Outcomes in patients with acute myeloid leukemia older than 70 years within the last 30 years, a single center experience

Outcomes in patients with acute myeloid leukemia older than 70 years within the last 30 years, a single center experience | 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 Research Article Outcomes in patients with acute myeloid leukemia older than 70 years within the last 30 years, a single center experience Felicitas Schulz, Claudia Roggenbuck, Andrea Kündgen, Annika Kasprzak, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5144621/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Annals of Hematology → Version 1 posted 10 You are reading this latest preprint version Abstract As median age of patients with acute myeloid leukemia is 72 years, older patients continue to be a vulnerable cohort representing significant challenges in clinical practice. Patient-specific comorbidities as well as leukemia-specific unfavorable molecular- and cytogenetics confer even poorer outcomes. Treatment of AML therefore needs to be less toxic to prevent harm while lowering or eradicating leukemic burden to prolong survival. In this retrospective analysis we included all 372 older AML patients from the Düsseldorf registry who were diagnosed and treated in our department of hematology over a period of 25 years. Most patients were treated with HMA (36.6%) followed by 35.5% of patients who received either low dose chemotherapy or BSC. 10% of patients were treated with induction chemotherapy while 8.3% of patients received a combination of HMA with venetoclax. 4% of patients underwent aHSCT. At the time of last follow up, 33 patients (8.9%) were still alive. Of those patients who were treated with induction chemotherapy or HMA + venetoclax, 18.9% and 25.8% were still alive, whereas 60% of the patients who underwent allogeneic stem cell transplantation were still alive (p.<0.001). Median overall survival of the entire patient population was 6 months. Longest survival was observed in patients who underwent aHSCT with an unreached median overall survival followed by patients who were treated with induction chemotherapy (19 months) or HMA plus venetoclax (18 months). The implementation of HMA + venetoclax and increasing numbers of aHSCT improved prognosis and survival even in older AML patients. Acute Myeloid Leukemia prognosis treatment strategies Figures Figure 1 Introduction Acute myeloid leukemia (AML) is a heterogeneous disease of older patients with a median age at initial diagnosis of 72 years [ 1 ]. The classification of different subtypes according to the World Health Organization (WHO) and International Consensus Classification (ICC) of 2022 is based on cytomorphological, cytogenetical and molecular characteristics. While the 5th edition of the WHO classification still defines AML presenting with a minimum of 20% myeloid blasts in the bone marrow, the ICC enables diagnosing AML with at least 10% myeloid marrow blasts [ 2 , 3 ]. Compared to the WHO classification of 2016, AML with myelodysplasia-related changes (AML-MRC), the most common subtype in older patients, is now called AML myelodysplasia-related (AML-MR) in WHO 2022 and is split up into AML with myelodysplasia-related gene mutations (AML-MR-M), AML with myelodysplasia-related cytogenetic abnormalities (AML-MR-C) and AML with mutated TP53 [ 2 , 3 , 4 ] in ICC 2022. Although today there are more therapeutic options to treat AML, treatment-related mortality as well as therapy resistance confer a poor prognosis in elderly patients (≥ 70 years) [ 5 , 6 ]. The proportion of patients with favorable genetic profiles as CBF translocations or isolated NPM1 mutations decreases, whereas the number of patients with unfavorable karyotypes and mutations, such as for example TP53, increases [ 7 , 8 , 9 ]. Based on the patients’ age and their concomitant comorbidities, a relevant number of patients is not suitable for intensive treatment such as induction therapy or allogeneic hematopoietic stem cell transplantation (aHSCT) while this remains the only curative option for patients suffering from secondary or therapy-related AML [ 5 ]. Both the National Comprehensive Cancer Network (NCCN) as well as the European LeukemiaNet (ELN) refrain from defining explicit criteria to decide whether an older patient is eligible for intensive treatment or not and both recommend considering surrogates such as the patients’ ECOG or comorbid conditions like cardiac or pulmonary disorders as well as renal or hepatic impairment [ 7 , 10 ]. In a considerable proportion of patients, best supportive care often remains the only option. Several analyses within the last years showed that standard induction therapy in patients older than 75 years of age led to inferior survival and higher early death rates while patients with an ECOG ≥ 3 even had a significantly increased risk of death compared to younger patients [ 5 , 6 , 7 ]. However, over the last decades, several therapeutic strategies with different mechanisms of action have emerged. These comprise therapies with hypomethylating agents (HMA) with or without the bcl2-inhibitor venetoclax [ 11 , 12 ], the addition of gemtuzumab ozogamicin to induction therapy [ 13 , 14 ], gilteritinib and midostaurin for patients with mutated FLT3 [ 15 , 16 ], and IDH inhibitors for patients with mutations in IDH1 or IDH2 [ 17 , 18 ]. In our present analyses, we focus on data from 372 AML patients with a median age of 75 years and a minimum age of 70 years treated at the university hospital in Düsseldorf over a period of approximately 3 decades to describe the impact of different therapies and changes in standard of care. Methods In this retrospective analysis we included 372 older AML patients from the Düsseldorf registry who were diagnosed and treated in our department of hematology over a period of 25 years. Patients were allocated to three different groups depending on time of diagnosis. The periods chosen were before the year 2000, between 2000 and 2017 and later than 2017 because of the rollout of HMAs in 2000 and venetoclax in 2018. Patient characteristics and treatment history were evaluated and survival times according to the various treatment modalities such as non-intensive cytotoxic chemotherapy, induction chemotherapy, allogeneic blood stem cell transplantation (aHSCT), hypomethylating agents (HMA) with or without venetoclax and best supportive care (BSC) including red blood cell and platelet transfusions as well as growth factors were calculated. Patients were classified according to the most intensive treatment they received during the course of the disease. Besides survival, the causes of death, ECOG and Karnofsky index, the ELN risk categories [ 19 ] as well as selected molecular genetics were evaluated. Descriptive statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25 (SPSS, Chicago, IL, USA). Clinical and hematological data at the time of diagnosis were compared using the χ 2 and Wilcoxon rank sum test. A two-sided p-value of less than 0.05 was considered as statistically significant. The probability of survival was estimated using Kaplan–Meier method [ 20 ]. Results Patient characteristics at the time of AML diagnosis are shown in Table 1 . Median age at diagnosis was 75 years (range 70–93) with 60% of patients being male. 68% of patients were diagnosed between 2000 and 2017. ECOG performance status at the time of diagnosis was 0 in 9.7% of patients, 1 in 23.1%, 2 in 19.1%, 3 in 8.9% and 4 in 2.2% of patients and remained unknown in 114 patients due to missing data. The majority of patients (56%) were classified as AML-MR while 11% of patients suffered from a myeloid neoplasm post cytotoxic therapy as shown in Table 2 . 