A Better Predictor…

You probably have heard from your oncologist/hematologist that there are multiple factors (variables) involved in predicting survival for patients with CLL, including:

    • age,
    • beta2-microglobulin (B2M) expression,
    • presence of an unmutated immunoglobulin variable region (IgVH),
    • high ZAP-70 expression,
    • high CD38 expression, and
    • certain cytogenetics (chromosomal abnormalities by FISH).


While, individually, each variable may contribute to prediction of survival, such a univariate (single variable) assessment is less accurate than a multivariate assessment, which includes a weighted combination of factors. Researchers performed multivariate analyses of these factors and used their findings to create a nomogram which enables us to obtain, easily and quickly,  a better prediction of survival.


Figure 1. Nomogram for survival of untreated patients with CLL. The nomogram is used by totaling the points identified on the top scale for each independent covariate. This total is then identified on the total points scale to identify the estimated median survival time (years) and the probability of 5- and 10-year survival. Sources: British Journal Haematology 2009;145:801-805 and Blood Journal.

For example, here are the points (in parentheses) for each variable and the survival estimates for the point total for a 72 year old male patient with the other conditions, noted:

    • Age: 72 (78)
    • B2M: 4 (24)
    • ALC: 0 (0)
    • Sex: Male (9)
    • RIA: II (0)
    • Nodes: 2 (0)
    • Total (1 1 1 )
    • Estimated Median Survival: ~ 6.3 years
    • 5-year Survival Probability: ~ .59
    • 10-year Survival Probability: < .2

If you have your values for the prognostics, above, give the nomogram a try, realizing that it is a better predictor than any values taken singly.


About Webmaster

Webmaster for gotCLL.com is a retired educator who has worked for more than 50 years in secondary, higher, medical, and life-long learning education.
This entry was posted in CLL, CLL Patient, Prediction and tagged , . Bookmark the permalink.

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