Survival Outcomes and Health-Related Quality of Life in Older Adults Diagnosed with Acute Myeloid Leukemia Receiving Frontline Therapy in Daily Practice

Citation: Ramos, F.; Hermosín, M.L.; Fuertes-Núñez, M.; Martínez, P.; Rodriguez-Medina, C.; Barrios, M.; Ibáñez, F.; Bernal, T.; Olave, M.T.; Álvarez, M.Á.; et al. Survival Outcomes and Health-Related Quality of Life in Older Adults Diagnosed with Acute Myeloid Leukemia Receiving Frontline Therapy in Daily Practice. J. Pers. Med. 2023,13, 1667. https://doi.org/ 10.3390/jpm13121667 Academic Editor: Franco Oreste Ranelletti Received: 2 August 2023 Revised: 25 October 2023 Accepted: 14 November 2023 Published: 28 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Personalized Medicine Article Survival Outcomes and Health-Related Quality of Life in Older Adults Diagnosed with Acute Myeloid Leukemia Receiving Frontline Therapy in Daily Practice Fernando Ramos 1,* , María Lourdes Hermosín2, Marta Fuertes-Núñez 1, Pilar Martínez 3, Carlos Rodriguez-Medina 4, Manuel Barrios 5, Francisco Ibáñez 6, Teresa Bernal 7, Maria Teresa Olave 8, Miguel Ángel Álvarez 9, María Vahí10, Teresa Caballero-Velázquez 11 , Bernardo González 12, Albert Altés13, Lorena García14, Pascual Fernández 15 , María Antonia Durán16, Rocío López 17, Montserrat Rafel 17 and Josefina Serrano 18 1Department of Hematology, Hospital Universitario de León, 24008 Leon, Spain 2Department of Hematology, Hospital Universitario de Jerez de la Frontera, 11407 Jerez de la Frontera, Spain 3Department of Hematology, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain 4Department of Hematology, Hospital Universitario de Gran Canaria Dr. Negrín, 35010 Las Palmas de Gran Canaria, Spain 5Department of Hematology, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain 6Department of Hematology, Hospital General Universitario de Valencia, 46014 Valencia, Spain 7Department of Hematology, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain 8Department of Hematology, Hospital Clinico Lozano Blesa, 50009 Zaragoza, Spain 9Department of Hematology, Hospital Universitario de Getafe, 28905 Getafe, Spain 10 Department of Hematology, Hospital Universitario Virgen de Valme, 41014 Sevilla, Spain 11 Department of Hematology, Instituto de Biomedicina de Sevilla, IBiS/Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain 12 Department of Hematology, Hospital Universitario de Canarias, 38320 La Laguna, Spain 13 Department of Hematology, Hospital Sant Joan de Deu de Manresa—FundacióAlthaia, 08243 Manresa, Spain 14 Department of Hematology, Complejo Hospitalario Universitario A Coruña (CHUAC), 15006 A Coruña, Spain 15 Department of Hematology, Hospital General Universitario de Alicante, 03010 Alicante, Spain 16 Department of Hematology, Hospital Universitario Son Espases, 07120 Palma de Mallorca, Spain 17 Medical Department, Hematology Area, Bristol Myers Squibb Company, Celgene, 28050 Madrid, Spain 18 Department of Hematology, Hospital Universitario Reina Sofía, IMIBIC UCO, 14004 Cordoba, Spain *Correspondence: [email protected] Abstract: Acute myeloid leukemia has a poor prognosis in older adults, and its management is often unclear due to its underrepresentation in clinical trials. Both overall survival (OS) and healthrelated quality-of-life (HRQoL) are key outcomes in this population, and patient-reported outcomes may contribute to patient stratification and treatment assignment. This prospective study included 138 consecutive patients treated in daily practice with the currently available non-targeted therapies (intensive chemotherapy [IC], attenuated chemotherapy [AC], hypomethylating agents [HMA], or palliative care [PC]). We evaluated patients’ condition at diagnosis (Life expectancy [Lee Index for Older Adults], Geriatric Assessment in Hematology [GAH scale], HRQoL [EQ-5D-5L questionnaire], and fatigue [fatigue items of the QLQ-C30 scale]), OS, early death (ED), treatment tolerability (TT) and change in HRQoL over 12 months follow-up. The median OS was 7.1 months (IC not reached, AC 5.9, HMA 8.8, and PC 1.0). Poor risk AML category and receiving just palliative care, as well as a higher Lee index score in the patients receiving active therapy, independently predicted a shorter OS. The Lee Index and GAH scale were not useful for predicting TT. The white blood cell count was a valid predictor for ED. Patients’ HRQoL remained