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The impact of comprehensive geriatric assessment for optimal treatment of older patients with cancer: A randomized parallel-group clinical trial

      Abstract

      Objectives

      The aim was to investigate if oncologic treatment decision based on G8 screening followed by comprehensive geriatric assessment (CGA) and a multidisciplinary team conference in patients with G8 ≤ 14 was better than treatment decision based on standard assessment. ClinicalTrials.gov Identifier: NCT02671994.

      Materials and Methods

      From January 2016 to June 2018, 96 patients with cancer, aged ≥70 years, were included. Patients were randomized to treatment decision based on the oncologist's clinical judgement (control) or based on screening with G8. If G8 > 14 treatment decision was made as in the control group and if G8 ≤ 14, patients were referred to CGA including intervention as needed and treatment decision after a multidisciplinary team conference (MDT).

      Results

      The study was closed early. 47 patients were randomized to the control group and 49 to the intervention group; 28 had a G8 ≤ 14, 24 of whom attended CGA. In the intervention group 48% completed treatment as planned compared to 54% in the control group (p = .208). Thirty-eight percent experienced grade 3–4 toxicity in the control group compared with only 20% in the intervention group (p = .055). Median overall survival (OS) was 14.2 months in the control group and 19.1 months in the intervention group (p = .911). Median progression-free survival (PFS) was 9.0 months in the control group and 7.8 months for the intervention group (p = .838).

      Conclusion

      Treatment decision based on G8 screening followed by CGA had no impact on completion rate of planned oncologic treatment, OS or PFS, but resulted in a borderline significant lower incidence of grade 3–4 toxicity.

      Keywords

      1. Introduction

      The number of older patients with cancer is increasing due to the aging of the population. Aging is associated with a progressive decline in the functional reserve of many organ systems and increased incidence of multimorbidity, including cancer [
      • Falandry C.
      • Bonnefoy M.
      • Freyer G.
      • Gilson E.
      Biology of cancer and aging: a complex association with cellular senescence.
      ,
      • Hansen J.
      Common cancers in the elderly.
      ,
      • Overcash J.
      Assessing the functional status of older cancer patients in an ambulatory care visit.
      ]. The prevalence of comorbidity in older patients with cancer varies from 36 to 94% [
      • Jorgensen T.L.
      • Hallas J.
      • Friis S.
      • Herrstedt J.
      Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality.
      ,
      • Extermann M.
      • Overcash J.
      • Lyman G.H.
      • Parr J.
      • Balducci L.
      Comorbidity and functional status are independent in older cancer patients.
      ,
      • Koroukian S.M.
      • Murray P.
      • Madigan E.
      Comorbidity, disability, and geriatric syndromes in elderly cancer patients receiving home health care.
      ,
      • Williams G.R.
      • Mackenzie A.
      • Magnuson A.
      • Olin R.
      • Chapman A.
      • Mohile S.
      • et al.
      Comorbidity in older adults with cancer.
      ] Comorbidity impacts survival, stage at diagnosis and risk of chemotherapy-related toxicity [
      • Jorgensen T.L.
      • Hallas J.
      • Friis S.
      • Herrstedt J.
      Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality.
      ,
      • Extermann M.
      • Overcash J.
      • Lyman G.H.
      • Parr J.
      • Balducci L.
      Comorbidity and functional status are independent in older cancer patients.
      ,
      • Koroukian S.M.
      • Murray P.
      • Madigan E.
      Comorbidity, disability, and geriatric syndromes in elderly cancer patients receiving home health care.
      ,
      • Williams G.R.
      • Mackenzie A.
      • Magnuson A.
      • Olin R.
      • Chapman A.
      • Mohile S.
      • et al.
      Comorbidity in older adults with cancer.
      ]. In newly diagnosed patients with cancer, the prevalence of polypharmacy is 35%–80% [
      • Prithviraj G.K.
      • Koroukian S.
      • Margevicius S.
      • Berger N.A.
      • Bagai R.
      • Owusu C.
      Patient characteristics associated with polypharmacy and inappropriate prescribing of medications among older adults with cancer.
      ,
      • Jorgensen T.L.
      • Herrstedt J.
      • Friis S.
      • Hallas J.
      Polypharmacy and drug use in elderly Danish cancer patients during 1996 to 2006.
      ]. Polypharmacy increases the risk of adverse drug events, drug-drug interactions, hospitalizations, potentially inappropriate medications, and mortality [
      • Turner J.P.
      • Shakib S.
      • Singhal N.
      • Hogan-Doran J.
      • Prowse R.
      • Johns S.
      • et al.
      Prevalence and factors associated with polypharmacy in older people with cancer.
      ,
      • Balducci L.
      • Goetz-Parten D.
      • Steinman M.A.
      Polypharmacy and the management of the older cancer patient.
      ]. Older patients with cancer have increased risk of toxicity to chemotherapy with a reported incidence of grade 3–5 toxicity of 53–64% in patients aged ≥70 years [
      • Hurria A.
      • Togawa K.
      • Mohile S.G.
      • Owusu C.
      • Klepin H.D.
      • Gross C.P.
      • et al.
      Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study.
      ,
      • Extermann M.
      • Boler I.
      • Reich R.R.
      • Lyman G.H.
      • Brown R.H.
      • DeFelice J.
      • et al.
      Predicting the risk of chemotherapy toxicity in older patients: the chemotherapy risk assessment scale for high-age patients (CRASH) score.
      ,
      • Hurria A.
      • Mohile S.
      • Gajra A.
      • Klepin H.
      • Muss H.
      • Chapman A.
      • et al.
      Validation of a prediction tool for chemotherapy toxicity in older adults with cancer.
      ]. Chronological age does not necessarily reflect physiological age, and older patients are often excluded from clinical trials creating a knowledge gap with limited evidence to guide treatment decisions in this population [
      • Scher K.S.
      • Hurria A.
      Under-representation of older adults in cancer registration trials: known problem, little progress.
      ].
      Oncologic treatment decisions are usually based on performance status (PS), such as Karnofsky PS (KPS) or Eastern Cooperative Oncology Group PS (ECOG PS) and the oncologist's judgement [
      • Oken M.M.
      • Creech R.H.
      • Tormey D.C.
      • Horton J.
      • Davis T.E.
      • McFadden E.T.
      • et al.
      Toxicity and response criteria of the eastern cooperative oncology group.
      ,
      • Peus D.
      • Newcomb N.
      • Hofer S.
      Appraisal of the Karnofsky performance status and proposal of a simple algorithmic system for its evaluation.
      ]. These tools have been shown to be insufficient in assessing functional status in older patients with cancer [
      • Jolly T.A.
      • Deal A.M.
      • Nyrop K.A.
      • Williams G.R.
      • Pergolotti M.
      • Wood W.A.
      • et al.
      Geriatric assessment-identified deficits in older cancer patients with normal performance status.
      ,
      • Repetto L.
      • Fratino L.
      • Audisio R.A.
      • Venturino A.
      • Gianni W.
      • Vercelli M.
      • et al.
      Comprehensive geriatric assessment adds information to eastern cooperative oncology group performance status in elderly cancer patients: an Italian group for geriatric oncology study.
      ].
      In geriatric medicine, comprehensive geriatric assessment (CGA) is “a multidimensional and usually interdisciplinary diagnostic process designed to determine a frail older person's medical condition, mental health, functional capacity, and social circumstances. The purpose is to plan and carry out a holistic plan for treatment, rehabilitation, support and long term follow up.” [
      • European Geriatric Medicine Society (EuGMS)
      ] A keystone in CGA is the assessment by a specialist in geriatric medicine and the implementation of interventions as needed.
      Both the American Society of Clinical Oncology (ASCO) and the International Society of Geriatric Oncology (SIOG) recommend a CGA in the evaluation of older patients and the Geriatric-8 (G8) as a screening tool [
      • Mohile S.G.
      • Dale W.
      • Somerfield M.R.
      • Schonberg M.A.
      • Boyd C.M.
      • Burhenn P.S.
      • et al.
      Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology.
      ,
      • Wildiers H.
      • Heeren P.
      • Puts M.
      • Topinkova E.
      • Janssen-Heijnen M.L.
      • Extermann M.
      • et al.
      International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.
      ]. Only a few randomized controlled trials (RCTs) have investigated the role of CGA in older patients with cancer [
      • Corre R.
      • Greillier L.
      • Le Caer H.
      • Audigier-Valette C.
      • Baize N.
      • Berard H.
      • et al.
      Use of a comprehensive geriatric assessment for the management of elderly patients with advanced non-small-cell lung cancer: the phase III randomized ESOGIA-GFPC-GECP 08-02 study.
      ,
      • Magnuson A.
      • Lemelman T.
      • Pandya C.
      • Goodman M.
      • Noel M.
      • Tejani M.
      • et al.
      Geriatric assessment with management intervention in older adults with cancer: a randomized pilot study.
      ,
      • Puts M.T.E.
      • Sattar S.
      • Kulik M.
      • MacDonald M.E.
      • McWatters K.
      • Lee K.
      • et al.
      A randomized phase II trial of geriatric assessment and management for older cancer patients.
      ].
      The aim of this study was to investigate if oncologic treatment decision based on G8 screening followed by CGA and a multidisciplinary team conference (MDT) in patients with G8 < 14 is superior to standard treatment decision based on the oncologist's clinical judgement in older patients with cancer.

      2. Materials and Methods

      This was a single-center randomized controlled open study including older patients with cancer, who were evaluated for chemotherapy or targeted therapy. Collection and storage of data was approved by The Regional Committees on Health Research Ethics for Southern Denmark (Journal number: S-20150093 CSF) and the Danish Data Protection Agency (Journal number: 15/20250). ClinicalTrials.gov Identifier: NCT02671994.

      2.1 Study Population

      Inclusion criteria were, 1) age ≥ 70 years, 2) a diagnosis of gynecological cancer (ovarian (ICD-10 codes C56–57, C48), endometrial cancer (C54–55)), urological cancer (prostate (C61), bladder (C65–57), kidney cancer (C64)), or non-small cell lung cancer (NSCLC (C34)), 3) starting chemotherapy or targeted therapy (tyrosine kinase inhibitor) for primary or recurrent disease, 4) able to understand/speak Danish, and 5) able to give informed consent. Exclusion criteria were a previous cancer diagnosis other than recurrence of the current cancer disease (carcinoma in situ of the cervix and non-melanoma skin cancer were allowed) and either surgery or curative intended radiation therapy within the last four weeks prior to inclusion (local radiotherapy of isolated lesions for palliative intent was allowed prior to and during the study).

