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A simple model for predicting incidence of chronic kidney disease in HIV-infected patients
Minoru Ando1, Naoki Yanagisawa, Atsushi Ajisawa
1Department of Nephrology, Tokyo Metropolitan Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-Ku, Tokyo 113-0021, Japan. nephrol@cick.jp
Insights
A new prediction model identifies human immunodeficiency virus (HIV)-infected individuals at high risk for chronic kidney disease (CKD). The model uses factors like age, CD4 count, diabetes, proteinuria, and baseline eGFR to predict incident CKD.
Area of Science:
- Nephrology
- Infectious Diseases
- Biostatistics
Background:
- Individuals with HIV face multiple risk factors for chronic kidney disease (CKD).
- Existing prediction models do not adequately quantify the combined risk of multiple factors for incident CKD in this population.
Purpose of the Study:
- To develop and validate a prognostic model for predicting the incidence of CKD in HIV-infected individuals.
- To identify key baseline factors associated with the development of new-onset CKD.
Main Methods:
- A 1-year prognostic model was developed using prospective data from a derivation cohort (n=623).
- Incident CKD was defined by a sustained decrease in estimated glomerular filtration rate (eGFR) below 60 ml/min/1.73 m².
- Multivariate logistic regression identified predictors, and the model was validated on a separate cohort (n=534).
Main Results:
- Eighteen subjects (2.9%) developed incident CKD.
- Five factors independently predicted CKD: age, CD4 cell count, diabetes, proteinuria, and baseline eGFR.
- The model demonstrated good discriminative ability with an area under the receiver operating characteristic curve (AROC) of 0.841 in the validation cohort.
Conclusions:
- The developed prediction model is a potentially valuable tool for identifying HIV-infected individuals at high risk of developing CKD.
- Early identification allows for targeted interventions to prevent or slow CKD progression in this vulnerable group.
Background:
Human immunodeficiency virus (HIV)-infected individuals have concurrent risk factors for chronic kidney disease (CKD), but there are no prediction models to quantify the effect of multiple factors on the development of incident CKD.
Methods:
A 1-year prognostic model was developed using prospective data between January 2008 and March 2009, from a derivation cohort of 623 patients with an estimated glomerular filtration rate (eGFR) exceeding 60 ml/min/1.73 m(2) at baseline. Incident CKD was defined as both an eGFR falling below 60 ml/min/1.73 m(2) and a decrease in eGFR of at least 25% during the period. Continuous variables were divided into categories using the mean value of the whole cohort. Multivariate logistic regression was used to determine baseline categories associated with incident CKD. The model was tested with prospective data between April 2009 and March 2010, from a validation cohort of 534 patients exceeding 60 ml/min/1.73 m(2) at baseline, consisting of part of the derivation cohort and newly enrolled patients. The discriminative ability was evaluated using the area under the receiver operating characteristic (AROC) curve.
Results:
Eighteen subjects developed incident CKD (2.9%). In a multivariate logistic regression analysis of the derivation cohort, 5 variables (age, CD4 cell count, diabetes, proteinuria, and eGFR at baseline) were independently associated with the incidence of CKD. The AROC curve was 0.841 (95% CI 0.799-0.894) in the validation cohort.
Conclusion:
This prediction model may be a useful tool for identifying HIV-infected individuals with a high likelihood of new-onset CKD.
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