stable during follow-up. Keywords: acute myeloid leukemia; elderly; survival; early death; health-related quality of life; life expectancy; geriatric assessment; fatigue J. Pers. Med. 2023,13, 1667. https://doi.org/10.3390/jpm13121667 https://www.mdpi.com/journal/jpm J. Pers. Med. 2023,13, 1667 2 of 16 1. Introduction Acute myeloid leukemia (AML) is the most commonly diagnosed type of acute leukemia [ 1 ] and occurs mainly in patients aged over 60 years. There are large differences between older and younger patients in clinical prognosis, with higher rates of early death (ED) and chemotherapy-related toxicities in the former [ 2 , 3 ]. The prognosis for older adults with AML can be estimated with the Medical Research Council/Leukemia Research Foundation (MRC/LRF), the MD Anderson Cancer Center, and the European LeukemiaNet prognostic scores [4–6], among others. Despite the higher prevalence of this hematologic cancer in the elderly, its management is still unclear in this patient population [ 7 ]. Fit older adults treated by intensive chemotherapy (IC), especially if they are included in the good risk category or followed by either allogeneic hematopoietic cell transplantation (HCT) or oral azacitidine maintenance, have prolonged their overall survival (OS) [ 8 – 11 ]. Nevertheless, many older patients have worse health status (poor performance status, multimorbidity, polypharmacy, malnutrition, cognitive impairment, etc.) and may have difficulties in following the above-mentioned clinical pathway and have reduced survival [ 11 , 12 ]. The available options for old and unfit patients are hypomethylating agents (HMA), such as azacitidine or decitabine [ 13 – 16 ], attenuated standard chemotherapy (AC), and palliative care (PC). Recently, the combination of azacitidine and venetoclax has shown improved overall survival, as compared to azacitidine plus placebo, in newly diagnosed unfit patients [17]. It is crucial to evaluate patient characteristics and disease heterogeneity thoroughly to offer appropriate personalized therapy [ 18 , 19 ]. Currently, patient evaluation is mainly based on traditional factors, including age, Eastern Cooperative Oncology Group (ECOG) performance status, and comorbidity, but this approach does not fully describe these patients [ 20 ]. Patient-reported outcomes (PROs) such as health-related quality-of-life (HRQoL), fatigue perception, etc., are currently gaining relevance in treatment decisionmaking. Studying the impact of both traditional factors and PROs in a multidimensional evaluation will help us to understand the reasons underlying treatment assignment and guide the choice of the most appropriate therapy. A prominent unmet clinical need is the lack of straightforward tools to predict early death (ED) and treatment tolerability (TT) in patients receiving the different forms of therapy considered since most available information comes from the IC setting [3,5,12,15,21–25]. In light of the above, we aimed to describe the OS of older adults diagnosed with AML and the change in their HRQoL as a function of the treatment strategy: to analyze the relative prognostic value for OS of life expectancy, geriatric assessment, fatigue, and HRQoL at diagnosis, adjusted for MRC/LRF risk categories; to study in depth the impact of white blood cell count (WBC) and other covariates on ED and TT; and, lastly, to describe the transfusion burden and the need for hospitalization for each treatment strategy, as well as the hematopoietic cell transplantation (HCT) rate. 2. Materials and Methods 2.1. Study Design and Participants SVLMA was a prospective observational study carried out by the Departments of Hematology of 40 Spanish hospitals according to the Helsinki Declaration and local regulations. It was approved by the Independent Ethics Committee of Hospital Clínico San Carlos (Madrid, Spain), and all patients gave their written informed consent. This study included all patients meeting the selection criteria who were consecutively recruited from February 2018 to April 2019. Eligible patients were aged ≥ 60 years and diagnosed with AML as defined in the World Health Organization (WHO) 2016 criteria [ 26 ]. Patients with previously treated AML or acute promyelocytic leukemia were excluded. Included patients were treated with all strategies available at the outset of the study in clinical practice: IC, AC, HMA, or PC. IC comprised any “3 + 7” scheme including cytarabine daily doses 100 mg/sqm and over, in combination with either (i) daunorubicin at a daily dose of at least 60 mg/sqm (or equivalent anthracycline dose) or (ii) fludarabine J. Pers. Med. 2023,13, 1667 3 of 16 (any dose), and the FLAG-IDA scheme. Lower doses of the same drugs or shorter schedules were considered AC. Each therapy was administered at the discretion of the participating physician and according to routine clinical practice. 