      2.2 Endpoints

      The primary endpoint was the rate of completion of oncologic treatment as scheduled (number of planned courses) without premature termination due to unacceptable toxicity, progression of disease, or death. Patients, who received treatment until progression, were followed for a maximum of 6 months during treatment. A maximum of 1 dose reduction and/or a maximum of 14 days of dose delay were allowed.
      Secondary endpoints
      • Rate of severe (grade 3–4 toxicity (National Cancer Common Terminology Criteria for Adverse Events (CTCAE) version 4).
      • Time from randomization to start of treatment
      • Progression-free survival (PFS) defined as time from randomization to disease progression or death.
      • Overall survival (OS) defined as time from randomization to death.

      2.3 Study Procedures and Intervention

      Eligible patients were randomized 1:1 to intervention versus control using block randomization with random blocks of four and six with stratification for cancer diagnosis (gynecological versus urological versus lung cancer), and prior chemotherapy and/or radiation (yes/no). The protocol was amended to include patients with lung cancer 12 months after study initiation.
      All patients had an ECOG PS score.
      For patients in the control group a treatment decision was based on the oncologist's clinical judgement.
      Participants in the invention group were screened with the G8. In patients with G8 > 14, treatment decision was based on the oncologist's clinical judgement. Patients with G8 ≤ 14 were referred to the Department of Geriatric Medicine, OUH for CGA including assessment of comorbidity, polypharmacy, nutrition, and physical, cognitive and social function. Activities of daily living (ADL) was assessed by the Barthel-20 Index, where a score of 20 was normal, and decreasing scores indicated increasing disability [
      • Collin C.
      • Wade D.T.
      • Davies S.
      • Horne V.
      The Barthel ADL index: a reliability study.
      ]. Muscle strength was assessed by the 30 s chair stand test (30s CST) with scores <10 indicating high risk for falls and hand grip strength test with strength <21 kg for men and < 15 kg for women considered abnormal [
      • Frederiksen H.
      • Hjelmborg J.
      • Mortensen J.
      • McGue M.
      • Vaupel J.W.
      • Christensen K.
      Age trajectories of grip strength: cross-sectional and longitudinal data among 8,342 Danes aged 46 to 102.
      ,
      • Csuka M.
      • McCarty D.J.
      Simple method for measurement of lower extremity muscle strength.
      ]. The Charlson Comorbidity Index (CCI) was used to assess comorbidity with score 0–1 indicating no/mild comorbidity, score 2–3 indicating moderate comorbidity, and score ≥ 4 indicating severe comorbidity [
      • Charlson M.E.
      • Pompei P.
      • Ales K.L.
      • MacKenzie C.R.
      A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
      ]. Cognitive function was screened with Orientation-Memory-Concentration (OMC) (score > 24: normal/minimal impairment, score 18–24: minimal/moderate impairment, score 8–17: moderate/severe impairment, score 0–7: severe impairment) [
      • Katzman R.
      • Brown T.
      • Fuld P.
      • Peck A.
      • Schechter R.
      • Schimmel H.
      Validation of a short orientation-memory-concentration test of cognitive impairment.
      ]. Nutrition was assessed by a modified nutrition assessment, where scores ≤6 indicated risk of malnutrition [
      • Guigoz Y.
      • Vellas B.
      • Garry P.J.
      Assessing the nutritional status of the elderly: the mini nutritional assessment as part of the geriatric evaluation.
      ]. Polypharmacy was assessed in accordance with the Screening Tool of Older Person's Prescriptions and Screening Tool to Alert Doctors to Right Treatment (STOPP-START) criteria, and numbers of changes within ATC groups were recorded [
      • O'Mahony D.
      • O'Sullivan D.
      • Byrne S.
      • O'Connor M.N.
      • Ryan C.
      • Gallagher P.
      STOPP/START criteria for potentially inappropriate prescribing in older people: version 2.
      ]. The extent and need for social support was clarified by geriatric nurses.

      2.4 Data Collection

      Patient baseline characteristics were collected by nurses and physicians. ECOG PS was assessed by an oncologist, who also performed the G8 screening for patients in the intervention group. CGA data on morbidities and medications were obtained by geriatricians, and functional data by geriatric nurses. Results of MDT were recorded by the treating oncologist.
      All patients were monitored for death and progression until March 15, 2019 or death.
      Data on toxicities were recorded at every visit using the National Cancer Common Terminology Criteria for Adverse Events CTCAE version 4, according to standard care. Furthermore, hospital admissions because of toxicity were considered as at least grade 3 toxicity (severe toxicity). The worst grade of toxicity during treatment was recorded.
      Quality of life (QoL) was assessed at baseline using the EQ-5D-5 L questionnaire [
      • Janssen M.F.
      • Pickard A.S.
      • Golicki D.
      • Gudex C.
      • Niewada M.
      • Scalone L.
      • et al.
      Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study.
      ,
      • Wittrup-Jensen K.U.
      • Lauridsen J.
      • Gudex C.
      • Pedersen K.M.
      Generation of a Danish TTO value set for EQ-5D health states.
      ,
      • Herdman M.
      • Gudex C.
      • Lloyd A.
      • Janssen M.
      • Kind P.
      • Parkin D.
      • et al.
      Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
      ].

