Prediction of CD4 count from CD4 percentage: experience from three laboratories
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Published on: October 31, 2010
P G Kidd1, S C Cheng, H Paxton
1Department of Laboratory Medicine, University of Washington, Seattle 98195.
Insights
CD4 percentages can substitute for CD4 counts in HIV monitoring when full counts are unavailable. This study confirms a strong correlation, aiding clinical decisions and research stratification.
Area of Science:
- Immunology
- Clinical Laboratory Science
- Virology
Background:
- CD4 counts are crucial for monitoring HIV disease progression and guiding therapy.
- The Centers for Disease Control and Prevention (CDC) now includes CD4 counts in HIV disease classification.
- CD4 percentage may be the sole available laboratory metric in certain clinical scenarios.
Purpose of the Study:
- To assess the correlation between CD4 percentages and CD4 counts.
- To determine if CD4 percentages can be reliably substituted for CD4 counts.
Main Methods:
- A predictive model was developed using 1000 consecutive CD4 measurements from the University of Washington.
- The model was validated with independent datasets of 1000 CD4 measurements each from Maryland Medical Laboratories and Rush-Presbyterian-St Luke's Medical Center.
- Patient data included diverse demographics and HIV statuses.
Main Results:
- A CD4 percentage of 14% or higher correlated with a CD4 count of 200 cells/µL or higher.
- A CD4 percentage of 27% or higher correlated with a CD4 count of 500 cells/µL or higher.
- These thresholds showed predictive accuracy across different laboratory datasets.
Conclusions:
- CD4 percentage correlations provide a viable alternative when CD4 counts cannot be directly calculated.
- This finding supports the use of CD4 percentages for HIV patient stratification and monitoring.
- The study highlights the utility of CD4 percentage in resource-limited settings or when complete blood counts are unavailable.
Objective:
CD4 counts have been used to monitor progression of disease in HIV infection as criteria for initiation of therapy, and to stratify and follow patients in clinical trials. Recently, the Centers for Disease Control and Prevention (CDC) has made CD4 counts part of the classification of HIV disease. Because a CD4 percentage may be the only laboratory information available, this study was initiated to determine whether the correlation between CD4 percentages and CD4 counts is sufficiently high to enable these measures to be substituted for each other.
Design, Setting And Patients:
One thousand consecutive CD4 measurements from the University of Washington (UW) were used to create a model that was tested using datasets of 1000 CD4 measurements each from Maryland Medical Laboratories (MML) and Rush-Presbyterian-St Luke's Medical Center (Rush). The patients were not selected for age, sex, risk group or treatment. All patients from MML and Rush were known to be HIV-positive, while the HIV status of all UW patients was unknown.
Results:
The model predicted that a patient with a CD4 percentage > or = 14% would have a CD4 count > or = 200 x 10(6)/l(if CD4 percentage of 14% was used, 9% of patients would have a CD4 count > or = 200 x 10(6)/l), and a patient with a CD4 percentage > or = 27% would have a CD4 count > or = 500 x 10(6)/l(if CD4 percentage of 27% was used, 17% of patients would have a CD4 count > or = 500 x 10(6)/l).
Conclusions:
These CD4 percentage correlations may be useful when a white blood cell and lymphocyte count are not available to calculate the CD4 count.