2.2. Assessments and Endpoints Patients were followed up for 12 months and assessed at 5 timepoints: baseline/ selection visit and four consecutive visits every 3 months (at 0, 3, 6, 9, and 12 months). The primary study endpoint was the median OS, defined as the time from diagnosis to death from any cause. Secondary efficacy endpoints included 1-year survival rate, treatment strategies, ED, TT, and change in patient HRQoL. We also studied the potential impact of patient conditions (life expectancy, performance status, geriatric assessment, and fatigue scores) on ED and TT, as well as the potential impact of WBC at diagnosis on ED. Additionally, data about treatment response, transfusion burden, need for hospitalization, HCT rate, and the date and reason of death were also collected. HRQoL was measured by the EuroQoL-5L-5D questionnaire [ 27 ], and fatigue was scored (FA-score) by using the 3 fatigue items of the QLQ-C30 [ 28 ]. Life expectancy (at 4 years) before AML diagnosis was calculated with the Lee Index for Older Adults [ 29 ]. Geriatric assessment was performed with the aid of the Geriatric Assessment in Hematology (GAH) scale [ 30 , 31 ]. Details on these scales can be consulted in the Supplementary Materials. 2.3. Statistical Methods Median OS and 1-year survival rate were analyzed using the Kaplan-Meier method. Those patients receiving HCT were censored. A Cox regression multivariate analysis was used to determine the impact of the covariates to predict OS. Variables showing significant differences (p< 0.150) in the univariate analyses were included in a multivariate analysis. ED was defined as the patient’s death within the first 8 weeks of treatment initiation [32]. TT was measured as the number of patients that needed either treatment termination or modification (i.e., at the patient’s request, when the patient had any adverse event that was considered severe or life-threatening by the investigator, and also when the patient experienced any deterioration of previous comorbidities or early death as a consequence of the adverse event). In addition, the physician’s subjective opinion on the observed patient tolerability on a scale of 0 (minimal) to 10 (maximal) was also collected. Treatment response was defined following the European LeukemiaNet criteria [6]. Transfusion burden was calculated as the number of packed red blood cell concentrates (PRBC) and the number of platelet transfusion procedures received by each patient during follow-up. Need for hospitalization was analyzed as the proportion of patients requiring hospitalization (excluding hospice or palliative care stays) during follow-up, as well as the proportion of days that the patients spent at the hospital as in-patients during the observation time. The impact of WBC on ED was analyzed by ANCOVA (accounting for time from diagnosis to treatment initiation) and a receiver operating characteristics (ROC) curve. The area under the curve (AUC) was calculated to identify the cut-off point, maximizing sensitivity and specificity. HRQoL was descriptively analyzed, including the frequency distributions of EQ-5D-5L dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) and measures of central tendency and dispersion of EQ-VAS and preference indexes; the latter was also analyzed using repeated-measures linear models. The Lee index and GAH scale validity as treatment-tolerability predictive values were analyzed by a full multiple linear regression model and multiple logistic regression. Optimal cut-off points were determined by ROC analysis. All the variables were analyzed in the overall series and adjusted by treatment. Missing data were not imputed and were left as missing. The hypothesis tests used were two-sided and with a significance level of 0.05. IBM SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA) was used for all the analysis. J. Pers. Med. 