Risk Factors for Progression of CKD with and without Diabetes

Abstract

Objective. We aim to identify independent risk factors to predict CKD progression to end stage renal disease (ESRD) in patients with or without diabetes. Methods. In this retrospective study, we enrolled CKD stage 3-4 patients between January 2013 and December 2018 and followed them until December 2020 or the initiation of dialysis. We used Kaplan-Meier to plot the survival curve. Univariate and multivariable Cox proportional hazards model was used to explore risk factors affecting the progression of CKD. The final model was used to construct nomogram for predicting CKD progression. Calibration plots and concordance index (C-index) were used to evaluate the accuracy and discrimination of the risk model. Results. We enrolled 309 CKD patients, including 80 cases in G3a, 98 cases in G3b, and 131 cases in G4. Among them, 141 patients had diabetes and 168 did not. The mean age of patients at enrolled was 57.86 ± 15.10 years, and 67% were male. The median follow-up time was 25.6 months. There were 81 patients (26.2%) that started dialysis in the total CKD cohort, 52 cases (36.9%) in the CKD with diabetes group, and 29 cases (17.3%) in the CKD without diabetes group. Hypoalbuminemia (HR =2.655, P < 0.001 ), proteinuria (HR =2.592, P = 0.042 ), increased LDL (HR =2.494, P < 0.001 ), diabetes (HR =2.759, P < 0.001 ), hypertension (HR =3.471, P = 0.037 ), and CKD stage (HR =2.001, P = 0.046 ) were risk factors for CKD progression to ESRD in the overall population. For those without diabetes, only hypoalbuminemia (HR =2.938, P = 0.030 ) was a risk factor for CKD progression to ESRD. For those with diabetes, both hypoalbuminemia (HR =2.758, P = 0.002 ), the increased level of LDL (HR =3.982, P < 0.001 ), and CKD stage (HR =3.781, P = 0.001 ) were risk factors for CKD progression to ESRD. The C-index of the final nomograms was 0.760 ( P < 0.001 ). Conclusions. The results from our risk factor model suggest that CKD disease progression can be predicted and early strategic intervention is necessary for CKD patients to avoid renal function deterioration.

Effect of Hypoalbuminemia in Patients Undergoing Parathyroidectomy for Primary Hyperparathyroidism

“…33 Studies have demonstrated the detrimental effects of low serum albumin on kidney function, and although surgery can have short-lived secondary consequences on the kidney through increased sympathetic nervous system activation, the combined stress may explain the increase in progressive renal insufficiency seen in HA patients. 34,35 HA patients in our study were two times more likely to experience unplanned reoperation compared with non-HA patients. Similarly, Kishawi et al found HA patients to be more than one and a half times more likely to undergo reoperation compared with non-HA following primary arthroplasty.…”

“…Ryan et al reported HA as a significant predictor of renal insufficiency following total joint arthroplasty 33 . Studies have demonstrated the detrimental effects of low serum albumin on kidney function, and although surgery can have short‐lived secondary consequences on the kidney through increased sympathetic nervous system activation, the combined stress may explain the increase in progressive renal insufficiency seen in HA patients 34,35 …”

“…In this analysis, HA was defined as serum albumin of less than 3.5 g/dL. To analyze differences between the non-HA and HA cohorts and to adjust for confounding factors, the following patient demographics and comorbidities provided by NSQIP were included in the analysis: sex (male, female), age (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49), 50-64, 65+ years), race (White, Black, Asian, Hawaiian/Pacific, and Native American), Hispanic ethnicity (Hispanic and non-Hispanic), obesity, diabetes, smoker, dyspnea, poor functional status, ventilator dependence, chronic obstructive pulmonary disease (COPD), ascites, congestive heart failure (CHF), hypertension, renal failure, dialysis, disseminated cancer, open wounds, steroid use, weight loss, bleeding disorders, preoperative blood transfusions, and systemic sepsis.…”

Income Level and Impaired Kidney Function Among Working Adults in Japan

“…These programs could have the potential to reduce health disparities; therefore, benchmarking the levels of income-based disparity in medical outcomes related to CKD in this population could advance our understanding of the socioeconomic status (SES) disparities in CKD, and provide clinicians and decision-makers with the crucial information needed to combat CKD. Given the difference in income levels by sex and difference in the risk of developing or progressing CKD according to diabetes status and baseline kidney function, 25 , 26 , 27 we also hypothesized that there would be heterogeneity in the income-CKD association according to these baseline characteristics. Therefore, considering these various factors, we sought to assess the association between individual income and subsequent rapid CKD progression and initiation of KRT in the context of universal health coverage.…”

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

“…However, previous ML prediction models have primarily integrated baseline laboratory data and clinical information, and many studies have focused on predicting ESKD rather than eGFR changes ( 25 ). Furthermore, studies on CKD-related risk factor screening or analysis have predominantly relied on a single model without considering hybrid approaches, especially when employing ML methods ( 26 , 27 , 29 , 30 ). According to a previous study, the mean annual eGFR decline in healthy individuals was estimated to be 0.97 ± 0.02 mL/min/1.73 m 2 ( 31 ).…”