Results

Demographics

The screening and enrollment of the potentially eligible participants are illustrated in supplemental Figure A (available on the Blood Web site). A total of 202 patients were diagnosed during the study period, 125 patients were eligible, and 105 patients agreed to participate. Ineligible patients received nonintensive chemotherapy (n = 60; decitabine, n = 53; low-dose cytarabine, n = 3; azacitidine, n = 3; and gilteritinib, n = 1) or best supportive care (n = 17; poor ECOG PS, n = 12; refusal of any chemotherapy, n = 5). The baseline characteristics are described in Table 1. Among the 105 enrolled patients, the median age was 64 years (range, 60-75 years), and 61.9% were male. Based on the ELN 2017 risk classification, 30.5% of the patients exhibited poor risk features, and 30.5% had secondary AML. We classified patients by using the existing survival prediction models (Table 1). The Wheatley index is a model used for predicting survival of older adults with AML by large cohorts of the Medical Research Council AML11 and the Leukemia Research Fund AML 14 trials.^[16] By the Wheatley index, 21.9% had poor survival risk. AML scores through a web-based application for risk assessment of intensive chemotherapy in older adults with AML were available to predict the probability of CR and the risk of ED along with survival.^[14] Median AML scores for CR and ED were 61.3% (range, 14.5%-90.6%) and 18.9% (range, 6.1%-52.4%), respectively. Ferrara criteria,^[15] which includes 9 covariates to classify fitness for intensive chemotherapy based on risks for ED and OS, classified 26.7% of patients as unfit.

GA measures

All enrolled patients participated in GAs and answered various questionnaires; there were no missing data. The median time from admission to administration of GAs was 3 days (range, 2-7 days), and approximately 40 minutes (a minimum of 30 minutes to a maximum of 1 hour) was spent evaluating each patient with a GA. Induction chemotherapy started 1 day after completion of GA measurements. The baseline GA scores are presented in Table 2. Almost all patients (92.4%) had various impairments in physical function (57.6%), nutritional status (33.3%), social support (32.4%), cognitive function (34.0%), and psychological function (depressive symptoms or distress; 69.5%). Regarding physical function, 35.2% exhibited impairment by objectively measured SPPB, whereas 9.5% of the Korean version of the modified Barthel index (K-MBI) and 29.5% of the Korean version of Instrumental Activities of Daily Living (K-IADL) self-reported measures captured recalled function status. Correlation analysis (supplemental Table A) revealed that impairments in SPPB were correlated with all other measures of physical function. Domains of physical function were commonly correlated with impairments in cognition (MMSE-KC), depression (SGDS-K and PHQ-9), and nutrition (MNA).

Treatment tolerance during induction chemotherapy according to GA measures

Clinical outcomes and adverse events during induction chemotherapy are listed in supplemental Table B. The median recovery period was 26 days (range, 24-29 days) for neutrophil counts and 30 days (range, 29-34 days) for platelet counts during induction chemotherapy. The median hospitalization for induction chemotherapy was 32 days (range, 16-104 days). In our cohort, 65.7% achieved first CR (CR1), 4.8% experienced ED within 60 days, and 58.1% underwent transplantation. Clinical outcomes and adverse events according to baseline characteristics and GA measures are listed in supplemental Table C. Among the baseline characteristics, poor ECOG PS was associated with grade 3 to 4 acute renal failure (21.1% vs 3.5%; P = .019) and high HCT-CI scores were associated with gastrointestinal complications (impaired vs unimpaired; 29.7% vs 12.2%; P = .037). Among the GA measures, impairments in physical function as measured by SPPB (impaired vs unimpaired; 72.9% vs 58.8%; P = .021) and K-IADL (impaired vs unimpaired; 80.6% vs 60.8%; P = .049) and cognitive impairment measured by MMSE-KC (impaired vs unimpaired; 80.0% vs 60.0%; P = .040) were associated with grade 3 to 4 infection. Physical dysfunction measured by SPPB was also associated with grade 3 to 4 acute renal failure (impaired vs unimpaired; 32.4% vs 10.3%; P = .005). Prolonged hospitalization from various adverse events was defined as longer than 40 days (75th percentile) and was associated with poor ECOG PS (impaired vs unimpaired; 17.4% vs 3.7%; P = .040) and impairment in MMSE-KC (impaired vs unimpaired; 40.0% vs 12.9%; P = .002). On multivariable analysis adjusted for age, ECOG PS, and HCT-CI (Figure 1), impairments in MMSE-KC (odds ratio [OR], 2.7; 95% confidence interval, 1.0-6.9; P = .044), and SPPB (OR, 3.0; 95% confidence interval, 1.2-7.8; P = .024) were associated with grade 3 to 4 infection, and SPPB was associated with grade 3 to 4 acute renal failure (OR, 3.9; 95% confidence interval, 1.3-11.4; P = .013). The MMSE-KC score was significantly associated with prolonged hospitalization (OR, 4.2; 95% confidence interval, 1.5-4.2; P = .005). Indeed, among 35 patients who had cognitive impairment on MMSE-KC, 13 developed delirium during induction chemotherapy, which was more frequent than in nonimpaired patients (37.1% vs 12.9%; P = .004).

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Figure 1. Forest plot of odds ratios for variables associated with treatment tolerance during induction chemotherapy. Variables that were significant on univariable analysis were adjusted by age, ECOG PS, and HCT-CI. Impairments in MMSE-KC and SPPB were associated with grade 3 to 4 infection, and SPPB was associated with grade 3 to 4 acute renal failure. The MMSE-KC was significantly associated with prolonged hospitalization. *P < .05; **P < .01.

