Nomogram to Predict Clinical Outcome in FLT3-ITD Acute Myeloid Leukemia

Nomogram to Predict Clinical Outcome in FLT3-ITD Acute Myeloid Leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nomogram to Predict Clinical Outcome in FLT3-ITD Acute Myeloid Leukemia Lili Hong, Richeng Hu, Xiaoli Guo, Kang Yu, Yixiang Han, Haifeng Zhuang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3998210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute myeloid leukemia (AML) with FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutation is a hematologic malignancy presenting with different clinical therapeutic outcomes and prognoses. Objective to explore clinical variables related to overall survival (OS) and relapse-free survival (RFS), integrate these factors and build a nomogram model to evaluate the individual prognosis risk. Methods Some clinical variables were incorporated, including disease-related characteristics and individual factors. The independent prognostic factors associated with OS and RFS were established by univariate and multivariate Cox regression analysis. Statistically significant factors determined by multivariate Cox regression analysis were incorporated and integrated to develop nomogram model. The distinguishability and accuracy of the nomogram model were confirmed by the drawing of the concordance index (C index) and calibration curve. Results A total of 66 patients with FLT 3-ITD acute myeloid leukemia were selected for this study. Four variables: age, Eastern Cooperative Oncology Group performance score (ECOG score), status of complete remission at the first time (CR1) and with favorable prognosis gene were included in the nomogram to predict OS. Two variables: status of complete remission at the first time (CR1) and with favorable prognosis gene were included in the nomogram to predict RFS. The nomogram with clinical variables showed good predictive ability, which was measured by C index (OS 0.80, RFS 0.87) and a calibration curve drawing. Conclusions A nomogram model for predicting the prognosis of OS and RFS in patients with FLT3-ITD AML was successfully established. This would help physicians to accurately assess individual prognosis risk and guide treatment. Acute myeloid leukemia FLT3-ITD Nomogram Over survival Relapse-free survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute myeloid leukemia (AML) is a common malignant clonal disease of hematopoietic stem progenitor cells. It is characterized by the high proliferation of blasts in myeloid lineage , with highly genetic heterogeneity and different clinical outcomes mainly dependent on the molecular abnormalities [1]. The FMS-like kinase 3-internal tandem duplication (FLT3-ITD) mutation is observed in approximately 25% of all AML cases. Many studies have shown that it is a prognostic factor related to poor clinical results [2-4] . The FLT3-ITD mutation gene had been brought into prognostic risk stratification of AML as an adverse prognostic factor, and also had been written by the guideline of the National Comprehensive Cancer Network (NCCN) of AML [5]. However, according to the classification of European Leukemia Network (ELN) in 2017, the allele ratio (AR) of FLT 3-ITD in acute myeloid leukemia is lower than 0.5, and the gene with NPM 1 mutation is defined as a favorable prognostic gene [6]. The conflicting results also had been reported in the literature [7]. It suggested that some factors were involved in the clinical outcome. Increasing studies have explored the influence of clinical and genetic factors on survival of AML patients[8]. The genetic factors play an essential role, with others contributed by clinical and patient-related variables. However, few prognostic model was reported to predict the clinical outcomes of FLT3-ITD AML patients incorporating all these related factors. In our study, we screened different kinds of variables and integrated valuable prognostic factors, including not only disease-related characteristics, such as leukemia FAB typing, cytogenetic and molecular abnormalities, but also patient-related factors, such as age, physical conditions, comorbidities and treatment-related factors. We sought to construct a nomogram to predict the OS and RFS of FLT3-ITD AML patients. It is convenient for the clinician to predict the individual risk by the prognostic model comprehensively incorporating the well-recognized factors. Materials & Methods Participants We reviewed 66 FLT3-ITD AML patients treated in Hematology Department of the First Affiliated Hospital of Wenzhou Medical University between 31st, December 2013 and 31st, March 2019. The diagnosis of FLT3-ITD AML was established according to World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia [ 9 ]. Patients diagnosed as acute promyelocytic leukemia (APL), therapy-related AML and secondary AML were not eligible for the study. The study was approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University(NO: 2022-084), all experiments were performed in accordance with relevant guidelines and no additional patient informed consent was required given its retrospective nature. Polymerase chain reaction (PCR) method was used to detect FLT3-ITD and other genetic mutations. The presence alone of the CBFB/MYH11 gene, AML1‑ETO gene, NPM1 mutation and CEBPA double mutation were defined as with favorable prognosis genes in FLT3‑ITD AML patients. In addition to this, other gene mutations were defined as with the adverse prognosis genes in FLT3‑ITD AML patients, such as multiple gene mutations and specific genes associated with a poor prognosis for AML, for example, C‑kit and DNMT3A mutations. Treatments The therapeutic regimen include induction and consolidation therapy. All the patients received standard induction regimen “3 + 7” (idarubicin 12mg/m 2 ×3 days + cytarabine 100mg/m 2 ×7 days). Consolidation therapy including 3–4 courses of cytarabine (2g/m 2 ) was given when patients achieved complete remission (CR). If the patients did not achieve CR, the first course of induction therapy plus sorafenib as a reinduction therapy was given again. Patients who had achieved CR1 and the refractory and relapse patients were proceeded to allogeneic stem cell transplant (allo-HSCT). Clinical data were collected, including patient-related clinical characteristics: sex, age, ECOG score (Eastern Cooperative Oncology Group performance score), body mass index (BMI), comorbidity; laboratory characteristics: white blood cells count (WBC), hemoglobin (HB), platelet (PLT), lactate dehydrogenase level (LDH), blasts in BM; disease-related characteristics : FAB type, chromosome karyotype, with or without favorable prognosis gene and treatment-related factors: chemotherapy combined with or without sorafenib, HSCT or not, if or not complete remission at the first time (CR1). Cutoff values were chosen according to the clinical experiences and previous literature studies. Definition of clinical end points CR1 was defined as complete remission after the first induction therapy, all the following criteria must been required: bone marrow blasts 1.0 × 10 9 /L, PLT > 100 × 10 9 /L and absence of blasts in the peripheral blood and no extramedullary leukemia. Relapse was defined as bone marrow blasts > 5%, reappearance of blasts in the peripheral blood, or extramedullary leukemia in patients previously achieved CR[ 10 ]. The primary endpoint was OS, defined as the date of diagnosis to death. Patients who were still alive at the follow-up time were censored for OS. As the secondary endpoint, relapse-free survival (RFS) was defined as the date of diagnosis to relapse, censoring at death in CR, or last follow-up. Statistical analysis Clinical variables associated with OS and RFS included continuous and categorical variables. All the variables were selected based on predictors previously reported in literature and clinical importance. The conversion of continuous variables to categorical variables was mainly on the basis of generally accepted criteria in clinical practice and previous reports. The OS and RFS probabilities were calculated with Kaplan-Meier survival analysis and the log-rank test were used to compare the difference. Cox regression univariate and multivariate analysis were used to evaluate the clinical variables with OS and RFS. The statistically significant variables selected from multivariate analysis ( P < 0.05) were incorporated in the nomogram. Data analysis was performed with SPSS (Version 23) and R (version 3.6.1). We evaluated the model performance through calibration and discrimination. The concordance index (C-index) can be estimated by analyzing the area under the curve. A calibration plot determined whether the predicted and actual OS were in concordance. All data were considered statistically significant at P < 0.05. Results Baseline characteristics Table 1 describes the baseline clinical features of FLT 3-ITD AML patients. A total of 66 patients with FLT3-ITD AML were followed up for a median of 18 months. The median age was 56 years (14–82 years) and the age stratification ratio were 54.5% (< 60y) and 45.5% (≥ 60y). Out of 66 patients, 39 were males (59.1%) and 27 females (40.9%). On the basis of the WHO classification of BMI and clinical practice, the patients were divided into group BMI ≥ 25 kg/m 2 and group BMI < 25 kg/m 2 . 