8.6% of patients were categorized as favorable according to ELN2022, 15.3% were allocated to the intermediate risk category and 34.4% of patients belonged to the adverse risk group while almost 41.7% had missing genetic data and could not be classified explicitly. Further details according to molecular genetics as well as cytogenetics at time of diagnosis and the resulting ELN 2022 risk categories can be found in Table 3 to 5 . Most patients were treated with HMA (36.6%) followed by 35.5% of patients who received either low dose chemotherapy or BSC. 10% of patients were treated with induction chemotherapy while 8.3% of patients received a combination of HMA with venetoclax. 5.6% of patients did not receive any treatment and 4% of patients underwent aHSCT as shown in Table 6 . Patients who did not receive any therapy as well as those who were treated with low dose chemotherapy alone had a median survival time of 1 month while those ones receiving best supportive care survived 3 months. The use of HMA increased the survival time up to 7 months (p < 0.05). A survival time of 18 and 19 months could be observed in patients treated with HMA in combination with venetoclax or induction chemotherapy. Patients who underwent aHSCT had the best prognosis with a median survival time of 36 months as shown in Fig. 1 . To further investigate patient’s outcomes, we additionally looked at patients being safely categorized according to ELN 2022 alone and analyzed those 218 patients separately. Patients who received induction chemotherapy survived longer (25 vs. 19 months) while the median overall survival of patients who underwent allogeneic stem cell transplantation was not reached. Detailed information is shown in Table 7 . Table 1 Patient characteristics at the time of AML diagnosis n (%) median (range) Year of diagnosis 2017 14 (3.8) 255 (68.5) 103 (27.7) Gender Female 39.8 Male 60.2 Age 75 (70–93) Medullary blast count (%) 35 (12–99) Blast count in peripheral blood (%) 28 (0–99) Hemoglobin g/dl 9.1 (2.1–14.9) WBC x 1000/µl 5.8 (3.0–36.5) ANC x 1000/µl 1.18 (0–11.3) Platelets x 1000/µl 59 (1–650) LDH U/l 350 (94–5212) Fever at diagnosis 36 (9.7) Infection at diagnosis 85 (22.8) Bleeding at diagnosis 24 (6.5) Extramedullary manifestation 14 (3.8) ECOG (n = 258) 0 36 (9.7) 1 86 (23.1) 2 71 (19.1) 3 33 (8.9) 4 8 (2.2) Table 2 AML subtypes according to WHO 2022 WHO Type n (%) AML with recurrent cytogenetics 249 (66.9) AML MR 208 (55.9) AML with NPM1 30 (8.1) AML with PML::RARA 7 (1.9) AML with CEBPA 3 (0.8) AML with MECOM-r 1 (0.3) AML defined by differentiation 77 (20.7) AML with minimal differentiation 5 (1.4) AML without maturation 24 (6.5) AML with maturation 21 (5.4) Acute myelomonocytic leukemia 10 (2.7) Acute monoblastic and monocytic leukemia 16 (4.3) Pure erythroid leukemia 1 (0.3) Myeloid neoplasm post cytotoxic therapy 41 (11.0) Unknown 5 (1.4) Table 3 Molecular genetics at time of diagnosis Type of mutation n (%) NPM1 30 (8.1) FLT3 ITD TKD 23 (6.2) 16 (4.3) 7 (1.9) IDH IDH1 IDH2 22 (5.9) 8 (2.1) 14 (3.8) ASXL1 18 (4.8) RUNX1 16 (4.3) CEBPA 10 (2.7) TP53 9 (2.4) Table 4 Cytogenetics according to IPSS-R risk groups Cytogenetic prognostic subgroups n (%) Very good 6 (1.6) Good 150 (40.3) Intermediate 64 (17.2) Poor 38 (10.2) Very poor 58 (15.6) Unknown 56 (15.1) Table 5 Patients’ risk categories according to ELN 2022 Risk category n (%) Favorable 32 (8.6) Intermediate 57 (15.3) Adverse 128 (34.4) Inexplicit 155 (41.7) Table 6 Major characteristics of the different treatment groups All patients (n = 372) No treatment (n = 21) BSC (n = 66) Cytoreduction (n = 66) HMA (n = 136) HMA + BCL2 inhibition (n = 31) Induction (n = 37) Allografting (n = 15) p-value Age, median 75 78 78 76 74 76 72 71 Male 224 (60.2%) 9 (42.8%) 40 (60.6%) 37 (56.1%) 85 (62.5%) 17 (54.8%) 22 (59.5%) 13 (86.7%) Year of diagnosis 2017 103 (27.7%) 1 (4.8%) 13 (19.7%) 8 (12.1%) 33 (24.3%) 31 (100%) 4 (10.8%) 11 (73.3%) Median survival in months 6 1 3 1 7 18 19 36 0.001 Table 7 Major characteristics of the different treatment groups, only patients with exact ELN2022 risk score (n = 218) All patients (n = 218) No treatment (n = 5) BSC (n = 22) Cytoreduction (n = 30) HMA (n = 94) HMA + BCL2 inhibition (n = 28) Induction (n = 26) Allografting (n = 13) p-value Age, median 74 71 80 77 75 77 73 72 Male 138 (63.3%) 4 (80%) 14 (63.6%) 19 (63.3%) 58 (61.7%) 16 (57.1%) 15 (57.7%) 12 (92.3%) Year of diagnosis 2017 83 (38.1%) 0 8 (36.4%) 5 (16.7%) 27 (28.7%) 28 (100%) 5 (19.2%) 13 (76.9%) Median survival in months 7 0 2 1 7 11 25 Not reached 0.001 Discussion Acute myeloid leukemia is a disease most frequently diagnosed in older, comorbid patients who are often not eligible for intensive treatment due to pre-existing conditions as well as disease-related problems mostly linked to associated cytopenia. Furthermore, the underlying disease biology and differences in treatment tolerance still lead to poor outcomes. Relying on chronological age alone as a surrogate for patients being eligible for intensive treatment remains a limitation and perpetuates the balancing act between under- and over-treatment resulting in the fact that these patients still comprise a challenge in clinical daily routine. To overcome this problem, various groups and studies have been trying to implement strategies to better characterize fitness of older patients in the context of therapy aiming to optimize decision-making. Common geriatric assessment including for example comorbidity, functional status, cognition and quality of life were evaluated using tools like the Hematopoietic Cell Transplant-Comorbidity Index (HCT-CI) [ 21 ], the mini-mental state examination (MMSE) [ 22 ], the European Organisation for Research and Treatment of Cancer QoL questionnaire (EORTC QLQ-C30) [ 23 ] and others, being able to predict outcomes better than age or performance status alone [ 24 – 28 ]. Proposed considerations for exemplary risk stratification and treatment in older adults with AML were the subdivision into ‘fit’, ‘vulnerable’ and ‘frail’ patients using a combination of ECOG, HCT-CI and impairments in (instrumental) activities of daily living (ADLs) with ‘fit’ patients having a maximum ECOG and HCT-CI of 1 and no impairment in ADLs and ‘frail’ patients having an ECOG ≥ 3, an HCT-CI > 2 and impairments in ADLs [ 29 ]. Patients characterized as ‘fit’ should be offered intensive therapy with even aHSCT after reduced-intensity conditioning in case of intermediate or unfavorable AML biology while ‘frail’ patients had a high treatment-related mortality and should receive lower intensity therapy or be considered for best supportive care only [ 29 ]. In 2020, the American Society of Hematology published guidelines for treating newly diagnosed acute myeloid leukemia in older adults based on six critical questions regarding type and duration of treatment, post-remission therapy and transfusion management [ 30 ]. Conclusion of these recommendations was that treatment is recommended over BSC and more-intensive treatment is recommended over less-intensive treatment when deemed tolerable always providing that during each patient’s disease treatment involves reevaluation consistently addressing goals of care and the value between cost and benefit [ 30 ]. Nevertheless, until today there is no consensus regarding optimal therapy and standard of care for older adults with