International journal of hematology · 2026
Personalized multivariable clinical prognostic model for patients with acute myeloid leukemia receiving intensive chemotherapy.
СтатьяНазвание и аннотация: Перевод в очереди
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Katsuki Kengo, Miyazaki Takuya, Komaki Moka, Saigusa Yusuke, Tachibana Takayoshi, Sakuma Takayuki, Hasegawa Kodai, Hirose Natsuki, Akimoto Masahiro, Izumi Akihiko, Ohashi Takuma, Matsumura Ayako, Suzuki Taisei, Ishii Yoshimi, Nakajima Yuki, Hagihara Maki, Tanaka Masatsugu, Fujisawa Shin, Nakajima Hideaki
Аннотация
PURPOSE: This study aimed to develop and validate a personalized prognostic model for patients with newly diagnosed with acute myeloid leukemia (AML) receiving intensive cytarabine- and anthracycline-based chemotherapy.
METHODS: We retrospectively analyzed 161 patients treated at a single center and externally validated the model in 184 patients from two institutions.
RESULTS: Using LASSO Cox regression, nine readily available clinical variables (age, sex, performance status, history of diabetes, respiratory disease, prior myelodysplastic syndrome or myeloproliferative neoplasm, serum albumin level, uric acid level, and adverse cytogenetic risk) were selected to construct a nomogram. The model demonstrated good discrimination of overall survival, with a concordance index of 0.721 and 0.678 in the training and validation cohorts, respectively. Calibration plots showed strong agreement between the predicted and observed outcomes. Based on the nomogram scores, the patients were stratified into three risk groups, with 3-year overall survival rates of 84.1%, 57.2%, and 11.9% in the good, intermediate, and poor-risk groups, respectively. To enhance clinical accessibility, an online tool was developed for use in real-world settings.
CONCLUSION: This nomogram integrates disease- and patient-related factors and allows for individualized prognostic assessment. This may support clinical assessment through personalized risk evaluation in AML patients.
METHODS: We retrospectively analyzed 161 patients treated at a single center and externally validated the model in 184 patients from two institutions.
RESULTS: Using LASSO Cox regression, nine readily available clinical variables (age, sex, performance status, history of diabetes, respiratory disease, prior myelodysplastic syndrome or myeloproliferative neoplasm, serum albumin level, uric acid level, and adverse cytogenetic risk) were selected to construct a nomogram. The model demonstrated good discrimination of overall survival, with a concordance index of 0.721 and 0.678 in the training and validation cohorts, respectively. Calibration plots showed strong agreement between the predicted and observed outcomes. Based on the nomogram scores, the patients were stratified into three risk groups, with 3-year overall survival rates of 84.1%, 57.2%, and 11.9% in the good, intermediate, and poor-risk groups, respectively. To enhance clinical accessibility, an online tool was developed for use in real-world settings.
CONCLUSION: This nomogram integrates disease- and patient-related factors and allows for individualized prognostic assessment. This may support clinical assessment through personalized risk evaluation in AML patients.