Elderly cancer patients are at increased risk for malnutrition. We aim to identify comprehensive geriatric assessment (CGA) based clinical factors associated with increased nutritional risk and develop a clinical scoring system to identify nutritional risk in elderly cancer patients.
Patients and Methods
CGA data was collected from 249 Asian patients aged 70 years or older. Nutritional risk was assessed based on the Nutrition Screening Initiative (NSI) checklist. Univariate and multivariate logistic regression analyses were applied to assess the association between patient clinical factors together with domains within the CGA and moderate to high nutritional risk. Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating characteristics curve (AUC). Internal validation was performed using simulated datasets via bootstrapping.
Among the 249 patients, 184 (74%) had moderate to high nutritional risk. Multivariate logistic regression analysis identified stage 3–4 disease (Odds Ratio [OR] 2.54; 95% CI, 1.14–5.69), ECOG performance status of 2–4 (OR 3.04; 95% CI, 1.57–5.88), presence of depression (OR 5.99; 95% CI, 1.99–18.02) and haemoglobin levels <12 g/dL (OR 3.00; 95% CI 1.54–5.84) as significant independent factors associated with moderate to high nutritional risk. The model achieved good calibration (Hosmer-Lemeshow test’s p = 0.17) and discrimination (AUC = 0.80). It retained good calibration and discrimination (bias-corrected AUC = 0.79) under internal validation.
Having advanced stage of cancer, poor performance status, depression and anaemia were found to be predictors of moderate to high nutritional risk. Early identification of patients with these risk factors will allow for nutritional interventions that may improve treatment tolerance, quality of life and survival outcomes.
Citation: Tan T, Ong WS, Rajasekaran T, Nee Koo K, Chan LL, Poon D, et al. (2016) Identification of Comprehensive Geriatric Assessment Based Risk Factors for Malnutrition in Elderly Asian Cancer Patients. PLoS ONE 11(5): e0156008. https://doi.org/10.1371/journal.pone.0156008
Editor: Masaru Katoh, National Cancer Center, JAPAN
Received: November 2, 2015; Accepted: May 6, 2016; Published: May 27, 2016
Copyright: © 2016 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: In line with the Personal Data Protection Act 2012 (Singapore), data are available upon request from the Singhealth Institutional Review Board.
Funding: This work was funded by the National Cancer Centre Singapore Research Fund Grant No. NRFGO08103 (to DP and RK). http://www.nccs.com.sg/GIVING/NCCRESEARCHFUND/Pages/Home.aspx. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Competing interests: The authors have declared no competing interests exist.
Malnutrition as defined by the World Health Organisation (WHO) refers to a deficiency of nutrition whilst cachexia is a complex metabolic syndrome associated with underlying cancer and characterized by loss of muscle with or without fat mass[2,3]. It is widely acknowledged that both malnutrition and cachexia are under diagnosed and under treated in patients with cancer[4–6]. Their prevalence varies largely depending on evaluation criteria and has been estimated to range up to as high as 85% in all cancer patients[3,7–10] Malnutrition and cachexia have been shown to be a predictors of risk of toxicity to chemotherapy, impaired quality of life and mortality[3,11–16]. In addition, the experience of weight loss by patients with advanced cancer is distressing for it is viewed as symbolizing proximity of death, loss of control and weakness both emotionally and physically.
The elderly patient is particularly prone to inadequate nutritional intake because of factors such as concomitant chronic diseases, polypharmacy, decreased mobility, social changes as well as age related physiological changes.
It is a general consensus that malnutrition or cachexia should ideally be recognised in the earlier phase of anti-cancer therapy which offers a window of opportunity for intervention. Early identification of elderly patients at nutritional risk would allow for a quick and timely referral to an appropriately trained professional for a comprehensive nutritional assessment and targeted nutritional intervention which is more likely to be effective before pronounce metabolic deficiencies render them resistant.
Evidence based guidelines for the management of elderly patients with cancer recommends a comprehensive geriatric assessment (CGA) to detect unrecognised problems and improve function as well as outcomes. Nutritional assessment is an important component of the CGA. A complete nutritional assessment is complex and usually performed by an appropriately trained professional such as a dietician. An in depth assessment would involve clinical, physical, psychological considerations in addition to anthropometry, biochemical and haematological assessments. This would not be practical for day to day use given the time and manpower constraint of a busy oncology practice.
Nutritional screening on the other hand is quick, easy and provides an indication of a patient’s nutritional risk. Several tools have been designed and available for use in specific patient groups however the absence of a universally agreed criteria in identifying malnutrition has resulted in a lack of consensus among experts as to the “best” or “correct” way of screening for nutritional status [20–23]
One such screening tool is the Nutrition Screening Initiative (NSI) Checklist. The NSI is an American national effort to increase public and health professional awareness of the importance of nutritional problems among older persons. It consists of a self-administered awareness checklist describing characteristics associated with poor nutritional status and was designed to predict adequacy of nutrient intake and overall perceived health. The NSI checklist identifies older persons at nutritional risk due to inadequate nutrient intake as defined by an intake of less than 75% of the recommended daily allowance (RDA) and is used throughout the United States in the assessment of nutrition risk.
To date there is a scarcity of studies evaluating malnutrition or nutritional risk in elderly Asian patients. Most of the validated screening tools consist of items such as current weight or body mass index (BMI), decreased dietary intake and unintentional weight loss. None of the available and validated screening tools are based on clinical factors in elderly patients in the setting of a diagnosis of cancer in Asia. We aim to identify CGA based clinical characteristics in elderly Asian cancer patients which are associated with moderate to high nutritional risk as determined by the NSI checklist. These clinical risk factors, which are routinely evaluated in the clinic can form the basis for a simplified screening tool for nutritional risk in the elderly Asian cancer patients.
Patients and Methods
Study Design and Patients
This is a retrospective analysis of the CGA data collected from elderly patients attending outpatient oncology clinics at the National Cancer Centre Singapore between May 2007 and November 2010. Patients aged 70 years and older with a diagnosis of cancer at any stage were interviewed by a research nurse prior to their first visit with an oncologist. All patients provided written informed consent before inclusion into the study. The study was approved by the local institutional review board and conducted according to the principles expressed in the Declaration of Helsinki.
The CGA questionnaire used in this study was previously described and was developed after a thorough review of the literature and guideline recommendations. The CGA consists of seven distinct domains. Functional status was assessed using Eastern Cooperative Oncology Group (ECOG) performance status, the index of activities of daily living (ADL), instrumental activities of daily living (IADL) of Lawton et al, the get up and go test, and the dominant handgrip strength test. Comorbidities were classified according to the Charlson comorbidity index. Cognitive status was assessed using the mini-mental state examination (MMSE)  and clock drawing test. Affective status was assessed via the Geriatric Depression Scale (GDS) Short Form 15. Polypharmacy was documented in terms of number of medications, appropriateness and interactions. Geriatric syndromes were those as described by Balducci et al and nutritional status, was assessed using the body mass index (BMI) and The NSI checklist (Table 1). The checklist classified patients into low (0 to 2 points), moderate (3 to 5 points) and high nutritional risk (≥6 points) groups. Clinical parameters such as age, sex, stage, tumour types and selected laboratory tests (e.g. haemoglobin, albumin, renal panel, liver function tests) routinely available to treating clinicians were also collected.
Demographic and clinical characteristics between patients with and without moderate to high nutritional risk were compared. Categorical characteristics were compared using the Chi-square test of Fisher’s exact test as appropriate. Mann-Whitney U test was used to compare continuous characteristics between 2 groups of patients. Logistic regression models were fitted to estimate the odds ratios to assess the association of various variables with moderate to high nutritional risk. Considering the large number of significant predictors from the univariate analysis and to avoid model over-fitting, multivariate analyses were performed only on variables with p<0.01 from the univariate analysis. Forward selection, backward elimination and stepwise selection algorithms were applied to identify independent predictors. Goodness of fit between the observed and predicted number of outcomes of the multivariate model were assessed based on the Hosmer-Lemeshow test and its discrimination ability assessed based on the area under the receiver operating characteristics curve (AUC). The AUC was further internally validated based on 200 simulated datasets via bootstrapping to correct for over-fit bias. All p-values were 2 sided and a p-value <0.05 was considered statistically significant. All analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC) and R 2.15.0 (http://www.R-project.org).
This analysis included 249 patients with a median age of 77 (range 70–94). Majority of the patients were male (61.4%) and of Chinese race (91.2%). Gastrointestinal (GI) tract cancers were the primary tumor sites in 67.1% of patients followed by lung cancer (11.6%) and genitourinary cancer (4.8%). Most of the patients had late stage cancer (84.7%) and poorer performance status of 2 or greater (66.7%). Table 2 lists the patient characteristics.