22 (33.3%) patients were overweight. 59.1% patients have complications such as diabetes, hypertension, chronic hepatitis B and lung disease. ECOG performance status ≥ 1 were 69.7%. Table 1 Clinical characteristics of FLT3-ITD AML patients Variable Number(%)/Median(range) Median Age (range),years 56.0 (14–82) Older (≥ 60y) 30 (45.5) Younger (< 60) 36 (54.5) Sex Male 39 (59.1) Female 27 (40.9) BMI (kg/m 2 ) < 25 44 (66.7) ≥ 25 22 (33.3) Comorbidity Yes 39 (59.1) No 27 (40.9) ECOG 0 20 (30.3) ≥ 1 46 (69.7) Initial CBC WBC, 10 9 /L 41.1 (1.25-444.51) <50 44 (66.7) ≥50 22 (33.3) HB, g/L 78.5 (37–132) <60 12 (18.2) ≥60–100 42 (63.6) ≥100 12 (18.2) PLT, 10 9 /L 48.5 (5-605) <20 10 (15.2) ≥20–50 25 (37.9) ≥50–100 22 (33.3) ≥100 9 (13.6) Blasts in BM, % 75.4 (10.8–96.5) <50 13 (19.7) ≥50 53 (80.3) LDH, U/L 584 (52-4018) <500 24 (36.4) ≥500 42 (63.6) ALB, g/L 34.7 (24.6–46.5) GLB, g/L 31.3 (22.4–47.9) AKP, U/L 70.5 (28–350) FAB M0 1 (1.5) M1 1 (1.5) M2 9 (13.6) M4 33 (50.0) M5 23 (33.3) Karyotype Normal karyotype 45 (68.2) Others 21 (31.8) With favorable prognosis gene 22 (33.3) Chemotherapy combined sorafenib 18 (27.3) HSCT 12 (18.2) CR or not CR 42 (63.6) NR 24 (36.4) Relapse Yes 42 (63.6) No 24 (36.4) Abbreviations: FLT3-ITD, fms-related tyrosine kinase 3 internal tandem duplications; BMI, body mass index; ECOG, Eastern Cooperative Oncology Group Performance Status ; CBC, complete blood count; WBC, white blood cell counts; HB, hemoglobin; PLT, platelet counts; BM, bone marrow; LDH, lactic dehydrogenase; ALB, albumin; GLB, globulin; AKP, alkaline phosphatase; FAB, French–American–British; HSCT, hematopoietic stem cell transplantation; CR, complete remission; NR, non-remission. Laboratory test results including initial complete blood counts [hemoglobin (HB), White blood cell count (WBC), platelet (PLT)], biochemical detection (globulin, albumin, lactate dehydrogenase, alkaline phosphatase), and bone marrow examination. The majority of patients had higher blasts in bone marrow (80.3%). The FLT3-ITD mutation was seen in all FAB classification system subgroups of AML except M6 and M7. It was most common in M4 (50.0%, 33/66), followed by M5 (33.3%, 23/66). Similar studies have also reported the same results [ 11 ]. Among these patients, 68.2% (45/66) had a normal cytogenetic karyotype, and 63.6% (42/66) got complete remission for the first time. The cases with favorable prognosis gene were 33.3% (22/66). The favorable prognosis genes were observed as follows: NPM1 mutation (14/22, 63.6%), CEBPA (5/22, 22.7%), RUNX1-RUNX1T1 (3/22 13.6%). The proportion of FLT3-ITD mono-mutation was 18.2% (12/66), and others 48.5% (32/66). Of all the patients, 18 cases received sorafenib combined with chemotherapy and 12 cases received HSCT treatment. Of 12 patients treated with HSCT, 7 did not have good prognosis genes. 63.6% patients achieved complete remission at the first time (CR1). At the follow-up time, 63.6% patients had recurrence of their diseases. Of all the 22 patients with favorable prognosis genes, 8 patients had disease recurrence. However, 77.3% (34/44) patients without favorable prognosis genes had disease recurrence. OS and RFS of all the patients were 36.9% and 22%, respectively. Univariate and multivariate analysis The results of univariate and multivariate analysis are described in Table 2 and Table 3 . A significant association was found between a shorter OS and age ≥ 60 years ( P = 0.024 ), ECOG score ≥ 1( P = 0.048 ), WBC ≥ 50(10 9 ) ( P = 0.018 ), without the favorable prognosis gene ( P = 0.038 ) and without complete remission at the first time( P < 0.001 ) in univariate analysis. The multivariate analysis for the OS confirmed that age ≥ 60 years ( p = 0.043), ECOG score ≥ 1 ( P = 0.004), without the favorable prognosis gene ( P = 0.006) and without complete remission at the first time ( P = 0.002) were independent prognostic factors (Table 2 ). In univariate analysis, we found a significant association between a shorter RFS and without the favorable prognosis gene ( P = 0.005) and without complete remission at the first time ( P < 0.001). The variables of without the favorable prognosis gene ( P = 0.039) and without complete remission at the first time ( P < 0.001) were prognostic factors for RFS confirmed by multivariate analysis (Table 3 ). Table 2 Overall Survival in Univariate and Multivariate analysis for the FLT3-ITD AML patients Univariate analysis Multivariate analysis Variable HR(95%CI) P HR(95%CI) P Age (years) < 60 1.0 (reference) ≥ 60 2.274 (1.117–4.630) 0.024 2.312 (1.028–5.201) 0.043 Sex Male 1.0(reference) Female 1.226 (0.607–2.474) 0.570 BMI (kg/m 2 ) < 25 1.0(reference) ≥ 25 0.796 (0.366–1.731) 0.565 Comorbidity Yes 1.298 (0.641–2.630) 0.469 No 1.0(reference) ECOG score 0 1.0(reference) ≥ 1 2.193 (0.944–5.096) 0.048 4.112(1.566–10.796) 0.004 WBC (10 9 /L) < 50 0.409 (0.195–0.860) ≥ 50 1.0(reference) 0.018 HB (g/L) < 60 1.0(reference) ≥ 60–100 0.661 (0.264–1.657) 0.377 ≥ 100 0.484(0.136–1.722) 0.262 PLT (10 9 /L) < 20 1.0(reference) ≥ 20–50 1.704 (0.543–5.350) 0.361 ≥ 50–100 1.426 (0.458–4.441) 0.541 ≥ 100 1.314 (0.326–5.295) 0.701 Blasts in BM (%) < 50 1.0(reference) ≥ 50 2.775 (0.963–7.999) 0.059 LDH (U/L) < 500 1.0(reference) ≥ 500 1.333 (0.648–2.743) 0.435 FAB M0 1.0(reference) M1 0(0) 0.980 M2 7.130 (0.841–60.443) 0.072 M4 1.211 (0.352–4.166) 0.761 M5 2.099 (0.887–4.968) 0.092 Karyotype Normal 1.0(reference) Others 1.196 (0.576–2.485) 0.631 With favorable prognosis gene Yes 1.0(reference) No 2.483 (1.053–5.854) 0.038 4.246 (1.501–12.007) 0.006 Chemotherapy combined Sorafenib Yes 1.0(reference) No 0.878 (0.415–1.858) 0.734 HSCT Yes 1.0(reference) No 1.807 (0.693–4.715) 0.226 CR or not CR 0.265 (0.131–0.539) < 0.001 0.276 (0.124–0.614) 0.002 NR 1.0(reference) Table 3 Relapse-Free Survival in Univariate and Multivariate analysis for the FLT3-ITD AML patients Univariate analysis Multivariate analysis Variable HR(95%CI) P HR(95%CI) P Age (years) < 60 1.0 (reference) ≥ 60 1.220 (0.660–2.256) 0.562 Sex Male 1.0(reference) Female 1.077 (0.578–2.007) 0.815 BMI (kg/m 2 ) < 25 1.0(reference) ≥ 25 1.270 (0.678–2.382) 0.456 Comorbidity Yes 1.028 (0.548–1.928) 0.932 No 1.0(reference) ECOG 0 1.0(reference) ≥ 1 1.625 (0.703–3.760) 0.256 WBC (10 9 /L) < 50 0.589 (0.308–1.126) 0.109 ≥ 50 1.0(reference) HB (g/L) < 60 1.0(reference) ≥ 60–100 0.7 (0.326–1.503) 0.360 ≥ 100 0.504 (0.178–1.428) 0.197 PLT (10 9 /L) < 20 1.0(reference) ≥ 20–50 0.890 (0.338–2.346) 0.814 ≥ 50–100 1.120 (0.436–2.874) 0.814 ≥ 100 1.571 (0.526–4.692) 0.418 Blasts in B (%) < 50 1.0(reference) ≥ 50 1.444 (0.639–3.261) 0.377 LDH (U/L) < 500 1.0(reference) ≥ 500 1.182 (0.624–2.241) 0.608 FAB M0 1.0(reference) M1 1.0 (0.063–15.988) 1 M2 0.281 0.304 (0.035–2.651) 0.281 M4 0.429 (0.057–3.225) 0.411 M5 0.369 (0.048–2.861) 0.340 Karyotype Normal 1.0(reference) Others 1.091 (0.566–2.106) 0.794 With favorable prognosis gene Yes 1.0(reference) No 3.218 (1.413–7.330) 0.005 2.276 (0.971–5.339) 0.039 Chemotherapy combined Sorafenib Yes 1.0(reference) No 0.621 (0.325–1.189) 0.151 HSCT Yes 1.0(reference) No 1.398 (0.587–3.326) 0.449 CR or not CR 0.071 (0.022–0.237) < 0.001 0.085 (0.025–0.285) < 0.001 NR 1.0(reference) Nomogram construction and validation The nomogram to predict OS of the FLT3-ITD AML were created based on the result of multivariate analysis including four variables: age, ECOG score, with the favorable prognosis gene and the status of complete remission at the first time. The results were shown in Fig. 1 . Each factor has a corresponding score. We calculate the probabilities of OS by adding up the corresponding score from each factor. The total value is displayed by the total number of point. A bootstrap resampling was used to validate the prognostic model. The predictive accuracy for OS were evaluated by C-index and the receiver operating characteristic (ROC) curve. The C-index was 0.80 (95% CI, 0.72–0.88) ( P < 0.001) and the ROC curve was listed in Fig. 3 A. A good correlation were expressed in calibration curve and the results of predicted and actual OS at 1 year, 2 year and 3 years was