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Frailty indicators and functional status in older patients after colorectal cancer surgery

Journal of Geriatric Oncology

Abstract

Objectives

The number of older survivors from colorectal cancer is increasing, but little is known regarding long-term consequences of cancer treatment in this patient group. Physical function is an important outcome for older patients, affecting both autonomy and quality of life. We aimed to investigate physical function in older patients with colorectal cancer before and after surgery, and to examine the role of individual frailty indicators as predictors of functional decline.

Material and Methods

We present 16–28months follow-up data of older patients after elective surgery for colorectal cancer. During a home-visit, physical function was evaluated by activities of daily living (ADL), instrumental activities of daily living (IADL), the timed up-and-go (TUG) test, and grip strength. Measurements were compared with those obtained preoperatively using the Wilcoxon signed rank test. Frailty indicators were dichotomized and implemented in logistic regression models to explore their associations to a decline in the physical function scores.

Results

Eighty-four patients were included and the median age was 82years. There was a significant decrease in ADL (p=0.04) and IADL scores (p0.001) at follow-up. We found no associations between frailty indicators and the risk of decline in physical functioning.

Conclusion

In our population of older patients with surgically treated colorectal cancer, there was a significant decline in ADL- and IADL-scores at follow-up. No change was found in TUG or grip strength, and frailty indicators did not predict decline in physical function.

Keywords: Colorectal cancer, Frailty, Physical function, Geriatric oncology, Geriatric surgery, Older cancer patients.

1. Introduction

Colorectal cancer is one of the most common cancer types. In Norway, approximately 85% of patients are older than 60years at diagnosis. Thus, colorectal cancer is a disease that mainly affects older individuals.1 Surgery is the main treatment modality, supplemented with adjuvant or palliative chemotherapy or radiation in selected cases. Survival from colorectal cancer is improving in all patient groups, and the number of older survivors is increasing. However, little is known of long-term effects of cancer surgery in this population.

In geriatric oncology, the use of a Geriatric Assessment (GA) is suggested to aid clinicians when caring for older patients with cancer.2 A GA generally includes evaluation of comorbid conditions, medications, physical function, nutrition, depression, cognitive function and social support, and may detect unknown – and possibly reversible – health problems.3 and 4 A systematic review from 2012 concluded that conducting a GA is feasible in the heterogeneous population of older patients with malignant disease, though studies investigating targeted interventions based on a GA are scarce.5 Furthermore, little is known about the possible predictive role of a GA with regards to long-term outcomes after cancer treatment.

The GA may serve as an approach to identifying frailty in an older individual.6 Frailty is defined as a state of increased vulnerability towards stressors, and several ways to measure frailty in clinical practice have been proposed. Among the definitions of frailty, the “frailty phenotype”, and the “accumulation of deficits”, developed by Fried et al. and Rockwood et al., respectively, are widely used.7 and 8 It is hypothesized that activation of inflammatory pathways and of the coagulation system contributes to the pathogenesis of frailty, and markers of inflammation such as C-reactive protein (CRP) and interleukin-6 (IL-6) have been linked to different frailty measures.9, 10, and 11 Identifying frailty in older patients with cancer may be relevant in order to optimize treatment and predict treatment outcomes. We have previously shown that frailty as determined by a GA can predict postoperative complications in older patients undergoing elective surgical resections of colorectal cancer, and that IL-6 is an independent predictor of postoperative complications in the same patient population.12 and 13

While several authors have dealt with postoperative mortality and morbidity after colorectal cancer surgery in older patients, only a few studies have investigated consequences for immediate and long-term functional status in this patient population. We therefore conducted a longitudinal study of older patients with colorectal cancer in order to describe and compare their physical function before and after elective surgery. Further, we explored the impact of the following variables on functional outcomes: individual frailty indicators, inflammatory biomarkers, and the occurrence of postoperative complications.

2. Material and Method

The study was approved by the Regional Committee for Medical and Health Research Ethics in Eastern Norway. All patients provided a written informed consent.