      2.5 Statistical Analysis

      Descriptive patient characteristics as well as outcomes were reported as counts with proportions for categorical measures and as medians with ranges or means with standard deviations (SD) for numerical measures. Difference in proportions was tested by Chi-square test or Fisher's exact test, depending on the number of counts) and differences in numerical data were tested by median test for age and Wilcoxon rank-sum test for other numerical variables. Progression-free survival was estimated from time of randomization to time of disease progression or death, and overall survival from the time of randomization to death of any cause, censoring at end of follow-up March 15th, 2019 by the Kaplan-Meier method comparing groups with log-rank test. Furthermore, hazard ratios (HR) were estimated by Cox regression analyses, both crude and adjusted for age, sex, ECOG PS, diagnosis group and line of treatment. All analyses were carried out in Stata 15, and p-values below 0.05 were considered statistically significant. The study was planned to include 182 patients to obtain a power of 80% with an expected increase in treatment completion rate from 60% to 80%.

      3. Results

      From January 2016 to June 2018, 114 patients were included. The study was closed after inclusion of 50% of the patients according to the prospectively calculated patient sample. An interim analysis of the primary effect parameter after inclusion of 110 patients showed that a numerically higher (but nonsignificant) number of patients in the control group than in the intervention group fulfilled the criteria for effect (see below). Therefore we concluded that it would be unrealistic to obtain a 20% difference in favor of the intervention arm if the study was continued to include patients as originally planned.
      The patient flow is shown in Fig. 1. Of the 114 patients, 59 were randomized to the control group and 55 to the intervention group. Eighteen patients were excluded (see CONSORT diagram, Fig. 1). Patient characteristics were well-balanced between the two groups, with the exception of median age, where patients in the control group were slightly older (Table 1). The majority of the patients had ECOG PS 0–1 and were treatment-naive. The diagnoses were predominately prostate (37% in the intervention group and 28% in the control group) and ovarian cancer (31% in the intervention group and 34% in the control group). Patients in the intervention group were not delayed in start of treatment compared to the control group. In the intervention group, 28 patients had a G8 score ≤ 14, and 24 completed CGA. Four patients were not referred to CGA.
      Table 1Patient characteristics.
      Control group (N = 47)Intervention group (N = 49)P-value
      Age (years)
       Median76.873.90.008
       Range70–8470–87
      Sex
       Female23 (49%)23 (47%)0.845
       Male24 (51%)26 (53%)
      Eastern Cooperative Oncology Group Performance Status (ECOG PS)
       017 (36%)21 (43%)0.343
       122 (47%)18 (37%)
       28 (17%)7 (14%)
       303 (6%)
      Line of treatment
       1st25 (54%)29 (60%)0.537
       2nd9 (20%)8 (17%)
       3rd9 (20%)5 (10%)
       4th3 (7%)4 (8%)
       5th02 (4%)
      Diagnosis
      Urological cancer24 (51%)25 (51%)0.812
      Prostate cancer13 (28%)18 (37%)
      Bladder cancer7 (15%)5 (10%)
      Kidney cancer4 (9%)2 (4%)
      Gynecological cancer19 (40%)20 (41%)
      Ovarian cancer16 (34%)15 (31%)
      Endometrial cancer3 (6%)5 (10%)
      Lung cancer, NSCLC4 (9%)4 (8%)
      EQ-5D health index score

