73m²) with 95% limits of agreement between - 6.4 and 8.0. A change in recorded age explained 95.6% (n = 78,903) of discordant values and 85.02% (n = 9371) of the discordant stages.

CONCLUSIONS

Values of retrospectively calculated eGFR can differ from automated values, but do not always result in a significant classification change. In very large datasets these small differences could become significant.

Data and knowledge standards for learning health: A population management example using chronic kidney disease

The widespread creation of learning health care systems (LHSs) will depend upon the use of standards for data and knowledge representation. Standards can facilitate the reuse of approaches for the identification of patient cohorts and the implementation of interventions. Standards also support rapid evaluation and dissemination across organizations. Building upon widely-used models for process improvement, we identify specific LHS activities that will require data and knowledge standards. Using chronic kidney disease (CKD) as an example, we highlight the specific data and knowledge requirements for a disease-specific LHS cycle, and subsequently identify areas where standards specifications, clarification, and tools are needed. The current data standards for CKD population management recommendations were found to be partially ambiguous, leading to barriers in phenotyping, risk identification, patient-centered clinical decision support, patient education needs, and care planning. Robust tools are needed to effectively identify patient health care needs and preferences and to measure outcomes that accurately depict the multiple facets of CKD. This example presents an approach for defining the specific data and knowledge representation standards required to implement condition-specific population health management programs. These standards specifications can be promoted by disease advocacy and professional societies to enable the widespread design, implementation, and evaluation of evidence-based health interventions, and the subsequent dissemination of experience in different settings and populations.

Adherence to chronic kidney disease guidelines in primary care patients is associated with comorbidity

BACKGROUND

GPs insufficiently follow guidelines regarding consultation and referral for chronic kidney disease (CKD).

OBJECTIVE

To identify patient characteristics and quality of care (QoC) in CKD patients with whom consultation and referral recommendations were not followed.

METHOD

A 14 month prospective observational cohort study of primary care patients with CKD stage 3-5. 47 practices participated, serving 207469 people. 2547 CKD patients fulfilled consultation criteria, 225 fulfilled referral criteria. We compared characteristics of patients managed by GPs with patients receiving nephrologist co-management. We assessed QoC as adherence to monitoring criteria, CKD recognition and achievement of blood pressure (BP) targets.

RESULTS

Patients treated in primary care despite a consultation recommendation (94%) had higher eGFR values (OR 1.07; 95% CI: 1.05-1.09), were less often monitored for renal function (OR 0.42; 95% CI: 0.24-0.74) and potassium (OR 0.56; 95% CI: 0.35-0.92) and CKD was less frequently recognised (OR 0.46; 95% CI: 0.31-0.68) than in patients with nephrologist co-management. Patients treated in primary care despite referral recommendation (70%) were older (OR 1.03; 95% CI:1.01-1.06) and had less cardiovascular disease (OR 0.37; 95% CI: 0.19-0.73). Overall, in patients solely managed by GPs, CKD recognition was 50%, monitoring disease progression in 36% and metabolic parameters in 3%, BP targets were achieved in 51%. Monitoring of renal function and BP was positively associated with diabetes (OR 3.10; 95% CI: 2.47-3.88 and OR 7.78; 95% CI: 3.21-18.87) and hypertension (OR 3.19; 95% CI: 2.67-3.82 and OR 3.35; 95% CI: 1.45-7.77).

CONCLUSION

Patients remaining in primary care despite nephrologists' co-management recommendations were inadequately monitored, and BP targets were insufficiently met. CKD patients without cardiovascular comorbidity or diabetes require extra attention to guarantee adequate monitoring of renal function and BP.

Patterns of primary care utilization before and after living kidney donation

BACKGROUND

Annual visits with a primary care provider (PCP) are recommended for living kidney donors to monitor long-term health postdonation, yet adherence to this recommendation is unknown.

METHODS

We surveyed 1170 living donors from our center from 1970 to 2012 to ascertain frequency of PCP visits pre- and postdonation. Interviews occurred median (IQR) 6.6 (3.8-11.0) years post-transplant. We used multivariate logistic regression to examine associations between donor characteristics and PCP visit frequency.

RESULTS

Overall, only 18.6% had less-than-annual PCP follow-up postdonation. The strongest predictor of postdonation PCP visit frequency was predonation PCP visit frequency. Donors who had less-than-annual PCP visits before donation were substantially more likely to report less-than-annual PCP visits postdonation (OR=9.8 14.421.0, P<.001). Men were more likely to report less-than-annual PCP visits postdonation (adjusted OR=1.2 1.62.3, P<.01); this association was amplified in unmarried/noncohabiting men (aOR=2.4 3.96.3, P<.001). Donors without college education were also more likely to report less-than-annual PCP visits postdonation (aOR=1.3 1.82.5 , P=.001).