2023,13, 1667 4 of 16 3. Results 3.1. Patient Disposition and Frontline Treatment A total of 151 patients were enrolled in the study, 13 of whom did not meet inclusion criteria, so 138 were finally evaluable (Figure 1, panel a). All treatments were administered according to the physician’s criteria and the local clinical practice. The study flow is displayed in Figure 1, panel b. and patient characteristics are shown in Table 1. Median (interquartile range, IQR) patient age was 75.2 (68.6–80.6) years, and most were male (57.2%). Patient ECOG performance status was predominantly good (ECOG 0–1 72.6%), but 27.4% of patients had an ECOG score of 2–3 (Table 1); 35 patients out of 137 (25.5%) had a previous hematological disease, and 27 (19.6%) had previously received chemotherapy and/or radiotherapy. Table 1. Patient characteristics at diagnosis. IC AC HMA PC Total pValue Patients receiving treatment, n (%) 41 (29.7) 22 (15.9) 53 (38.4) 22 (15.9) 138 (100.0) Age (years), median (IQR) 67.0 (63.8–69.8) 76.3 (73.0–81.2) 76.2 (72.0–81.5) 80.6 (78.0–86.3) 75.2 (68.6–80.6) <0.001 Men, n (%) 19 (46.3) 13 (59.1) 33 (62.3) 14 (63.6) 79 (57.2) 0.402 BMI (kg/m2), mean ±SD 26.9 ±4.2 29.9 ±5.4 28.0 ±5.2 25.3 ±3.2 27.5 ±4.9 0.012 Previous hematological disease 8 (19.5) 4 (18.2) 13 (24.5) 10 (47.6) 35 (25.5) 0.078 Lee Index score, mean ±SD 6.9 ±2.6 9.6 ±3.7 10.2 ±3.3 12.5 ±3.7 9.5 ±3.7 <0.001 GAH scale score, mean ±SD 39.1 ±29.3 52.2 ±19.4 52.6 ±23.3 51.3 ±26.9 47.8 ±25.8 0.195 ECOG PS, n (%) 0–1 30 (76.9) 18 (81.8) 40 (76.9) 10 (45.5) 98 (72.6) 0.019 2–3 9 (23.1) 4 (18.2) 12 (23.1) 12 (54.5) 37 (27.4) EQ-VAS score, mean ±SD 67.1 ±22.6 54.7 ±22.5 56.5 ±23.1 53.3 ±27.0 58.9 ±23.8 0.166 Marrow blast % at diagnosis, median (IQR) 38.0 (21.0–70.0) 47.5 (39.0–89.0) 35.0 (23.0–54.0) 39.0 (17.0–67.5) 39.9 (22.0–67.5) 0.219 Cytogenetics, n (%) Normal 24 (58.5) 15 (68.2) 29 (55.8) 14 (63.6) 82 (59.9) Abnormal 17 (41.5) 7 (31.8) 23 (44.2) 8 (36.4) 55 (40.1) Not available 0 (0.0) 0 (0.0) 1 (0.7) 0 (0.0) 1 (0.7) Prevalence of recurrent cytogenetic abnormalities *, n (%) t(8;21) 2 (4.9) 0 (0.0) 0 (0.0) 0 (0.0) 2 (1.4) Inv (16) 1 (2.4) 1 (14.3) 0 (0.0) 0 (0.0) 2 (1.4) t(9;11) 1 (2.4) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.7) Other ** 13 (31.7) 6 (27.3) 23 (44.2) 8 (36.4) 50 (36.5) Patients with complex karyotype, n (%) 2 (4.9) 2 (9.0) 9 (17.3) 4 (18.2) 17 (12.4) MRC/LRF risk, n (%) Good 17 (41.5) 3 (13.6) 3 (5.7) 1 (4.5) 24 (17.4) <0.001 Standard 16 (39.0) 6 (27.3) 13 (24.5) 4 (18.2) 39 (28.3) Poor 8 (19.5) 13 (59.1) 37 (69.8) 17 (77.3) 75 (54.3) Fatigue assessment, n (%) Need to stop 0.273 Not at all 12 (29.3) 7 (31.8) 6 (11.5) 1 (5.3) 26 (19.4) A little 13 (31.7) 7 (31.8) 20 (38.5) 8 (42.1) 48 (35.8) Quite a bit 7 (17.1) 5 (22.7) 14 (26.9) 7 (36.8) 33 (26.4) Very much 9 (22.0) 3 (13.6) 12 (23.1) 3 (15.8) 27 (20.1) Feeling weak Not at all 12 (29.3) 5 (22.7) 11 (21.2) 2 (10.0) 30 (22.2) 0.651 A little 11 (26.8) 7 (31.8) 16 (30.8) 8 (40.0) 42 (31.1) Quite a bit 13 (31.7) 6 (27.3) 18 (34.6) 4 (20.0) 41 (30.4) Very much 5 (12.2) 4 (18.2) 7 (13.5) 6 (30.0) 22 (16.3) Feeling tired Not at all 7 (17.7) 5 (22.7) 1 (1.9) 1 (5.3) 14 (10.4) 0.158 A little 15 (36.6) 6 (27.3) 22 (42.3) 9 (47.4) 52 (38.8) Quite a bit 10 (24.4) 7 (31.8) 16 (30.8) 3 (15.8) 36 (26.9) Very much 9 (22.0) 4 (18.2) 13 (25.0) 6 (31.6) 32 (23.9) AC, attenuated chemotherapy; BMI, body mass index; Complex cytogenetics, 3 or more abnormalities; ECOG, Eastern Cooperative Oncology Group; EQ-VAS, EuroQoL Visual Analogue Scale; GAH, Geriatric Assessment in Hematology; HMA, hypomethylating agents; IC, intensive chemotherapy; IQR, interquartile range; MRC/LRF, Medical Research Council and Leukemia Research Foundation; NA, not available; PC, palliative care; PS, performance status; SD, standard deviation. * Proportions within treatment groups are shown. ** Including complex karyotypes. J. Pers. Med. 2023,13, 1667 5 of 16 J. Pers. Med. 2023, 13, x FOR PEER REVIEW 4 of 17 during follow-up. Need for hospitalization was analyzed as the proportion of patients requiring hospitalization (excluding hospice or palliative care stays) during follow-up, as well as the proportion of days that the patients spent at the hospital as in-patients during the observation time. The impact of WBC on ED was analyzed by ANCOVA (accounting for time from diagnosis to treatment initiation) and a receiver