The patients were recruited from an observational prospective cohort study evaluating predictors of postoperative complications in older patients with colorectal cancer.12 All participants were aged ≥70years, and had undergone elective resection of colorectal cancer in one of three Norwegian public hospitals; Oslo University Hospital–Ullevål or Aker divisions, or Akershus University Hospital. TNM-stage and scoring of American Society of Anesthesiologists Physical Status Classification System (ASA) were retrieved from patients' medical records. A preoperative GA was performed by a physician trained in geriatrics (SRK), and formed the basis for classification of patients into frail or non-frail. The GA included measurements of ADL- and IADL-function by the Barthel index and the Nottingham Extended Activities of Daily Living Scale (NEADL), respectively; cognitive function by the Mini Mental State Examination (MMSE); nutritional status by the Mini Nutritional Assessment (MNA); depressive symptoms by the Geriatric Depression Scale (GDS); and comorbidity by the Cumulative Illness Rating Scale (CIRS).14, 15, 16, 17, and 18 Physical performance measures included the timed “up-and-go” (TUG) test and grip strength.19 In addition, The European Organisation of Research and Treatment of Cancer Quality of Life Questionnaire C-30 (EORTC-QLQ C30) and the European Cooperative Oncology Group Performance Status (ECOG PS) were used.20 and 21 These scales evaluate self-rated physical, psychological and social function, and physical performance status, respectively. Postoperative complications were retrospectively registered from patients' medical records, and classified as minor (grade 1), potentially life-threatening without (grade 2) or with (grade 3) permanent sequelae, or fatal (grade 4), based on the classification system developed by Clavien et al.22

Preoperative blood samples were collected and serum was obtained by centrifugation at 3400–3700rpm for 10–12min, and stored at −70°C until analyzed. CRP levels were determined by an enzyme linked immunosorbent assay (DRG Instruments GmbH, Germany), with a detection limit of 0.1mg/L and a coefficient of variation (CV) of <5%. Levels of IL-6 were determined using another commercially available enzyme linked immunosorbent assay (R&D Systems Europe, Abingdon, Oxon, UK, CV 10.5%).

Patients from the original study were consecutively contacted by post and telephone in order to inform them about the follow-up study. Patients who consented to participation received a home visit from the head researcher (BR) between 16 and 28months after surgery. During this visit, functional dependency was assessed by the Barthel index and the NEADL-scale, while physical performance was measured with TUG and grip strength. TUG was measured as the number of seconds spent on standing up from a chair, walking a distance of 3m, turning, walking back, and sitting down again.19 Grip strength was measured in kilograms with a Jamar® handheld dynanometer. The highest value of three attempts on either hand was noted.

3. Statistical Approach and Definition of Cut-off Values

Non-parametric statistical methods were applied due to skewed distribution of most variables. To determine significant changes in functional measure scores before surgery and at follow-up, the Wilcoxon signed rank test was used. Exploratory analyses indicated that there was no homogeneous relationship between preoperative frailty indicators and the odds of certain postoperative functional outcomes. Accordingly, the requirements for analyzing the explanatory variables as continuous were not fulfilled. We therefore dichotomized our variables and built logistic regression models.

From a clinical point of view, it is very important whether a patient's functional performance has deteriorated or remained stable. Accordingly, the Barthel-score, NEADL-score, time to complete TUG, and grip strength at follow-up were all dichotomized into whether or not the individual's performance had decreased from the pre-operative measurements. These were our primary endpoints. We were not able to find validated cut-offs for clinically significant changes in the chosen assessment tools. Thus, for the Barthel-index, a one-point reduction led to categorization in the reduced group. For NEADL-score, a four-point reduction was set as the cut-off, representing loss of independence in one of the measured activities. TUG-scores were dichotomized into an increase of 1s or more versus stable or shorter time than preoperative measurements, while grip strength was dichotomized as any reduction versus no reduction.

