Validation of the 2007 kidney disease outcomes quality initiative clinical practice guideline for the diagnosis of diabetic nephropathy and nondiabetic renal disease in Chinese patients

Liu, Xiaomin; Dong, Zheyi; Zhang, Weiguang; Liu, Moyan; Zhou, Jianhui; Wang, Qian; Sun, Xuefeng; Wang, Yong; Cao, Xueying; Chen, Xiangmei

doi:10.1016/j.diabres.2018.11.008

Cited by 8 publications

(10 citation statements)

References 25 publications

(31 reference statements)

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“…The previous non-invasive identification guidelines widely used internationally are the 2007 KDOQI guidelines (18), which were evaluated in Chinese patients and found that many of the predictors were binary categorical variables. The low specificity of the guidelines renders them not suitable for diagnostic criteria (9). So we built the models using the logistic regression method in 2008 and 2014 (13,14) 48), n = 207)] also used the logistic regression method to build the diagnostic models, and all the models found the index like DR and DM course are important.…”

Section: Discussionmentioning

confidence: 99%

“…Laboratory data obtained included measurements of HbA1c, hemoglobin (Hb), serum creatinine (sCr), serum albumin (ALB), estimated glomerular filtration rate (eGFR), and serum lipid levels. Variable definitions have been provided in previous studies ( 9 ).…”

Section: Methodsmentioning

confidence: 99%

“…Compared to DN, NDRD would have a better renal prognosis and longer survival in most cases. The 2007 Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines are widely used for the clinical distinguish between DN and NDRD, but our previous study found that the specificity (40.63%) of the KDOQI guidelines was insufficient ( 9 ).…”

Section: Introductionmentioning

confidence: 98%

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New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

et al. 2022

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BackgroundThe disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD.MethodsWe retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017–April 2019).ResultsIn total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively.ConclusionWe successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods.Clinical Trial RegistrationClinicalTrial.gov, NCT03865914.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

et al. 2022

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“…However, further evaluation found that the diagnostic efficacy of the guidelines for Chinese patients was not satisfactory enough. [ 53 ] Therefore, 929 patients with type 2 diabetes who underwent renal biopsy from 2005 to 2017 were screened by Liu et al [unpublished]. Based on the clinical test and renal pathological diagnosis results, differential diagnosis models of DN and NDRD based on SVM and RF were established.…”

Section: In Nephrologymentioning

confidence: 99%

Machine learning in nephrology: scratching the surface

Fan

Han

et al. 2020

Chinese Medical Journal

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Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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“…However, nephrologists inconsistently adhere to the existing guidelines regarding KB indications in patients with T2D [10]. This is further supported by a recent study reconsidering the diagnostic ability of these guidelines in clinical practice [11]. These discrepancy between on the one hand guidelines and study results and actual clinical practice on the other hand probably results from 2 misconception.…”

Section: Introductionmentioning

confidence: 99%

Kidney Biopsy in Type 2 Diabetes: A Multicenter Cross-Sectional Study

et al. 2021

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Introduction: Kidney biopsies (KBs) are performed in patients with type 2 diabetes (T2D) to diagnose non-diabetic or hypertensive kidney disease (NDHKD) potentially requiring specific management compared to diabetic and or hypertensive nephropathy (absence of NDHKD). Indications for KB are based on the presence of atypical features compared to the typical course of diabetic nephropathy. In this study, we assessed the association of different patterns of atypical features, or KB indications, with NDHKD. Methods: Native KBs performed in patients with T2D were analyzed. Data were collected from the patients’ records. KB indications were determined according to the presence of different atypical features considered sequentially: (1) presence of any feature suggesting NDHKD which is not among the following ones, (2) recent onset of nephrotic syndrome, (3) low or rapidly declining estimated glomerular filtration rate (eGFR), (4) rapid increase in proteinuria, (5) short duration of diabetes, (6) presence of hematuria, or (7) normal retinal examination. Results: Among the 463 KBs analyzed, NDHKD was diagnosed in 40% of the total population and 54, 40, 24, and 7% of the KBs performed for indications 1–4 respectively. Conversely, no patient who underwent KB for indications 5–7 displayed NDHKD. Logistic regression analyses identified eGFRCKD-EPI >15 mL/min/1.73 m2, urinary protein-to-Cr ratio <0.3 g/mmol, hematuria, HbA1c <7%, and diabetes duration <5 years as predictors of NDHKD, independently from the indication group. Conclusion: NDHKD is frequent in T2D. Despite the association of hematuria with NDHKD, our results suggest that presence of hematuria and absence of DR are insufficient to indicate KB in the absence of concurrent atypical features. Conversely, rapid progression of proteinuria and rapid deterioration of eGFR are major signals of NDHKD.

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Validation of the 2007 kidney disease outcomes quality initiative clinical practice guideline for the diagnosis of diabetic nephropathy and nondiabetic renal disease in Chinese patients

Cited by 8 publications

References 25 publications

New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

Machine learning in nephrology: scratching the surface

Kidney Biopsy in Type 2 Diabetes: A Multicenter Cross-Sectional Study

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