operating characteristics (ROC) curve. The area under the curve (AUC) was calculated to identify the cut-off point, maximizing sensitivity and specificity. HRQoL was descriptively analyzed, including the frequency distributions of EQ-5D-5L dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) and measures of central tendency and dispersion of EQ-VAS and preference indexes; the latter was also analyzed using repeated-measures linear models. The Lee index and GAH scale validity as treatment-tolerability predictive values were analyzed by a full multiple linear regression model and multiple logistic regression. Optimal cut-off points were determined by ROC analysis. All the variables were analyzed in the overall series and adjusted by treatment. Missing data were not imputed and were left as missing. The hypothesis tests used were twosided and with a significance level of 0.05. IBM SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA) was used for all the analysis. 3. Results 3.1. Patient Disposition and Frontline Treatment A total of 151 patients were enrolled in the study, 13 of whom did not meet inclusion criteria, so 138 were finally evaluable (Figure 1, panel a). All treatments were administered according to the physician’s criteria and the local clinical practice. The study flow is displayed in Figure 1, panel b. and patient characteristics are shown in Table 1. Median (interquartile range, IQR) patient age was 75.2 (68.6–80.6) years, and most were male (57.2%). Patient ECOG performance status was predominantly good (ECOG 0–1 72.6%), but 27.4% of patients had an ECOG score of 2–3 (Table 1); 35 patients out of 137 (25.5%) had a previous hematological disease, and 27 (19.6%) had previously received chemotherapy and/or radiotherapy. (a) J. Pers. Med. 2023, 13, x FOR PEER REVIEW 5 of 17 (b) Figure 1. (a) Patient enrollment flowchart. (b). Study flowchart. AML, acute myeloid leukemia; LE, Life expectancy; GA, Geriatric assessment; QoL, quality-of-life; WBC, white blood cell count; IC, intensive chemotherapy; AC, attenuated chemotherapy; HMA, hypomethylating agents; PC, palliative care; wk, week; m, month; HCT, hematopoietic cell transplantation. Fifty-five patients (39.9%) had an abnormal karyotype, and in 17 (13.2%) it was complex. In one case of the HMA group, cytogenetics was not available. Among the patients with normal cytogenetics, the prevalence of mutations in the genes NPM1, FLT3, and CEBPA was 9.3%, 18.6%, and 3.2%, respectively. Details on WHO classification are displayed in Figure 2 (genomic data available in the Supplementary Materials, Table S1). Figure 1. ( a ) Patient enrollment flowchart. ( b ). Study flowchart. AML, acute myeloid leukemia; LE, Life expectancy; GA, Geriatric assessment; QoL, quality-of-life; WBC, white blood cell count; IC, intensive chemotherapy; AC, attenuated chemotherapy; HMA, hypomethylating agents; PC, palliative care; wk, week; m, month; HCT, hematopoietic cell transplantation. Fifty-five patients (39.9%) had an abnormal karyotype, and in 17 (13.2%) it was complex. In one case of the HMA group, cytogenetics was not available. Among the patients with normal cytogenetics, the prevalence of mutations in the genes NPM1,FLT3, and CEBPA J. Pers. Med. 2023,13, 1667 6 of 16 was 9.3%, 18.6%, and 3.2%, respectively. Details on WHO classification are displayed in Figure 2(genomic data available in the Supplementary Materials, Table S1). J. Pers. Med. 2023, 13, x FOR PEER REVIEW 6 of 17 Figure 2. AML type following WHO 2016 classification criteria. AC, attenuated chemotherapy; AML, acute myeloid leukemia; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. Most patients (54.3%) were included in the poor MRC/LRF risk category. Good MRC/LRF risk score was predominant in the patients receiving IC, while poor risk score was predominant in the rest of the patients (Figure 3). These differences were statistically significant (p < 0.001). Figure 3. MRC/LRF categories by treatment group. AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. The need for PRBC at diagnosis was maximal for patients who were later assigned to PC and minimal for those assigned IC (Figure S1), but the differences between groups were not statistically significant (p = 0.101). Life expectancy prior to AML diagnosis was maximal in the IC group, minimal in the PC, and intermediate in those treated by AC and HMA (Table 