A number of explanatory variables representing frailty indicators are presented in Table 1, and were created as follows: age was dichotomized into groups of under 80years versus 80years and older. Tumor stage was dichotomized in TNM stages 0–II versus stage III and higher, defining groups with low or high risk of systemic disease. ASA-scores were dichotomized into values <3 and ≥3, representing patients with lower and higher surgical risk, respectively. Patients were classified as non-frail or frail based on a GA, as described in the original study.12 The Barthel-index and NEADL-scores were dichotomized into dependent or independent by scores of 18 or less versus 19 and above and 43 or less versus 44 and above, respectively. For the MMSE, a score of 24 is a commonly used cut-off, indicating risk of cognitive impairment. However, as our population was overall cognitively well-functioning, we chose to dichotomize on the median score, to gain statistical strength. The MNA-scores were dichotomized into risk of malnutrition versus no risk, by scores below 24 versus 24 and higher, as described by the authors of the tool.16 For GDS, a cut-off of 13 points was used to define normal mood versus depressed mood. The CIRS scores were dichotomized into severe comorbidity versus lower grade comorbidity, severe comorbidity being defined as having any grade 4 comorbidity or three or more grade 3 comorbidities. Self-reported exhaustion was defined as being present or not present, based on questions from the EORTC QLQ-C30, this criterion being positive when a patient answered “quite a bit” or “very much” on question 12 — “Have you felt weak?” or question 18 — “Were you tired?”, as described in a recent paper.23 For TUG, different cut-off values have been proposed. We chose a cut-off of <19s versus ≥19s, as described by Rockwood et al.24 Grip strength was dichotomized according to age- and gender-specific cut-offs as described in the literature.7 ECOG PS scores were dichotomized into 0 to 1 versus 2 to 3 (no patients had ECOG PS 4).

Table 1 Explanatory variables including frailty indicators.

Variable   Measure
Age <80 vs. ≥80 Years
Tumor stage 0–II vs. ≥III TNM
ASA-score 1–2 vs. >2  
Frailty Non-frail vs. frail GA
ADL-function Not dependent (≥19) vs. dependent (<19) Barthel-index
IADL-function Not dependent (≥44) vs. dependent (<44) NEADL
Cognitive function Above median (≥29) vs. below median MMSE
Malnutrition No risk (≥24) vs. risk of malnutrition (<24) MNA
Depression No risk (<13) vs. risk of depression (≥13) GDS
Comorbidity Low grade vs. high grade comorbidity CIRS
Self-reported exhaustion Not present vs. present EORTC- QLQ C30
TUG <19 vs. ≥19 Seconds
Grip strength Above vs. below age- and gender-specific cut-off Reference values8
Performance status 0–1 vs. 2–3 ECOG PS
CRP Below vs. above 25th percentile  
IL-6 Below vs. above median  
Postoperative complications Not present vs. present  
Severe postoperative complications Not present vs. present  

ASA=American Society of Anesthesiologists Physical Status Classification System.

GA=Geriatric Assessment.

ADL=activities of daily living.

IADL=instrumental activities of daily living.

NEADL=Nottingham Extended Activities of Daily Living.

MMSE=Mini Mental State Examination.

MNA=Mini Nutritional Assessment.

GDS=Geriatric Depression Scale.

CIRS=Cumulative Illness Rating Scale.

EORTC-QLQ C30=European Organisation of Research and Treatment of Cancer Quality of Life Questionnaire.

TUG=timed up-and-go.

ECOG-PS=European Cooperative Oncology Group Performance Status.

CRP=C-reactive protein.

IL-6=interleukin-6.

We were not able to find validated cut-off criteria for low-grade elevation in the inflammatory biomarkers CRP and IL-6. Based on preliminary distribution analyses we dichotomized into values below/above the 25th percentile (3.0mg/mL) for CRP, and below/above the median (3.4pg/mL) for IL-6.13 Finally, two different explanatory variables based on the occurrence of postoperative complications were created; no complications versus any complication, and any complication versus severe complications.12

All analyses were performed using SPSS 19.0 software (Chicago, IL).

