Poor adherence to clinical practice guidelines: A call to action for increased albuminuria testing in patients with type 2 diabetes

Treatment strategies to reduce cardiovascular risk in persons with chronic kidney disease and Type 2 diabetes

Chronic kidney disease (CKD) is a prevalent and progressive condition associated with significant mortality and morbidity. Diabetes is a common cause of CKD, and both diabetes and CKD increase the risk of cardiovascular disease (CVD), the leading cause of death in individuals with CKD. This review will discuss the importance of early detection of CKD and prompt pharmacological intervention to slow CKD progression and delay the development of CVD for improving outcomes. Early CKD is often asymptomatic, and diagnosis usually requires laboratory testing. The combination of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) measurements is used to diagnose and determine CKD severity. Guidelines recommend at least annual screening for CKD in at-risk individuals. While eGFR testing rates are consistently high, rates of UACR testing remain low. This results in underdiagnosis and undertreatment of CKD, leaving many individuals at risk of CKD progression and CVD. UACR testing is an actionable component of the CKD definition. A four-pillar treatment approach for slowing the progression of diabetic kidney disease is suggested, comprising a renin-angiotensin-system (RAS) inhibitor, a sodium-glucose cotransporter 2 inhibitor, a glucagon-like peptide 1 receptor agonist, and the nonsteroidal mineralocorticoid receptor antagonist finerenone. The combination of these agents provides a greater cardiorenal risk reduction compared with RAS inhibitors alone. Early detection of CKD and prompt intervention with guideline-directed medical therapy are crucial for reducing CVD risk in individuals with CKD and diabetes. Evidence from ongoing studies will advance our understanding of optimal therapy in this population.

A Machine Learning-Based Prediction Model for Diabetic Kidney Disease in Korean Patients with Type 2 Diabetes Mellitus

Background/Objectives: Diabetic kidney disease (DKD) is a major cause of end-stage kidney disease and a leading contributor to morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). However, predictive models for DKD onset in Korean patients with T2DM remain underexplored. This study aimed to develop and validate a machine learning (ML)-based DKD prediction model for this population. Methods: This retrospective study utilized electronic health records from six secondary or tertiary hospitals in Korea. The Jeonbuk National University Hospital cohort was used for model development (ratio training: test data, 8:2), whereas datasets from five other hospitals supported external validation. We employed multiple ML algorithms, including lasso, ridge, and elastic net regression; random forest; XGBoost; support vector machines; and neural networks. The model incorporated demographic variables, comorbidities, medications, and laboratory test results. Results: Among 5120 patients with T2DM, 1361 (26.6%) developed DKD. In the development cohort, XGBoost achieved the highest predictive performance (AUC: 0.8099), followed by random forest and logistic regression models (AUCs: 0.7977-0.8019). External validation confirmed the model's robustness with high AUCs (XGBoost: 0.8113, logistic regression models: 0.8228-0.8271). Key predictive factors included age; baseline estimated glomerular filtration rate; and creatinine, hemoglobin, and hemoglobin A1c levels. Conclusions: Our findings highlight the potential of ML-based approaches in predicting DKD in patients with T2DM. The superior performance of XGBoost and logistic regression models underscores their clinical utility. External validation supports the model's generalizability. This model is a valuable tool for the early DKD risk assessment of Korean patients with T2DM.

[Association Between the Aggregate Index of Systemic Inflammation and Albuminuria: A Cross-Sectional Study of National Health and Nutrition Examination Survey 2007-2018]

Objective

Prior studies have established a connection between albuminuria and various inflammatory reactions, highlighting that an increase in C-reactive protein by 1 mg/L increases the likelihood of albuminuria by 2%. Recent investigations indicate a positive correlation between the systemic immune-inflammation index (SII) and increased urinary protein excretion. In addition, elevated levels of the systemic inflammatory response index (SIRI) also correlate with a higher prevalence of albuminuria. The aggregate index of systemic inflammation (AISI) offers a more comprehensive indicator of inflammation, providing an extensive assessment of systemic inflammatory status compared to SII and SIRI. Yet, the specific relationship between AISI and albuminuria remains unclear. This study aims to explore this association in U.S. adults.

