Table 1.

Prognostic algorithms for fitness assessment.

Study	Prognosticators	Aim	Limits
Walter et al.; J Clin Oncol (2011) [8]	Age, platelet count, percentage of blasts in peripheral blood, albumin level, diagnosis of secondary AML, creatinine, WBC count and PS	To predict 28-days treatment-related mortality after induction chemotherapy	Able to predict mortality, not a proper fitness score
Wheatley et al.; Br J Haematology (2009) [9]	Age, PS, cytogenetic risk and AML type (newly diagnosed vs. secondary)	To predict survival after intensive chemotherapy according to patient- and disease-related factors	Able to predict survival, not a proper fitness score
Deschler et al.; Haematologica, (2013) [12]	PS, Health-related quality-of-life scale, Activities of daily living (ADL), Instrumental activities of daily living (IADL), Charlson comorbidity index (CCI), Hematopoietic Cell Transplantation specific Comorbidity Indices (HCT-CI), Get-up and Go Test, Geriatric Depression Scale (GDS), Mini-Mental State Examination (MMSE), EORTC Quality of life questionnaire, percentage of bone marrow blasts, cytogenetics, IPSS in MDS, peripheral blood leukocytes, hemoglobin, serum LDH, serum creatinine, creatinine clearance and serum albumin.	To offer a comprehensive geriatric/quality of life assessment aside from established disease-specific variables	Lack of information regarding the effects of different treatment intensities on outcomes
Ferrara et al.; Leukemia (2013) [13]	Age, PS, comorbidities	To select treatment intensity based on a multi-organ functional evaluation, regardless of disease-related factors	Lack of prospective validation
NCCN clinical practice guideline in oncology [10]	Application of cognition, depression, distress, physical function and comorbidities scales	To predict survival in older hematological patients through cognitive and physical function evaluation	Time-consuming, requires a multidisciplinary team and procedures outside clinical practice