shown in Fig. 3 B. The nomogram to predict RFS of the FLT3-ITD-positive AML were created based on two variables: with or without the favorable prognosis gene and the status of complete remission at the first time. The results shown in Fig. 2 . The C-index was 0.87 (95% CI, 0.81–0.94) ( P < 0.001). The ROC curve was listed in Fig. 4 A. The predicted and actual RFS at 1 and 2 years were showed in calibration curve (Fig. 4 B). Discussion The diversity and heterogeneity of clinical outcomes in FLT3-ITD AML patients prompt us to consider potential related influencing factors. The prognostic factors related to long-term survival and disease relapse had been put forward in some studies [ 12 – 14 ], while lack of a predictive model for individual prognosis assessment. It is urgent to evaluate individual prognosis with an accurate prognosis model combining clinical factors. The study reviewed the clinical characteristic of the FLT3-ITD AML patients, identified some independent prognosis factors and developed a simple feasible prognostic model to predict OS and RFS by combining disease-related characteristics and individual factors. A wide range of clinical variables, including age, body mass index, exercise status, physical condition, complications, morphology, cytogenetic status and treatment response, are considered to explore the factors related to patients and diseases, so as to construct an individual prognosis evaluation. Independent factors selected by the multivariate variable analysis, were incorporated and integrated into the model to evaluate the clinical outcome. Age < 60y, ECOG score = 0, CR1 and with the favorable prognosis gene were the dependent factors for OS. CR1 and with the favorable prognosis gene were the good factors for RFS. Age has been well recognized as an important clinical variable, which is elaborated in all kinds of AML not just the one of FLT3-ITD AML [ 15 ]. Older patients usually have more complications and lower response to induction therapy. Also, it is associated with the accumulation of molecular events during aging [ 16 ]. Old age has a negative impact on OS, but it did not affect the RFS. Poor performance status was an important predictor for OS, it was also reported in other studies[ 17 ]. High WBC counts and increasing blasts in BM reflect disease burden, some studies have reported that it was associated with shorter OS[ 18 ]. In our current research, we showed that it has nothing to do with WBC counts and primitive cells in bone marrow. We believe that the number of white blood cells and primitive cells in bone marrow reflects the burden of cancer, but it did not affect the CR. Among the 66 FLT3‑ITD AML patients; 68.2% (45/66) patients had normal karyotypes, and 21 patients had abnormal karyotypes. It shows no differences between different karyotypes in the OS and RFS. At the same time, it demonstrated that good prognostic genes and the state of first complete remission are closely related to the OS and RFS. Consistent with the important role of favorable prognosis gene in the ELN risk stratification [ 19 ], it is not surprising that with the favorable prognosis gene was also a powerful predictor in our model. 80% of FLT3-ITD AML patients with normal cytogenetics achieved CR, while half of them relapsed. The research also emphasizes the important role of gene expression related to OS and recurrence [ 20 ].FLT3-ITD AML patients harboring with other gene mutations usually exhibits considerable molecular complexity. Different genetic mutations showed different clinical outcomes. Coexistence with nucleophosmin1 (NPM1) double mutations was a favorable prognostic marker, and the clinical prognosis of these patients is better than that of patients with FLT 3-ITD single mutation [ 7 , 21 ].CEBPA mutation was found to have a positive impact on OS[ 22 ]. A DNMT3A co-mutation is an adverse prognostic indicator [ 23 ]. In our study, we found FLT3-ITD AML patients harboring with favorable prognosis genes had better OS and RFS. All the patients had received the first complete remission rate of 63.6%, which was similar to other results[ 14 ]. 22 patients accompanied with favorable prognosis genes; of the 18 (81.8%) patients achieved complete remission at the first time. In addition, the recurrence rate of patients with FLT 3-ITD AML with good prognosis gene was lower than that of patients without good prognosis gene (36.4% vs 77.3%). Therefore, the results also indicate that patients with favorable prognosis genes might had deeper induction remission, thus achieving longer no disease survival. The patients who achieved CR at the first time and with the favorable prognosis gene were independent factors for OS and RFS. Sorafenib, a tyrosine kinase inhibitor, combined with chemotherapy has become the standard treatment for patients with FLT 3-ITD AML [ 24 , 25 ]. In our study, 18 patients received combination therapy, however, they did not obtain the long-time benefit from the sorafenib. It did not improve the OS and RFS. The same conclusion was also reported in another study [ 26 ]. It indicated that sorafenib may not obtain a persistent and deep remission, it needs other strategies such as HSCT to improve the long-term results. It is suggested that patients with FLT 3-ITD AML should undergo allogeneic hematopoietic stem cell transplantation (allogeneic HSCT) under CR 1[ 27 ]. 12 patients received HSCT in our study, of which 9 were patients in CR1 and 3 in non-complete remission. However, the treatment of HSCT was not incorporated in the nomogram model. Maybe it need more data to discuss the value of HSCT. In short, the role of tyrosine kinase inhibitors and HSCT may require more clinical data and further research to prove this conclusion. Our nomogram to predicting the OS and RFS in FLT3-ITD AML patients demonstrated good discriminative ability and calibration. It integrates several risk factors into consideration and directly presents the scores and individual clinical outcomes. Rare model to completely and directly predict the clinical outcomes of the FLT3-ITD AML patients. Even though, our study still had several limitations. First, the prognostic model was constructed based on the clinical variables of the retrospective study. The forecast value of this model needs more research to verify. Second, the model did not incorporate some variables, such as the FLT3 allele ratio, because some independent prognostic markers were not widely available. The study took into account a series of well-known and easily acquired predictors. Also some factors, such as were unavailable, the potential effect could not be assessed. Thus, further studies are needed to identify. In conclusion, we integrated comprehensive factors to develop a new model to predict OS and RFS of FLT3-ITD AML patients. Nomogram can accurately and quickly calculate individual survival probability, evaluate the individual prognosis, help physicians accurately estimate the individual clinical outcomes, and meet the needs of precise medical care. Conclusions Based on a series of clinical factors, a nomogram was successfully constructed to predict the individual OS and RFS of FLT 3-ITD AML patients. This is a convenient tool for physicians to predict the individual OS and RFS prior to treatment. Multicenter data and more studies are needed to validate the value of prognostic model. Declarations Acknowledgements Author Contributions: Lili Hong,Haifeng Zhuang and Yifen Shi designed the research and wrote the paper; Xaoli Guo collected the data; Richeng Hu analyzed the data. All authors read and approved the final manuscript. The authors declare that they have no competing interests. Funding information The study was funded by the Natural Science Foundation of Zhejiang Province (grant number: LQ19H080002), the Public Welfare Science and Technology Project of Wenzhou (grant numbers: Y20190119), the Open Research Fund Program of Key Laboratory of Blood Safety Research of Zhejiang province(2023KF003) and the 2024 Zhejiang Blood Transfusion Association Green Kor scientific Research Fund(ZJB-LK-2024-003). The funders did not participate in the study other than provide financial support. Ethical approval statement The study was approved by the institutional review board of the First Affiliated Hospital of Wenzhou Medical University. Consent waiver statement Consent was waived by the The First affiliated hospital of Wenzhou medical university review board due to retrospective nature of this study, yet confidentialities of patients were protected. 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Epub 2023/01/04. doi: 10.1002/ajh.26822. PMID: 36594187; Christian Thiede CS, Brigitte Mohr, Markus Schaich, Ulrike Scha¨ kel, Uwe Platzbecker, Martin Wermke, Martin Bornha¨user, Markus Ritter, Andreas Neubauer, Gerhard Ehninger, and Thomas Illmer. 