-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
- Cosmetic Ingredient
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Recently, the team of Professor Jiang Qian & Huang Xiaojun of the Institute of Hematology of Peking University published a clinical prediction model
on the failure of chronic myeloid leukemia to receive imatinib.
The advent of tyrosine kinase inhibitors (TKIs) has significantly improved survival outcomes in patients with chronic myeloid leukemia (CML-CP
).
In the past, a number of risk scoring systems, such as Sokal and ELTS scoring systems, have been proposed internationally, which are mainly used to predict the outcome
of overall survival of patients.
Today, thanks to TKI treatment, the vast majority of patients can survive
for a long time.
In the context of long-term survival, treatment failure has become a topic of more concern, and early identification and prediction of high-risk groups of first-line imatinib treatment failure, and intervention and strengthened management in order to further improve the overall survival outcome
of patients 。 Therefore, the team retrospectively analyzed 1364 patients with newly diagnosed CML-CP who were treated with first-line imatinib in our hospital from January 2006 to April 2021, and based on the principle of simple machine learning, combined with common clinical factors (high white blood cell count, low hemoglobin level, high basophil percentage, and medium and high risk of ELTS) at initial diagnosis, a simple model
was established to predict imatinib treatment failure.
The model could divide patients into five risk subgroups: very low, low, intermediate, high and very high risk, and the treatment failure rate was statistically significantly different
between the groups.
The model can also be extended to predict patient disease progression and survival outcomes
.
.
Write | Zhang Xiaoshuai, Jiang Qian
Audit | Jiang Qian
Corresponding author
Jiang Qian
National Center PI
Professor, chief physician, doctoral supervisor
National Clinical Research Center for Hematologic Diseases
Institute of Hematology, Peking University
Deputy Director of the Department of Hematology, Peking University People's Hospital
Co-corresponding author
Huang Xiaojun
National Center PI
Professor, chief physician, doctoral supervisor
Director of the National Clinical Research Center for Hematologic Diseases
Director of the Institute of Hematology, Peking University
Director of the Department of Hematology, Peking University People's Hospital
First author
Zhang Xiaoshuai
2020 research-oriented graduate students
Supervisor: Professor Jiang Qian
National Clinical Research Center for Hematologic Diseases
Institute of Hematology, Peking University
Department of Hematology, Peking University People's Hospital
The study included 1364 patients with newly diagnosed CML-CP who received first-line imatinib treatment at Peking University People's Hospital from January 2006 to April 2021, and were randomly divided into model training cohort (908 cases) and validation cohort (456 cases).
In the training cohort, a predictive model was established using multivariate COX regression, and the final model included: white blood cell count ≥ 120×109/L at initial diagnosis, hemoglobin level < 115g/L, peripheral blood basophil ratio ≥12%, and ELTS score at medium and high risk
.
According to the regression coefficient, the Imatinib-therapy failure model (IMTF) was established, and the calculation method was as follows:
Figure 1.
Imatinib treatment failure (IMTF) model calculation method
Using this model, patients can be stratified into five risk subgroups
: very low (0 points), low (1 point), intermediate (2 points), high (3 points), and very high risk (≥ 4 points).
There were significant differences in cumulative treatment failure rate and failure-free survival rate between risk subgroups
.
Figure 2.
The cumulative treatment failure rate of each risk subgroup was predicted according to the IMTF scoring system
In addition, we used the Bootstrap internal verification method to perform time-dependent calibration curves, subject working curves and clinical decision curve analysis: the calibration curves showed that the model predicted treatment failure outcomes at 1, 3 and 5 years and the actual observed outcomes were in good agreement.
The working curve of time-dependent subjects showed that the area under the curve was 0.
78-0.
85, and the analysis of the clinical decision curve suggested that patients could benefit from the use of this model to effectively stratify the risk, identify high-risk imatinib failure patients early, and strengthen management and intervention in time, which is conducive to clinicians choosing the treatment decision
of first-line TKI.
We developed and validated a model (IMTF) for predicting first-line imatinib treatment failure with good differentiation, calibration and net benefit, which can effectively predict the probability of imatinib treatment failure in patients, help early identification of high-risk imatinib failure patients, timely strengthen management and intervention, facilitate clinicians' treatment decision-making choices, and further improve overall patient outcomes
.
References:
, Gale, R.
P.
, Zhang, MJ.
, Huang XJ & Jiang Q.
A predictive scoring system for therapy-failure in persons with chronic myeloid leukemia receiving initial imatinib therapy.
Leukemia (2022).
https://doi.
org/10.
1038/s41375-022-01527-y Original link:
