Chronic kidney disease (CKD) is associated with high incidence, low awareness, and high disability rates among the population. Moreover, the disease significantly affects the physical and mental health of patients. Approximately 25% of patients with CKD develop end-stage renal disease (ESRD) within 20 years of diagnosis and have to rely on renal replacement therapy, which is associated with high mortality, heavy economic burden, and symptoms including fatigue, pain, insomnia, uremia pruritus, and restless leg syndrome. Currently, the means to delay the progress of CKD are insufficient; therefore, developing strategies for delaying CKD progression has important practical implications. In recent years, more and more people are accepting the traditional Chinese medical technique “acupuncture.” Acupuncture has been shown to improve the uncomfortable symptoms of various diseases through stimulation (needling, medicinal moxibustion, infrared radiation, and acupressure) of acupoints. Its application has been known for thousands of years, and its safety and efficacy have been verified. As a convenient and inexpensive complementary therapy for CKD, acupuncture has recently been gaining interest among clinicians and scientists. Nevertheless, although clinical trials and meta-analysis findings have demonstrated the efficacy of acupuncture in reducing albuminuria, improving glomerular filtration rate, relieving symptoms, and improving the quality of life of patients with CKD, the underlying mechanisms involved are still not completely understood. Few studies explored the correlation between acupuncture and renal pathological diagnosis. The aim of this study was to conduct a literature review summarizing the currently known mechanisms by which acupuncture could delay the progress of CKD and improve symptoms in patients with ESRD. This review help provide a theoretical basis for further research regarding the influence of acupuncture on renal pathology in patients with CKD, as well as the differences between specific therapeutic mechanisms of acupuncture in different renal pathological diagnosis. The evidence in this review indicates that acupuncture may produce marked effects on blocking and reversing the critical risk factors of CKD progression (e.g., hyperglycemia, hypertension, hyperlipidemia, obesity, aging, and anemia) to improve the survival of patients with CKD via mechanisms including oxidative stress inhibition, reducing inflammatory effects, improving hemodynamics, maintaining podocyte structure, and increasing energy metabolism.

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate’s method of 99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland–Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland–Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.