2002 Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: association with FAB subtypes and identification of subgroups with poor prognosis. BLOOD.99, 12. Yalniz F, Abou Dalle I, Kantarjian H, Borthakur G, Kadia T, Patel K, et al., 2019 Prognostic significance of baselineFLT3‐ITD mutant allele level in acute myeloid leukemia treated with intensive chemotherapy with/without sorafenib. American Journal of Hematology.94(9):984-91. doi: 10.1002/ajh.25553. Grob T, Sanders MA, Vonk CM, Kavelaars FG, Rijken M, Hanekamp DW, et al., 2023 Prognostic Value of FLT3-Internal Tandem Duplication Residual Disease in Acute Myeloid Leukemia. J Clin Oncol.41(4):756-65. Epub 2022/11/01. doi: 10.1200/jco.22.00715. PMID: 36315929; Niparuck P, Limsuwanachot N, Pukiat S, Chantrathammachart P, Rerkamnuaychoke B, Magmuang S, et al., 2019 Cytogenetics and FLT3-ITD mutation predict clinical outcomes in non transplant patients with acute myeloid leukemia. Exp Hematol Oncol.8:3. Epub 2019/02/08. doi: 10.1186/s40164-019-0127-z. PMID: 30729065; Ambayya A, Moorman AV, Sathar J, Eswaran J, Sulong S, Hassan R. 2021 Genetic Profiles and Risk Stratification in Adult De Novo Acute Myeloid Leukaemia in Relation to Age, Gender, and Ethnicity: A Study from Malaysia. Int J Mol Sci.23(1). Epub 2022/01/12. doi: 10.3390/ijms23010258. PMID: 35008684; Appelbaum FR, Gundacker H, Head DR, Slovak ML, Willman CL, Godwin JE, et al., 2006 Age and acute myeloid leukemia. Blood.107(9):3481-5. Epub 2006/02/04. doi: 10.1182/blood-2005-09-3724. PMID: 16455952; Fateen MA, El Demerdash DM, Zayed RA, Mattar MM. 2019 Role of physical function in predicting short-term treatment outcome in Egyptian acute myeloid leukemia patients: a single center experience. Hematol Transfus Cell Ther.41(1):17-24. Epub 2019/02/23. doi: 10.1016/j.htct.2018.05.003. PMID: 30793100; Chen Y, Xie Y, Fang Y, Hong M, Shi J, Qian S. 2023 Correlation of blood cell counts with mutant subtypes and impact prognosis in acute myeloid leukemia patients with FLT3 mutations. Hematology.28(1):2172296. Epub 2023/02/05. doi: 10.1080/16078454.2023.2172296. PMID: 36738279; Boddu PC, Kadia TM, Garcia-Manero G, Cortes J, Alfayez M, Borthakur G, et al., 2019 Validation of the 2017 European LeukemiaNet classification for acute myeloid leukemia with NPM1 and FLT3-internal tandem duplication genotypes. Cancer.125(7):1091-100. Epub 2018/12/07. doi: 10.1002/cncr.31885. PMID: 30521114; Walker CJ, Mrózek K, Ozer HG, Nicolet D, Kohlschmidt J, Papaioannou D, et al., 2021 Gene expression signature predicts relapse in adult patients with cytogenetically normal acute myeloid leukemia. Blood Advances.5(5):1474-82. doi: 10.1182/bloodadvances.2020003727. Pasic I, Da'na W, Lam W, Law A, Lipton JH, Viswabandya A, et al., 2019 Influence of FLT3-ITD and NPM1 status on allogeneic hematopoietic cell transplant outcomes in patients with cytogenetically normal AML. Eur J Haematol.102(4):368-74. Epub 2019/02/02. doi: 10.1111/ejh.13216. PMID: 30706524; Wang H, Chu TT, Han SY, Qi JQ, Tang YQ, Qiu HY, et al., 2019 FLT3-ITD and CEBPA Mutations Predict Prognosis in Acute Myelogenous Leukemia Irrespective of Hematopoietic Stem Cell Transplantation. Biol Blood Marrow Transplant.25(5):941-8. Epub 2018/12/07. doi: 10.1016/j.bbmt.2018.11.031. PMID: 30503388; Zhang Q, Wu X, Cao J, Gao F, Huang K. 2019 Association between increased mutation rates in DNMT3A and FLT3-ITD and poor prognosis of patients with acute myeloid leukemia. Exp Ther Med.18(4):3117-24. Epub 2019/10/02. doi: 10.3892/etm.2019.7891. PMID: 31572552; Daver N, Venugopal S, Ravandi F. 2021 FLT3 mutated acute myeloid leukemia: 2021 treatment algorithm. Blood Cancer J.11(5):104. Epub 2021/05/29. doi: 10.1038/s41408-021-00495-3. PMID: 34045454; Zhang C, Lam SSY, Leung GMK, Tsui SP, Yang N, Ng NKL, et al., 2020 Sorafenib and omacetaxine mepesuccinate as a safe and effective treatment for acute myeloid leukemia carrying internal tandem duplication of Fms-like tyrosine kinase 3. Cancer.126(2):344-53. Epub 2019/10/04. doi: 10.1002/cncr.32534. PMID: 31580501; Choi S, Kim BK, Ahn HY, Hong KT, Choi JY, Shin HY, et al., 2020 Outcomes of pediatric acute myeloid leukemia patients with FLT3-ITD mutations in the pre-FLT3 inhibitor era. Blood Res.55(4):217-24. Epub 2020/11/25. doi: 10.5045/br.2020.2020127. PMID: 33232940; Hunter BD, Chen YB. 2020 Current Approaches to Transplantation for FLT3-ITD AML. Curr Hematol Malig Rep.15(1):1-8. Epub 2020/02/09. doi: 10.1007/s11899-020-00558-5. PMID: 32034660; Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3998210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280924870,"identity":"c39fc3ec-9151-45d4-b132-bd336214ad93","order_by":0,"name":"Lili Hong","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University(Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Hong","suffix":""},{"id":280924871,"identity":"268a205e-57b9-48c7-9dec-8259b8757ebd","order_by":1,"name":"Richeng Hu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Richeng","middleName":"","lastName":"Hu","suffix":""},{"id":280924872,"identity":"272e99c5-6932-484c-96b2-55190fe08f60","order_by":2,"name":"Xiaoli Guo","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Guo","suffix":""},{"id":280924873,"identity":"dcd154d7-0888-4fd3-af7f-502175a76d35","order_by":3,"name":"Kang Yu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Yu","suffix":""},{"id":280924874,"identity":"07b4a599-5803-4947-afcb-250cd4e5d4be","order_by":4,"name":"Yixiang Han","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixiang","middleName":"","lastName":"Han","suffix":""},{"id":280924875,"identity":"b07c4078-cd05-486f-8301-388042207648","order_by":5,"name":"Haifeng Zhuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3PMUvDQBTA8TsO3vRi1jui5CukBCJitV8lEqhLBidxPCjcFJxTFPwQgjheCTgF/QBxcencIEqoGYyNa68ZBe8/3PHg/YZHiM32F+ObVxNgmhDajtF15VACcUfkdF/keiAhPSnGgYzNwr/JRu8X61d/D9jT28fjCwZE01Wdbif0tgy9+fVypADOw4OywkMmmZg/bCeMp4HnZAVVfhZ5HCo8khqYYyDA0/CrIxMF7mdHnjHQsZkgTyMPm+JMAYKold5NOJ9eHjuySLpbIo+UCYp8MTPe4ufJfYVtcXKn2FI0V6cT150tVrWBbKKq/xn2o9yx/1P7S5sBuzabzfb/+ga+4E/fBj6ZFQAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University(Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":true,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Zhuang","suffix":""},{"id":280924877,"identity":"d5f8e111-ed96-41cc-a29f-70eac703dc3b","order_by":6,"name":"Yifen Shi","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifen","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-02-29 02:00:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3998210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3998210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53010275,"identity":"27d3c62c-95e2-4d5f-a829-bf05dd2aab92","added_by":"auto","created_at":"2024-03-19 15:26:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram to predict the OS of FLT3-ITD AML patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe four independent prognostic factors associated with the OS screened from the multivariate analysis were used to build the nomogram. Each prognostic factor has a corresponding score. The total value is showed by the total point scale. We can calculate the survival probability of 1-year，2-year and 3-year by adding up the values of each variable.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3998210/v1/6327bd454c77a9c0ca128b10.png"},{"id":53010274,"identity":"f6fea527-030a-4721-8f06-5a7cb6675cb9","added_by":"auto","created_at":"2024-03-19 15:26:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram to predict the RFS of FLT3-ITD AML patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two independent prognostic factors associated with the RFS screened from the multivariate analysis were used to build the nomogram. Each prognostic factor has a corresponding score. The total value is showed by the total point scale. We can calculate the survival probability of 1-year and 2-year by adding up the values of each variable.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3998210/v1/48cf19161a2e660a8c1a6bc7.png"},{"id":53010276,"identity":"f38ba1a5-147c-49bc-9dda-6d96f1349c44","added_by":"auto","created_at":"2024-03-19 15:26:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInternal validation of the nomogram to predict OS in FLT3-ITD AML patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Discrimination: the area under the receiver operating characteristic curve (AUC) was 0.772.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Calibration: the calibration curve for the prediction of 1-year, 2-year and 3-year survival; the X and Y axis represent the predicted and actual survival probability.