Ting Zhou1, Yiyun Wang2, Li Shen3, Xiaomei Li1, Qiong Jiao1, Ze Li1, Junjie Jia1, Li He1, Qunzi Zhang1, Niansong Wang1, Ying Fan1. 1. Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. 2. Department of Emergency, Shanghai United Family Hospital, Shanghai, China. 3. Clinical Research Unit, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Abstract
INTRODUCTION: Clinical indicators or pathological features alone cannot reliably predict renal survival in patients with biopsy-confirmed diabetic nephropathy (DN). Therefore, this analysis sought to develop and validate a predictive model incorporating both clinical and pathological markers to predict renal outcomes in patients with biopsy-confirmed DN. METHODS: A predictive nomogram was developed based upon data pertaining to 194 patients with biopsy-confirmed DN. The prognostic relevance of individual clinicopathological variables was assessed through univariate and multivariate Cox regression analyses. A prognostic nomogram was then developed and validated based upon concordance (C)-index values and calibration curves. Internal validation was conducted through bootstrap resampling, while the clinical utility of this model was assessed via a decision curve analysis (DCA) approach. RESULTS: Nephrotic-range 24-h proteinuria, late-stage CKD, glomerular classification III-IV, and IFTA score 2-3 were all identified as independent predictors of poor renal outcomes in DN patients and were incorporated into our final nomogram. Calibration curves revealed good agreement between predicted and actual 3- and 5-year renal survival in DN patients with the C-index value for this nomogram at 0.845 (95% CI: 0.826-0.864). DCA revealed that our nomogram was superior to models based solely upon clinical indicators. CONCLUSION: A predictive nomogram incorporating clinical and pathological indicators was developed and validated for the prediction of renal survival outcomes in patients with biopsy-confirmed DN. This model will be of value to clinicians, as it can serve as an easy-to-use and reliable tool for physicians to guide patient management based on individualized risk.
INTRODUCTION: Clinical indicators or pathological features alone cannot reliably predict renal survival in patients with biopsy-confirmed diabetic nephropathy (DN). Therefore, this analysis sought to develop and validate a predictive model incorporating both clinical and pathological markers to predict renal outcomes in patients with biopsy-confirmed DN. METHODS: A predictive nomogram was developed based upon data pertaining to 194 patients with biopsy-confirmed DN. The prognostic relevance of individual clinicopathological variables was assessed through univariate and multivariate Cox regression analyses. A prognostic nomogram was then developed and validated based upon concordance (C)-index values and calibration curves. Internal validation was conducted through bootstrap resampling, while the clinical utility of this model was assessed via a decision curve analysis (DCA) approach. RESULTS: Nephrotic-range 24-h proteinuria, late-stage CKD, glomerular classification III-IV, and IFTA score 2-3 were all identified as independent predictors of poor renal outcomes in DN patients and were incorporated into our final nomogram. Calibration curves revealed good agreement between predicted and actual 3- and 5-year renal survival in DN patients with the C-index value for this nomogram at 0.845 (95% CI: 0.826-0.864). DCA revealed that our nomogram was superior to models based solely upon clinical indicators. CONCLUSION: A predictive nomogram incorporating clinical and pathological indicators was developed and validated for the prediction of renal survival outcomes in patients with biopsy-confirmed DN. This model will be of value to clinicians, as it can serve as an easy-to-use and reliable tool for physicians to guide patient management based on individualized risk.
Authors: Celine Q F Klessens; Tess D Woutman; Kimberley A M Veraar; Malu Zandbergen; Elisabeth J J Valk; Joris I Rotmans; Ron Wolterbeek; Jan A Bruijn; Ingeborg M Bajema Journal: Kidney Int Date: 2016-04-09 Impact factor: 10.612