Information from the abstract
Introduction Despite the development of advanced therapeutic approaches in the last two decades, acute myeloid leukemia (AML) has a poor prognosis, especially in older patients. The main causes of death are refractory/relapsed disease, fatal bleeding, or serious infection. A model to predict survival in patients with AML is necessary for clinicians to make decisions regarding appropriate treatment. In this study, we aimed to evaluate the predictive factors for death and generate a model to predict survival in patients with AML. Methods We conducted a multicenter prospective cohort study across nine tertiary medical care institutes in Thailand, enrolling patients aged ≥ 18 years with newly diagnosed AML between January 1, 2014, and December 31, 2023. Patients with acute promyelocytic leukemia were excluded. Multivariable Cox proportional hazards regression analyses identified the predictors of mortality, and the final model was constructed using backward stepwise regression with Akaike Information Criterion selection. Model performance was assessed using Harrell’s C-index and calibration plots, with internal validation performed via bootstrapping. Results A total of 1,055 patients were included. The median overall survival was 10.9 months, with a 10-year survival rate of 22.4%. Eight variables were independently associated with survival outcomes: age > 55 years, Eastern Cooperative Oncology Group performance status, tumor lysis syndrome, leukostasis, disseminated intravascular coagulation, white blood cell count, genetic risk, and type of induction therapy. The THAI-LEDGE model demonstrated a good discriminatory ability (C-index = 0.743) and satisfactory calibration. The internal validation yielded a C-index of 0.737, confirming the robustness of the model. Conclusions The THAI-LEDGE model performed well in predicting the survival of AML patients. However, external validation of the model in other ethnic populations and healthcare settings are necessary before widespread clinical implementation.
Why this record is monitored
This record has an Impact Signal of 77/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.
Related topics: Acute Myeloid Leukemia Research · Neutropenia and Cancer Infections · Sepsis Diagnosis and Treatment
Thai researcher and institutional participation
Pirun Saelue · Jakrawadee Julamanee · Adisak Tantiworawit · Thanawat Rattanathammethee · Weerapat Owattanapanich · Smith Kungwankiattichai · Chantiya Chanswangphuwana · Chantana Polprasert · Wasithep Limvorapitak · Supawee Saengboon · Kannadit Prayongratana · Chantrapa Sriswasdi · Pimjai Niparuck · Teeraya Puavilai · Chajchawan Nakhakes · Chinadol Wanitpongpun · Prince of Songkla University · Chiang Mai University · Siriraj Hospital · Chulalongkorn University · King Chulalongkorn Memorial Hospital · Thammasat University · Phramongkutklao Hospital · Ramathibodi Hospital · Rajavithi Hospital · Khon Kaen University
Data limitations
This page is a bibliographic record based on abstract-level information, not a full analysis or quality assessment. Verify the DOI and original article before citation.