Algorithm for predicting the clinical course and treatment effectiveness for patients with chronic myeloid leukemia using markers of metabolic intoxication.

OBJECTIVE

Aim: To develop a prognostic algorithm, taking into account the blood concentrations of medium-mass molecules (MMM), pyruvic acid (PA), and lactic acid (LA) as markers of metabolic intoxication (MI), in order to optimize the prediction of the clinical course and treatment effectiveness in patients with chronic myeloid leukemia (CML).

PATIENTS AND

METHODS

Materials and

Methods

The study was conducted on 97 individuals (45 men, 52 women). The main group consisted of 77 patients with CML, aged (M ± m) 47.5 ± 1.4 years: 19 in stage I (chronic), 33 in stage II (acceleration), and 25 in stage III (blast crisis). The control group included 20 healthy individuals, aged 38.9 ± 1.3 years. A sequential Wald analysis, in a modified version with a 0.05 p-level threshold, was performed based on interim study results (levels of MMM, PA, and LA in blood, in addition to standard tests).

RESULTS

Results

The prediction algorithm aimed at identifying patients at high risk of CML progression and evaluating treatment effectiveness was developed, considering the MI markers. Reaching the predictive coefficient threshold sum is a criterion for determining the risk: if equal to or lower than "-19.8," the risk is high; if greater than "-19.8," but lower than "+19.8," the risk is uncertain; if equal to or greater than "+19.8," the risk is low.

CONCLUSION

Conclusions

The algorithm enables the stratification of patients with CML into risk groups. The incorporation of MMM, PA, and LA into the prognostic framework has the potential to enhance the predictive capacity of the model regarding clinical deterioration, treatment failure, etc.