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3998210/v1/9285d42b7fd931e7e6ca41bb.png"},{"id":53010277,"identity":"a459704a-30ef-4752-b80d-ceaf6bf272bc","added_by":"auto","created_at":"2024-03-19 15:26:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":410167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInternal validation of the nomogram to predict RFS in FLT3-ITD AML patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eDiscrimination: the area under the receiver operating characteristic curve (AUC) was 0.822.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Calibration: the calibration curve for the prediction of 1-year and 2-year survival; the X and Y axis represent the predicted and actual survival probability.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3998210/v1/b866c676b0319bf9f3a66b9b.png"},{"id":65361074,"identity":"80b2050b-d212-4246-9c31-cc146fb71fd3","added_by":"auto","created_at":"2024-09-26 13:17:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3998210/v1/fffe189a-d844-4c71-b739-0c4423a230df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram to Predict Clinical Outcome in FLT3-ITD Acute Myeloid Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a common malignant clonal disease of hematopoietic stem progenitor cells. It is characterized by the high proliferation of blasts in myeloid lineage , with highly genetic heterogeneity and different clinical outcomes mainly dependent on the molecular abnormalities [1]. The FMS-like kinase 3-internal tandem duplication (FLT3-ITD) mutation is observed in approximately 25% of all AML cases. Many studies have shown that it is a prognostic factor related to poor clinical results [2-4]\u003cstrong\u003e.\u003c/strong\u003e The FLT3-ITD mutation gene had been brought into prognostic risk stratification of AML as an adverse prognostic factor, and also had been written by the guideline of the National Comprehensive Cancer Network (NCCN) of AML [5]. However, according to the classification of European Leukemia Network (ELN) in 2017, the allele ratio (AR) of FLT 3-ITD in acute myeloid leukemia is lower than 0.5, and the gene with NPM 1 mutation is defined as a favorable prognostic gene [6]. The conflicting results also had been reported in the literature [7]. It suggested that some factors were involved in the clinical outcome. Increasing studies have explored the influence of clinical and genetic factors on survival of AML patients[8]. The genetic factors play an essential role, with others contributed by clinical and patient-related variables. However, few prognostic model was reported to predict the clinical outcomes of FLT3-ITD AML patients incorporating all these related factors.\u003c/p\u003e\n\u003cp\u003eIn our study, we screened different kinds of variables and integrated valuable prognostic factors, including not only disease-related characteristics, such as leukemia FAB typing, cytogenetic and molecular abnormalities, but also patient-related factors, such as age, physical conditions, comorbidities and treatment-related factors. We sought to construct a nomogram to predict the OS and RFS of FLT3-ITD AML patients. It is convenient for the clinician to predict the individual risk by the prognostic model comprehensively incorporating the well-recognized factors.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e We reviewed 66 FLT3-ITD AML patients treated in Hematology Department of the First Affiliated Hospital of Wenzhou Medical University between 31st, December 2013 and 31st, March 2019. The diagnosis of FLT3-ITD AML was established according to World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Patients diagnosed as acute promyelocytic leukemia (APL), therapy-related AML and secondary AML were not eligible for the study. The study was approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University(NO: 2022-084), all experiments were performed in accordance with relevant guidelines and no additional patient informed consent was required given its retrospective nature.\u003c/p\u003e \u003cp\u003ePolymerase chain reaction (PCR) method was used to detect FLT3-ITD and other genetic mutations. The presence alone of the CBFB/MYH11 gene, AML1‑ETO gene, NPM1 mutation and CEBPA double mutation were defined as with favorable prognosis genes in FLT3‑ITD AML patients. In addition to this, other gene mutations were defined as with the adverse prognosis genes in FLT3‑ITD AML patients, such as multiple gene mutations and specific genes associated with a poor prognosis for AML, for example, C‑kit and DNMT3A mutations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTreatments\u003c/h2\u003e \u003cp\u003eThe therapeutic regimen include induction and consolidation therapy. All the patients received standard induction regimen \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; (idarubicin 12mg/m\u003csup\u003e2\u003c/sup\u003e \u0026times;3 days\u0026thinsp;+\u0026thinsp;cytarabine 100mg/m\u003csup\u003e2\u003c/sup\u003e \u0026times;7 days). Consolidation therapy including 3\u0026ndash;4 courses of cytarabine (2g/m\u003csup\u003e2\u003c/sup\u003e) was given when patients achieved complete remission (CR). If the patients did not achieve CR, the first course of induction therapy plus sorafenib as a reinduction therapy was given again. Patients who had achieved CR1 and the refractory and relapse patients were proceeded to allogeneic stem cell transplant (allo-HSCT).\u003c/p\u003e \u003cp\u003eClinical data were collected, including patient-related clinical characteristics: sex, age, ECOG score (Eastern Cooperative Oncology Group performance score), body mass index (BMI), comorbidity; laboratory characteristics: white blood cells count (WBC), hemoglobin (HB), platelet (PLT), lactate dehydrogenase level (LDH), blasts in BM; disease-related characteristics : FAB type, chromosome karyotype, with or without favorable prognosis gene and treatment-related factors: chemotherapy combined with or without sorafenib, HSCT or not, if or not complete remission at the first time (CR1). Cutoff values were chosen according to the clinical experiences and previous literature studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of clinical end points\u003c/h2\u003e \u003cp\u003eCR1 was defined as complete remission after the first induction therapy, all the following criteria must been required: bone marrow blasts\u0026thinsp;\u0026lt;\u0026thinsp;5%, normal maturation of all cell lineages, absolute neutrophil count\u0026thinsp;\u0026gt;\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, PLT\u0026thinsp;\u0026gt;\u0026thinsp;100 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e /L and absence of blasts in the peripheral blood and no extramedullary leukemia. Relapse was defined as bone marrow blasts\u0026thinsp;\u0026gt;\u0026thinsp;5%, reappearance of blasts in the peripheral blood, or extramedullary leukemia in patients previously achieved CR[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The primary endpoint was OS, defined as the date of diagnosis to death. Patients who were still alive at the follow-up time were censored for OS. As the secondary endpoint, relapse-free survival (RFS) was defined as the date of diagnosis to relapse, censoring at death in CR, or last follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eClinical variables associated with OS and RFS included continuous and categorical variables. All the variables were selected based on predictors previously reported in literature and clinical importance. The conversion of continuous variables to categorical variables was mainly on the basis of generally accepted criteria in clinical practice and previous reports. The OS and RFS probabilities were calculated with Kaplan-Meier survival analysis and the log-rank test were used to compare the difference.\u003c/p\u003e \u003cp\u003eCox regression univariate and multivariate analysis were used to evaluate the clinical variables with OS and RFS. The statistically significant variables selected from multivariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were incorporated in the nomogram. Data analysis was performed with SPSS (Version 23) and R (version 3.6.1). We evaluated the model performance through calibration and discrimination. The concordance index (C-index) can be estimated by analyzing the area under the curve. A calibration plot determined whether the predicted and actual OS were in concordance. All data were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e describes the baseline clinical features of FLT 3-ITD AML patients. A total of 66 patients with FLT3-ITD AML were followed up for a median of 18 months. The median age was 56 years (14\u0026ndash;82 years) and the age stratification ratio were 54.5% (\u0026lt;\u0026thinsp;60y) and 45.5% (\u0026ge;\u0026thinsp;60y). Out of 66 patients, 39 were males (59.1%) and 27 females (40.9%). On the basis of the WHO classification of BMI and clinical practice, the patients were divided into group BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e and group BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e. 22 (33.3%) patients were overweight. 