Patient characteristics by nutritional risk
A significant proportion of patients (73.9%) were at moderate to high nutritional risk. Compared with patients with low nutritional risk, there were significantly more patients with moderate to high nutritional risk who had primary tumour in the GI tract (73% vs 51%), ECOG performance status 2–4 (76% vs 42%), advanced stage of disease at diagnosis (90% vs 71%), depression based on geriatric depression scale (36% vs 6%), low MMSE scores (< 24 points) (38% vs 17%), imposed mild to severe burden to their caregivers (27% vs 11%), had more than 4 prescribed drugs (66% vs 44%) and the presence of geriatric syndromes (69% vs 37%) (all p < 0.02).
Patients with moderate to high nutritional risk also had significantly lower median BMI values (20.9 vs 23.7), haemoglobin levels (11.1 vs 12.5 g/dL), and albumin levels (29.0 vs 34.0 g/L) (all p < 0.001).
There were no significant differences in age, gender, comorbidity risk, renal and liver functions between the 2 groups of patients.
Univariate logistic regression analysis
Factors that were significantly associated with moderate to high nutritional risk included an advanced stage at diagnosis [odds ratio (OR) 3.57; 95% confidence interval (CI) 1.74–7.29], a higher ECOG performance status of 2–4 (OR 4.35; 95% CI 2.39–7.90), being dependent in ADL (OR 6.11; 95% CI 1.83–20.47), a lower score in IADL (OR 1.43; 95% CI 1.23–1.67), a lower score in dominant handgrip strength test (OR 0.95; 95% CI 0.94–0.97), MMSE score < 24 (OR 2.94; 95% CI 1.43–6.01), GDS score > 5 (OR 8.46; 95% CI 2.94–24.33), presence of geriatric syndromes (OR 3.81; 95% CI 2.10–6.89), imposing mild to severe burden to caregivers (OR 3.05; 95% CI 1.30–7.13), having more than 4 prescribed drugs (OR 2.53; 95% CI 1.42–4.52), a lower BMI value (OR 1.23; 95% CI 1.14–1.35), lower haemoglobin levels (OR 1.43; 95% CI 1.22–1.69) and lower albumin levels (OR 1.14; 95% CI 1.08–1.20) (Table 3).
Multivariate logistic regression analysis
Multivariate logistic regression analysis using forward selection, backward elimination and stepwise selection algorithms identified identical predictors for moderate to high nutritional risk (Table 4). Stage 3–4 at diagnosis (OR 2.54; 95% CI 1.14–5.69; p = 0.023), ECOG performance status of 2–4 (OR 3.04; 95% CI 1.57–5.88; p = 0.001), presence of depression as measured by GDS (OR 5.99; 95% CI 1.99–18.02; p = 0.001) and haemoglobin levels < 12 g/dl (OR 3.00; 95% CI 1,54–5.84; p = 0.001) were all statistically significant independent factors associated with moderate to high nutritional risk.
Clinical scoring system
A nomogram was constructed based on the multivariate model as shown in Fig 1. The model achieved both calibration (Hosmer-Lemeshow test’s p = 0.172) and discrimination (AUC = 0.799). Based on bootstrapping, the bias-corrected AUC of the multivariate model was slightly lower at 0.788, indicating that the model retained a good discrimination. The predicted probabilities of moderate to high nutritional risk based on the model approximated the actual outcomes well (Fig 2).
The predicted probability of moderate to high nutritional risk of a patient is obtained by first locating the patient’s stage at diagnosis, Eastern Cooperative Oncology Group [ECOG] performance status, geriatric depression scale and haemoglobin on each axis. Draw a vertical line to the “points” axis to determine the number of points to assign for each variable’s value. Sum all the points for all variables, locate the total sum on the “Total Points,” and draw a straight line down to locate the probability of moderate to high nutritional risk corresponding to the sum.
We have previously reported nutritional risk as assessed using the NSI to be predictive of survival in elderly Asian patients with cancer. We have shown here a high prevalence (73.9%) of nutritional risk in our cohort of elderly Asian cancer patients. To our knowledge, our study is the first to investigate the relationship between nutritional risk, defined by the NSI and all domains of the CGA in addition to readily available clinical parameters specifically in a cohort of elderly Asian patients with cancer. We have identified four factors; presence of depression, advanced stage, poor performance status, and anaemia as significantly associated on multivariate analysis with moderate to high nutritional risk.
