End-stage renal disease risk equations for Hong Kong Chinese patients with type 2 diabetes: Hong Kong Diabetes Registry

Department of Medicine and Therapeutics, The Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, People's Republic of China.
Diabetologia (Impact Factor: 6.67). 10/2006; 49(10):2299-308. DOI: 10.1007/s00125-006-0376-3
Source: PubMed


The objective of the study was to investigate risk factors and develop risk equations for end-stage renal disease (ESRD) in Chinese patients with type 2 diabetes.
A prospective cohort of 4,438 patients with type 2 diabetes mellitus and without ESRD (median observation period 2.9 years, interquartile range 1.6-4.1 years) was included in the analysis. The end-point (ESRD) was defined by: (1) death due to diabetes with renal manifestations or renal failure; (2) hospitalisation due to renal failure; (3) estimated GFR (eGFR) <15 ml min(-1) 1.73 m(-2). Cox proportional hazards regression was used to develop risk equations. The data were randomly and evenly divided into the training data for development of the risk equations and the test data for validation. The validation was performed using the area under the receiver operating characteristic curve (aROC), which takes into account follow-up time and censoring.
During the observation period, 159 patients or 12.45 per 1,000 person-years (95% CI 10.52-14.37 per 1,000 person-years) developed ESRD. Known duration of diabetes, systolic blood pressure, log(10) total cholesterol:HDL cholesterol ratio and retinopathy were significant predictors of ESRD. After further adjusting for eGFR, log(10) spot albumin:creatinine ratio (ACR) and haematocrit, only eGFR, haematocrit and log(10) ACR remained as independent predictors of ESRD. The risk equation derived from these three independent predictors had good discrimination, with an aROC of 0.97.
Estimated GFR, haematocrit and ACR were independent predictors of ESRD and the derived risk equation performed well in Chinese patients with type 2 diabetes.

Download full-text


Available from: Xilin Yang,
  • Source
    • "All patients underwent a structured 4-hour clinical and biochemical assessment, details of which have been described [9]. In brief, anthropometric measurements and blood pressure (BP) were obtained. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naive Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNP) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naive Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD.
    BMC Nephrology 07/2013; 14(1):162. DOI:10.1186/1471-2369-14-162 · 1.69 Impact Factor
  • Source
    • "Although the JADE was developed based on data from patients receiving secondary or tertiary care, it forms the base for the stratification of local diabetic patients into very high risk, high risk, medium risk and low risk in the RAMP at primary care level. Apart from stratifying diabetic patients into different risk levels, equations integrating different patient’s clinical parameters have been formulated to predict the diabetic patient’s 5-year risk of coronary heart disease, stroke, end-stage renal disease, and all-cause mortality [12-15]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Type 2 Diabetes Mellitus (DM) is a common chronic disease associated with multiple clinical complications. Management guidelines have been established which recommend a risk-stratified approach to managing these patients in primary care. This study aims to evaluate the quality of care (QOC) and effectiveness of a multi-disciplinary risk assessment and management programme (RAMP) for type 2 diabetic patients attending government-funded primary care clinics in Hong Kong. The evaluation will be conducted using a structured and comprehensive evidence-based evaluation framework. Method/design For evaluation of the quality of care, a longitudinal study will be conducted using the Action Learning and Audit Spiral methodologies to measure whether the pre-set target standards for criteria related to the structure and process of care are achieved. Each participating clinic will be invited to complete a Structure of Care Questionnaire evaluating pre-defined indicators which reflect the setting in which care is delivered, while process of care will be evaluated against the pre-defined indicators in the evaluation framework. Effectiveness of the programme will be evaluated in terms of clinical outcomes, service utilization outcomes, and patient-reported outcomes. A cohort study will be conducted on all eligible diabetic patients who have enrolled into RAMP for more than one year to compare their clinical and public service utilization outcomes of RAMP participants and non-participants. Clinical outcome measures will include HbA1c, blood pressure (both systolic and diastolic), lipids (low-density lipoprotein cholesterol) and future cardiovascular diseases risk prediction; and public health service utilization rate will include general and specialist outpatient, emergency department attendances, and hospital admissions annually within 5 years. For patient-reported outcomes, a total of 550 participants and another 550 non-participants will be followed by telephone to monitor quality of life, patient enablement, global rating of change in health and private health service utilization at baseline, 6, 12, 36 and 60 months. Discussion The quality of care and effectiveness of the RAMP in enhancing the health for patients with type 2 diabetes will be determined. Possible areas for quality enhancement will be identified and standards of good practice can be established. The information will be useful in guiding service planning and policy decision making.
    BMC Family Practice 12/2012; 13(1):116. DOI:10.1186/1471-2296-13-116 · 1.67 Impact Factor
  • Source
    • "Details of the 4-h assessment of complications and risk factors have been reported [8] [9] [21]. Briefly, on the day of assessment, patients attended the centre after 8 h of fasting and underwent anthropometric measurements and laboratory investigations . "
    [Show abstract] [Hide abstract]
    ABSTRACT: High white blood cell (WBC) predicted cancer-associated mortality and renin-angiotensin system (RAS) inhibitors have immunomodulating effects. We hypothesize that RAS inhibitors may reduce cancer risk associated with high WBC in type 2 diabetes mellitus (T2DM). A prospective cohort of 4570 Chinese T2DM patients, free of cancer at enrolment, were analyzed. Biological interaction between WBC groups and use of RAS inhibitors was estimated using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (S). RERI>0, AP>0 or S>1 indicates biological interaction. During 4.89 years of follow-up, 205 (4.49%) patients developed cancer. WBC > or = 8.2 x 10(9) counts/L plus non-use of RAS inhibitors was associated with elevated cancer risks in multivariable models. The RERI and AP for interaction between WBC > or = 8.2 x 10(9) counts/L and non-use of RAS inhibitors were, respectively, 1.26 (95% CI: 0.22-2.31) and 0.50 (0.23-0.78). In patients with WBC > or = 8.2 x 10(9) counts/L, use of RAS inhibitors was associated with 64% (31-81%) cancer risk reduction in multivariable analysis. In T2DM, increased WBC predicts cancer while use of RAS inhibitors may reduce cancer risks associated with high WBC count.
    Diabetes research and clinical practice 11/2009; 87(1):117-25. DOI:10.1016/j.diabres.2009.10.012 · 2.54 Impact Factor
Show more