Saturday, June 10, 2023
| Characteristic | No. | % | Median (range) |
|---|---|---|---|
| Age at diagnosis, y | 64 (60-75) | ||
| 60-64 | 54 | 51.4 | |
| 65-70 | 37 | 35.3 | |
| 71-75 | 14 | 13.3 | |
| Sex | |||
| Male | 65 | 61.9 | |
| Female | 40 | 38.1 | |
| AML disease type | |||
| De novo | 73 | 69.5 | |
| Secondary | 32 | 30.5 | |
| ELN 2017 criteria | |||
| Favorable | 24 | 22.9 | |
| Intermediate | 49 | 46.7 | |
| Poor | 32 | 30.5 | |
| Genetic mutation | |||
| Biallelic CEBPA | 6 | 5.7 | |
| NPM1 without FLT3- ITD or with FLT3-ITD (low) | 13 | 12.4 | |
| NPM1 with FLT3-ITD (high) | 10 | 9.5 | |
| FLT3-ITD (high) without NPM1 | 9 | 8.6 | |
| RUNX1 | 10 | 9.5 | |
| ASXL1 | 9 | 8.6 | |
| TP53 | 2 | 1.9 | |
| Laboratory findings at baseline | |||
| WBC × 109/L | 3.8 (0.3-345.7) | ||
| Hemoglobin | 9.1 (5.2-13.0) | ||
| Platelet count × 109/L | 68.0 (9.0-827.0) | ||
| Creatinine, mg/dL | 0.9 (0.5-1.7) | ||
| Albumin, g/dL | 3.8 (2.8-5.0) | ||
| Fibrinogen, mg/dL | 344.0 (57.0-500.0) | ||
| Lactate dehydrogenase, U/L | 471.0 (184.0-13 200.0) | ||
| Basic assessment | |||
| Cardiac function, LVEF (%) | 64.0 (52.0-74.2) | ||
| Pulmonary function | |||
| FEV-1 (%) | 88.0 (57.0-115.0) | ||
| Adjusted DLCO (%) | 77.0 (42.0-119.0) | ||
| ECOG PS | |||
| 0-1 | 98 | 93.3 | |
| 2 | 7 | 6.7 | |
| HCT-CI | |||
| ≥3 | 24 | 22.9 | |
| ≥4 | 15 | 14.3 | |
| ≥5 | 9 | 8.6 | |
| Wheatley index* | |||
| Score | 7 (4-14) | ||
| Good risk (4-6) | 52 | 49.5 | |
| Standard risk (7-8) | 30 | 28.6 | |
| Poor risk (≥9) | 23 | 21.9 | |
| AML scores† | |||
| ED score, % | 18.9 (6.1-52.4) | ||
| 1st quartile | 26 | 24.8 | |
| 2nd quartile | 26 | 24.8 | |
| 3rd quartile | 24 | 22.9 | |
| 4th quartile | 29 | 27.6 | |
| CR score, % | 61.3 (14.5-90.6) | ||
| 1st quartile | 27 | 25.7 | |
| 2nd quartile | 26 | 24.8 | |
| 3rd quartile | 28 | 26.7 | |
| 4th quartile | 24 | 22.8 | |
| Ferrara criteria† | |||
| Age 75 years or older | 1 | ||
| ECOG PS ≥3 | 0 | ||
| Heart (LVEF ≤50%) | 0 | ||
| Lungs (DLCO ≤65% or FEV-1 ≤65%) | 21 | ||
| Kidney (on dialysis) | 3 | ||
| Liver (LFT >3× normal values) | 4 | ||
| Infection (resistant to anti-infective therapy) | 0 | ||
| Mental illness or uncontrolled cognitive status | 0 | ||
| Any other comorbidity that the physician judged to be incompatible with chemotherapy | 0 | ||
| Unfit‡ | 28 | 26.7 |
Table 1. Baseline characteristics of the study cohort (N = 105)
DLCO, diffusing capacity of lungs for carbon monoxide; FEV-1, forced expiratory volume at 1 second; ITD, internal tandem duplication; LFT, liver function test; LVEF, left ventricular ejection fraction; WBC, white blood cell count.