Methods

We analyzed data from the National Health and Nutrition Examination Survey (NHANES) for 2007-2018, excluding pregnant women and individuals under 18. Cases with missing data on AISI, urinary albumin concentration, and other covariates were also excluded. AISI was computed using the formula: AISI=(platelet count×neutrophil count×monocyte count)/lymphocyte count. Albuminuria was defined as the urinary albumin-to-creatinine ratio exceeding 30 mg/g. Continuous variables were presented in the form of the mean±standard error, and categorical variables in percentages. We utilized weighted t-tests and chi-square tests for baseline comparisons. We applied weighted multivariable logistic regression and generalized additive models (GAM) to explore the association between AISI and albuminuria and to assess potential nonlinear relationships.

Results

The study included 32273 participants, with an average age of (46.75±0.24) years old. The cohort comprised 48.73% males and 51.27% females. The prevalence of albuminuria was 9.64%. The average logarithmic value of log2AISI was 7.95±0.01, and were categorized into tertiles as follows: Quartile 1 (Q1) (4.94 to 7.49), Q2 (7.49 to 8.29), and Q3 (8.29 to 10.85). As log2AISI increased, so did the prevalence of hypertension, diabetes, congestive heart failure, and albuminuria, all showing statistically significant increases (P<0.001). Similarly, the use of antihypertensive, lipid-lowering, and hypoglycemic drugs was also more prevalent (P<0.001). Statistically significant differences were observed across the three groups concerning age, race and ethnicity, formal education, alcohol consumption, smoking status, systolic and diastolic blood pressures, body mass index, estimated glomerular filtration rate, HbA1c, alanine aminotransferase, aspartate aminotransferase, albumin, creatinine, uric acid, and high-density lipoprotein cholesterol (P<0.05). However, no significant differences were noted in the total cholesterol or the sex ratios among the groups. The association between log2AISI and albuminuria was assessed using weighted multivariable logistic regression, and the detailed results are presented in Table 2. In model 1, without adjusting for covariates, each unit increase in log2AISI was associated with a 32% increase in the risk of albuminuria (odds ratio [OR]=1.32, 95% confidence interval [CI]: 1.27-1.38, P<0.001). Model 2 was adjusted for age, gender, race, and education level, and showed a similar trend, with each unit increase in log2AISI associated with a 31% increased risk (OR=1.31, 95% CI: 1.26-1.37, P<0.001). Model 3, which was further adjusted for all covariates, revealed that each unit increase in log2AISI was associated with a 20% increase in the risk of albuminuria (OR=1.20, 95% CI: 1.15-1.26, P<0.001). The study also transformed log2AISI from a continuous to a categorical variable for analysis. Compared with Q1, the risk of albuminuria in Q3, after adjusting for all covariates, significantly increased (OR=1.37, 95% CI: 1.22-1.55, P<0.001). Q2 also demonstrated a higher risk compared with Q1 (OR=1.13, 95% CI: 1.06-1.36, P=0.004). The trend test indicated a dose-effect relationship between increasing log2AISI and the rising risk of albuminuria. GAM revealed a nonlinear relationship between log2AISI and albuminuria, with distinct trends noted between sexes. Segmented regression based on turning points showed significant effects among women, although the slope difference between the segments was not significant. In men, a significant threshold effect was observed; below the log2AISI of 7.25, increases in log2AISI did not enhance the risk of albuminuria, but above this threshold, the risk significantly increased. As part of a sensitivity analysis, weighted multivariable logistic regression was performed by changing the outcome variable to macroalbuminuria and adjusting for all covariates. The analysis showed that for every unit increase in log2AISI, the risk of developing macroalbuminuria increased by 31% (OR=1.31, 95% CI: 1.15-1.49, P<0.001). Compared with Q1, the risk of albuminuria in Q3 increased by 69% (OR=1.69, 95% CI: 1.27-2.25, P<0.001), and in Q2, it increased by 40% (OR=1.40, 95% CI: 1.03-1.92, P=0.030). Subgroup analysis and interaction results showed that the positive association between AISI and proteinuria risk was stronger in men than in women. Similarly, the association was stronger in people with hypertension compared with those with normal blood pressure, and higher in overweight people compared with those of normal weight. Furthermore, smokers and drinkers showed a stronger positive association between AISI and the risk of proteinuria than non-smokers and non-drinkers do. These results suggest that sex, blood pressure, body mass index, smoking, and alcohol consumption interact with AISI to influence the risk of proteinuria.

Conclusion

There is a robust positive association between AISI and increased risks of albuminuria in US adults. As log2AISI increases, so does the risk of albuminuria. However, further validation of this conclusion through large-scale prospective studies is warranted.

Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review

Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.