title: Charlson comorbidity index helps predict the risk of mortality for patients with type 2 diabetic nephropathy.
abstract: Our intent is to examine the predictive role of Charlson comorbidity index (CCI) on mortality of patients with type 2 diabetic nephropathy (DN). Based on the CCI score, the severity of comorbidity was categorized into three grades: mild, with CCI scores of 1-2; moderate, with CCI scores of 3-4; and severe, with CCI scores ≥5. Factors influencing mortality and differences between groups stratified by CCI were determined by logistical regression analysis and one-way analysis of variance (ANOVA). The impact of CCI on mortality was assessed by the Kaplan-Meier analysis. A total of 533 patients with type 2 DN were enrolled in this study, all of them had comorbidity (CCI score >1), and 44.7% (238/533) died. The mortality increased with CCI scores: 21.0% (50/238) patients with CCI scores of 1-2, 56.7% (135/238) patients with CCI scores of 3-4, and 22.3% (53/238) patients with CCI scores ≥5. Logistical regression analysis showed that CCI scores, hemoglobin, and serum albumin were the potential predictors of mortality (P<0.05). One-way ANOVA analysis showed that DN patients with higher CCI scores had lower levels of hemoglobulin, higher levels of serum creatinine, and higher mortality rates than those with lower CCI scores. The Kaplan-Meier curves showed that survival time decreased when the CCI scores and mortality rates went up. In conclusion, CCI provides a simple, readily applicable, and valid method for classifying comorbidities and predicting the mortality of type 2 DN. An increased awareness of the potential comorbidities in type 2 DN patients may provide insights into this complicated disease and improve the outcomes by identifying and treating patients earlier and more effectively.
authors.affiliation: Division of Nephrology, West China Hospital of Sichuan University, Chengdu 610041, China.
authors.forename: You-qun
authors.initials: YQ
authors.lastname: Huang
authors.forename: Rong
authors.initials: R
authors.lastname: Gou
authors.forename: Yong-shu
authors.initials: YS
authors.lastname: Diao
authors.forename: Qing-hua
authors.initials: QH
authors.lastname: Yin
authors.forename: Wen-xing
authors.initials: WX
authors.lastname: Fan
authors.forename: Ya-ping
authors.initials: YP
authors.lastname: Liang
authors.forename: Yi
authors.initials: Y
authors.lastname: Chen
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.forename: Li
authors.initials: L
authors.lastname: Zang
authors.forename: Ling
authors.initials: L
authors.lastname: Li
authors.forename: Jing
authors.initials: J
authors.lastname: Zang
authors.forename: Lu
authors.initials: L
authors.lastname: Cheng
authors.forename: Ping
authors.initials: P
authors.lastname: Fu
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
mesh_terms: D017677:Age Distribution; D000368:Aged; D002681:China / Q000453:epidemiology; D015897:Comorbidity; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000401:mortality*; D003928:Diabetic Nephropathies / Q000175:diagnosis / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D015994:Incidence; D008297:Male; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D016019:Survival Analysis