1 and Figure 4). Patient condition, according to the GAH scale, was intermediate and similar across treatment groups (Table 1). Some GAH dimension subscores, more specifically, the number of drugs, difficulties in performing activities of daily life, comorbidities, and smoking habits, Figure 2. AML type following WHO 2016 classification criteria. AC, attenuated chemotherapy; AML, acute myeloid leukemia; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. Most patients (54.3%) were included in the poor MRC/LRF risk category. Good MRC/LRF risk score was predominant in the patients receiving IC, while poor risk score was predominant in the rest of the patients (Figure 3). These differences were statistically significant (p< 0.001). J. Pers. Med. 2023, 13, x FOR PEER REVIEW 6 of 17 Figure 2. AML type following WHO 2016 classification criteria. AC, attenuated chemotherapy; AML, acute myeloid leukemia; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. Most patients (54.3%) were included in the poor MRC/LRF risk category. Good MRC/LRF risk score was predominant in the patients receiving IC, while poor risk score was predominant in the rest of the patients (Figure 3). These differences were statistically significant (p < 0.001). Figure 3. MRC/LRF categories by treatment group. AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. The need for PRBC at diagnosis was maximal for patients who were later assigned to PC and minimal for those assigned IC (Figure S1), but the differences between groups were not statistically significant (p = 0.101). Life expectancy prior to AML diagnosis was maximal in the IC group, minimal in the PC, and intermediate in those treated by AC and HMA (Table 1 and Figure 4). Patient condition, according to the GAH scale, was intermediate and similar across treatment groups (Table 1). Some GAH dimension subscores, more specifically, the number of drugs, difficulties in performing activities of daily life, comorbidities, and smoking habits, Figure 3. MRC/LRF categories by treatment group. AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. J. Pers. Med. 2023,13, 1667 7 of 16 The need for PRBC at diagnosis was maximal for patients who were later assigned to PC and minimal for those assigned IC (Figure S1), but the differences between groups were not statistically significant (p= 0.101). Life expectancy prior to AML diagnosis was maximal in the IC group, minimal in the PC, and intermediate in those treated by AC and HMA (Table 1and Figure 4). Patient condition, according to the GAH scale, was intermediate and similar across treatment groups (Table 1). Some GAH dimension subscores, more specifically, the number of drugs, difficulties in performing activities of daily life, comorbidities, and smoking habits, were statistically different between the treatment strategies (Table S2). EQ-5D-VAS and fatigue were also similar in the different groups (Table 1; details on the different domains are shown in Table S3). J. Pers. Med. 2023, 13, x FOR PEER REVIEW 8 of 17 Need to stop 0.273 Not at all 12 (29.3) 7 (31.8) 6 (11.5) 1 (5.3) 26 (19.4) A little 13 (31.7) 7 (31.8) 20 (38.5) 8 (42.1) 48 (35.8) Quite a bit 7 (17.1) 5 (22.7) 14 (26.9) 7 (36.8) 33 (26.4) Very much 9 (22.0) 3 (13.6) 12 (23.1) 3 (15.8) 27 (20.1) Feeling weak Not at all 12 (29.3) 5 (22.7) 11 (21.2) 2 (10.0) 30 (22.2) 0.651 A little 11 (26.8) 7 (31.8) 16 (30.8) 8 (40.0) 42 (31.1) Quite a bit 13 (31.7) 6 (27.3) 18 (34.6) 4 (20.0) 41 (30.4) Very much 5 (12.2) 4 (18.2) 7 (13.5) 6 (30.0) 22 (16.3) Feeling tired Not at all 7 (17.7) 5 (22.7) 1 (1.9) 1 (5.3) 14 (10.4) 0.158 A little 15 (36.6) 6 (27.3) 22 (42.3) 9 (47.4) 52 (38.8) Quite a bit 10 (24.4) 7 (31.8) 16 (30.8) 3 (15.8) 36 (26.9) Very much 9 (22.0) 4 (18.2) 13 (25.0) 6 (31.6) 32 (23.9) AC, attenuated chemotherapy; BMI, body mass index; Complex cytogenetics, 3 or more abnormalities; ECOG, Eastern Cooperative Oncology Group; EQ-VAS, EuroQoL Visual Analog Scale; GAH, Geriatric Assessment in Hematology; HMA, hypomethylating agents; IC, intensive chemotherapy; IQR, interquartile range; MRC/LRF, Medical Research Council and Leukemia Research Foundation; NA, not available; PC, palliative care; PS, performance status; SD, standard deviation. * Proportions within treatment groups are shown. ** Including complex karyotypes. Figure 4. Lee Index for Older Adults by treatment group. (a) Distribution of