Clinical significance of single and persistent elevation of serum high-sensitivity C-reactive protein levels for prediction of kidney outcomes in patients with impaired fasting glucose or diabetes mellitus

A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital

BACKGROUND

Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM), with patients typically remaining asymptomatic until reaching an advanced stage. We aimed to develop and validate a predictive model for DKD in patients with an initial diagnosis of type 2 diabetes mellitus (T2DM) using real-world data.

METHODS

We retrospectively examined data from 3,291 patients (1740 men, 1551 women) newly diagnosed with T2DM at Ningbo Municipal Hospital of Traditional Chinese Medicine (2011-2023). The dataset was randomly divided into training and validation cohorts. Forty-six readily available medical characteristics at initial diagnosis of T2DM from the electronic medical records were used to develop prediction models based on linear, non-linear, and SuperLearner approaches. Model performance was evaluated using the area under the curve (AUC). SHapley Additive exPlanation (SHAP) was used to interpret the best-performing models.

RESULTS

Among 3291 participants, 563 (17.1%) were diagnosed with DKD during median follow-up of 2.53 years. The SuperLearner model exhibited the highest AUC (0.7138, 95% confidence interval: [0.673, 0.7546]) for the holdout internal validation set in predicting any DKD stage. Top-ranked features were WBC_Cnt*, Neut_Cnt, Hct, and Hb. High WBC_Cnt, low Neut_Cnt, high Hct, and low Hb levels were associated with an increased risk of DKD.

CONCLUSIONS

We developed and validated a DKD risk prediction model for patients with newly diagnosed T2DM. Using routinely available clinical measurements, the SuperLearner model could predict DKD during hospital visits. Prediction accuracy and SHAP-based model interpretability may help improve early detection, targeted interventions, and prognosis of patients with DM.

Nonlinear association between proteinuria levels and the risk of cardiovascular disease events and all-cause mortality among chronic kidney disease patients

BACKGROUND

The association between proteinuria levels and cardiovascular disease (CVD) development and all-cause mortality in chronic kidney disease (CKD) patients remains controversial.

METHODS

In this investigation, we conducted a retrospective analysis involving 1138 patients who were registered in the CKD-Research of Outcomes in Treatment and Epidemiology (ROUTE) study. The primary outcome of this study was the composite of cardiovascular events or all-cause death. Cox proportional hazards regression, smooth curve fitting, piecewise linear regression, and subgroup analyses were used.

RESULTS

The mean age of the included individuals was 67.3 ± 13.6 years old. Adjusted hazard ratios (HRs) for UPCR in middle and high groups, compared to the low group, were 1.93 (95% CI: 1.28-2.91) and 4.12 (95% CI: 2.87-5.92), respectively, after multivariable adjustment. Further adjustments maintained significant associations; HRs for middle and high groups were 1.71 (95% CI: 1.12-2.61) and 3.07 (95% CI: 2.08-4.54). A nonlinear UPCR-primary outcome relationship was observed, with an inflection point at 3.93 g/gCr.

CONCLUSION

Among non-dialyzed patients with stage G2-G5 CKD, a nonlinear association between UPCR and the primary outcome was observed. A higher UPCR (when UPCR < 3.93 g/gCr) was an independent predictor of the primary outcome. Importantly, our study predates SGLT2 inhibitor use, showcasing outcomes achievable without these medications. Future research considerations will involve factors like SGLT-2 inhibitor utilization.

Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

OBJECTIVES

Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings.

METHODS

We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively.

RESULTS

The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender.

CONCLUSIONS

Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.