AML which is why we analyzed 372 AML patients with a median age of 75 years treated at our department of hematology over a period of approximately 3 decades. Looking at our cohort, with a minimum age of 70 and the highest age of 93 years, patients were quite old compared to the literature where being categorized as an ‘old patient’ predominantly begins with the age of 60 years [ 5 ]. Compared to a large analysis within the United States where between 2000 and 2010 only 40% of patients being newly diagnosed with AML in an age > 60 years received AML-directed therapy [ 31 ], the number of patients within our cohort who received no treatment or best supportive care was quite low with only 24% between 2000 and 2017. After 2017, 78% of patients were treated with at least an hypomethylating agent being in line with the trend of recent studies towards more frequent use of leukemia-directed therapy in adults aged 65–80 years in the US [ 32 , 33 ]. With AML-MR being the most frequent and myeloid neoplasm post cytotoxic therapy being the second AML subtype of our cohort, the composition was representative [ 34 ]. Since analyses of molecular genetics via next generation sequencing have been further developed and improved over the last 20 years [ 35 ], referring data was missing and in our cohort, with NPM1 being the most detected mutation and TP53 mutation only occurring in 2.4% of patients, not representative. Hereby, allocating patients to the different risk categories of ELN 2022, was only possible in 58% of cases. Regarding the most intensive treatment option patients did receive over the three decades, treatment with hypomethylating agents like azacytidine or decitabine was the most frequent option in 36.6% of patients followed by cytoreduction and best supportive care each in a frequency of almost 18%. The median overall survival of 7 months in patients treated with hypomethylating agents was in line with data found in the literature ranging from 7 to 9 months in older AML patients treated with either azacytidine or decitabine [ 36 , 37 ]. The small number of patients treated with a combination of azacytidine + venetoclax was the result of the approval for treatment with venetoclax in 2021 and its rollout in 2018 and fitted the fact that only patients with date of diagnosis in 2018 or later received this type of therapy. The median survival time of 18 months was totally in line with the results of DiNardo et al. who observed a median overall survival of 17.5 months in elderly patients [ 38 ]. Comparable duration of median overall survival with 19 months was seen in our patients undergoing intensive induction chemotherapy which was quite long compared to results of previous studies with a median overall survival < 1 year regarding the well-known 7 + 3 induction regimen as well as CPX-351 [ 5 , 6 , 39 ]. Regarding relapse rates, early mortality or complications like infections or febrile neutropenia, the combination of hypomethylating agents and venetoclax compared to induction therapy turned out to be equivalent or even better [ 40 , 41 ] being in line with the development within our cohort to treat only a few justified exceptional cases with induction therapy or hypomethylating agents alone instead of a combination of HMA + venetoclax after 2018. Longest median overall survival of 36 months (and even an unreached median overall survival when only looking at the smaller group of 218 patients with a safely known ELN category) could be observed in patients who underwent allografting with only the smallest amount of 4% receiving an allograft but observing increasing numbers with only 4 patients undergoing aHSCT between 2000 und 2017 and 11 patients after 2017. This was again in line with data of the US where the number of aHSCT in older patients has increased visibly in the past decades, rising from less than 0.1% of transplants in 2000 to almost 4% by 2013 [ 42 ] and further increasing every year. Expanded knowledge and handling of transplant complications, increasing accessibility to unrelated donors and development of reduced-intensity conditioning strategies helped to improve transplant outcome and survival over time while low-intensive induction regimens such as HMA/venetoclax now serve as bridging therapy for remission induction prior to aHSCT making allografting a realistic option even for older patients with AML or other hematologic malignancies. Other therapeutic options we were not able to discuss due to missing data were IDH-inhibitors, FLT3-inhibitors, Menin-Inhibitors as well as triplet combinations. In patients with IDH1 mutation, ivosidenib in combination with HMA improved median overall survival as well as event free survival and response rates compared to monotherapy with HMA [ 43 ] while IDH-mutated AML patients who were considered too frail for HMA-based treatment may be offered monotherapy with IDH1/IDH2 inhibitors [ 17 , 44 ]. The role of FLT3-inhibitors in older patients remains limited as it was mainly combined to intensive induction chemotherapy, but gilteritinib has been approved in the relapsed/refractory setting as monotherapy with a median overall survival of almost ten months [ 45 ]. The role of Menin inhibitors in previously untreated, older AML-patients with NPM1 mutations and KMT2A rearrangement is still under investigation in current clinical trials [ 46 ] and same applies to triplet combinations like IDH- or FLT3-inhibitors with HMA and venetoclax [ 47 , 48 ]. Our analyses of 372 older AML patients diagnosed at our department of hematology over the last three decades has limitations. Looking at the distribution of patients within our cohort, a relevant number of almost 70% of patients were diagnosed between 2000 and 2017 with only 3.8% of patients being diagnosed before the year 2000 leading to a time bias. Since genetic analyses have evolved over the last 20 years and molecular testing has become more frequent, there is a huge lack of data making important gain of information like ELN classification of the whole cohort impossible. As our analyses are retrospective and documentation of patients has not always been as extensive and disposable as today, we were not able to give evidence about interesting end points like event-free survival, remission or relapse rates as well as treatment-related mortality. Conclusion Older patients suffering from acute myeloid leukemia and hematologic malignancies in general continue to be a vulnerable patient cohort representing significant challenges in clinical daily practice. Patient-specific factors like comorbidities as well as leukemia-specific factors such as underlying unfavorable molecular- and cytogenetics presuppose even poorer outcomes than in younger cohorts. Treatment of AML therefore needs less toxic and more targeted options to prevent harm maintaining quality of life while lowering or eradicating leukemic burden to prolong survival. As the combination of venetoclax has elevated treatment of AML onto a new level and other therapeutic options in terms of targeted therapies are evolving, the paradigm of conventional 7 + 3 induction is no longer a favored option in vulnerable patient cohorts. With more targeted and simultaneously less toxic therapies, the aim is to widen the landscape of treatment possibilities for elderly patients with AML while prolonging survival and reducing treatment-related mortality. In conclusion, therapy for older patients with AML has evolved while more therapeutic options are in the pipeline reinforcing even more that care of older and unfit adults needs to essentially stay personalized. Declarations Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Heinrich-Heine University in Duesseldorf. Informed consent statement : Informed consent was obtained from all subjects in the study. Conflicts of interest: G.