CONCLUSIONS

The importance of annual PCP visits should be emphasized to all living donors, especially those with less education, men (particularly single men), and donors who did not see their PCP annually before donation.

Ensuring Patient Safety During the Transition to ESRD

Patients with chronic kidney disease (CKD) are at risk for complications both inherent to the disease and as a consequence of its treatment. The dangers that CKD patients face change across the spectrum of the disease. Providers who are well-versed in these safety threats are best poised to safeguard patients as their CKD progresses.

Early recognition of multiple sclerosis using natural language processing of the electronic health record

BACKGROUND

Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.

METHODS

An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.

RESULTS

Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.

CONCLUSIONS

Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

The Healthy People 2020 Objectives for Kidney Disease: How Far Have We Come, and Where Do We Need to Go?

The Healthy People 2020 initiative established goals for patients with CKD and ESRD. We assessed United States progress toward some of these key goals. Using data from the Centers for Medicare and Medicaid Services ESRD database, we created yearly cohorts of patients on incident and prevalent dialysis from 2000 to 2013. Change in event rate or proportion change over the study years was modeled using Poisson regression with adjustment for age, race, sex, and primary cause of ESRD. For all-cause mortality in prevalent patients, Healthy People 2020 sought approximately 0.8% relative annual improvement; actual improvement was 2.7%. Improvement was greatest for patients ages 18-44 years old (3.8%; P<0.01 versus 2.8% for ages 65-74 years old) and 2.3% even for patients ages ≥75 years old. For mortality in incident patients, the relative annual decrease was 2.1% overall, a twofold improvement over the goal; mortality decreased nearly twice as much in black as in white patients (3.2% versus 1.8%; P<0.001). Geographic variation was substantial; the relative annual decrease was 0.6% in the Midwest and more than fourfold greater (2.7%) in the South. Cardiovascular mortality in prevalent patients decreased dramatically at 5.0% per year, far exceeding the annual goal of approximately 0.8%; the decrease was greatest in patients ages ≥75 years old (5.5%; P<0.001 versus ages 65-74 years old; 5.1%). The relative annual increase in percentages of patients with a fistula at dialysis initiation was 2.4%, roughly three times the goal; the increase was greater for black than white patients (3.2% versus 2.3%; P<0.01). Adjusted regional differences varied greater than twofold: 2.0% for the South versus 4.1% for the Midwest. Thus, although gains have been substantial, not all groups have benefitted equally. Goal development for Healthy People 2030 should consider changes in goal paradigms, such as tailoring by geographic region and incorporating patient-centered goals.

Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study

Background

The capacity of electronic health record (EHR) data to guide targeted surveillance in chronic kidney disease (CKD) is unclear. We sought to leverage EHR data for predicting risk of progressing from CKD to end-stage renal disease (ESRD) to help inform surveillance of CKD among vulnerable patients from the healthcare safety-net.

Methods

We conducted a retrospective cohort study of adults (n = 28,779) with CKD who received care within 2 regional safety-net health systems during 1996–2009 in the Western United States. The primary outcomes were progression to ESRD and death as ascertained by linkage with United States Renal Data System and Social Security Administration Death Master files, respectively, through September 29, 2011. We evaluated the performance of 3 models which included demographic, comorbidity and laboratory data to predict progression of CKD to ESRD in conditions commonly targeted for disease management (hypertension, diabetes, chronic viral diseases and severe CKD) using traditional discriminatory criteria (AUC) and recent criteria intended to guide population health management strategies.

Results

Overall, 1730 persons progressed to end-stage renal disease and 7628 died during median follow-up of 6.6 years. Performance of risk models incorporating common EHR variables was highest in hypertension, intermediate in diabetes and chronic viral diseases, and lowest in severe CKD. Surveillance of persons who were in the highest quintile of ESRD risk yielded 83–94 %, 74–95 %, and 75–82 % of cases who progressed to ESRD among patients with hypertension, diabetes and chronic viral diseases, respectively. Similar surveillance yielded 42–71 % of ESRD cases among those with severe CKD. Discrimination in all conditions was universally high (AUC ≥0.80) when evaluated using traditional criteria.

Conclusions

Recently proposed discriminatory criteria account for varying risk distribution and when applied to common clinical conditions may help to inform surveillance of CKD in diverse populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-016-0272-0) contains supplementary material, which is available to authorized users.

