Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease

Tan, Hui Zhuan; Choo, Jason Chon Jun; Fook‐Chong, Stephanie; Chin, Yok Mooi; Chan, Choong Meng; Tan, Chieh Suai; Woo, Keng Thye; Kwek, Jia Liang

doi:10.1007/s11255-022-03299-x

Cited by 5 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three recent studies [21][22][23] have examined the associations of various clinical features with a kidney biopsy diagnosis and have used these associations to develop predictive models. Diabetic retinopathy was the only clinical feature which was included in all predictive models.…”

Section: Discussionmentioning

confidence: 99%

“…Diabetic retinopathy was the only clinical feature which was included in all predictive models. The duration of diabetes was explored in one model [21] but was not significant. Once again, these associations were retrospectively determined after obtaining a biopsy diagnosis, and predictive models were not prospectively tested.…”

Section: Discussionmentioning

confidence: 99%

“…These studies all involved single centres, Asian populations, and focused only on patients with type 2 DM. It is notable that two of these studies specifically involved patients with proteinuria [21,23], who would normally be assumed to have DKD, according to ADA criteria [2].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

The Diagnostic Performance of a Clinical Diagnosis of Diabetic Kidney Disease

Tan

McDonald

Hoy

2023

Life

View full text Add to dashboard Cite

Background: Diabetic kidney disease (DKD), a common cause of CKD and kidney failure, is usually diagnosed clinically. However, there is little evidence comparing the performance of a clinical diagnosis to biopsy-proven diagnosis. Purpose of the study: Diagnostic performance of a clinical diagnosis was determined in a group of patients with diabetes and chronic kidney disease who underwent kidney biopsy after an initial clinical diagnosis. Methods: A data analysis of 54 patients who were part of a study cohort for a prospective analysis of cardiovascular and kidney outcomes and who had undergone kidney biopsy after an initial clinical diagnosis of DKD or non-DKD (NDKD) at enrolment was used. We determined the sensitivity, specificity, and positive and negative predictive values of a clinical diagnosis of DKD. Results: A total of 37 of 43 patients clinically diagnosed with DKD also had biopsy-proven DKD, whilst only 1 of 11 patients who had clinically diagnosed NDKD had biopsy-proven DKD. Sensitivity was 97.4%, specificity was 62.5%, positive predictive value 86%, and negative predictive value 90.9%. Comparable values were obtained when analysis was restricted to those with primary rather than secondary diagnosis of DKD or when restricted to those with only DKD found at biopsy. Conclusion: A clinical diagnosis of DKD has high sensitivity and is unlikely to overlook cases but may lead to overdiagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Diagnostic Performance of a Clinical Diagnosis of Diabetic Kidney Disease

Tan

McDonald

Hoy

2023

Life

View full text Add to dashboard Cite

show abstract

“…Furthermore, Zou Y et al 10 analyzed the clinical data of 390 DKD patients and created a research model to predict the probability of end-stage renal disease. Tan HZ et al 11 examined 102 individuals with T2DM. They observed that the length of T2DM, HbA1c levels, absence of hematuria, presence of diabetic retinopathy, and lack of positive systemic biomarkers were all independent predictors of DKD.…”

Section: Example Of Radiomics Nomogrammentioning

confidence: 99%

Two-Dimensional Ultrasound-Based Radiomics Nomogram for Diabetic Kidney Disease: A Pilot Study

Huang,

Hu,

Zhang

et al. 2024

IJGM

View full text Add to dashboard Cite

To establish a radiomics nomogram based on two-dimensional ultrasound for risk assessment of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: This study retrospectively collected two-dimensional ultrasound images and clinical data from 52 patients with T2DM who underwent renal biopsy in our hospital from January 2023 to August 2023. Based on the pathological results, all patients were categorized into two groups: DKD (n=33) and non-DKD (n=19). The radiomic features of the segmented kidney in ultrasound pictures were retrieved and selected to calculate each patient's rad-score. A predictive nomogram based on rad-score and clinical features was then constructed and validated based on the calibration curve. Results:The rad-score for all patients were computed based on five imaging characteristics extracted from the ultrasound images. The predictive nomogram was developed with the rad-score, diabetic retinopathy, duration of diabetes, and glycosylated hemoglobin. Moreover, This radiomics nomogram showed outstanding calibration capability, discrimination as well as therapeutic usefulness. Conclusion:We constructed a nomogram based on two-dimensional ultrasound for DKD in T2DM patientsThe model has been proven to have good predictive performance, showing its potential in identifying DKD in T2DM patients and assisting in making appropriate early interventions.

show abstract