This Sokal score for CML calculator prognoses survival rates in patients with chronic myeloid leukemia based on spleen size, platelet count and myeloblasts. You can find more information on this index and instructions on how to use it below the form.
How does this Sokal score for CML calculator work?
This health tool offers a general prognostic of survival rates in patients with chronic myeloid leukemia (CML) based on clinical data and laboratory results.
The Sokal Index is based on a study from the 1980s with patients diagnosed with chronic phase CML between 1962 and 1981. The main criticism of the model is that it underestimates survival rates due to the fact that before the 1980, tyrosine kinase inhibitors were yet to be introduced as a treatment and that survival rates were lower than currently. At that time, the main chemotherapy agent was busulfan.
There are four variables, 4 independent prognostic factors that are used in the calculation:
■ Patient age – elder patients are more susceptible to develop CML.
■ Spleen size measured in cm – in order to identify and quantify splenomegaly.
■ Platelet count measured in x 109/L – assesses whether platelet count is higher than the normal range which is between 150,000 and 400,000 mcL.
■ Myeloblasts percentage – peripheral blood blasts is the laboratory test obtained by flow cytometry and hematologic counting. Myeloblasts are immature blood cells found in the bone marrow that have not yet developed into white blood cells.
The formula used in the Sokal score for CML calculator is as follows:
Sokal Score = Exp (0.0116 x (Age in years – 43.4)) + (0.0345 x (Spleen size in cm – 7.51)) + (0.188 x ((Platelets in 109/L / 700)2 – 0.563)) + (0.0887 x (blasts in % – 2.10))
Given the time and medical evolution since the score was first implemented, the Sokal is not deemed to be accurate in assessing patients that have undergone interferon- alpha based treatments.
There are two more recent CML survival prediction scores available: the Hasford in the 1990s and the EUTOS (European Treatment and Outcome Study) from the 2000s. The Hasford score is said to identify more low risk patients and has less specificity for the high risk group.
Sokal score interpretation
The main benefit of the Sokal index is that it allows a stratification of risk groups in three main categories based on the risk factors employed. The table shows the 2 year survival rates and the median survival time according to each of the three prognostic groups.
|Sokal score||Risk group||2 year survival rate||Median survival time|
|< 0.8||Low||90%||5 years|
|0.8 – 1.2||Intermediate||65 – 90%||2.5 – 5 years|
|> 1.2||High||65%||2.5 years|
95% of chronic myeloid leukemia patients are found to have a genetic defect between chromosomes 9 and 2, called the Philadelphia chromosome.
In cases of adult leukemia, 15 – 20% is of CML with a higher incidence in elderly patients. CML terminates in lethal acute leukemia and the only curative option (in 35% of cases possible due to limiting factors) is allogeneic bone marrow transplantation.
The four main types of leukemia are:
■ Acute myeloid leukemia (AML);
■ Chronic myeloid leukemia (CML);
■ Acute lymphocytic leukemia (ALL);
■ Chronic lymphocytic leukemia (CLL).
The main differences between the four consist in their progression rates and the location of malignancy. While the main causes of leukemia are not entirely known, these are considered a combination of environmental and genetic factors which affect the normal development of the white blood cells.
Amongst the risk factor there is age for some types, smoking for AML, blood disorders or certain chemotherapy drugs.
Typical symptoms include swollen lymph nodes, fatigue, bleeding and bruising easily, frequent infections, weight loss, joint pain or impairment and splenomegaly and/or hepatomegaly.
Main diagnosis methods include blood tests, clinical examination of lymph nodes, bone marrow biopsy and cell markers.
1) Sokal JE, Cox EB, Baccarani M, Tura S, Gomez GA, Robertson JE, Tso CY, Braun TJ, Clarkson BD, Cervantes F, et al. (1984) . Blood; 63(4):789-99.
2) Sinha SK, Sinha S, Mandal PK, Bhattacharyya NK, Pandey A, Gupta P. (2013) . Indian J Pathol Microbiol; 56(3):216-20.
3) Bonifazi F, De Vivo A, Rosti G, Tiribelli M, Russo D, Trabacchi E, Fiacchini M, Montefusco E, Baccarani M. (2000) . Br J Haematol; 111(2):587-95.24 Feb, 2016