Clinical predictive factors and prediction models for end‐stage renal disease in Chinese patients with type 2 diabetes mellitus
Dear Editor Diabetes mellitus (DM) has become a significant chronic condition that seriously affects human health.1 Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM).2 The increasing prevalence of DM ex...
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Published in: | Clinical and translational medicine Vol. 13; no. 7; pp. e1323 - n/a |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
John Wiley & Sons, Inc
01-07-2023
John Wiley and Sons Inc Wiley |
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Online Access: | Get full text |
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Summary: | Dear Editor Diabetes mellitus (DM) has become a significant chronic condition that seriously affects human health.1 Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM).2 The increasing prevalence of DM exacerbates the incidence of end-stage renal disease (ESRD).3 T2DM-related ESRD not only reduces survival rate and health-related quality of life but also places a significant cost on patients as well as society.4–6 To identify clinical predictive factors and develop prediction models for ESRD risk in T2DM patients, we used the study population extracted from the China Renal Data System, a database containing the information of more than seven million patients attended at 19 hospitals in the Chinese mainland, as previously described.7 ESRD, including an eGFR of 15 mL/min/1.73 m2 or less, or the commencement of dialysis or kidney transplantation due to ESRD, was classified as the outcome. Ten clinical predictive factors, including age, hypertension, diabetes retinopathy (DR), hemoglobin (HGB), serum albumin (ALB), serum creatinine (Scr), serum uric acid, Low-density lipoprotein cholesterol (LDL-C), serum fibrinogen, and urinary protein were selected into the final model (Table S3). Predictors Model 1 (full model) Model 2 (laboratory model) Model 3 (Simplified model) Age (incremented by 1 year) 0.993 / .988 Hypertension 1.651 / 1.829 DR 1.433 / / HGB (incremented by 1 g/L) 0.982 0.982 .980 Serum ALB (incremented by 1 g/L) 0.962 0.963 / Scr (incremented by 1 μmol/L) 1.009 1.009 1.010 Serum uric acid (incremented by 1 μmol/L) 1.001 1.001 / LDL-C (incremented by 1 mmol/L) 1.091 1.110 / Serum fibrinogen (incremented by 1 g/L) 1.055 1.070 / Urinary protein† 4.608 5.159 6.000 Urinary protein‡ 7.647 8.930 12.309 / / AUC (derivation cohort) 0.926 (0.919, 0.934) 0.924 (0.917, 0.932) .916 (0.908, 0.924) AUC (internal derivation cohort) 0.927 (0.920, 0.935) 0.925 (0.917, 0.932) 0.914 (0.906, 0.922) AUC (eternal validation cohort) 0.882 (0.871, 0.894) 0.877 (0.866, 0.889) 0.868 (0.856, 0.881) Abbreviations: ALB, albumin; AUC, area under the curve; DR, diabetic retinopathy; HGB, hemoglobin; LDL-C, low-density lipoprotein cholesterol; Scr, serum creatinine. † The level of urine protein was 1+ or 2+. ‡ The level of urine protein was 3+ or 4+. [...]a risk score was developed as follows: age (year; ≥ 56 scores 0 and < 56 scores 1), hypertension (yes scores 2 and no scores 0), DR (yes scores 1 and no scores 0), HGB (g/L; ≥ 108 scores 0 and < 108 scores 2), serum ALB (g/L; ≥ 33 scores 0 and < 33 scores 2), Scr (μmol/L; < 115 scores 0 and ≥ 115 scores 5), serum uric acid (μmol/L; < 435 scores 0 and ≥ 435 scores 1), LDL-C (mmol/L; < 4 scores 0 and ≥ 4 scores 1), serum fibrinogen (g/L; < 4 scores 0 and ≥ 4 scores 1) and urinary protein (0 – ± scores 0, 1+−2+ scores 4 and 3+−4+ scores 8), as shown in Table 2. |
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Bibliography: | Yueming Gao and Zhi Shang contributed equally to this work and share first authorship. SourceType-Other Sources-1 ObjectType-Article-2 content type line 63 ObjectType-Correspondence-1 |
ISSN: | 2001-1326 2001-1326 |
DOI: | 10.1002/ctm2.1323 |