Abstract
INTRODUCTION
Most patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) are of advanced age and it is often difficult to identify those who may benefit from specific treatment strategies. The comprehensive geriatric assessment (CGA) is considered the gold standard tool to classify older patients according to their frailty profile. A multidisciplinary approach that includes a geriatrician is essential. CGA can be helpful in personalizing the treatment plan and detecting conditions that may be reversible through geriatric interventions. Our objective is to evaluate the impact of CGA on therapeutic decisions in patients with AML and MDS.
METHODS
From January 2018 to April 2021, 97 elderly patients with AML and MDS, who were candidates to receive any treatment, were systematically evaluated through the CGA, which includes validated instruments to assess comorbidity, polypharmacy, functional status, geriatric syndromes, mood, cognition and social state. According to the CGA, the patients were classified into 3 frailty categories: fit, medium fit and unfit.
RESULTS
The mean age was 78 years (range 67-90); 55% were men, 50 patients (51,5%) with AML and 47 (37.1%) with MDS (Table 1). Diagnoses were classified according to the 2017 WHO's AML criteria: 7 (7.2%) patients had AML and related neoplasm (unclassifiable), 11 (11.3%) AML with recurrent genetic abnormalities, 14 (14.4%) AML NOS, 18 (18.5%) AML with dysplasia-related changes and 6 (6.2%) Therapy Related Myeloid Neoplasm. According to 2017 WHO's MDS criteria: 13 (13.4%) had MDS-EB, 11 (11.4%) CMML, 2 (2.1%) MDS-RS, 1 (1%) MDS with isolated del (5q), 8 (8.2%) MDS-MLD, 5 (5.1%) MDS-RS-MLD and 1 (1%) MDS unclassifiable. R-IPSS assessment for MDS was: 2 patients (6.1%) very low, 7 (21.2%) low, 10 (30.3%) intermediate, 9 (27.27%) high, and 5 (15.15%) very high risk. As for CMML prognostic, CPSS was: 4 (44.4%) high, 3 int-1(33.3%) and 2 (22.2%) low. For AML, 2017 European Leukemia Network (ELN) categories were 23 (37.7%) favorable, 24 (39.3%) intermediate and 14 (22.9%) adverse. According to the CGA, in AML, 23 (46%) patients were classified as fit, 23 (46%) as medium fit and 4 (8%) as unfit. In the MDS, 25 (54.2%), 14 (29.8%) and 8 (17%) were fit, medium fit and unfit, respectively. Regarding treatment, a total of 85.4% of fit, 78.9% of medium fit and 45.5% of unfit patients received hemato-specific treatment (p 0.03). According to the CGA category, 35.4% of fit, 50% of medium fit and 100% of unfit patients required intervention (p 0.001). Furthermore, for the CGA domains taken into consideration, depression and cognitive deficit were detected in 31 (32%) and 9 (9.3%) of patients, respectively. Also, 5 (5,2%) and 17 (17.5%) of patients had basic activities of daily livings (bADL) and instrumental activities of daily livings (iADL) deficiencies, respectively. This indicates dependence on assistance for tasks such as managing finances, use the phone, prepare meals or manage medicines. Regarding Charlson Comorbidity Index (CCI), 55 (56,7%) of patients scored ≥2 and 6 (6.2%) of patients had falls (Table 1). In addition, 48.5% of patients (54% AML) required intervention in different measures by physiotherapy, nutrition, pharmacy, psychology, social work or palliative treatment.
Geriatric assessed frailty categories were a powerful OS predictor and could discriminate three different groups regarding OS. Patients classified as fit had better median overall survival (OS;1.8 years 95% CI 1.4-2.1) compared to medium fit (1.1 y 95% CI 0.8-1.4) and unfit patients (0.8 y 95% CI 0.3-1.3) (p 0.016; Figure 1). Multivariate analysis performed included gender, age CGA categories and hemato-specific treatment showed that medium fit and unfit categories were associated with poor survival, independent of hemato-specific treatment, age and gender (HR 2.1; 95% CI, 1.1-4.2; p 0.022 and HR 2.4; 95% CI, 0.98-5.99; p 0.05)
CONCLUSIONS
Incorporating CGA within a multidisciplinary approach provides the opportunity to better classify patients according to frailty profiles to guide interventions and treatment decisions. CGA showed efficacy in predicting survival and demonstrates potential implications for shaping the decision-making process for hematologic therapies
Figure 1 Figure 1.
Disclosures
Sureda: Roche: Other: Support for attending meetings and/or travel; GSK: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Mundipharma: Consultancy; Bluebird: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Research Funding, Speakers Bureau; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Speakers Bureau. Arnan: BMS/Celgene: Consultancy, Other: Participation in clinical trials; Takeda: Other: Participation in clinical trials; Novartis: Consultancy, Other: Participation in clinical trials; Astellas: Other: Participation in clinical trials; Jazz: Other: Participation in clinical trials.
Personalizing initial therapy in acute myeloid leukemia: incorporating novel agents into clinical practice