4. Results

A total of 84 patients (46% of those included at baseline, 69% of those eligible for participation) were included, 34 (41%) men and 50 (59%) women. Only one patient lived in an institutional care facility. Median age was 82years (72–95) and median time to follow-up was 22months (16–28). A flow-chart of the patient inclusion process is presented in Fig. A.

gr1

Fig. A Inclusion process.

Comparisons of pre- and postoperative functional measures are displayed in Table 2. We found a statistically significant decrease in Barthel- and NEADL-scores from the preoperative to the postoperative condition (p=0.04 and <0.001, respectively). No significant changes in TUG-score or grip strength were identified.

Table 2 Pre- and postoperative functional measures.

  At baseline At baseline, only patients included in follow-up At follow-up Mean change (SD) p-Valuea
Barthel-index
N 182 84 84 0.25 0.04
Median (IQR) 20 (19–20) 20 (19–20) 20 (18–20) (1.3)  
NEADL
N 182 84 84 7.08 <0.001
Median (IQR) 58 (48–63) 60 (51–63) 52 (41–58) (9.2)  
TUG, seconds
N 79 38 38 3.8 0.44
Median (IQR) 12 (8–17) 13 (8–16) 11 (8–17) (17.8)  
Grip strength b
N 73 39 39 0.5 0.17
Median (SD) 29.4 (9.9) 29.5 (9.1) 29 (13) (6.5)  

a By the Wilcoxon signed rank test.

b Kilograms.

IQR=interquartile range.

SD=standard deviation.

NEADL=Nottingham Extended Activities of Daily Living.

TUG=timed up-and-go.

The results from bivariate logistic regression analyses are shown in Table 3. In total, 26 (31%) patients had lost ADL-function as measured by the Barthel-index, and in 58 (69%) patients the IADL-function had decreased. For TUG, 15 patients (39%) performed poorer at follow-up than at baseline, and 15 (38%) had lower grip strength.

Table 3 Frailty indicators and decline in physical function results from logistic regression models.

  Barthel decreased (N=26) NEADL decreased (N=58) TUG increased (N=15) Grip strength decreased (N=15)
N OR 95% CI N OR 95% CI N OR 95% CI N OR 95% CI
Age80 15 1.4 0.5–3.6 32 1.4 0.6–3.6 5 0.3 0.1–1.3 9 2.63 0.7–10.1
TNMIII 8 1.4 0.5–4.1 18 3.5 0.9–13.1 6 3.8 0.8–17.9 6 3.0 0.7–13.7
ASA >2 12 1.6 0.6–4.1 23 1.5 0.6–4.0 4 0.4 0.1–1.6 8 1.7 0.4–6.8
Frailty 9 1.2 0.5–3.3 18 0.9 0.4–2.4 5 0.9 0.2–3.4 8 3.0 0.8–12.1
Barthel-dependent 2 0.5 0.1–2.3 6 0.5 0.1–1.8 2 0.7 0.1–4.6 2 0.9 0.1–6.7
NEADL-dependent 5 1.7 0.5–6.1 6 0.4 0.1–1.3 2 0.4 0.1–2.0 5 3.2 0.6–16.1
MMSE28 12 1.3 0.5–3.5 27 2.1 0.8–5.6 4 0.3 0.1–1.3 7 1.1 0.3–3.9
MNA24 12 0.8 0.3–2.1 25 0.4 0.2–1.1 4 0.3 0.1–1.1 6 0.7 0.2 2.5
GDS13 1 0.7 0.1–6.4 3 0.7 0.1–4.7 1 1.7 0.1–30.5 0 NA NA
Severe comorbidity 7 2.4 0.8–7.5 10 0.9 0.3–3.0 3 1.3 0.2–6.9 5 5.0 0.8–30.5
Exhaustion 12 1.9 0.7–4.9 21 0.9 0.4–2.4 5 0.8 0.2–3.0 5 1.0 0.2–3.9
TUG19s 4 3.4 0.7–16.8 5 1.2 0.2–6.5 1 0.5 0.1–5.1 3 NA NA
Low grip strength 1 0.3 0.1–2.8 2 0.2 0.1–1.7 0 NA NA 1 0.3 0.1–3.2
ECOG PS 2–3 6 1.4 0.5–4.4 10 0.7 0.2–2.2 2 0.2 0.1–1.3 6 3.0 0.7–13.4
CRP >25th percentile 16 1.2 0.4–3.7 32 0.2 0.1–1.2 12 1.5 0.2–9.6 11 1.4 0.2–8.9
IL-6>median 8 1.4 0.5–4.1 15 0.5 0.2–1.7 4 1.0 0.2–4.7 6 2.6 0.6–11.4
Any complication 17 2.4 0.9–5.9 26 0.4 0.2–1.2 10 1.3 0.3–5.0 13 6.5 1.2–35.8
Severe complications 12 1.7 0.7–4.4 18 0.5 0.2–1.2 5 0.6 0.1–2.1 8 2.0 0.5–7.6