59.1% patients have complications such as diabetes, hypertension, chronic hepatitis B and lung disease. ECOG performance status\u0026thinsp;\u0026ge;\u0026thinsp;1 were 69.7%.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClinical characteristics of FLT3-ITD AML patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber(%)/Median(range)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian Age (range),years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0 (14\u0026ndash;82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOlder (\u0026ge;\u0026thinsp;60y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYounger (\u0026lt;\u0026thinsp;60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial CBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.1 (1.25-444.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.5 (37\u0026ndash;132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;60\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.5 (5-605)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;20\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlasts in BM, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.4 (10.8\u0026ndash;96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e584 (52-4018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.7 (24.6\u0026ndash;46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3 (22.4\u0026ndash;47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKP, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.5 (28\u0026ndash;350)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaryotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal karyotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith favorable prognosis gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy combined sorafenib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviations:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFLT3-ITD, fms-related tyrosine kinase 3 internal tandem duplications; BMI, body mass index; ECOG, Eastern Cooperative Oncology Group Performance Status ; CBC, complete blood count; WBC, white blood cell counts; HB, hemoglobin; PLT, platelet counts; BM, bone marrow; LDH, lactic dehydrogenase; ALB, albumin; GLB, globulin; AKP, alkaline phosphatase; FAB, French\u0026ndash;American\u0026ndash;British; HSCT, hematopoietic stem cell transplantation; CR, complete remission; NR, non-remission.\u003c/p\u003e\n \u003cp\u003eLaboratory test results including initial complete blood counts [hemoglobin (HB), White blood cell count (WBC), platelet (PLT)], biochemical detection (globulin, albumin, lactate dehydrogenase, alkaline phosphatase), and bone marrow examination. The majority of patients had higher blasts in bone marrow (80.3%).\u003c/p\u003e\n \u003cp\u003eThe FLT3-ITD mutation was seen in all FAB classification system subgroups of AML except M6 and M7. It was most common in M4 (50.0%, 33/66), followed by M5 (33.3%, 23/66). Similar studies have also reported the same results [\u003cspan\u003e11\u003c/span\u003e]. Among these patients, 68.2% (45/66) had a normal cytogenetic karyotype, and 63.6% (42/66) got complete remission for the first time.\u003c/p\u003e\n \u003cp\u003eThe cases with favorable prognosis gene were 33.3% (22/66). The favorable prognosis genes were observed as follows: NPM1 mutation (14/22, 63.6%), CEBPA (5/22, 22.7%), RUNX1-RUNX1T1 (3/22 13.6%). The proportion of FLT3-ITD mono-mutation was 18.2% (12/66), and others 48.5% (32/66). Of all the patients, 18 cases received sorafenib combined with chemotherapy and 12 cases received HSCT treatment. Of 12 patients treated with HSCT, 7 did not have good prognosis genes.\u003c/p\u003e\n \u003cp\u003e63.6% patients achieved complete remission at the first time (CR1). At the follow-up time, 63.6% patients had recurrence of their diseases. Of all the 22 patients with favorable prognosis genes, 8 patients had disease recurrence. However, 77.3% (34/44) patients without favorable prognosis genes had disease recurrence. OS and RFS of all the patients were 36.9% and 22%, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eUnivariate and multivariate analysis\u003c/h2\u003e\n \u003cp\u003eThe results of univariate and multivariate analysis are described in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. A significant association was found between a shorter OS and age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024 ), ECOG score\u0026thinsp;\u0026ge;\u0026thinsp;1( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048 ), WBC\u0026thinsp;\u0026ge;\u0026thinsp;50(10\u003csup\u003e9\u003c/sup\u003e) ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018 ), without the favorable prognosis gene ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038 ) and without complete remission at the first time( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) in univariate analysis. The multivariate analysis for the OS confirmed that age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), ECOG score\u0026thinsp;\u0026ge;\u0026thinsp;1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), without the favorable prognosis gene (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and without complete remission at the first time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were independent prognostic factors (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). In univariate analysis, we found a significant association between a shorter RFS and without the favorable prognosis gene (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and without complete remission at the first time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The variables of without the favorable prognosis gene (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) and without complete remission at the first time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were prognostic factors for RFS confirmed by multivariate analysis (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOverall Survival in Univariate and Multivariate analysis for the FLT3-ITD AML patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.274\u003c/p\u003e\n \u003cp\u003e(1.117\u0026ndash;4.630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.312\u003c/p\u003e\n \u003cp\u003e(1.028\u0026ndash;5.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.226\u003c/p\u003e\n \u003cp\u003e(0.607\u0026ndash;2.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003cp\u003e(0.366\u0026ndash;1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003cp\u003e(0.641\u0026ndash;2.630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECOG score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.193\u003c/p\u003e\n \u003cp\u003e(0.944\u0026ndash;5.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.112(1.566\u0026ndash;10.796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003cp\u003e(0.195\u0026ndash;0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003cp\u003e(0.264\u0026ndash;1.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.484(0.136\u0026ndash;1.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;20\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.704\u003c/p\u003e\n \u003cp\u003e(0.543\u0026ndash;5.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.426\u003c/p\u003e\n \u003cp\u003e(0.458\u0026ndash;4.441)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.314\u003c/p\u003e\n \u003cp\u003e(0.326\u0026ndash;5.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlasts in BM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.775\u003c/p\u003e\n \u003cp\u003e(0.963\u0026ndash;7.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003cp\u003e(0.648\u0026ndash;2.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.130\u003c/p\u003e\n \u003cp\u003e(0.841\u0026ndash;60.443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003cp\u003e(0.352\u0026ndash;4.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.099\u003c/p\u003e\n \u003cp\u003e(0.887\u0026ndash;4.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaryotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.196\u003c/p\u003e\n \u003cp\u003e(0.576\u0026ndash;2.