Wheatley risk score comprises cytogenetic risk group, WBC group, ECOG PS, age group, and AML type.16
AML scores calculate the probability of CR or ED (%) with appropriate formula, including initial body temperature, hemoglobin, platelet count, fibrinogen level, lactate dehydrogenase level, age, cytogenetic/molecular risk classification, and AML type.14
Ferrara operation criteria define unfitness for intensive chemotherapy in AML. The definition of unfitness for intensive chemotherapy should require the fulfillment of ≥1 of 9 criteria.44
| GA category | Score | No. | % | Median (range) |
|---|---|---|---|---|
| Physical function assessment | ||||
| K-MBI as ADL measurement | 105 (24-05) | |||
| Impaired K-MBI | ≤100 | 10 | 9.5 | |
| K-IADL | 10 (10-28) | |||
| Impaired K-IADL | ≥12 | 31 | 29.5 | |
| SPPB | 10 (3-12) | |||
| Impaired SPPB | ≤8 | 37 | 35.2 | |
| Standing balance consists of 3 subsequent balance tests | ≤3 points | |||
| Side-by-side stand <10 s | 0 points | 0 | ||
| Semitandem stand <10 s | 0 points | 3 | 2.9 | |
| Tandem stand <10 s | 18 | 17.2 | ||
| 3.0-9.9 s | 1 point | 9 | 50.0 | |
| >3.0 s or cannot perform | 0 points | 9 | 50.0 | |
| Gait speed assessment (4 meters), ≥4.82 s | ||||
| <4.82 s | 4 points | 48 | 45.7 | |
| 4.82-6.20 s | 3 points | 27 | 25.7 | |
| 6.21-8.70 s | 2 points | 14 | 13.3 | |
| >8.70 s | 1 point | 6 | 5.7 | |
| Cannot perform | 0 points | 10 | 9.5 | |
| Sit-and-stand speed, 5 times (≥11.19 s) | ||||
| <11.19 s | 4 points | 46 | 43.8 | |
| 11.19-13.69 s | 3 points | 21 | 20.0 | |
| 13.70-16.69 s | 2 points | 17 | 16.2 | |
| >16.7 s | 1 point | 9 | 8.6 | |
| <60 s or cannot perform | 0 points | 12 | 11.4 | |
| Handgrip strength | ||||
| Dominant hand strength, kg | 28 (12-46) | |||
| Male | 34 (12-46) | |||
| Female | 21 (13-28) | |||
| Impaired handgrip strength, dominant hand (≤4th quartile) | 24 | 22.9 | ||
| Male | 10 | |||
| Female | 14 | |||
| Nutritional status assessment | ||||
| MNA | 25.5 (10.5-33.0) | |||
| Impaired MNA | ≤23.5 | 35 | 33.3 | |
| Social support assessment | ||||
| OARS | 16 (8-24) | |||
| Impaired OARS | ≥18 | 34 | 32.4 | |
| Cognition function assessment | ||||
| MMSE-KC | 26 (15-30) | |||
| Impaired MMSE-KC | ≤23 | 35 | 33.3 | |
| No cognitive impairment | 24-30 | 70 | 66.7 | |
| Mild cognitive impairment | 18-23 | 31 | 29.5 | |
| Severe cognitive impairment | 0-17 | 4 | 3.8 | |
| KNU-DESC | 0 (0-3) | |||
| Impaired KNU-DESC | ≥2 | 2 | 1.9 | |
| Psychological function assessment | ||||
| SGDS-K | 2 (0-15) | |||
| Impaired SGDS-K, moderate depressive symptom | ≥6 | 19 | 18.1 | |
| No depression | 0-5 | 86 | 81.9 | |
| Moderate depressive symptom | 6-9 | 9 | 8.6 | |
| Major depression | ≥10 | 10 | 9.5 | |
| PHQ-9 | 5 (0-27) | |||
| Impaired PHQ-9, mild depression | ≥6 | 50 | 47.6 | |
| No depression | 0-5 | 55 | 52.4 | |
| Mild depression | 6-8 | 18 | 17.1 | |
| Moderate depression | 9-14 | 19 | 18.1 | |
| Severe depression | ≥15 | 13 | 12.4 | |
| NCCN distress thermometer | 3 (0-10) | |||
| Impaired NCCN distress thermometer | ≥3 | 64 | 61.0 | |
Table 2. Baseline GA measures for the study cohort (N = 105)
ADL, activity of daily living.
This activity is intended for hematologists, oncologists, internists, geriatricians, and other clinicians caring for older patients (age > 60 years) with acute myeloid leukemia (AML).