ASA=American Society of Anesthesiologists Physical Status Classification System.

NEADL=Nottingham Extended Activities of Daily Living.

TUG=timed up-and-go.

OR=odds ratio.

CI=confidence interval.

ECOG PS=European Cooperative Oncology Group Performance Status.

MNA=Mini Nutritional Assessment.

GDS=Geriatric Depression Scale.

MMSE=Mini Mental State Examination.

CRP=C-reactive protein.

IL-6=interleukin-6.

There was a statistically significant association between postoperative complications and decreased grip strength at follow-up; however, we were not able to identify any predictors of poorer functional outcome. We also performed linear regression models using the degree of change in the outcomes as continuous variables, but with this strategy we were still unable to identify significant associations between our explanatory variables and the outcome variables (data not shown).

5. Discussion

In our group of 84 patients with surgically treated colorectal cancer, we found a significant reduction in ADL and IADL scores from the preoperative to the postoperative state. Approximately one-third of the patients had lost ADL-function, while two-thirds had lost IADL-function. We found no change in the physical performance measures i.e. TUG and grip strength. Except for the occurrence of any postoperative complication, our chosen explanatory variables, which mainly represented frailty indicators, were not able to predict a postoperative decline in functional outcome.

Though statistically significant, the reduction in primary ADL performance was small and hardly clinically relevant, and more than half the patients still had a maximum Barthel-score at follow-up. However, a one-point decline in ADL-score as measured by the Barthel-index may represent a substantial impact on an individual's functional dependency. There was a somewhat larger decline in instrumental ADL, indicating that complex activities were affected to a higher degree than basic daily functions. No reduction in TUG or grip strength was identified. Psychological and social factors may contribute to a patient's understanding of his or her health and functional status, making objective measures insufficient in assessing physical function.

These findings are overall in concordance with other studies. Amemiya et al. found that only a small fraction of patients over 75years exhibited decline in ADL six months after cancer surgery, and this was predicted by a preoperative risk assessment.25 Mastracci and co-workers likewise concluded that most older patients with colorectal cancer maintain their preoperative functional level within a follow-up time of up to five years.26 Functional independence has been shown to be an important treatment outcome for older patients. In one study of patient preferences, including 226 patients over 60years with a diagnosis of cancer, heart failure or chronic obstructive pulmonary disease, 74% stated that they would refuse, or be reluctant to, receiving treatment leading to severe functional impairment.27 Colorectal cancer is a lethal disease when left untreated, so there is little doubt that surgery is a proper course of action. It may still be important and reassuring for the older patient that the expected loss of physical function is small, and may very well represent a reduction that is to be expected with normal aging.

When exploring frailty indicators and postoperative complications as predictors of functional outcomes, we did not find any significant associations. No consensus has to date been reached on what truly constitutes frailty as a concept or syndrome. This is the reason why we chose to explore individual frailty markers. However, most of our hypothesized frailty indicators are included in one or more frailty definitions, and it was surprising that no predictive values were identified. We cannot exclude inclusion-bias, as it is possible that better or poorer physical function affected patients' acceptance of participation. It is also a possibility that the preoperative frailty indicators were associated with mortality before follow-up. If this is true, our population was pre-selected, and we plan to further investigate these issues with a study of the association between frailty indicators and long-term survival at a later time.

