Kidney Failure Risk Equation and Cost of Care in Patients with Chronic Kidney Disease

Advanced chronic kidney disease increases complications in anterior cervical discectomies with fusions: An analysis of 75,508 patients

Background

Although anterior cervical discectomy and fusion (ACDF) procedures for cervical spine disease have been increasing amid a growing population of patients with kidney dysfunction, there is a scarcity of literature focusing on kidney dysfunction as a risk-factor for post-operative ACDF complications. The purpose is to evaluate the differential impact of kidney dysfunction on perioperative outcomes including surgical and medical complications, extended length of hospital stay (LOS), and death within 30 days following ACDF.

Patient Sample

This was a retrospective cohort study of prospectively collected data using the American College of Surgeons National Surgical Quality Improvement Program database to identify patients who had undergone an elective ACDF procedure between 2011-2021 using Current Procedural Terminology code 22551. Patients were categorized into five cohorts based on eGFR according to the "Kidney Disease: Improving Global Outcomes" Classification: values of: ≥ 90(reference cohort), 60-89 (G2), 30-59 (G3), 15-29 (G4), and <15 (G5). One-way ANOVA for continuous variables and chi-square tests for categorical variables were used to identify differences in perioperative variables between the five groups. Multivariable logistic regression analysis assessed the effect of kidney dysfunction on post-operative surgical outcomes. Significance was defined as p<.05.

Results

About 75,508 ACDF patients were included, of who 57,480 were G1, 15,186 were G2, 2,192 were G3, 312 were G4, and 338 were G5. G4 and G5 independently increased the risk of medical complications (OR: 1.893, 95% CI [1.296-2.705]; OR: 2.241, 95% CI [1.222-3.964]) and blood transfusion. Only G5 independently increased the risk for extended LOS (OR: 2.410, 95% CI [1.281-4.371], p=.005).

Conclusion

High grade CKD is an independent risk factor for medical complications, extended hospital LOS, and blood transfusions following ACDF, underscoring the importance of risk stratification to optimize perioperative management and reduce the burden of complications and healthcare costs. Conversely, low grade CKD does not increase the risk of complications in ACDF.

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance

There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down 3 sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution's CDS governance group, we show how health system-based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity.

Validation of the Klinrisk chronic kidney disease progression model in the FIDELITY population

Background

Chronic kidney disease (CKD) affects >800 million individuals worldwide and is often underrecognized. Early detection, identification and treatment can delay disease progression. Klinrisk is a proprietary CKD progression risk prediction model based on common laboratory data to predict CKD progression. We aimed to externally validate the Klinrisk model for prediction of CKD progression in FIDELITY (a prespecified pooled analysis of two finerenone phase III trials in patients with CKD and type 2 diabetes). In addition, we sought to identify evidence of an interaction between treatment and risk.

Methods

The validation cohort included all participants in FIDELITY up to 4 years. The primary and secondary composite outcomes included a ≥40% decrease in estimated glomerular filtration rate (eGFR) or kidney failure, and a ≥57% decrease in eGFR or kidney failure. Prediction discrimination was calculated using area under the receiver operating characteristic curve (AUC). Calibration plots were calculated by decile comparing observed with predicted risk.

Results

At time horizons of 2 and 4 years, 993 and 1795 patients experienced a primary outcome event, respectively. The model predicted the primary outcome accurately with an AUC of 0.81 for 2 years and 0.86 for 4 years. Calibration was appropriate at both 2 and 4 years, with Brier scores of 0.067 and 0.115, respectively. No evidence of interaction between treatment and risk was identified for the primary composite outcome (P = .31).

Conclusions

Our findings demonstrate the accuracy and utility of a laboratory-based prediction model for early identification of patients at the highest risk of CKD progression.

Associations of urinary fetuin-A with histopathology and kidney events in biopsy-proven kidney disease

Background

Fetuin-A is implicated in the pathogenesis of vascular calcification in chronic kidney disease (CKD); however, the relationship between fetuin-A, histopathologic lesions and long-term kidney outcomes in patients with various types of kidney disease remains unclear.