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac. Competing Interests F.N. : Stock in Gilead and 270Bio, equity in ORNA, MPM entrepreneur partner. G.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac. U.G. : Institutional research support: BMS, Abbvie, Jazz. Speaker honoraria: BMS, Jansen. Funding: This research received no external funding. Author Contribution F.S., C.R. and U.G. wrote the main manuscript text, prepared figures and tables and did statistical analyses. A.Kuendgen, A. Kasprzak, K.N., P.J., G.K., S.D. and F.N. edited the manuscript. All authors reviewed the manuscript and agreed to the published version. Data availability statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions. References Kraywinkel K, Spix C (2017) Epidemiology of acute leukemia in Germany. 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Blood Adv 6(13):3997–4005. 10.1182/bloodadvances.2022007265 Muffly L, Pasquini MC, Martens M, Brazauskas R, Zhu X, Adekola K, Aljurf M, Ballen KK, Bajel A, Baron F, Battiwalla M, Beitinjaneh A, Cahn JY, Carabasi M, Chen YB, Chhabra S, Ciurea S, Copelan E, D'Souza A, Edwards J, Foran J, Freytes CO, Fung HC, Gale RP, Giralt S, Hashmi SK, Hildebrandt GC, Ho V, Jakubowski A, Lazarus H, Luskin MR, Martino R, Maziarz R, McCarthy P, Nishihori T, Olin R, Olsson RF, Pawarode A, Peres E, Rezvani AR, Rizzieri D, Savani BN, Schouten HC, Sabloff M, Seftel M, Seo S, Sorror ML, Szer J, Wirk BM, Wood WA, Artz A (2017) Increasing use of allogeneic hematopoietic cell transplantation in patients aged 70 years and older in the United States. 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Blood, The Journal of the American Society of Hematology, 135(7), 463–471 Perl AE, Martinelli G, Cortes JE, Neubauer A, Berman E, Paolini S, Montesinos P, Baer MR, Larson RA, Ustun C, Fabbiano F, Erba HP, Di Stasi A, Stuart R, Olin R, Kasner M, Ciceri F, Chou WC, Podoltsev N, Recher C, Yokoyama H, Hosono N, Yoon SS, Lee JH, Pardee T, Fathi AT, Liu C, Hasabou N, Liu X, Bahceci E, Levis MJ (2019) Gilteritinib or Chemotherapy for Relapsed or Refractory FLT3-Mutated AML. N Engl J Med 381(18):1728–1740. 10.1056/NEJMoa1902688 Issa, G. C., Aldoss, I., DiPersio, J. F., Cuglievan, B., Stone, R. M., Arellano, M.L., … Stein, E. (2022). The menin inhibitor SNDX-5613 (revumenib) leads to durable responses in patients (Pts) with KMT2A-rearranged or NPM1 mutant AML: updated results of a phase (Ph) 1 study. Blood, 140(Supplement 1), 150–152 Maiti A, DiNardo CD, Daver NG, Rausch CR, Ravandi F, Kadia TM, Pemmaraju N, Borthakur G, Bose P, Issa GC, Short NJ, Yilmaz M, Montalban-Bravo G, Ferrajoli A, Jabbour EJ, Jain N, Ohanian M, Takahashi K, Thompson PA, Loghavi S, Montalbano KS, Pierce S, Wierda WG, Kantarjian HM, Konopleva MY (2021) Triplet therapy with venetoclax, FLT3 inhibitor and decitabine for FLT3-mutated acute myeloid leukemia. Blood Cancer J 11(2):25. 10.1038/s41408-021-00410-w Lachowiez, C. A., Borthakur, G., Loghavi, S., Zeng, Z., Kadia, T. M., Masarova, L.,… Dinardo, C. D. (2021). A phase Ib/II study of ivosidenib with venetoclax+/-azacitidine in IDH1-mutated myeloid malignancies Additional Declarations Competing interest reported. F.N. : Stock in Gilead and 270Bio, equity in ORNA, MPM entrepreneur partner. G.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac. U.G. : Institutional research support: BMS, Abbvie, Jazz. Speaker honoraria: BMS, Jansen. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviews received at journal 04 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers invited by journal 03 Oct, 2024 Editor assigned by journal 02 Oct, 2024 Submission checks completed at journal 02 Oct, 2024 First submitted to journal 24 Sep, 2024 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. 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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-5144621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375898968,"identity":"615a982e-5b0c-496c-be22-5844b57868ee","order_by":0,"name":"Felicitas 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11:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5144621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5144621/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00277-025-06196-2","type":"published","date":"2025-01-11T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71477600,"identity":"db70cd52-3617-4070-b29c-f4dde86866d1","added_by":"auto","created_at":"2024-12-16 05:26:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":290547,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival time according to most intensive treatment category\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5144621/v1/6c1fcc35a914e20146d0ed50.png"},{"id":73694323,"identity":"0f27b073-35a9-4c7d-ac45-7b15977320c9","added_by":"auto","created_at":"2025-01-13 16:13:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5144621/v1/0a283418-4486-486e-ae5d-32a6a29b3927.pdf"}],"financialInterests":"Competing interest reported. F.N. : Stock in Gilead and 270Bio, equity in ORNA, MPM entrepreneur partner. \nG.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac. \nU.G. : Institutional research support: BMS, Abbvie, Jazz. Speaker honoraria: BMS, Jansen.","formattedTitle":"Outcomes in patients with acute myeloid leukemia older than 70 years within the last 30 years, a single center experience","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a heterogeneous disease of older patients with a median age at initial diagnosis of 72 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The classification of different subtypes according to the World Health Organization (WHO) and International Consensus Classification (ICC) of 2022 is based on cytomorphological, cytogenetical and molecular characteristics. While the 5th edition of the WHO classification still defines AML presenting with a minimum of 20% myeloid blasts in the bone marrow, the ICC enables diagnosing AML with at least 10% myeloid marrow blasts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compared to the WHO classification of 2016, AML with myelodysplasia-related changes (AML-MRC), the most common subtype in older patients, is now called AML myelodysplasia-related (AML-MR) in WHO 2022 and is split up into AML with myelodysplasia-related gene mutations (AML-MR-M), AML with myelodysplasia-related cytogenetic abnormalities (AML-MR-C) and AML with mutated TP53 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] in ICC 2022. Although today there are more therapeutic options to treat AML, treatment-related mortality as well as therapy resistance confer a poor prognosis in elderly patients (\u0026ge;\u0026thinsp;70 years) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The proportion of patients with favorable genetic profiles as CBF translocations or isolated NPM1 mutations decreases, whereas the number of patients with unfavorable karyotypes and mutations, such as for example TP53, increases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Based on the patients\u0026rsquo; age and their concomitant comorbidities, a relevant number of patients is not suitable for intensive treatment such as induction therapy or allogeneic hematopoietic stem cell transplantation (aHSCT) while this remains the only curative