Some of the chosen frailty indicators showed non-significant trends towards poorer functional outcome at follow-up, and we cannot rule out that there is an association between frailty markers and functional outcomes that fails to show because of the limited sample size (type II error). We were only able to include 84 of the original population of 182 patients, because 26% were dead and 31% of those eligible for inclusion denied participation. Predictors of early (median 20months) mortality in this cohort have been discussed in a previous paper, and included ECOG performance status, severe comorbidity, and impaired nutrition.28

We chose to create patient groups based on dichotomization, but the lack of statistically significant associations was confirmed in linear regression analyses. Another explanation is that our proposed frailty indicators may in fact not be related to the development of reduced physical function in this patient population. Identifying frailty might be more useful when planning health services, deciding treatment intensity, and optimizing pre- and postoperative care, than for the prediction of long-term functional outcomes. For instance, a recent paper concluded that the frailty phenotype predicted postoperative complications and length of stay, as well as discharge to a skilled or assisted-living facility in patients over 65years undergoing various elective surgical procedures.29

A strength of the study is the use of both functional status and physical performance measures as outcome variables. Our data represent an important addition to current knowledge about the postoperative functional trajectory in this patient group. Physical function affects autonomy as well as quality of life in older adults. Our results support the practice of resecting colorectal tumors in patients with advanced age and some degree of frailty, as patients are likely to retain their physical function level up to several months after surgery.

The GA is a useful preoperative assessment tool for predicting short-term outcomes in older surgical patients.5 Its role in predicting long-term physical function remains unclear. Further clarification of GA's possible role in pre-, peri- and postoperative care for frail patients with cancer warrants more observational studies as well as intervention studies.

Disclosures and Conflict of Interest Statements

The authors declare that there are no conflicts of interest.

Author Contributions

Concept and design: B. Rønning, T. Wyller, and S. Kristjansson.

Data collection: A. Nesbakken, A. Bakka, and I. Seljeflot.

Analysis and interpretation of data: B. Rønning, T. Wyller, and S. Kristjansson.

Manuscript writing and approval: B. Rønning, S. Kristjansson, T. Wyller, M. Jordhøy, A. Bakka, I. Seljeflot, and A. Nesbakken.

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Footnotes

a Department of Geriatric Medicine, Oslo University Hospital, Pb 4956 Nydalen, 0424 Oslo, Norway Department of Geriatric Medicine, Oslo University Hospital, Pb 4956 Nydalen, Oslo, 0424, Norway

b Institute of Clinical Medicine, University of Oslo, Oslo, Norway Institute of Clinical Medicine, University of Oslo, Oslo, Norway

c Department of Internal Medicine, Innlandet Hospital Trust Gjøvik, Gjøvik, Norway Department of Internal Medicine, Innlandet Hospital Trust Gjøvik, Gjøvik, Norway

d Regional Center of Excellence in Palliative Care, Oslo University Hospital, Oslo, Norway Regional Center of Excellence in Palliative Care, Oslo University Hospital, Oslo, Norway

e Department of Gastroenterological Surgery, Oslo University Hospital, Oslo, Norway Department of Gastroenterological Surgery, Oslo University Hospital, Oslo, Norway

f Department of Digestive Surgery, Akershus University Hospital, University of Oslo, Lorenskog, Norway Department of Digestive Surgery, Akershus University Hospital, University of Oslo, Lorenskog, Norway

g Center for Clinical Heart Research, Department of Cardiology, Oslo University Hospital Ullevål, Oslo, Norway Center for Clinical Heart Research, Department of Cardiology, Oslo University Hospital Ullevål, Oslo, Norway

h Diakonhjemmet Hospital, Department of Medicine, Diakonvn 12, 0319 Oslo, Norway Diakonhjemmet Hospital, Department of Medicine, Diakonvn 12, Oslo, 0319, Norway

lowast Corresponding author at: Borger Withs gate 1, 0482 Oslo, Norway. Tel.: +47 48270639.


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