Lee Index Score by quartiles. (b). Distribution of Lee Index Score by categories (score < 6 pts and score ≥ 6 pts). AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. One hundred and thirty-eight patients initiated frontline therapy according to standard clinical practice (Table S4). The mean time (±SD) from diagnosis to therapy was 9.8 ± 14.3 days. Forty-one patients (29.7%) received IC, 22 (15.9%) AC, 53 (38.4%) HMA, and 22 (15.9%) did not receive active therapy but just PC. Figure 4. Lee Index for Older Adults by treatment group. ( a ) Distribution of Lee Index Score by quartiles. ( b ). Distribution of Lee Index Score by categories (score < 6 pts and score ≥ 6 pts). AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. One hundred and thirty-eight patients initiated frontline therapy according to standard clinical practice (Table S4). The mean time ( ± SD) from diagnosis to therapy was 9.8 ± 14.3 days. Forty-one patients (29.7%) received IC, 22 (15.9%) AC, 53 (38.4%) HMA, and 22 (15.9%) did not receive active therapy but just PC. 3.2. Overall Survival and Prognostic Factors Fifty-six patients (40.6%) died during follow-up. Median OS for the overall series was 7.1 months (95%CI 4.7–9.5), with significant differences by treatment groups: median IC not reached; AC, 5.9 (95% CI 3.3–8.5); HMA, 8.8 (95% CI 6.5–11.0), and PC 1.0 (95% CI 0.2–1.8) (p< 0.001) (Figure 5). J. Pers. Med. 2023,13, 1667 8 of 16 J. Pers. Med. 2023, 13, x FOR PEER REVIEW 9 of 17 3.2. Overall Survival and Prognostic Factors Fifty-six patients (40.6%) died during follow-up. Median OS for the overall series was 7.1 months (95%CI 4.7–9.5), with significant differences by treatment groups: median IC not reached; AC, 5.9 (95% CI 3.3–8.5); HMA, 8.8 (95% CI 6.5–11.0), and PC 1.0 (95% CI 0.2– 1.8) (p < 0.001) (Figure 5). Figure 5. Overall survival by treatment group. AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. Overall, the 1-year survival rate was 31.9% (95% CI 23.1–40.7). When stratified by treatment strategy, 1-year survival was 61.7% (95% CI 43.8–79.6) for patients on IC, 29.8% (95% CI 9.0–50.6) for AC, and 29.5% (95% CI 16.5–42.6) for HMA (Figure S2). Given the absence of data at 12 months follow-up, this variable could not be calculated for patients on PC. As regards the impact of variables predicting OS, multivariate analysis showed that patients in the MRC/LRF poor category (HR = 2.69, 95% CI = 1.66–4.37, p < 0.001) and those receiving PC (HR = 4.97, 95% CI = 2.93–8.43, p < 0.001) had a higher mortality risk. No other relevant prognostic factors for OS were identified. 3.3. Early Death: Impact of WBC at Diagnosis Twenty-eight patients (21.5%) died before 8 weeks from diagnosis, fourteen of whom (50%) were receiving PC. The study hypothesis set out that WBC could be a relevant predictor of ED. When we compared the mean (±SD) WBC of the patients who died early vs. the WBC of those surviving that period, we found higher values for patients with ED (55.0 ± 79.7 × 10 9 /L vs. 19.1 ± 45.7 × 10 9 /L; p < 0.001) and a positive correlation between ED proportion and WBC strata (p = 0.002, Figures S3 and S4). The ANCOVA analysis, performed from diagnosis to treatment onset, showed similar results (53.0 × 10 9 /L, 95% CI 32.2–73.8 vs. 19.6 × 10 9 /L, 95% CI 8.9–30.4; p = 0.006). These results were confirmed by a ROC analysis, demonstrating the validity of WBC for predicting ED in the overall series (AUC = 0.718, 95% CI 0.606– 0.829, p < 0.001) at the expense of patients treated with alternative chemotherapy (either Figure 5. Overall survival by treatment group. AC, attenuated chemotherapy; IC, intensive chemotherapy; HMA, hypomethylating agents; PC, palliative care. Overall, the 1-year survival rate was 31.9% (95% CI 23.1–40.7). When stratified by treatment strategy, 1-year survival was 61.7% (95% CI 43.8–79.6) for patients on IC, 29.8% (95% CI 9.0–50.6) for AC, and 29.5% (95% CI 16.5–42.6) for HMA (Figure S2). Given the absence of data at 12 months follow-up, this variable could not be calculated for patients on PC. As regards the impact of variables predicting OS, multivariate analysis showed that patients in the MRC/LRF poor category (HR = 2.69, 95% CI = 1.66–4.37, p< 0.001) and those receiving PC (HR = 4.97, 95% CI = 2.93–8.43, p< 0.001) had a higher mortality risk. No other relevant prognostic factors for OS were identified. 