      Mean (95% CI)
      0.79 (0.73; 0.85)0.78 (0.74; 0.82)0.545
      VAS score

      Mean (95% CI)
      74 (67; 81)71 (64; 77)0.389
      Time to treatment

      Days (mean (SD))
      10.4 (10.3)8.8 (6.8)0.886
      NSCLC: non-small cell lung cancer, VAS: Visual Analogue Scale.
      Table 2 shows the results of the CGA. The most common comorbidities were diabetes without complications (n = 7) and myocardial infarction (n = 5). The five most common ATC groups that were adjusted were analgesic drugs (opioids), drugs for constipation, nutritional agents, and urological drugs (drugs used in prostate hyperplasia).
      Table 2Results of the different domains of the comprehensive geriatric assessment N = 24).
      Functional status
      Barthel-20 Index1 (median (IQR))20 (18.5; 20)
       Normal (score = 20)15 (63%)
       Minimal disability (score 18–19)6 (25%)
       Moderate/severe disability (score < 18)3 (13%)
      30s CST2 (median (IQR))10 (8; 13)
       Normal (score ≥ 10)12 (50%)
       Abnormal (score 1–9)11 (46%)
       Unknown1 (4%)
      Handgrip strength test
      Strength <21 kg for men and < 15 kg for women considered abnormal [3].
      Female
       Abnormal, right hand2 (20%)
       Abnormal, left hand2 (20%)
      Male
       Abnormal, right hand0 (0%)
       Abnormal, left hand0 (0%)
      Comorbidity
      CCI
       None/mild comorbidity (score 0–1)11 (46%)
       Moderate comorbidity (score 2–3)7 (29%)
       Severe comorbidity (score ≥ 4)6 (25%)
      Cognition
      OMC (median (IQR))26 (24; 28)
       Normal/minimal impairment (score > 24)13 (54%)
       Minimal/moderate impairment (score 18–24)8 (33%)
       Moderate/severe impairment (score 8–17)3 (13%)
       Severe impairment (score 0–7)0
      Nutrition
      MNA
       Normal (score > 6)7 (29%)
       Risk of malnutrition (score ≤ 6)17 (71%)
      30s CST: 30 s Chair Stand Test, CCI: Charlson Comorbidity Index, OMC: Orientation-Memory-Concentration, MNA: Mini Nutritional Assessment.
      a Strength <21 kg for men and < 15 kg for women considered abnormal [
      • Overcash J.
      Assessing the functional status of older cancer patients in an ambulatory care visit.
      ].
      Seventy-one percent of the patients only had one visit to the Geriatric Clinic, but for 75% of the patients a health intervention was implemented. The most common intervention concerned changes in medications, most frequently as new prescriptions (33%) (8/24), deprescriptions in 29% (7/24) and dose changes in 30% (7/24). All interventions are shown in Table 3.
      Table 3Implemented interventions based on the comprehensive geriatric assessment (N = 24).
      Referrals to other departments/examinations/general practitioner/other
      Examinations (x-ray of thorax, measurement of peripheral or orthostatic blood pressure, DXA scan)8
      Assessment of physical function by physical therapist4
      Rehabilitation plan by physician2
      Prepared rehabilitation plan for municipal training2
      General practitioner4
      Other2
      Nutritional interventions
      Rehydration1
      Dietary counselling4
      Nutritional preparations4
      Changes within categories of Anatomical Therapeutic Chemical (ATC) Classification System.
      Social situation
      Increased aid at home (personal care, practical aid, management of medication, food arrangement)6 (11)
      Number of patients (number of times)
      a Number of patients (number of times)
      b Changes within categories of Anatomical Therapeutic Chemical (ATC) Classification System.
      A total of 94 patients were scheduled to start oncologic treatment. Eighty-eight were to start chemotherapy (42 in the control group and 46 in the intervention group), while 6 were scheduled to start targeted therapy (4 in the control group and 2 in the intervention group). Targeted therapy consisted of TKI-inhibitors for patients with kidney cancer.
      In the control group, 39 (83%) of the patients were planned to start standard dose oncologic treatment and 7 (15%) in a reduced dose, while 1 (2%) was evaluated unfit for treatment (Table 4). In the intervention group, 44 (90%) of the patients were planned to start standard dose oncologic treatment. Twenty-one of these patients had a G8 score of >14, while 28 had a G8 score G8 ≤ 14. 5 (10%) patients were not considered eligible for treatment in standard dose at the MDT; four received treatment in a reduced dose, while one was unfit for treatment. Reasons included comorbidity (1), impaired nutritional status (2), impaired functional status (3) and/or impaired cognition (1), low hemoglobin (1), or social situation (1). Some patients had more than one reason.
      Table 4Oncologic treatment and primary endpoint.
      All patients N = 96Control group N = 47Intervention group N = 49P-value
      Planned treatment at standard dose83 (86%)39 (83%)44 (90%)

      G8 > 14: 21

      G8 ≤ 14: 23
      0.672
      Planned treatment at reduced dose11 (11%)7 (15%)4 (8%)

      G8 > 14: 0

      G8 ≤ 14: 4
      Unfit for treatment2 (2%)1 (2%)1 (2%)

      G8 > 14: 0

      G8 ≤ 14: 1
      Premature treatment termination43 (45%)18 (39%)25 (51%)0.208
      Reasons for premature treatment terminationUnacceptable toxicity (11)

      Disease progression (5)

      Death (1)

      Other complication (2)
      Unacceptable toxicity (15)

      Disease progression (8)

      Death (1)

      Other complication (3)