Validation of the Korean coefficient for the modification of diet in renal disease study equation

BACKGROUND/AIMS

Race and ethnicity are important determinants when estimatingglomerular filtration rate (GFR). The Korean coefficients for the isotope dilution mass spectrometry (IDMS) Modification of Diet in Renal Disease (MDRD) Study equations were developed in 2010. However, the coefficients have not been validated. The aim of this study was to validate the performance of the Korean coefficients for the IDMS MDRD Study equations.

METHODS

Equation development and validation were performed in separate groups (development group, n = 147 from 2008 to 2009; validation group, n = 125 from 2010 to 2012). We compared the performance of the original IDMS MDRD equations and modified equations with Korean coefficients. Performance was assessed by comparing correlation coefficients, bias, and accuracy between estimated GFR and measured GFR, with systemic inulin clearance using a single injection method.

RESULTS

The Korean coefficients for the IDMS MDRD equations developed previously showed good performance in the validation group. The new Korean coefficients for the four- and six-variable IDMS MDRD equations using both the development and validation cohorts were 1.02046 and 0.97300, respectively. No significant difference was detected for the new Korean coefficients, in terms of estimating GFR, between the original and modified IDMS MDRD Study equations.

CONCLUSIONS

The modified equations with Korean coefficients for the IDMS MDRD Study equations were not superior to the original equations for estimating GFR. Therefore, we recommend using the original IDMS MDRD Study equation without ethnic adjustment in the Korean population.

Clinical implications of the CKD epidemiology collaboration (CKD-EPI) equation compared with the modification of diet in renal disease (MDRD) study equation for the estimation of renal dysfunction in patients with cardiovascular disease

The CKD-EPI equation is more accurate than the MDRD equation in the general population. We performed this study to establish whether chronic kidney disease (CKD) is commonly recognized by clinicians and whether the CKD-EPI equation improves prognosis estimation in patients with chronic cardiovascular disease (CVD). We analyzed data on 12394 CVD patients consecutively examined at the Cardiovascular Center of Trieste (Italy) between November 2009 and October 2013. The outcomes were all-cause death and a composite outcome of death/hospitalization for CV events (D+cvH). CKD-EPI formula reclassified 1786 (14.4 %) patients between KDIGO categories compared to the MDRD: 2.3 % (n = 280) placed in a lower risk and 12.1 % (n = 1506) into a higher risk group. CKD, defined as eGFR-CKD-EPI formula <60 ml/min, was present in 3083 patients (24.9 %) but not recognized by clinicians in 1946 (63.1 % of patients with CKD). The lack of recognition of CKD was inversely proportional to the KDIGO class for both equations. There were 986 deaths and 2726 D+cvH during 24 months follow-up. The incidence of death and D+cvH was about twice as high in patients with unrecognized CKD than in those with normal renal function (31 % vs. 17.1 %, aHR: 1.35, 95 % CI: 1.15 to 1.60), even in those patients with eGFR-MDRD >60 but eGFR-CKD-EPI formula <60 (31.1 % vs 17.1 %, p < 0.001). CKD-EPI equation provides more accurate risk stratification than MDRD equation in patients with CVD. CKD was unrecognized in nearly two-thirds of these patients but clinical outcomes were similar in those for patients with recognized CKD.

Trends in Arteriovenous Fistula Use at Dialysis Initiation After Automated eGFR Reporting

The purpose of this study was to examine trends in the presence of an arteriovenous fistula (AVF) at dialysis initiation before and after eGFR reporting. All incident dialysis patients from four Canadian provinces that implemented province-wide, automated laboratory reporting of eGFR with known vascular access at dialysis initiation were included in the study (N = 25,201) from 2001 to 2010. The primary outcome was the change in proportion of patients with an AVF at dialysis initiation using an interrupted time series and adjusted multilevel logistic regression models. AVF usage at dialysis initiation decreased gradually over the study period from 19.0% to 14.6%. After implementation of automated eGFR reporting, there was attenuation in the decline in AVF usage in models adjusted for case-mix, facility, and the downward trajectory in AVF use over time. The adjusted odds ratio for initiating dialysis with an AVF 1 year post-eGFR reporting compared to pre-eGFR reporting was more pronounced in older patients (age tertile >73; OR: 1.40; 95% CI: 1.04-1.90). Laboratory-based eGFR reporting was associated with a possible attenuation in the decline of AVF at dialysis initiation and this was more pronounced in older patients.