option for patients suffering from secondary or therapy-related AML [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Both the National Comprehensive Cancer Network (NCCN) as well as the European LeukemiaNet (ELN) refrain from defining explicit criteria to decide whether an older patient is eligible for intensive treatment or not and both recommend considering surrogates such as the patients\u0026rsquo; ECOG or comorbid conditions like cardiac or pulmonary disorders as well as renal or hepatic impairment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In a considerable proportion of patients, best supportive care often remains the only option. Several analyses within the last years showed that standard induction therapy in patients older than 75 years of age led to inferior survival and higher early death rates while patients with an ECOG\u0026thinsp;\u0026ge;\u0026thinsp;3 even had a significantly increased risk of death compared to younger patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, over the last decades, several therapeutic strategies with different mechanisms of action have emerged. These comprise therapies with hypomethylating agents (HMA) with or without the bcl2-inhibitor venetoclax [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the addition of gemtuzumab ozogamicin to induction therapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], gilteritinib and midostaurin for patients with mutated FLT3 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and IDH inhibitors for patients with mutations in IDH1 or IDH2 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our present analyses, we focus on data from 372 AML patients with a median age of 75 years and a minimum age of 70 years treated at the university hospital in D\u0026uuml;sseldorf over a period of approximately 3 decades to describe the impact of different therapies and changes in standard of care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this retrospective analysis we included 372 older AML patients from the D\u0026uuml;sseldorf registry who were diagnosed and treated in our department of hematology over a period of 25 years. Patients were allocated to three different groups depending on time of diagnosis. The periods chosen were before the year 2000, between 2000 and 2017 and later than 2017 because of the rollout of HMAs in 2000 and venetoclax in 2018. Patient characteristics and treatment history were evaluated and survival times according to the various treatment modalities such as non-intensive cytotoxic chemotherapy, induction chemotherapy, allogeneic blood stem cell transplantation (aHSCT), hypomethylating agents (HMA) with or without venetoclax and best supportive care (BSC) including red blood cell and platelet transfusions as well as growth factors were calculated. Patients were classified according to the most intensive treatment they received during the course of the disease. Besides survival, the causes of death, ECOG and Karnofsky index, the ELN risk categories [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] as well as selected molecular genetics were evaluated. Descriptive statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25 (SPSS, Chicago, IL, USA). Clinical and hematological data at the time of diagnosis were compared using the χ\u003csup\u003e2\u003c/sup\u003e and Wilcoxon rank sum test. A two-sided p-value of less than 0.05 was considered as statistically significant. The probability of survival was estimated using Kaplan\u0026ndash;Meier method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics at the time of AML diagnosis are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Median age at diagnosis was 75 years (range 70\u0026ndash;93) with 60% of patients being male. 68% of patients were diagnosed between 2000 and 2017. ECOG performance status at the time of diagnosis was 0 in 9.7% of patients, 1 in 23.1%, 2 in 19.1%, 3 in 8.9% and 4 in 2.2% of patients and remained unknown in 114 patients due to missing data. The majority of patients (56%) were classified as AML-MR while 11% of patients suffered from a myeloid neoplasm post cytotoxic therapy as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. 8.6% of patients were categorized as favorable according to ELN2022, 15.3% were allocated to the intermediate risk category and 34.4% of patients belonged to the adverse risk group while almost 41.7% had missing genetic data and could not be classified explicitly. Further details according to molecular genetics as well as cytogenetics at time of diagnosis and the resulting ELN 2022 risk categories can be found in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMost patients were treated with HMA (36.6%) followed by 35.5% of patients who received either low dose chemotherapy or BSC. 10% of patients were treated with induction chemotherapy while 8.3% of patients received a combination of HMA with venetoclax. 5.6% of patients did not receive any treatment and 4% of patients underwent aHSCT as shown in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePatients who did not receive any therapy as well as those who were treated with low dose chemotherapy alone had a median survival time of 1 month while those ones receiving best supportive care survived 3 months. The use of HMA increased the survival time up to 7 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A survival time of 18 and 19 months could be observed in patients treated with HMA in combination with venetoclax or induction chemotherapy. Patients who underwent aHSCT had the best prognosis with a median survival time of 36 months as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To further investigate patient\u0026rsquo;s outcomes, we additionally looked at patients being safely categorized according to ELN 2022 alone and analyzed those 218 patients separately. Patients who received induction chemotherapy survived longer (25 vs. 19 months) while the median overall survival of patients who underwent allogeneic stem cell transplantation was not reached. Detailed information is shown in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics at the time of AML diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedian (range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003cp\u003e2000\u0026ndash;2017\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (3.8)\u003c/p\u003e \u003cp\u003e255 (68.5)\u003c/p\u003e \u003cp\u003e103 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (70\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedullary blast count (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (12\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlast count in peripheral blood (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (0\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1 (2.1\u0026ndash;14.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC x 1000/\u0026micro;l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8 (3.0\u0026ndash;36.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC x 1000/\u0026micro;l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0\u0026ndash;11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets x 1000/\u0026micro;l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (1\u0026ndash;650)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH U/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350 (94\u0026ndash;5212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary manifestation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG (n\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAML subtypes according to WHO 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with recurrent cytogenetics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (66.9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208 (55.