      No reason specified (2)
      At least one dose reduction31 (33%)18 (39%)13 (27%)0.214
      > 1 dose reduction4 (4%)4 (9%)00.054
      Dose delay >14 days2 (2%)2 (4%)00.237
      Three patients (all in the control group had): premature termination + > 1 dose reduction, premature termination + dose delay >14 days, respectively, more than one dose reduction + dose delay >14 days.
      In the control group, 25 patients (54%) completed treatment as planned without premature termination and a maximum of 1 dose reduction and/or a maximum of 14 days of dose delay (primary effect parameter) as compared to 23 patients (48%) in the intervention group (p = .533). In the control group, 23 patients (59%), who started treatment in standard dose continued treatment in a 100% dose throughout their course of treatment, whereas 12 (31%) had one dose reduction. In the control group, five patients (71%), who started treatment in a reduced dose did not experience additional dose reductions, while two (29%) did. In the intervention group, 31 patients (70%), who started treatment in standard dose did not experience dose reductions, and 13 (30%) had one dose reduction. The four patients in the intervention group, who started treatment in a reduced dose, did not experience additional dose reductions. Only patients in the control group experienced >1 dose reduction and/or dose delay >14 days (Table 2). Premature termination of planned treatment was predominantly caused by unacceptable toxicity.
      Eighty-six patients (95%) experienced toxicity, 44 (98%) in the control group, and 41 (92%) in the intervention group (Table 5). Most of the patients experienced grade 1–2 toxicity – 60% in the control group and 72% in the intervention group. Overall, the most common toxicities were fatigue, peripheral sensory neuropathy, nausea and diarrhea. None of the patients experienced grade 5 toxicity. Grade 3–4 was experienced by 17 (38%) in the control group as compared to 9 (20%) in the intervention group (p = .055) (Table 3). Most of the severe toxicities were recorded during hospitalizations. The most common severe toxicities were febrile neutropenia, anemia, fatigue and various infections.
      Table 5Treatment-related toxicity.
      All patients (N = 94)Control group (N = 46)Intervention group (N = 48)P-valueMissing
      Worst toxicity experienced (including hospitalizations)
      Grade 05 (5%)1 (2%)4 (9%)3
      Grade 1–260 (66%)27 (60%)33 (72%)
      Grade 3–4 (incl. hosp)26 (29%)17 (38%)9 (20%)0.055
      Hospitalization experienced23 (24%)15 (33%)8 (17%)0.0720
      Febrile neutropenia (grade 1–4)1 (1%)1 (2%)00.4949
      Febrile neutropenia (grade 3–4)1 (1%)1 (2%)00.494
      Nausea (grade 1–4)31 (36%)16 (36%)15 (35%)0.8857
      Nausea (grade 3–4)1 (1%)01 (2%)0.494
      Vomiting (grade 1–4)8 (9%)5 (11%)3 (7%)0.7137
      Vomiting (grade 3–4)000NA
      Diarrhea (grade 1–4)25 (29%)14 (33%)11 (26%)0.4768
      Diarrhea (grade 3–4)000NA
      Fatigue (grade 1–4)78 (91%)41 (95%)37 (86%)0.2658
      Fatigue (grade 3–4)4 (5%)2 (5%)2 (5%)1.000
      Peripheral sensory neuropathy (grade 1–4)30 (35%)18 (43%)12 (28%)0.1499
      Peripheral sensory neuropathy (grade 3–4)1 (1%)01 (2%)1.000
      Hand-Foot Syndrome (grade 1–4)23 (28%)15 (37%)8 (19%)0.07411
      Hand-Foot Syndrome (grade 3–4)1 (1%)1 (2%)00.494
      Discoloration of nail (grade 1–4)10 (14%)5 (14%)5 (15%)0.88225
      Discoloration of nail (grade 3–4)000NA
      Loss of nail (grade 1–4)2 (3%)2 (6%)00.49326
      Loss of nail (grade 3–4)000NA
      Other (grade 1–4)35
      Three of these patients have two different other toxicities.
      (37%)
      17 (37%)18 (38%)0
      Other (grade 3–4)42 (5%)2 (5%)
      a Three of these patients have two different other toxicities.
      The median follow-up was 14.1 months (95% CI: 11.7–17.7). Median OS was 14.2 months (95% CI: 11.6-not reached) in the control group and 19.1 months (95% CI: 11.8-not reached) in the intervention group (p = .911). Crude HR was 0.97 (95% CI: 0.57–1.65) (p = .911), and HR adjusted for age, sex, ECOG PS, diagnosis group and line of treatment was 1.24 (95% CI: 0.68–2.24) (p = .484). Median PFS was 9.0 months (95% CI: 7.3–10.5) in the control group and 7.8 months (95% CI: 6.4–9.3) for the intervention group (p = .838). Fig. 2 shows the Kaplan-Meier curves for OS and PFS for the two groups.
      Fig. 2
      Fig. 2Overall survival (OS) and progression-free survival (PFS).