In a recent cohort study (The ELCAPA-05), the Mini Nutritional Assessment (MNA) was used as the primary evaluation criterion. This tool requires a professional to complete and evaluates risk of undernutrition through measures of anthropometry, dietary and clinical global assessment in addition to self-perception of health and nutritional status. A total of 643 patients were included in the survey. Similar to our study, the authors highlighted a high prevalence of malnutrition of 20.7% and 43.5% at risk of malnutrition in their cohort of elderly French cancer patients. The presence of geriatric syndromes such as cognitive impairment, depressed mood and fall risk were independent risk factors for malnutrition. In particular, depressed mood was associated with a 1.5–3 times risk for malnutrition in their cohort of patients. The relationship between nutritional status and psychological status in patients with colorectal cancers was investigated in a Canadian study not limited to elderly patients. Depression was identified as an independent predictor of risk of malnutrition when controlling for age, gender, marital status and weight change. Further work is required to investigate the causal relationship between depression and malnutrition.
Advanced tumor stage, a consequence of disease progression is a well-established poor prognostic factor[14,24]. Nutritional risk likely reflects the consequence of having advanced disease and the general health of patients. In a large study of 14972 Korean cancer patients, the proportion of patients with high risk for malnutrition as defined by BMI, serum albumin, total lymphocyte count and dietary intake, increased with cancer stage. Similarly, in the SCReening the Nutritional status in Oncology (SCRINIO) study of 1000 oncology outpatients in Italy, weight loss was higher in patients with more advanced stage of disease and compromised performance status.
Nutritional risk as defined by the Nutritional Risk Score (NRS) was noted to be higher in patients with poorer performance status. Similarly, a multicenter observational study conducted in France identified a WHO performance status score of 2 or more as a risk factor for malnutrition as defined by 2 anthropometric indicators, the level of weight loss and BMI. Performance status is a commonly cited factor independently associated with mortality.
Anaemia, a common finding in patients with cancer may adversely influence the management of elderly cancer patients by limiting dose intensity of treatment and hence affecting efficacy. In a prospective survey, Mancuso et al analyzed the correlation between CGA parameters and anemia. Functional decline, cognitive decline, depression and poor quality of life were identified as associated with low haemoglobin levels. In a review of the literature of elderly cancer patients, anaemia has not yet been found to be a predictor for risk of malnutrition. Hence this is the first study to report this association.
Early identification of malnutrition allows for timely referral to appropriately trained health care professionals leading to interventions that may modify risk factors and potentially improve outcomes. We report here an exploratory analysis identifying four factors that should be further explored for subsequent use in clinical trials and therapeutic recommendations. We have incorporated these four factors in developing a clinical scoring system to predict an individual elderly patient’s risk for malnutrition. As far as we are aware, this is the first scoring system utilizing clinical factors and parameters providing an individualised malnutrition risk assessment in this unique population of patients.
There are however some limitations to our study. The NSI checklist was originally applied in a cohort of non-instituitionalised, white, older persons without a specific diagnosis of cancer. Few studies have validated the NSI checklist and data for its predictive value with regards to mortality remains weak[18,40–42]. Our study population is small and heterogeneous in terms of the tumor types. The patients included in our analysis are outpatients representing a group of fitter patients. The majority of our patients had GI tract cancers with an underrepresentation of other solid tumour types. This reflects selection bias in the conduct of this study the results of which may therefore not be completely extrapolated to the general elderly cancer patient population. GI tract cancer patients may have higher risk of malnutrition due to the site and nature of their disease compared to those with other tumor types. Given several reports on varying prevalence of malnutrition based on primary tumour sites, future studies should be conducted focusing on specific tumor types.
In taking the findings of this study to the next step, we plan to prospectively validate this score in a separate population of elderly Asian cancer patients.
In conclusion, a significant number of elderly Asian cancer patients are at nutritional risk. Physicians need to have a strong index of suspicion of under nutrition in the elderly population. Advanced stage of cancer, poor performance status, depression and anaemia are independent predictors of moderate to high nutritional risk.
Conceived and designed the experiments: RK DP. Performed the experiments: KNK LLC RK DP. Analyzed the data: TT WSO RK. Contributed reagents/materials/analysis tools: WSO DP RK. Wrote the paper: TT WSO TR KNK LLC DP ARC LK RK.
- 1. Blössner M, Onis M De, Organization WH. Malnutrition: quantifying the health impact at national and local levels. Environ Burd Dis Ser 2005;12:43.