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with NPM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with PML::RARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with CEBPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with MECOM-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAML defined by differentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e77 (20.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with minimal differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML without maturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with maturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myelomonocytic leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute monoblastic and monocytic leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure erythroid leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyeloid neoplasm post cytotoxic therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e41 (11.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5 (1.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular genetics at time of diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of mutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003cp\u003eITD\u003c/p\u003e \u003cp\u003eTKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (6.2)\u003c/p\u003e \u003cp\u003e16 (4.3)\u003c/p\u003e \u003cp\u003e7 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDH\u003c/p\u003e \u003cp\u003eIDH1\u003c/p\u003e \u003cp\u003eIDH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (5.9)\u003c/p\u003e \u003cp\u003e8 (2.1)\u003c/p\u003e \u003cp\u003e14 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASXL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEBPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCytogenetics according to IPSS-R risk groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytogenetic prognostic subgroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150 (40.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (15.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; risk categories according to ELN 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128 (34.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInexplicit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMajor characteristics of the different treatment groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;372)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBSC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCytoreduction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHMA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHMA\u0026thinsp;+\u0026thinsp;BCL2 inhibition\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAllografting\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003cp\u003e(60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e(62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e(54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e(86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis\u0026thinsp;\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis 2000\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003cp\u003e(68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e(69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003cp\u003e(84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003cp\u003e(75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e(89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis\u0026thinsp;\u0026gt;\u0026thinsp;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003cp\u003e(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e(19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e(24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian survival in months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMajor characteristics of the different treatment groups, only patients with exact ELN2022 risk score (n\u0026thinsp;=\u0026thinsp;218)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;218)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBSC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCytoreduction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHMA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHMA\u0026thinsp;+\u0026thinsp;BCL2 inhibition\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAllografting\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e(63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e(63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003cp\u003e(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis\u0026thinsp;\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis 2000\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003cp\u003e(61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e(59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003cp\u003e(71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of diagnosis\u0026thinsp;\u0026gt;\u0026thinsp;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003cp\u003e(38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e(76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian survival in months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNot reached\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAcute myeloid leukemia is a disease most frequently diagnosed in older, comorbid patients who are often not eligible for intensive treatment due to pre-existing conditions as well as disease-related problems mostly linked to associated cytopenia. Furthermore, the underlying disease biology and differences in treatment tolerance still lead to poor outcomes. Relying on chronological age alone as a surrogate for patients being eligible for intensive treatment remains a limitation and perpetuates the balancing act between under- and over-treatment resulting in the fact that these patients still comprise a challenge in clinical daily routine. To overcome this problem, various groups and studies have been trying to implement strategies to better characterize fitness of older patients in the context of therapy aiming to optimize decision-making. Common geriatric assessment including for example comorbidity, functional status, cognition and quality of life were evaluated using tools like the Hematopoietic Cell Transplant-Comorbidity Index (HCT-CI) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the mini-mental state examination (MMSE) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the European Organisation for Research and Treatment of Cancer QoL questionnaire (EORTC QLQ-C30) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and others, being able to predict outcomes better than age or performance status alone [\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProposed considerations for exemplary risk stratification and treatment in older adults with AML were the subdivision into \u0026lsquo;fit\u0026rsquo;, \u0026lsquo;vulnerable\u0026rsquo; and \u0026lsquo;frail\u0026rsquo; patients using a combination of ECOG, HCT-CI and impairments in (instrumental) activities of daily living (ADLs) with \u0026lsquo;fit\u0026rsquo; patients having a maximum ECOG and HCT-CI of 1 and no impairment in ADLs and \u0026lsquo;frail\u0026rsquo; patients having an ECOG \u0026ge; 3, an