      4. Discussion

      To our knowledge, this study represents one of the largest randomized trials examining the significance of CGA in older patients with cancer. In addition, it is the first RCT to examine the effect of CGA in older patients with cancer with G8 ≤ 14, followed by an MDT, on oncologic treatment decision and outcomes.
      The study demonstrated that implementation of CGA in the management of older patients with cancer was feasible, and referral for CGA did not delay start of treatment. The intervention did not lead to a higher completion rate of planned oncologic treatment as compared to the control group. However, only patients in the control group (n = 4) needed more than one dose reduction and experienced dose delay >14 days. There was a borderline significant tendency of a greater proportion of patients in the control group experiencing severe toxicity (grade 3–4) than in the intervention group. The intervention did not influence OS or PFS.
      Corre et al. conducted a RCT in patients aged ≥70 years with advanced NSCLC and ECOG PS of 0–2, in which treatment allocation was based on GA, and found no difference in treatment-failure free survival (TFFS), progression-free survival (PFS), overall survival (OS) or grade 3–4 toxicity, but a reduction in all-grade toxicity and fewer treatment failures due to toxicity were observed in the GA arm [
      • Corre R.
      • Greillier L.
      • Le Caer H.
      • Audigier-Valette C.
      • Baize N.
      • Berard H.
      • et al.
      Use of a comprehensive geriatric assessment for the management of elderly patients with advanced non-small-cell lung cancer: the phase III randomized ESOGIA-GFPC-GECP 08-02 study.
      ]. Our results are in accordance with this regarding grade 3–4 toxicity, although we only found a near significant reduction. Contrary to the findings by Corre et al., we did not find a difference in treatment failures. However, our study differs from the study by Corre et al. First of all, even though the authors refer to the assessment in their study as a CGA, it is in fact a GA, as detection of problems was not met with specific interventions. CGA entails that the diagnostic process/assessment by a geriatrician is followed by relevant interventions to optimize the health status of the patient [
      • Wildiers H.
      • Heeren P.
      • Puts M.
      • Topinkova E.
      • Janssen-Heijnen M.L.
      • Extermann M.
      • et al.
      International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.
      ]. It is reasonable to assume that implementing relevant interventions based on an assessment with the purpose of optimizing general health status affects outcomes differently than the assessment alone.
      Secondly, Corre et al. included a more uniform population in terms of diagnosis. Even though, the diagnoses were evenly distributed between our two groups, the pooling of patients with different cancer diagnoses that vary in characteristics such as cause, treatment and prognosis may potentially reduce the total effect of the intervention. Finally, the treatment allocation in the Corre study was completely pre-specified in both the control group (based on PS and age) and in the intervention group (based on GA). Our design allowed for a more individual selection of treatment at the discretion of the oncologist.
      A non-randomized study in patients aged ≥70 years compared an observational group with an intervention group, where high-risk patients underwent a CGA. They found that patients in the intervention group were more likely to complete oncologic treatment as planned and needed fewer treatment modifications. A nonsignificant trend towards less grade 3+ toxicity rate was also found in the intervention group [
      • Kalsi T.
      • Babic-Illman G.
      • Ross P.J.
      • Maisey N.R.
      • Hughes S.
      • Fields P.
      • et al.
      The impact of comprehensive geriatric assessment interventions on tolerance to chemotherapy in older people.
      ]. The latter finding is in line with our study. Although Kalsi et al. found an effect of CGA on completion of treatment as planned, it is important to notice that 33.8% achieved this in the intervention group vs. 11.4% in the control group, which must be considered as relatively small proportions of the populations. Differences in study populations, as they included a large proportion of patients with GI cancer, may also account for the different findings. Furthermore, they used the CGA-GOLD questionnaire to select high risk patients, whereas we used the G8 screening tool. As these methods comprise different approaches for selecting high risk/frail patients, it is natural that this can cause a difference in the populations undergoing CGA's in the two studies.
      A recent study by Magnuson et al. comparing a usual care arm and an intervention arm with GA and intervention recommendations to oncologists found no significant differences in GA measures, grade 3–5 toxicity, hospitalizations, dose reductions, dose delays, early treatment discontinuation, or hospice enrollments. Furthermore, there was no notable difference in anticipated toxicity according to the Cancer and Aging Research Group (CARG) tool and observed toxicity in the two arms [
      • Magnuson A.
      • Lemelman T.
      • Pandya C.
      • Goodman M.
      • Noel M.
      • Tejani M.
      • et al.
      Geriatric assessment with management intervention in older adults with cancer: a randomized pilot study.
      ]. This is more in line with our findings. Their study population was more similar to ours, as they included patients aged ≥70 years, stage III-IV solid tumors and with a diagnosis of GI-, gynecologic- or lung cancer. They also concluded that it was feasible to conduct CGA in a timely manner in older patients with cancer, as is the case in our study.
      We found a lower incidence of severe toxicity in this population compared to what has previously been reported in older cancer populations receiving chemotherapy [
      • Hurria A.
      • Togawa K.
      • Mohile S.G.
      • Owusu C.
      • Klepin H.D.
      • Gross C.P.
      • et al.
      Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study.
      ,
      • Extermann M.
      • Boler I.
      • Reich R.R.
      • Lyman G.H.
      • Brown R.H.
      • DeFelice J.
      • et al.
      Predicting the risk of chemotherapy toxicity in older patients: the chemotherapy risk assessment scale for high-age patients (CRASH) score.
      ,
      • Hurria A.
      • Mohile S.
      • Gajra A.
      • Klepin H.
      • Muss H.
      • Chapman A.
      • et al.
      Validation of a prediction tool for chemotherapy toxicity in older adults with cancer.
      ]. This may be explained by the fact that the vast majority of our patients in both the intervention arm and in the control arm had a good PS, which is known to be associated with lower risk of chemotherapy-related toxicity [
      • Extermann M.
      • Boler I.
      • Reich R.R.
      • Lyman G.H.
      • Brown R.H.
      • DeFelice J.
      • et al.
      Predicting the risk of chemotherapy toxicity in older patients: the chemotherapy risk assessment scale for high-age patients (CRASH) score.
      ,
      • Wildiers H.
      • Heeren P.
      • Puts M.
      • Topinkova E.
      • Janssen-Heijnen M.L.
      • Extermann M.
      • et al.
      International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.
      ].
      Even though the majority of our population only experienced grade 1–2 toxicity, unacceptable toxicity was the main reason for discontinuing treatment. This indicates that some grade 2 toxicities may also be significant in older patients with cancer [
      • Hurria A.
      • Togawa K.
      • Mohile S.G.
      • Owusu C.
      • Klepin H.D.