3.3. Early Death: Impact of WBC at Diagnosis Twenty-eight patients (21.5%) died before 8 weeks from diagnosis, fourteen of whom (50%) were receiving PC. The study hypothesis set out that WBC could be a relevant predictor of ED. When we compared the mean ( ± SD) WBC of the patients who died early vs. the WBC of those surviving that period, we found higher values for patients with ED (55.0 ± 79.7 × 10 9 /L vs. 19.1 ± 45.7 × 10 9 /L; p< 0.001) and a positive correlation between ED proportion and WBC strata (p= 0.002, Figures S3 and S4). The ANCOVA analysis, performed from diagnosis to treatment onset, showed similar results (53.0 × 10 9 /L, 95% CI 32.2–73.8 vs. 19.6 × 10 9 /L, 95% CI 8.9–30.4; p= 0.006). These results were confirmed by a ROC analysis, demonstrating the validity of WBC for predicting ED in the overall series (AUC = 0.718, 95% CI 0.606–0.829, p< 0.001) at the expense of patients treated with alternative chemotherapy (either AC or HMA; AUC = 0.805, 95% CI = 0.698–0.911, p= 0.002), but could not be confirmed for those receiving IC (p= 0.231). The optimal cut-off was 5.8 × 10 9 leucocytes/L in the overall series and 9.5 ×109leucocytes/L in those patients receiving alternative chemotherapy. 3.4. Treatment Tolerability TT, according to the investigator, was considered intermediate: overall (mean ± SD) 6.4 ± 2.9; IC 6.0 ± 2.7; AC 5.5 ± 3.4 and HMA 6.9 ± 2.3. Objective TT was poor in J. Pers. Med. 2023,13, 1667 9 of 16 30/116 (25.9%) of the patients receiving active antileukemic treatment, with no statistically significant differences between the treatment groups (IC 24.4%, AC 22.7%, HMA 28.3%, p= 0.85). Reasons for treatment termination and types of treatment modification due to toxicity are shown in Tables S5 and S6. 3.5. Treatment Response The response could not be evaluated in 34 out of 116 (29.3%) patients receiving active treatment, mainly because of the lack of longitudinal bone marrow data. The overall response rate was 54.3% (45/81). By treatment groups, IC-treated patients had a higher response rate than those receiving alternative chemotherapy (IC 28/34, 82.4%; AC or HMA 16/47, 34.0%; p< 0.001). 3.6. Patient Condition and Patient-Reported Outcomes Neither Lee index/GAH scores nor fatigue score at diagnosis/EQ-VAS showed independent prognostic value on OS in the overall series (Table 2). When we restricted the OS analysis to those patients receiving active therapy (i.e., excluding PC), MRC/LRF poor category (HR = 2.02, 95% CI = 1.11–3.64, p= 0.020) and a higher Lee score (HR = 1.08, 95%CI = 1.002–1.156, p= 0.044) were the only covariates linked to a higher mortality risk. Table 2. Analysis of prognostic factors for overall survival (N = 138). Overall Survival pValue HR 95% CI Univariate analysis MRC/LRF risk group Standard/rest risk group 0.150 2.07 0.77–5.58 Unfavorable/rest risk group 0.001 4.48 1.80–11.16 Baseline EQ-VAS value 0.455 1.00 0.99–1.01 Lee scale score <0.001 1.13 1.07–1.19 Lee ≥6/Lee < 6 0.004 2.33 1.31–4.15 GAH scale score 0.385 1.00 0.99–1.01 GAH > 42/GAH ≤42 0.899 1.04 0.59–1.81 FA score 0.392 1.00 0.99–1.01 Frontline treatment Palliative care/rest <0.001 8.45 4.10–17.41 Multivariate analysis MRC/LRF risk group Unfavorable/rest risk group <0.001 2.69 1.66–4.37 Frontline treatment Palliative care/rest <0.001 4.97 2.93–8.42 CI, confidence interval; EQ-VAS, EuroQoL Visual Analogue Score; FA, fatigue assessment; GAH, Geriatric Assessment in Hematology; HR, hazard ratio. Lee and GAH scores were not associated with ED (p= 0.142 and p= 0.310, respectively), and as regards TT, neither of them can be considered appropriate tools for predicting TT (AUC 0.429, 95% CI 0.311–0.547, p= 0.222 and AUC 0.684, 95% CI 0.521–0.847, p= 0.140; respectively). Most patients were allocated to the most favorable category of each EQ-5D dimension, and this status was maintained until the end of the study (Figure 6). The score obtained in the preference index was stable during the study follow-up and closer to 1 (perfect health) than to 0 (death). From month 6 onwards, data from patients on PC care were no longer available due to the high mortality rate, so they were not included in the comparative analysis. 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Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States. Ann. Hematol. 2015 ,94, 1127–1138. [CrossRef] [PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.