HCT-CI\u0026thinsp;\u0026gt;\u0026thinsp;2 and impairments in ADLs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Patients characterized as \u0026lsquo;fit\u0026rsquo; should be offered intensive therapy with even aHSCT after reduced-intensity conditioning in case of intermediate or unfavorable AML biology while \u0026lsquo;frail\u0026rsquo; patients had a high treatment-related mortality and should receive lower intensity therapy or be considered for best supportive care only [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In 2020, the American Society of Hematology published guidelines for treating newly diagnosed acute myeloid leukemia in older adults based on six critical questions regarding type and duration of treatment, post-remission therapy and transfusion management [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conclusion of these recommendations was that treatment is recommended over BSC and more-intensive treatment is recommended over less-intensive treatment when deemed tolerable always providing that during each patient\u0026rsquo;s disease treatment involves reevaluation consistently addressing goals of care and the value between cost and benefit [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, until today there is no consensus regarding optimal therapy and standard of care for older adults with AML which is why we analyzed 372 AML patients with a median age of 75 years treated at our department of hematology over a period of approximately 3 decades. Looking at our cohort, with a minimum age of 70 and the highest age of 93 years, patients were quite old compared to the literature where being categorized as an \u0026lsquo;old patient\u0026rsquo; predominantly begins with the age of 60 years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Compared to a large analysis within the United States where between 2000 and 2010 only 40% of patients being newly diagnosed with AML in an age\u0026thinsp;\u0026gt;\u0026thinsp;60 years received AML-directed therapy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the number of patients within our cohort who received no treatment or best supportive care was quite low with only 24% between 2000 and 2017. After 2017, 78% of patients were treated with at least an hypomethylating agent being in line with the trend of recent studies towards more frequent use of leukemia-directed therapy in adults aged 65\u0026ndash;80 years in the US [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. With AML-MR being the most frequent and myeloid neoplasm post cytotoxic therapy being the second AML subtype of our cohort, the composition was representative [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Since analyses of molecular genetics via next generation sequencing have been further developed and improved over the last 20 years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], referring data was missing and in our cohort, with NPM1 being the most detected mutation and TP53 mutation only occurring in 2.4% of patients, not representative. Hereby, allocating patients to the different risk categories of ELN 2022, was only possible in 58% of cases. Regarding the most intensive treatment option patients did receive over the three decades, treatment with hypomethylating agents like azacytidine or decitabine was the most frequent option in 36.6% of patients followed by cytoreduction and best supportive care each in a frequency of almost 18%. The median overall survival of 7 months in patients treated with hypomethylating agents was in line with data found in the literature ranging from 7 to 9 months in older AML patients treated with either azacytidine or decitabine [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The small number of patients treated with a combination of azacytidine\u0026thinsp;+\u0026thinsp;venetoclax was the result of the approval for treatment with venetoclax in 2021 and its rollout in 2018 and fitted the fact that only patients with date of diagnosis in 2018 or later received this type of therapy. The median survival time of 18 months was totally in line with the results of DiNardo et al. who observed a median overall survival of 17.5 months in elderly patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Comparable duration of median overall survival with 19 months was seen in our patients undergoing intensive induction chemotherapy which was quite long compared to results of previous studies with a median overall survival\u0026thinsp;\u0026lt;\u0026thinsp;1 year regarding the well-known 7\u0026thinsp;+\u0026thinsp;3 induction regimen as well as CPX-351 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Regarding relapse rates, early mortality or complications like infections or febrile neutropenia, the combination of hypomethylating agents and venetoclax compared to induction therapy turned out to be equivalent or even better [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] being in line with the development within our cohort to treat only a few justified exceptional cases with induction therapy or hypomethylating agents alone instead of a combination of HMA\u0026thinsp;+\u0026thinsp;venetoclax after 2018. Longest median overall survival of 36 months (and even an unreached median overall survival when only looking at the smaller group of 218 patients with a safely known ELN category) could be observed in patients who underwent allografting with only the smallest amount of 4% receiving an allograft but observing increasing numbers with only 4 patients undergoing aHSCT between 2000 und 2017 and 11 patients after 2017. This was again in line with data of the US where the number of aHSCT in older patients has increased visibly in the past decades, rising from less than 0.1% of transplants in 2000 to almost 4% by 2013 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and further increasing every year. Expanded knowledge and handling of transplant complications, increasing accessibility to unrelated donors and development of reduced-intensity conditioning strategies helped to improve transplant outcome and survival over time while low-intensive induction regimens such as HMA/venetoclax now serve as bridging therapy for remission induction prior to aHSCT making allografting a realistic option even for older patients with AML or other hematologic malignancies. Other therapeutic options we were not able to discuss due to missing data were IDH-inhibitors, FLT3-inhibitors, Menin-Inhibitors as well as triplet combinations. In patients with \u003cem\u003eIDH1\u003c/em\u003e mutation, ivosidenib in combination with HMA improved median overall survival as well as event free survival and response rates compared to monotherapy with HMA [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] while IDH-mutated AML patients who were considered too frail for HMA-based treatment may be offered monotherapy with IDH1/IDH2 inhibitors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The role of FLT3-inhibitors in older patients remains limited as it was mainly combined to intensive induction chemotherapy, but gilteritinib has been approved in the relapsed/refractory setting as monotherapy with a median overall survival of almost ten months [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The role of Menin inhibitors in previously untreated, older AML-patients with NPM1 mutations and KMT2A rearrangement is still under investigation in current clinical trials [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and same applies to triplet combinations like IDH- or