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith favorable prognosis gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.483\u003c/p\u003e\n \u003cp\u003e(1.053\u0026ndash;5.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.246\u003c/p\u003e\n \u003cp\u003e(1.501\u0026ndash;12.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy combined Sorafenib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003cp\u003e(0.415\u0026ndash;1.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.807\u003c/p\u003e\n \u003cp\u003e(0.693\u0026ndash;4.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003cp\u003e(0.131\u0026ndash;0.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003cp\u003e(0.124\u0026ndash;0.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRelapse-Free Survival in Univariate and Multivariate analysis for the FLT3-ITD AML patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003cp\u003e(0.660\u0026ndash;2.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003cp\u003e(0.578\u0026ndash;2.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.270\u003c/p\u003e\n \u003cp\u003e(0.678\u0026ndash;2.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003cp\u003e(0.548\u0026ndash;1.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.625\u003c/p\u003e\n \u003cp\u003e(0.703\u0026ndash;3.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003cp\u003e(0.308\u0026ndash;1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003cp\u003e(0.326\u0026ndash;1.503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003cp\u003e(0.178\u0026ndash;1.428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;20\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003cp\u003e(0.338\u0026ndash;2.346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.120\u003c/p\u003e\n \u003cp\u003e(0.436\u0026ndash;2.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.571\u003c/p\u003e\n \u003cp\u003e(0.526\u0026ndash;4.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlasts in B (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.444\u003c/p\u003e\n \u003cp\u003e(0.639\u0026ndash;3.261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.182\u003c/p\u003e\n \u003cp\u003e(0.624\u0026ndash;2.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003cp\u003e(0.063\u0026ndash;15.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003cp\u003e(0.035\u0026ndash;2.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003cp\u003e(0.057\u0026ndash;3.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003cp\u003e(0.048\u0026ndash;2.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaryotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003cp\u003e(0.566\u0026ndash;2.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith favorable prognosis gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.218\u003c/p\u003e\n \u003cp\u003e(1.413\u0026ndash;7.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.276\u003c/p\u003e\n \u003cp\u003e(0.971\u0026ndash;5.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy combined Sorafenib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003cp\u003e(0.325\u0026ndash;1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.398\u003c/p\u003e\n \u003cp\u003e(0.587\u0026ndash;3.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003cp\u003e(0.022\u0026ndash;0.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003cp\u003e(0.025\u0026ndash;0.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eNomogram construction and validation\u003c/h2\u003e\n \u003cp\u003eThe nomogram to predict OS of the FLT3-ITD AML were created based on the result of multivariate analysis including four variables: age, ECOG score, with the favorable prognosis gene and the status of complete remission at the first time. The results were shown in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. Each factor has a corresponding score. We calculate the probabilities of OS by adding up the corresponding score from each factor. The total value is displayed by the total number of point.\u003c/p\u003e\n \u003cp\u003eA bootstrap resampling was used to validate the prognostic model. The predictive accuracy for OS were evaluated by C-index and the receiver operating characteristic (ROC) curve. The C-index was 0.80 (95% CI, 0.72\u0026ndash;0.88) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the ROC curve was listed in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA. A good correlation were expressed in calibration curve and the results of predicted and actual OS at 1 year, 2 year and 3 years was shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB.\u003c/p\u003e\n \u003cp\u003eThe nomogram to predict RFS of the FLT3-ITD-positive AML were created based on two variables: with or without the favorable prognosis gene and the status of complete remission at the first time. The results shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. The C-index was 0.87 (95% CI, 0.81\u0026ndash;0.94) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC curve was listed in Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA. The predicted and actual RFS at 1 and 2 years were showed in calibration curve (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe diversity and heterogeneity of clinical outcomes in FLT3-ITD AML patients prompt us to consider potential related influencing factors. The prognostic factors related to long-term survival and disease relapse had been put forward in some studies [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while lack of a predictive model for individual prognosis assessment. It is urgent to evaluate individual prognosis with an accurate prognosis model combining clinical factors. The study reviewed the clinical characteristic of the FLT3-ITD AML patients, identified some independent prognosis factors and developed a simple feasible prognostic model to predict OS and RFS by combining disease-related characteristics and individual factors.\u003c/p\u003e \u003cp\u003eA wide range of clinical variables, including age, body mass index, exercise status, physical condition, complications, morphology, cytogenetic status and treatment response, are considered to explore the factors related to patients and diseases, so as to construct an individual prognosis evaluation. Independent factors selected by the multivariate variable analysis, were incorporated and integrated into the model to evaluate the clinical outcome. Age\u0026thinsp;\u0026lt;\u0026thinsp;60y, ECOG score\u0026thinsp;=\u0026thinsp;0, CR1 and with the favorable prognosis gene were the dependent factors for OS. CR1 and with the favorable prognosis gene were the good factors for RFS. Age has been well recognized as an important clinical variable, which is elaborated in all kinds of AML not just the one of FLT3-ITD AML [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Older patients usually have more complications and lower response to induction therapy. Also, it is associated with the accumulation of molecular events during aging [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Old age has a negative impact on OS, but it did not affect the RFS. Poor performance status was an important predictor for OS, it was also reported in other studies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. High WBC counts and increasing blasts in BM reflect disease burden, some studies have reported that it was associated with shorter OS[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our current research, we showed that it has nothing to do with WBC counts and primitive cells in bone marrow. We believe that the number of white blood cells and primitive cells in bone marrow reflects the burden of cancer, but it did not affect the CR.\u003c/p\u003e \u003cp\u003eAmong the 66 FLT3‑ITD AML patients; 68.2% (45/66) patients had normal karyotypes, and 21 patients had abnormal karyotypes. It shows no differences between different karyotypes in the OS and RFS. At the same time, it demonstrated that good prognostic genes and the state of first complete remission are closely related to the OS and RFS. Consistent with the important role of favorable prognosis gene in the ELN risk stratification [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], it is not surprising that with the favorable prognosis gene was also a powerful predictor in our model. 