- 2. Evans WJ, Morley JE, Argiles J, Bales C, Baracos V, Guttridge D, et al. Cachexia: a new definition. Clin Nutr 2008;27:793–9. pmid:18718696
- 3. Aapro M, Arends J, Bozzetti F, Fearon K, Grunberg SM, Herrstedt J, et al. Early recognition of malnutrition and cachexia in the cancer patient: A position paper of a European School of Oncology Task Force. Ann Oncol 2014;25:1492–9. pmid:24569913
- 4. Wells JL, Dumbrell AC. Nutrition and aging: assessment and treatment of compromised nutritional status in frail elderly patients. Clin Interv Aging 2006;1:67–79. pmid:18047259
- 5. Attar A, Malka D, Sabaté JM, Bonnetain F, Lecomte T, Aparicio T, et al. Malnutrition Is High and Underestimated During Chemotherapy in Gastrointestinal Cancer: An AGEO Prospective Cross-Sectional Multicenter Study. Nutr Cancer 2012;64:535–42. pmid:22494155
- 6. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: An international consensus. Lancet Oncol 2011;12:489–95. pmid:21296615
- 7. Paillaud E, Liuu E, Laurent M, Le Thuaut A, Vincent H, Raynaud-Simon A, et al. Geriatric syndromes increased the nutritional risk in elderly cancer patients independently from tumoursite and metastatic status. The ELCAPA-05 cohort study. Clin Nutr 2014;33:330–5. pmid:23786899
- 8. Davies M. Nutritional screening and assessment in cancer-associated malnutrition. Eur J Oncol Nurs 2005;9 Suppl 2:S64–73. pmid:16437759
- 9. Bozzetti F, Mariani L, Lo Vullo S, Group SW, Amerio ML, Biffi R, et al. The nutritional risk in oncology: a study of 1,453 cancer outpatients. Support Care Cancer 2012;20:1919–28. pmid:22314972
- 10. Prevost V, Grach M-C. Nutritional support and quality of life in cancer patients undergoing palliative care. Eur J Cancer Care (Engl) 2012;21:581–90.
- 11. Landi F, Zuccalà G, Gambassi G, Incalzi RA, Manigrasso L, Pagano F, et al. Body mass index and mortality among older people living in the community. J Am Geriatr Soc 1999;47:1072–6. pmid:10484248
- 12. Aaldriks AA, van der Geest LGM, Giltay EJ, le Cessie S, Portielje JEA, Tanis BC, et al. Frailty and malnutrition predictive of mortality risk in older patients with advanced colorectal cancer receiving chemotherapy. J Geriatr Oncol 2013;4:218–26. pmid:24070460
- 13. Aaldriks AA, Giltay EJ, le Cessie S, van der Geest LGM, Portielje JEA, Tanis BC, et al. Prognostic value of geriatric assessment in older patients with advanced breast cancer receiving chemotherapy. Breast 2013;22:753–60. pmid:23414850
- 14. Soubeyran P, Fonck M, Blanc-Bisson C, Blanc J-F, Ceccaldi J, Mertens C, et al. Predictors of early death risk in older patients treated with first-line chemotherapy for cancer. J Clin Oncol 2012;30:1829–34. pmid:22508806
- 15. Pressoir M, Desné S, Berchery D, Rossignol G, Poiree B, Meslier M, et al. Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres. Br J Cancer 2010;102:966–71. pmid:20160725
- 16. Barret M, Malka D, Aparicio T, Dalban C, Locher C, Sabate JM, et al. Nutritional status affects treatment tolerability and survival in metastatic colorectal cancer patients: results of an AGEO prospective multicenter study. Oncology 2011;81:395–402. pmid:22269999
- 17. Hopkinson J, Wright D, Corner J. Exploring the experience of weight loss in people with advanced cancer. J Adv Nurs 2006;54:304–12. pmid:16629915
- 18. Beck AM, Ovesen L, Osler M. The “Mini Nutritional Assessment” (MNA) and the “Determine Your Nutritional Health” Checklist (NSI Checklist) as predictors of morbidity and mortality in an elderly Danish population. Br J Nutr 1999;81:31–6. pmid:10341673
- 19. Extermann M, Aapro M, Bernabei R, Cohen HJ, Droz J-P, Lichtman S, et al. Use of comprehensive geriatric assessment in older cancer patients: recommendations from the task force on CGA of the International Society of Geriatric Oncology (SIOG). Crit Rev Oncol Hematol 2005;55:241–52. pmid:16084735
- 20. Posner BM, Jette AM, Smith KW, Miller DR. Nutrition and health risks in the elderly: the nutrition screening initiative. Am J Public Health 1993;83:972–8. pmid:8328619
- 21. Stratton RJ, Hackston A, Longmore D, Dixon R, Price S, Stroud M, et al. Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the “malnutrition universal screening tool” (‘MUST') for adults. Br J Nutr 2004;92:799–808. pmid:15533269
- 22. Wolinsky FD, Coe RM, McIntosh WA, Kubena KS, Prendergast JM, Chavez MN, et al. Progress in the development of a nutritional risk index. J Nutr 1990;120 Suppl: 1549–53.
