Presence of early CKD-related metabolic complications predict progression of stage 3 CKD: a case-controlled study

A systematic review of statistical methodology used to evaluate progression of chronic kidney disease using electronic healthcare records

Background

Electronic healthcare records (EHRs) are a useful resource to study chronic kidney disease (CKD) progression prior to starting dialysis, but pose methodological challenges as kidney function tests are not done on everybody, nor are tests evenly spaced. We sought to review previous research of CKD progression using renal function tests in EHRs, investigating methodology used and investigators’ recognition of data quality issues.

Methods and findings

We searched for studies investigating CKD progression using EHRs in 4 databases (Medline, Embase, Global Health and Web of Science) available as of August 2021. Of 80 articles eligible for review, 59 (74%) were published in the last 5.5 years, mostly using EHRs from the UK, USA and East Asian countries. 33 articles (41%) studied rates of change in eGFR, 23 (29%) studied changes in eGFR from baseline and 15 (19%) studied progression to binary eGFR thresholds. Sample completeness data was available in 44 studies (55%) with analysis populations including less than 75% of the target population in 26 studies (33%). Losses to follow-up went unreported in 62 studies (78%) and 11 studies (14%) defined their cohort based on complete data during follow up. Methods capable of handling data quality issues and other methodological challenges were used in a minority of studies.

Conclusions

Studies based on renal function tests in EHRs may have overstated reliability of findings in the presence of informative missingness. Future renal research requires more explicit statements of data completeness and consideration of i) selection bias and representativeness of sample to the intended target population, ii) ascertainment bias where follow-up depends on risk, and iii) the impact of competing mortality. We recommend that renal progression studies should use statistical methods that take into account variability in renal function, informative censoring and population heterogeneity as appropriate to the study question.

A systematic review and participant-level meta-analysis found little association of retinal microvascular caliber with reduced kidney function

Previously, variation in retinal vascular caliber has been reported in association with chronic kidney disease (CKD) but findings remain inconsistent. To help clarify this we conducted individual participant data meta-analysis and aggregate data meta-analysis on summary estimates to evaluate cross-sectional associations between retinal vascular caliber and CKD. A systematic review was performed using Medline and EMBASE for articles published until October 2018. The aggregate analysis used a two-stage approach combining summary estimates from eleven studies (44,803 patients) while the individual participant analysis used a one-stage approach combining raw data from nine studies (33,222 patients). CKD stages 3-5 was defined as an estimated glomerular filtration rate under 60 mL/min/1.73m2. Retinal arteriolar and venular caliber (central retinal arteriolar and venular equivalent) were assessed from retinal photographs using computer-assisted methods. Logistic regression estimated relative risk of CKD stages 3-5 associated with a 20 μm decrease (approximately one standard deviation) in central retinal arteriolar and venular equivalent. Prevalence of CKD stages 3-5 was 11.2% of 33,222 and 11.3% of 44,803 patients in the individual participant and aggregate data analysis, respectively. No significant associations were detected in adjusted analyses between central retinal arteriolar and venular equivalent and CKD stages 3-5 in the aggregate analysis for central retinal arteriolar relative risk (0.98, 95% confidence interval 0.94-1.03); venular equivalent (0.99, 0.95-1.04) or individual participant central retinal arteriolar (0.99, 0.95-1.04) or venular equivalent (1.01, 0.97-1.05). Thus, meta-analysis provided little evidence to suggest that cross sectional direct measurements of retinal vascular caliber was associated with CKD stages 3-5 in the general population. Hence, meta-analyses of longitudinal studies evaluating the association between retinal parameters and CKD stages 3-5 may be warranted.

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

BACKGROUND

Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.

OBJECTIVE

To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches.

METHODS

The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked.

RESULTS

The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important.

CONCLUSIONS

Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.

Contemporary rates and predictors of fast progression of chronic kidney disease in adults with and without diabetes mellitus

Background

Chronic kidney disease (CKD) is highly prevalent but identification of patients at high risk for fast CKD progression before reaching end-stage renal disease in the short-term has been challenging. Whether factors associated with fast progression vary by diabetes status is also not well understood. We examined a large community-based cohort of adults with CKD to identify predictors of fast progression during the first 2 years of follow-up in the presence or absence of diabetes mellitus.

Methods

Within a large integrated healthcare delivery system in northern California, we identified adults with estimated glomerular filtration rate (eGFR) 30–59 ml/min/1.73 m² by CKD-EPI equation between 2008 and 2010 who had no previous dialysis or renal transplant, who had outpatient serum creatinine values spaced 10–14 months apart and who did not initiate renal replacement therapy, die or disenroll during the first 2 years of follow-up. Through 2012, we calculated the annual rate of change in eGFR and classified patients as fast progressors if they lost > 4 ml/min/1.73 m² per year. We used multivariable logistic regression to identify patient characteristics that were independently associated with fast CKD progression stratified by diabetes status.

Results

We identified 36,195 eligible adults with eGFR 30–59 ml/min/1.73 m² and mean age 73 years, 55% women, 11% black, 12% Asian/Pacific Islander and 36% with diabetes mellitus. During 24-month follow-up, fast progression of CKD occurred in 23.0% of patients with diabetes vs. 15.3% of patients without diabetes. Multivariable predictors of fast CKD progression that were similar by diabetes status included proteinuria, age ≥ 80 years, heart failure, anemia and higher systolic blood pressure. Age 70–79 years, prior ischemic stroke, current or former smoking and lower HDL cholesterol level were also predictive in patients without diabetes, while age 18–49 years was additionally predictive in those with diabetes.

Conclusions

In a large, contemporary population of adults with eGFR 30–59 ml/min/1.73 m², accelerated progression of kidney dysfunction within 2 years affected ~ 1 in 4 patients with diabetes and ~ 1 in 7 without diabetes. Regardless of diabetes status, the strongest independent predictors of fast CKD progression included proteinuria, elevated systolic blood pressure, heart failure and anemia.