80% of FLT3-ITD AML patients with normal cytogenetics achieved CR, while half of them relapsed. The research also emphasizes the important role of gene expression related to OS and recurrence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].FLT3-ITD AML patients harboring with other gene mutations usually exhibits considerable molecular complexity. Different genetic mutations showed different clinical outcomes. Coexistence with nucleophosmin1 (NPM1) double mutations was a favorable prognostic marker, and the clinical prognosis of these patients is better than that of patients with FLT 3-ITD single mutation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].CEBPA mutation was found to have a positive impact on OS[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A DNMT3A co-mutation is an adverse prognostic indicator [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, we found FLT3-ITD AML patients harboring with favorable prognosis genes had better OS and RFS. All the patients had received the first complete remission rate of 63.6%, which was similar to other results[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 22 patients accompanied with favorable prognosis genes; of the 18 (81.8%) patients achieved complete remission at the first time. In addition, the recurrence rate of patients with FLT 3-ITD AML with good prognosis gene was lower than that of patients without good prognosis gene (36.4% vs 77.3%).\u003c/p\u003e \u003cp\u003eTherefore, the results also indicate that patients with favorable prognosis genes might had deeper induction remission, thus achieving longer no disease survival. The patients who achieved CR at the first time and with the favorable prognosis gene were independent factors for OS and RFS. Sorafenib, a tyrosine kinase inhibitor, combined with chemotherapy has become the standard treatment for patients with FLT 3-ITD AML [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our study, 18 patients received combination therapy, however, they did not obtain the long-time benefit from the sorafenib. It did not improve the OS and RFS. The same conclusion was also reported in another study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It indicated that sorafenib may not obtain a persistent and deep remission, it needs other strategies such as HSCT to improve the long-term results. It is suggested that patients with FLT 3-ITD AML should undergo allogeneic hematopoietic stem cell transplantation (allogeneic HSCT) under CR 1[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 12 patients received HSCT in our study, of which 9 were patients in CR1 and 3 in non-complete remission. However, the treatment of HSCT was not incorporated in the nomogram model. Maybe it need more data to discuss the value of HSCT. In short, the role of tyrosine kinase inhibitors and HSCT may require more clinical data and further research to prove this conclusion.\u003c/p\u003e \u003cp\u003eOur nomogram to predicting the OS and RFS in FLT3-ITD AML patients demonstrated good discriminative ability and calibration. It integrates several risk factors into consideration and directly presents the scores and individual clinical outcomes. Rare model to completely and directly predict the clinical outcomes of the FLT3-ITD AML patients. Even though, our study still had several limitations. First, the prognostic model was constructed based on the clinical variables of the retrospective study. The forecast value of this model needs more research to verify. Second, the model did not incorporate some variables, such as the FLT3 allele ratio, because some independent prognostic markers were not widely available. The study took into account a series of well-known and easily acquired predictors. Also some factors, such as were unavailable, the potential effect could not be assessed. Thus, further studies are needed to identify.\u003c/p\u003e \u003cp\u003eIn conclusion, we integrated comprehensive factors to develop a new model to predict OS and RFS of FLT3-ITD AML patients. Nomogram can accurately and quickly calculate individual survival probability, evaluate the individual prognosis, help physicians accurately estimate the individual clinical outcomes, and meet the needs of precise medical care.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on a series of clinical factors, a nomogram was successfully constructed to predict the individual OS and RFS of FLT 3-ITD AML patients. This is a convenient tool for physicians to predict the individual OS and RFS prior to treatment. Multicenter data and more studies are needed to validate the value of prognostic model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor Contributions: Lili Hong,Haifeng Zhuang and Yifen Shi designed the research and wrote the paper; Xaoli Guo collected the data; Richeng Hu analyzed the data. All authors read and approved the final manuscript. The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Natural Science Foundation of Zhejiang Province (grant number: LQ19H080002), the Public Welfare Science and Technology Project of Wenzhou (grant numbers: Y20190119),\u0026nbsp;the\u0026nbsp;Open Research Fund Program of Key Laboratory of Blood Safety Research of Zhejiang province(2023KF003) and the 2024 Zhejiang Blood Transfusion Association Green Kor scientific Research Fund(ZJB-LK-2024-003). The funders did not participate in the study other than provide financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional review board of the First Affiliated Hospital of Wenzhou Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent waiver statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent was waived\u0026nbsp;by the The First affiliated hospital of Wenzhou medical university review board due to retrospective nature of this study, yet confidentialities of patients were protected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to [reasons of sensitivity e.g. human data] and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of conflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the study had no commercial or financial relationship.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShort NJ, Rytting ME, Cortes JE. 2018 Acute myeloid leukaemia. 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PMID: 32034660; \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute myeloid leukemia, FLT3-ITD, Nomogram, Over survival, Relapse-free survival","lastPublishedDoi":"10.21203/rs.3.rs-3998210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3998210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAcute myeloid leukemia (AML) with FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutation is a hematologic malignancy presenting with different clinical therapeutic outcomes and prognoses. Objective to explore clinical variables related to overall survival (OS) and relapse-free survival (RFS), integrate these factors and build a nomogram model to evaluate the individual prognosis risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSome clinical variables were incorporated, including disease-related characteristics and individual factors. The independent prognostic factors associated with OS and RFS were established by univariate and multivariate Cox regression analysis. Statistically significant factors determined by multivariate Cox regression analysis were incorporated and integrated to develop nomogram model. The distinguishability and accuracy of the nomogram model were confirmed by the drawing of the concordance index (C index) and calibration curve.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 66 patients with FLT 3-ITD acute myeloid leukemia were selected for this study. Four variables: age, Eastern Cooperative Oncology Group performance score (ECOG score), status of complete remission at the first time (CR1) and with favorable prognosis gene were included in the nomogram to predict OS. Two variables: status of complete remission at the first time (CR1) and with favorable prognosis gene were included in the nomogram to predict RFS. The nomogram with clinical variables showed good predictive ability, which was measured by C index (OS 0.80, RFS 0.87) and a calibration curve drawing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA nomogram model for predicting the prognosis of OS and RFS in patients with FLT3-ITD AML was successfully established. This would help physicians to accurately assess individual prognosis risk and guide treatment.\u003c/p\u003e","manuscriptTitle":"Nomogram to Predict Clinical Outcome in FLT3-ITD Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 15:25:57","doi":"10.21203/rs.3.rs-3998210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae451ba4-fad3-48be-9aad-fd29d51fb104","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-26T13:09:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 15:25:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3998210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3998210","identity":"rs-3998210","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}