A Simple Algorithm to Predict Incident Kidney Disease

Kshirsagar, Abhijit V.; Bang, Heejung; Bomback, Andrew S.; Vupputuri, Suma; Shoham, David A.; Kern, Lisa M.; Klemmer, Philip J.; Mazumdar, Madhu; August, Phyllis

doi:10.1001/archinte.168.22.2466

Cited by 113 publications

(120 citation statements)

References 39 publications

(18 reference statements)

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“…The area under the ROC curve (0.88) was good, and also the performance in an external dataset was acceptable (UAC of the ROC curve 0.71) (28). The second prediction model was developed in a population-based cohort in the United States and resulted in a prediction model for incident CKD (defined as eGFR Ͻ60 ml/min per 1.73 m 2 ) within a follow-up of 9 years (29). The authors also performed a sensitivity analysis defining CKD as developing an eGFR Ͻ60 ml/min per 1.73 m 2 and a renal function decline of at least 10 ml/min per 1.73 m 2 during the 9-year follow-up period.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a General Population Renal Risk Score

Halbesma¹,

Jansen

Heymans

et al. 2011

Clinical Journal of the American Society of Nephrology

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SummaryBackground and objectives There is a need for prediction scores that identify individuals at increased risk for developing progressive chronic kidney disease (CKD). Therefore, this study was performed to develop and validate a "renal risk score" for the general population.Design, setting, participants, & measurements For this study we used data from the PREVEND (Prevention of Renal and Vascular ENdstage Disease) study, a prospective population-based cohort study with a median follow-up of 6.4 years. Participants with two or three consecutive estimated GFR (eGFR) measurements during follow-up were included. Participants within the group who had the most renal function decline (top 20% of the total population) and had an eGFR value Ͻ60 ml/min per 1.73 m 2 during follow-up were defined as having progressive CKD. Possible predictors for progressive CKD were selected on the basis of univariable logistic regression analyses.Results A final prediction model was built using backward logistic regression analysis. Besides baseline eGFR, the model contained age, urinary albumin excretion, systolic BP, C-reactive protein, and known hypertension. The area under the receiver operating characteristic (ROC) curve was 0.84. We performed internal validation by using a bootstrapping procedure. As expected, after the regression coefficients were corrected for optimism, the area under the ROC curve was still 0.84. For clinical use we divided all predictors in meaningful clinical categories to develop a score chart. The area under the ROC curve was 0.83, indicating the high discriminative value of this model. ConclusionsGiven the high internal validity of this renal risk score, this score can be helpful to identify individuals at increased risk for progressive CKD.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a General Population Renal Risk Score

Halbesma¹,

Jansen

Heymans

et al. 2011

Clinical Journal of the American Society of Nephrology

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“…Existing prediction models for kidney disease have been developed in the US, 18 Canada, 19 the Netherlands, 18,20 and Taiwan; 21 however, the performance data to support these models are, at best, comparable 19,20 to the QKidney scores without serum creatinine. There are also no data to support their use in the UK.…”

Section: Discuss This Articlementioning

confidence: 99%

Predicting the risk of chronic kidney disease in the UK: an evaluation of QKidney® scores using a primary care database

Collins

Altman

2012

Br J Gen Pract

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“…The patient population drawn from government community-based polyclinics in Singapore were more similar to KEEP with a high percentage (95% CI) of pre-existing hypertension 83.2% (82.9-83.4%) and/or diabetes 42.8% (42.5-43.1%), and 25.9% (25.6-26.2%) of the individuals screened had CKD. The observed OR for CKD was 1.71 (1.63-1.79) among individuals with hypertension in KEEP, and 1.72 (1.51-1.96) in NHANES (1999-2004) cohorts, while the OR for CKD for individuals with diabetes was 1.45 (1.39-1.52) and 2.68 (2.17-3.33), respectively [21,22]. In contrast, in our study, hypertension almost tripled the odds of CKD (OR 2.89 [2.69-3.11]) while diabetes resulted in 7-fold higher odds of CKD (OR 7.23 [6.95-7.52]).…”

Section: Physician Documentation Of Ckd In Ehrmentioning

confidence: 96%

Factors Associated with Chronic Kidney Disease and Their Clinical Utility in Primary Care Clinics in a Multi-Ethnic Southeast Asian Population

Lew

Allen

Nguyen

et al. 2017

Nephron

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Background: Chronic kidney disease (CKD) is a major global public health challenge. We investigated determinants of CKD and their clinical utility in an ethnically diverse Southeast Asian population. Methods: Electronic health records (EHR) of adults ≥40 years who visited any one of 4 government polyclinics in Singapore from January 1, 2012 to December 31, 2015 were analyzed. CKD was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² or 1+ dipstick proteinuria excretion, based on 2 measurements ≥3 months apart. CKD-associated factors and their clinical utility for predicting odds of CKD were investigated using multiple logistic regression analysis. Results: Based on the study criteria, 25.9% (95% CI 25.6–26.2) of the 88,765 eligible study individuals had CKD. The factors (OR and 95% CI) independently associated with CKD were older age ≥65 years (2.54 [2.44–2.64] vs. ≤65 years), respectively; men (1.13 [1.09–1.18]); Malay (1.27 [1.20–1.33]) and Indian (0.77 [0.71–0.83]) vs. Chinese ethnicity; overweight (body mass index [BMI] ≥27.5 kg/m²; 1.10 [1.04–1.16]) vs. normal weight (BMI 18 to <23 kg/m²); government (1.22 [1.15–1.31]) vs. private housing; and with hypertension (3.32 [3.09–3.56]), diabetes (6.93 [6.67–7.20]) or stroke (1.46 [1.36–1.56]) vs. without each co-morbidity, respectively. The area under the receiver operating characteristic curve (95% CI) for the model to predict the probability of CKD using hypertension, diabetes, and age was 0.808 (0.805–0.811). Only 28.5% (27.9–29.1%) of individuals with CKD had physician documentation of their CKD status. However, documentation of CKD status was associated with age ≥65 years (1.11 [1.04–1.20] vs. <65 years), men (1.35 [1.26–1.44]) vs. women, with vs. without hypertension (1.24 [1.07–1.44]), Indian (0.80 [0.69–0.92]) compared to Chinese ethnicity, ever smokers (0.89 [0.81–0.99]) vs. non-smokers, and those with vs. without stroke (0.83 [0.75–0.93]). Conclusions: CKD prevalence in our Southeast Asian population is high and under-documented even in high-risk patients. Our findings highlight factors associated with CKD, and the predictive value of hypertension, diabetes, and advancing age as EHR-based screening targets for CKD. Our results also suggest that complementary educational efforts will be needed to increase physician detection and optimize the management of CKD, especially in high risk and marginalized groups across all clinics in Singapore, and possibly in the region.

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A Simple Algorithm to Predict Incident Kidney Disease

Cited by 113 publications

References 39 publications

Development and Validation of a General Population Renal Risk Score

Development and Validation of a General Population Renal Risk Score

Predicting the risk of chronic kidney disease in the UK: an evaluation of QKidney® scores using a primary care database

Factors Associated with Chronic Kidney Disease and Their Clinical Utility in Primary Care Clinics in a Multi-Ethnic Southeast Asian Population

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