The goal of this activity is to describe the prognostic value of multiparameter geriatric assessment (GA) domains on treatment tolerance and outcomes after intensive chemotherapy with cytarabine and idarubicin in 105 newly diagnosed older adults (age > 60 years) with AML, according to a single-institution prospective cohort study.
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Given that there are only a few prospective studies with conflicting results, we investigated the prognostic value of multiparameter geriatric assessment (GA) domains on tolerance and outcomes after intensive chemotherapy in older adults with acute myeloid leukemia (AML). In all, 105 newly diagnosed patients with AML who were older than age 60 years and who received intensive chemotherapy consisting of cytarabine and idarubicin were enrolled prospectively. Pretreatment GA included evaluations for social and nutritional support, cognition, depression, distress, and physical function. The median age was 64 years (range, 60-75 years), and 93% had an Eastern Cooperative Oncology Group performance score <2. Between 32.4% and 69.5% of patients met the criteria for impairment for each domain of GA. Physical impairment by the Short Physical Performance Battery (SPPB) and cognitive dysfunction by the Mini-Mental State Examination in the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Assessment Packet (MMSE-KC) were significantly associated with nonfatal toxicities, including grade 3 to 4 infections (SPPB, P = .024; MMSE-KC, P = .044), acute renal failure (SPPB, P = .013), and/or prolonged hospitalization (≥40 days) during induction chemotherapy (MMSE-KC, P = .005). Reduced physical function by SPPB and depressive symptoms by the Korean version of the short form of geriatric depression scales (SGDS-K) were significantly associated with inferior survival (SPPB, P = .027; SGDS-K, P = .048). Gait speed and sit-and-stand speed were the most powerful measurements for predicting survival outcomes. Notably, the addition of SPPB and SGDS-K, gait speed and SGDS-K, or sit-and-stand speed and SGDS-K significantly improved the power of existing survival prediction models. In conclusion, GA improved risk stratification for treatment decisions and may inform interventions to improve outcomes for older adults with AML. This study was registered at the Clinical Research Information Service as #KCT0002172.
Acute myeloid leukemia (AML) is a disease of the elderly with a median age at diagnosis between 68 and 72 years.[1,2] Older adults with AML (usually defined as age ≥60) have worse survival outcomes than younger patients with AML because they have different biology and more frequently have unfavorable cytogenetics, a decline in performance status, and acquired comorbidities.[3] The mutational spectrum in older adults with AML also differs from that in younger patients,[4] and differentiated mutational patterns could aid precise prognostication.[5] Selected cases of older adults with AML can benefit from intensive chemotherapy, including that containing anthracycline and cytarabine, despite the risk for increased toxicity from treatment.[3,6,7] Several prognostic models have been developed to identify patients at high risk of early death (ED), treatment resistance, or poor survival after conventional intensive AML therapy.[8] However, they were limited by low accuracy and the need for reassessment to reflect changes resulting from continuous improvement in supportive care.[8]
Chronological age, performance status, and comorbidities are commonly used to determine fitness for intensive treatment. These variables are relatively easy to assess but are limited in capturing the heterogeneity of older patients with hematologic malignancies.[9-11] Therefore, additional assessment tools are needed to better characterize fitness in the context of therapy and to capture the frailty that arises from “decreased reserves in multiple organ systems, which are initiated by disease, lack of activity, inadequate nutritional intake, stress, and/or the physiologic changes by aging.”[10,11] Among various frailty assessments, multiparameter geriatric assessment (GA) offers more comprehensive evaluations, including functional ability, physical health, cognition, psychological health, nutritional status, and social support.[10,11] Despite the growing evidence that GA can detect unrecognized vulnerabilities in patients with hematologic malignancies to help predict treatment tolerance and survival, GA is limited by lack of standardization and consensus regarding its prognostic value in older adults with AML.[10,11] Two previous prospective studies of GA in older adults with AML had conflicting results regarding the role of physical performance measures as survival predictors, suggesting the need for further prospective validation of GAs.[12,13] Furthermore, the degree to which preexisting survival prediction models, such as web-based prediction models for AML (AML scores),[14] Ferrara criteria,[15] or Wheatley index,[16] can be improved by integrating components of GA still needs to be determined.[8] Here, we report the results of a single-institution prospective cohort study that included newly diagnosed older adults with AML who received homogeneous intensive induction chemotherapy to determine which patient-related characteristics assessed by GA predict treatment tolerance and outcomes and how much they can improve survival prediction tools.