FLT3-inhibitors with HMA and venetoclax [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analyses of 372 older AML patients diagnosed at our department of hematology over the last three decades has limitations. Looking at the distribution of patients within our cohort, a relevant number of almost 70% of patients were diagnosed between 2000 and 2017 with only 3.8% of patients being diagnosed before the year 2000 leading to a time bias. Since genetic analyses have evolved over the last 20 years and molecular testing has become more frequent, there is a huge lack of data making important gain of information like ELN classification of the whole cohort impossible. As our analyses are retrospective and documentation of patients has not always been as extensive and disposable as today, we were not able to give evidence about interesting end points like event-free survival, remission or relapse rates as well as treatment-related mortality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOlder patients suffering from acute myeloid leukemia and hematologic malignancies in general continue to be a vulnerable patient cohort representing significant challenges in clinical daily practice. Patient-specific factors like comorbidities as well as leukemia-specific factors such as underlying unfavorable molecular- and cytogenetics presuppose even poorer outcomes than in younger cohorts. Treatment of AML therefore needs less toxic and more targeted options to prevent harm maintaining quality of life while lowering or eradicating leukemic burden to prolong survival.\u003c/p\u003e \u003cp\u003eAs the combination of venetoclax has elevated treatment of AML onto a new level and other therapeutic options in terms of targeted therapies are evolving, the paradigm of conventional 7\u0026thinsp;+\u0026thinsp;3 induction is no longer a favored option in vulnerable patient cohorts. With more targeted and simultaneously less toxic therapies, the aim is to widen the landscape of treatment possibilities for elderly patients with AML while prolonging survival and reducing treatment-related mortality.\u003c/p\u003e \u003cp\u003eIn conclusion, therapy for older patients with AML has evolved while more therapeutic options are in the pipeline reinforcing even more that care of older and unfit adults needs to essentially stay personalized.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Heinrich-Heine University in Duesseldorf.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatement\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects in the study.\u003c/p\u003e\n\u003ch4\u003eConflicts of interest:\u003c/h4\u003e\n\u003cp\u003eG.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.N. : Stock in Gilead and 270Bio, equity in ORNA, MPM entrepreneur partner. G.K. : Advisory Role, Speaker Honoraria and/or travel support: MSD, Pfizer, Amgen, Novartis, Gilead, BMS-Celgene, Abbvie, Medac, Biotest, Takeda, Eurocept. Financing of scientific research: BMS-Celgene, Amgen, Abbvie, Eurocept, Medac. U.G. : Institutional research support: BMS, Abbvie, Jazz. Speaker honoraria: BMS, Jansen.\u003c/p\u003e\n\u003ch3\u003eFunding:\u003c/h3\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch3\u003eAuthor Contribution\u003c/h3\u003e\n\u003cp\u003eF.S., C.R. and U.G. wrote the main manuscript text, prepared figures and tables and did statistical analyses. A.Kuendgen, A. Kasprzak, K.N., P.J., G.K., S.D. and F.N. edited the manuscript. All authors reviewed the manuscript and agreed to the published version.\u003c/p\u003e\n\u003ch3\u003eData availability statement:\u003c/h3\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKraywinkel K, Spix C (2017) Epidemiology of acute leukemia in Germany. 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A phase Ib/II study of ivosidenib with venetoclax+/-azacitidine in IDH1-mutated myeloid malignancies\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute Myeloid Leukemia, prognosis, treatment strategies","lastPublishedDoi":"10.21203/rs.3.rs-5144621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5144621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs median age of patients with acute myeloid leukemia is 72 years, older patients continue to be a vulnerable cohort representing significant challenges in clinical practice. Patient-specific comorbidities as well as leukemia-specific unfavorable molecular- and cytogenetics confer even poorer outcomes. Treatment of AML therefore needs to be less toxic to prevent harm while lowering or eradicating leukemic burden to prolong survival. In this retrospective analysis we included all 372 older AML patients from the D\u0026uuml;sseldorf registry who were diagnosed and treated in our department of hematology over a period of 25 years. Most patients were treated with HMA (36.6%) followed by 35.5% of patients who received either low dose chemotherapy or BSC. 10% of patients were treated with induction chemotherapy while 8.3% of patients received a combination of HMA with venetoclax. 4% of patients underwent aHSCT. At the time of last follow up, 33 patients (8.9%) were still alive. Of those patients who were treated with induction chemotherapy or HMA\u0026thinsp;+\u0026thinsp;venetoclax, 18.9% and 25.8% were still alive, whereas 60% of the patients who underwent allogeneic stem cell transplantation were still alive (p.\u0026lt;0.001). Median overall survival of the entire patient population was 6 months. Longest survival was observed in patients who underwent aHSCT with an unreached median overall survival followed by patients who were treated with induction chemotherapy (19 months) or HMA plus venetoclax (18 months). The implementation of HMA\u0026thinsp;+\u0026thinsp;venetoclax and increasing numbers of aHSCT improved prognosis and survival even in older AML patients.\u003c/p\u003e","manuscriptTitle":"Outcomes in patients with acute myeloid leukemia older than 70 years within the last 30 years, a single center experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 05:25:49","doi":"10.21203/rs.3.rs-5144621/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T19:02:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T23:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77509907063563331177641296082559516927","date":"2024-10-07T22:51:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215040283270146162102850254748929399855","date":"2024-10-07T14:28:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T14:01:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10806574983692198988370411077585003392","date":"2024-10-04T06:43:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T20:00:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-02T13:16:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-02T13:16:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2024-09-24T11:17:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7e751d8b-e529-4488-a943-475898dcef54","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:07:45+00:00","versionOfRecord":{"articleIdentity":"rs-5144621","link":"https://doi.org/10.1007/s00277-025-06196-2","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2025-01-11 15:58:00","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-12-16 05:25:49","video":"","vorDoi":"10.1007/s00277-025-06196-2","vorDoiUrl":"https://doi.org/10.1007/s00277-025-06196-2","workflowStages":[]},"version":"v1","identity":"rs-5144621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5144621","identity":"rs-5144621","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}