Methods

We measured urinary fetuin-A levels in 335 individuals undergoing clinically indicated native kidney biopsy. The expressions of fetuin-A mRNA and protein in the kidney were assessed using RNA sequencing and immunohistochemistry. The association of urinary fetuin-A with histopathologic lesions and major adverse kidney events (MAKE), defined as a decline in estimated glomerular filtration rate (eGFR) of at least 40%, kidney failure or death, was analyzed.

Results

Urinary fetuin-A levels showed a positive correlation with albuminuria (rs = 0.67, P < .001) and a negative correlation with eGFR (rs = -0.46, P < .001). After multivariate adjustment, higher urinary fetuin-A levels were associated with glomerular inflammation, mesangial expansion, interstitial fibrosis and tubular atrophy, and arteriolar sclerosis. Using a 1 transcript per million gene expression cutoff, we found kidney fetuin-A mRNA levels below the threshold in both individuals with normal kidney function and those with CKD. Additionally, immunohistochemistry revealed reduced fetuin-A staining in tubular cells of CKD patients compared with normal controls. During a median 21-month follow-up, 115 patients experienced MAKE, and Cox regression analysis confirmed a significant association between elevated urinary fetuin-A and MAKE. This association remained significant after adjusting for potential confounding factors.

Conclusion

Urinary fetuin-A is associated with chronic histological damage and adverse clinical outcomes across a spectrum of biopsy-proven kidney diseases.

Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus

People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

Costs Associated With Progression of Mildly Reduced Kidney Function Among Medicare Advantage Enrollees

Rationale & Objective

The prevalence of early chronic kidney disease (CKD) in older adults has increased in the past 2 decades, yet CKD disease progression, overall, is variable. It is unclear whether health care costs differ by progression trajectory. The purpose of this study was to estimate the trajectories of CKD progression and examine Medicare Advantage (MA) health care costs of each trajectory over a 3-year period in a large cohort of MA enrollees with mildly reduced kidney function.

Study Design

Cohort study.

Setting & Population

421,187 MA enrollees with stage G2 CKD in 2014-2017.

Outcomes

We identified 5 trajectories of kidney function over time.

Model Perspective & Timeframe

Mean total health care costs for each of the trajectories were described in each of the following 3 years from a payer perspective: 1 year before and 2 years after the index date establishing stage G2 CKD (study entry).

Results

The mean estimated glomerular filtration rate (eGFR) at study entry was 75.9 mL/min/1.73 m2 and the median (interquartile range) follow-up period was 2.6 (1.6, 3.7) years. The cohort had a mean age of 72.6 years and had predominantly female participants (57.2%), and White (71.2%). We identified the following 5 distinct trajectories of kidney function: a stable eGFR (22.3%); slow eGFR decline with a mean eGFR at study entry of 78.6 (30.2%); slow eGFR decline with an eGFR at study entry of 70.9 (28.4%); steep eGFR decline (16.3%); and accelerated eGFR decline (2.8%). Mean costs of enrollees with accelerated eGFR decline were double the MA enrollees' mean costs in each of the other 4 trajectories in every year ($27,738 vs $13,498 for a stable eGFR 1 year after study entry).

Limitations

Results may not generalized beyond MA and a lack of albumin values.

Conclusions

The small fraction of MA enrollees with accelerated eGFR decline has disproportionately higher costs than other enrollees with mildly reduced kidney function.

Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study

This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran’s hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.

Patient Perspectives on Integrating Risk Prediction Into Kidney Care: Opinion Piece

Although Chronic Kidney Disease is common, only a relatively small proportion of individuals will reach kidney failure requiring dialysis or transplantation. Validated risk equations using routine laboratory tests have been developed that can easily be used at the bedside to help clinicians accurately predict the risk of kidney failure in their patient population, in turn informing patient-centered conversations, guiding appropriate nephrology referrals, improving the timing of dialysis treatment planning, and identifying individuals who are most likely to benefit from interventions. In this article, individuals living with kidney disease share why access to individualized prediction of kidney failure risk can help patients manage their disease and why it should be considered an essential component of kidney care.