      • Gross C.P.
      • et al.
      Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study.
      ].
      This study has some strengths. It showed that it is feasible to implement CGA without treatment delay, and the fact that CGA was performed by geriatricians with implementation of relevant geriatric interventions is also a strength. Overall, studies with geriatrician-delivered CGA interventions in older patients with cancer have been rare, especially RCTs. Most studies so far have focused on GA rather than CGA, where certain GA elements such as impairments regarding IADL, cognitive function, physical functional and nutritional status have been associated to higher risk of chemotherapy-related toxicity [
      • Hurria A.
      • Togawa K.
      • Mohile S.G.
      • Owusu C.
      • Klepin H.D.
      • Gross C.P.
      • et al.
      Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study.
      ,
      • Extermann M.
      • Boler I.
      • Reich R.R.
      • Lyman G.H.
      • Brown R.H.
      • DeFelice J.
      • et al.
      Predicting the risk of chemotherapy toxicity in older patients: the chemotherapy risk assessment scale for high-age patients (CRASH) score.
      ,
      • Mohile S.G.
      • Dale W.
      • Somerfield M.R.
      • Schonberg M.A.
      • Boyd C.M.
      • Burhenn P.S.
      • et al.
      Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology.
      ,
      • Wildiers H.
      • Heeren P.
      • Puts M.
      • Topinkova E.
      • Janssen-Heijnen M.L.
      • Extermann M.
      • et al.
      International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.
      ]. We tried to account for the heterogeneity of the population in terms of cancer diagnosis and treatment line by stratification of the randomization and adjustment in the survival analysis, rather than subgroup analysis. The patients in our study had a relatively high median age, and inclusion was not restricted by an upper age limit. Even though some studies operate with cut-offs for CGA, these are not well-defined [
      • Pottel L.
      • Lycke M.
      • Boterberg T.
      • Pottel H.
      • Goethals L.
      • Duprez F.
      • et al.
      Serial comprehensive geriatric assessment in elderly head and neck cancer patients undergoing curative radiotherapy identifies evolution of multidimensional health problems and is indicative of quality of life.
      ,
      • Orum M.
      • Gregersen M.
      • Jensen K.
      • Meldgaard P.
      • Damsgaard E.M.S.
      Frailty status but not age predicts complications in elderly cancer patients: a follow-up study.
      ,
      • Kirkhus L.
      • Saltyte Benth J.
      • Rostoft S.
      • Gronberg B.H.
      • Hjermstad M.J.
      • Selbaek G.
      • et al.
      Geriatric assessment is superior to oncologists' clinical judgement in identifying frailty.
      ,
      • Smets I.H.
      • Kempen G.I.
      • Janssen-Heijnen M.L.
      • Deckx L.
      • Buntinx F.J.
      • van den Akker M.
      Four screening instruments for frailty in older patients with and without cancer: a diagnostic study.
      ,
      • Handforth C.
      • Clegg A.
      • Young C.
      • Simpkins S.
      • Seymour M.T.
      • Selby P.J.
      • et al.
      The prevalence and outcomes of frailty in older cancer patients: a systematic review.
      ]. Therefore, our study avoided the use of constructed cut-offs and instead opted for the development of an individual, personalized care and treatment plan for each patient.
      Unfortunately, we did not manage to include the scheduled number of patients. We did not find an effect of CGA on completion of oncologic treatment as planned. It is possible that a larger study would have been able to disclose more differences between the two study groups. Recording of initial treatment plan was not performed in this study, but this would have facilitated an evaluation of, whether CGA and MDT were able to affect treatment decision. A review of 35 studies has shown that geriatric evaluation (geriatric consultation, a multidisciplinary geriatric evaluation, or a GA) is able to affect oncologic treatment decision, leading to modification of initial treatment plan in a median of 28% of patients, mainly an alteration of the treatment plan to a less aggressive option [
      • Hamaker M.E.
      • Te Molder M.
      • Thielen N.
      • van Munster B.C.
      • Schiphorst A.H.
      • van Huis L.H.
      The effect of a geriatric evaluation on treatment decisions and outcome for older cancer patients - a systematic review.
      ].
      This study had an overweight of patients in good health condition. In this context, it is important to be aware of the steps necessary to reach enrollment in the study. First of all, there is the referral from the general practitioner to the surgical department. The GPs might not refer the oldest and most frail patients for further diagnostic processes and treatment. A surgical department might reach a similar conclusion and decide against further referral to the department of oncology. Another factor of importance is the reluctance of some frail patients to be referred – but as already described, physicians' attitude is a significant factor in the decision-making process of patient treatment, so how information is presented to these patients is essential [
      • Townsley C.A.
      • Naidoo K.
      • Pond G.R.
      • Melnick W.
      • Straus S.E.
      • Siu L.L.
      Are older cancer patients being referred to oncologists? A mail questionnaire of Ontario primary care practitioners to evaluate their referral patterns.
      ]. It may still be an obstacle to transform from an approach based on chronological age to one based on physiological age. This means that in studies, where recruitment takes place in an oncological department, the study population may not reflect the older cancer population in general. The heterogeneity of the population may also comprise a limitation, as it can dilute the effect of the intervention, when patients with different cancers that entail different prognoses and different treatment regimens are assessed together.
      The decision to exclude patients, who had radiotherapy or surgery <4 weeks before inclusion, was made to make the population as comparable as possible, as it was assumed that undergoing these treatment modalities closely up to inclusion would affect the general health status of the patients and, thus, reflect CGA outcome. CGA in older patients with cancer with G8 ≤ 14 and subsequent final oncologic treatment decision at an MDT did not significantly improve the completion rate of planned treatment as compared to oncologic treatment decision based on the oncologist's clinical judgement. However, it might reduce the incidence of severe toxicity. Further research is needed to clarify the place for CGA in the treatment decision in older patients with cancer.

      Declarations of Competing Interests

      None of the authors have any conflicts of interest.

      Author Contributions

      Conception and design (SN, LM, TLJ, JH), data collection (SN, LM, TLJ, JH, SJ, AK, LD), analysis and interpretation of data (SN, LM, TLJ, JH, SM), manuscript writing (the manuscript was drafted by SN and modified by SN, LM, TLJ, LD, AK, SJ, SM, JH), approval of the final manuscript (SN, LM, TLJ, LD, AK, SJ, JH, SM).

      Acknowledgements and Funding

      We would like to thank the Danish Cancer Society [grant number R90-A6232 ] and The Velux Foundation [grant number 9631 ] for funding this study. The sponsors did not have any role in the study design, the collection and analysis of data; in the interpretation of results; in the writing of the report; or in the decision to submit the article for publication.

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