- 23. Isenring E, Elia M. Which screening method is appropriate for older cancer patients at risk for malnutrition? Nutrition 2015;31:594–7. pmid:25770324
- 24. Kanesvaran R, Li H, Koo K-N, Poon D. Analysis of prognostic factors of comprehensive geriatric assessment and development of a clinical scoring system in elderly Asian patients with cancer. J Clin Oncol 2011;29:3620–7. pmid:21859998
- 25. Oken MMMD a, Creech RHMD b, Tormey DCMDPD c, Horton JMDCB d, Davis TEMD c, McFadden ET e, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982;5:649–56. pmid:7165009
- 26. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA1963;185:914–9. pmid:14044222
- 27. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9:179–86. pmid:5349366
- 28. Mathias S, Nayak US, Isaacs B. Balance in elderly patients: the “get-up and go” test. Arch Phys Med Rehabil 1986;67:387–9. pmid:3487300
- 29. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–83. pmid:3558716
- 30. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–98. pmid:1202204
- 31. Shulman KI. Clock-drawing: Is it the ideal cognitive screening test? Int. J. Geriatr. Psychiatry, vol. 15, 2000, p. 548–61. pmid:10861923
- 32. Van Marwijk HWJ, Wallace P, De Bock GH, Hermans J, Kaptein AA, Mulder JD. Evaluation of the feasibility, reliability and diagnostic value of shortened versions of the geriatric depression scale. Br J Gen Pract 1995;45:195–9. pmid:7612321
- 33. Balducci L, Yates J. General guidelines for the management of older patients with cancer. Oncology (Williston Park) 2000;14:221–7.
- 34. Guigoz Y, Lauque S, Vellas BJ. Identifying the elderly at risk for malnutrition the mini nutritional assessment. Clin Geriatr Med 2002;18:737–57. pmid:12608501
- 35. Daudt HML, Cosby C, Dennis DL, Payeur N, Nurullah R. Nutritional and psychosocial status of colorectal cancer patients referred to an outpatient oncology clinic. Support Care Cancer 2012;20:1417–23. pmid:21748465
- 36. Wie G-A, Cho Y-A, Kim S-Y, Kim S-M, Bae J-M, Joung H. Prevalence and risk factors of malnutrition among cancer patients according to tumor location and stage in the National Cancer Center in Korea. Nutrition 2010;26:263–8. pmid:19665873
- 37. Bozzetti F, Group SW. Screening the nutritional status in oncology: a preliminary report on 1,000 outpatients. Support Care Cancer 2009;17:279–84. pmid:18581148
- 38. Jang RW, Caraiscos VB, Swami N, Banerjee S, Mak E, Kaya E, et al. Simple Prognostic Model for Patients With Advanced Cancer Based on Performance Status. J Oncol Pr 2014:JOP.2014.001457 –.
- 39. Mancuso A, Migliorino M, De Santis S, Saponiero A, De Marinis F. Correlation between anemia and functional/cognitive capacity in elderly lung cancer patients treated with chemotherapy. Ann Oncol 2006;17:146–50. pmid:16251202
- 40. Sahyoun NR, Jacques PF, Dallal GE, Russell RM. Nutrition Screening Initiative Checklist may be a better awareness/educational tool than a screening one. J Am Diet Assoc 1997;97:760–4. pmid:9216553
- 41. Sinnett S, Bengle R, Brown A, Glass AP, Johnson MA, Lee JS. The validity of Nutrition Screening Initiative DETERMINE Checklist responses in older Georgians. J Nutr Elder 2010;29:393–409. pmid:21104511
- 42. de Groot LC, Beck AM, Schroll M, van Staveren WA. Evaluating the DETERMINE Your Nutritional Health Checklist and the Mini Nutritional Assessment as tools to identify nutritional problems in elderly Europeans. Eur J Clin Nutr 1998;52:877–83. pmid:9881882