Survival outcomes according to GA measures

With a median follow-up of 13.7 months (range, 0.2-48.3 months), the cohort median OS was 24.9 months. However, median NRM was not reached in this study. The 2-year estimated OS was 52.2% (95% confidence interval, 41.5%-61.8%), and the estimated NRM was 36.5% (95% confidence interval, 26.9%-46.2%). Among the GA measures, physical function (SPPB; gait speed and sit-and-stand speed test as a part of SPPB), psychological function (SGDS-K), and nutrition (MNA) were significantly associated with OS and/or NRM on univariable analysis (Figure 2; supplemental Table D). Because of the significant correlations between those measures (supplemental Table A), we performed multivariable analysis of each GA measure with other significant covariates (Figure 3). In multivariable analysis model 1, patients with impaired physical function by SPPB had a higher risk of death (1.9-fold; 95% confidence interval, 1.1- to 3.4-fold; P = .027) and a higher risk of NRM (2.0-fold; 95% confidence interval, 1.1- to 3.9-fold; P = .033). Patients with impaired gait (model 2) had a 2.8-fold (95% confidence interval, 1.5- to 5.2-fold; P = .002) higher risk of death; those with impaired sit-and-stand speed (model 3) had a 3.6-fold (95% confidence interval, 1.9- to 7.0-fold; P < .001) higher risk of death. Patients with impaired gait (model 2) had a 2.5-fold (95% confidence interval, 1.2- to 4.9-fold; P = .011) higher risk of NRM; those with impaired sit-and-stand speed (model 3) had a 3.8-fold (95% confidence interval, 1.8- to 8.2-fold; P < .001) higher risk of NRM. Patients with depressive symptoms based on the SGDS-K (model 4) exhibited a 1.9-fold (95% confidence interval, 1.0- to 3.6-fold; P = .048) higher risk of death and a trend toward higher risk of NRM (hazard ratio, 1.8; 95% confidence interval, 0.9-3.5; P = .097). Overall, 48 patients were referred to psychiatrists because of psychological symptoms during treatment, and 15 patients were confirmed with major depressive disorder (MDD) during the postremission treatment course. All patients with MDD died, mostly as a result of NRM (71.1%). Among 19 patients with impairment measured by SGDS-K, 6 developed MDD, which was more frequent than in patients who were not impaired (31.6% vs 10.5%; P = .028). Nutrition impairment measured by MNA (model #5) was significantly associated with a 2.1-fold (95% confidence interval, 1.1- to 4.0-fold; P = .024) higher risk of NRM.

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Figure 2. Kaplan-Meier survival curves according to GA measures. Kaplan-Meier survival curves according to GA measures for physical function with SPPB (A), gait speed (B), and sit-and-stand speed (C) as part of SPPB and for depression with SGDS-K scores (D). Impairments in physical and psychological health were associated with inferior OS.

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Figure 3. Forest plot of hazard ratio (HR) for variables associated with survival outcomes. We performed multivariable analysis for survival outcomes with variables that were significant on univariable analysis. (A) Among GA measures, SPPB, gait speed, sit-and-stand speed, and SGDS-K impairment were significantly associated with inferior OS. (B) SPPB, gait speed, sit-and-stand speed, and MNA impairment were significantly associated with higher NRM. *P < .05; **P < .01; ***P < .001.

Improvement of existing survival prediction models by GA measures

We evaluated the prognostic values of the existing survival prediction models (supplemental Table E). The Wheatley index and AML scores were significantly associated with worse OS. Figure 4 and supplemental Table F show the explanatory power of survival prediction models and GA measures for OS. The IDI can be interpreted as the proportion of variance explained by the model, similar to r ², which is a measure of how well a regression line fits the data points in linear regression. The Wheatley index score explained 32.1% of the variability in OS. The addition of SPPB and SGDS-K explained an additional 10.1%. Adding gait speed and SGDS-K or sit-and-stand speed and SGDS-K explained 14.8% or 19.1% of the variability of the Wheatley index score. Another prediction model of AML scores for ED exhibited similar results. The addition of SPPB and SGDS-K, gait speed and SGDS-K, or sit-and-stand speed and SGDS-K explained an additional 10.0%, 17.5%, or 23.2% of variability, respectively. Conversely, AML scores for CR demonstrated an additional 10.5% or 13.7% explanatory power when gait speed and SGDS-K or sit-and-stand speed and SGDS-K were added. However, adding SPPB and SGDS-K did not significantly improve the explanatory power.

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Figure 4. Explanatory power of known prognostic scoring systems to predict OS. (A) The addition of SPPB and SGDS-K improved the power of existing survival prediction models of the Wheatley index (without to with SPPB+SGDS-K; 32.1% to 42.2%; P < .001) and AML score for ED (without to with SPPB+SGDS-K; 25.7% to 35.7%; P = .007) but not in AML score for CR (without to with SPPB+SGDS-K; 37.0% to 41.5%; P = .093). (B) Adding gait speed and SGDS-K improved the prediction power of the Wheatley index (without to with gait speed+SGDS-K; 32.1% to 46.9%; P < .001), AML score for ED (without to with gait speed+SGDS-K; 25.7% to 43.2%; P < .001), and AML score for CR (without to with gait speed+SGDS-K; 37.0% to 47.5%; P = .013). (C) Adding sit-and-stand speed and SGDS-K improved the prediction power of the Wheatley index (without to with sit-and-stand speed+SGDS-K; 32.1% to 51.2%; P < .001), AML score for ED (without to with sit-and-stand speed+SGDS-K; 25.7% to 48.9%; P < .001), and AML score for CR (without to with sit-and-stand speed+SGDS-K; 37.0% to 50.7%; P = .027). *P < .05; **P < .01; ***P < .001.