Tanujaa Rajasekaran1, Tira Tan1, Whee Sze Ong2, Khai Nee Koo3, Lili Chan1, Donald Poon4, Anupama Roy Chowdhury5, Lalit Krishna6, Ravindran Kanesvaran7. 1. Department of Medical Oncology, National Cancer Centre, Singapore. 2. Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre, Singapore. 3. Perdana University Graduate School of Medicine, Serdang, Malaysia. 4. Raffles Cancer Centre, Singapore; Duke-NUS Graduate Medical School, Singapore. 5. Changi General Hospital, Singapore. 6. Department of Medical Oncology, National Cancer Centre, Singapore; Duke-NUS Graduate Medical School, Singapore. 7. Department of Medical Oncology, National Cancer Centre, Singapore; Duke-NUS Graduate Medical School, Singapore. Electronic address: ravindran.kanesvaran@singhealth.com.sg.
Abstract
OBJECTIVE: This study aims to identify Comprehensive Geriatric Assessment (CGA) based risk factors to help predict caregiver burden among elderly patients with cancer. MATERIALS AND METHOD: The study evaluated 249 patients newly diagnosed with cancer, aged 70years and above, who attended the geriatric oncology clinic at the National Cancer Centre Singapore between 2007 and 2010. RESULTS: Out of 249 patients, 244 patients had information available on family caregiver burden and were analysed. On univariate analysis, ADL dependence, lower IADL scores, ECOG performance status of 3-4, higher fall risk, lower scores in dominant hand grip strength test and mini mental state examination, polypharmacy, higher nutritional risk, haemoglobin <12g/dL and presence of geriatric syndromes were significantly associated with mild to severe caregiver burden. On multivariate analysis, only ECOG performance status of 3-4 (odds ratio [OR], 4.47; 95% confidence interval [CI], 2.27-8.80) and haemoglobin <12g/dL (OR, 2.38; 95% CI, 1.14-4.99) were associated with an increased probability of mild to severe caregiver burden. The model achieved a good fit (Hosmer-Lemeshow's p=0.196) and discrimination (area under the curve [AUC]=0.742; bias-corrected AUC=0.737). Based on this, patients were stratified into 3 risk groups with different proportion of patients with increased caregiver burden (low risk: 3.9% vs intermediate risk: 18.8% vs high risk: 39.6%; p<0.001). CONCLUSION: ECOG performance status and haemoglobin were associated with increased caregiver burden among elderly patients with cancer. Using these two factors in the clinic may help clinicians identify caregivers at risk and take preventive action to mitigate that.
OBJECTIVE: This study aims to identify Comprehensive Geriatric Assessment (CGA) based risk factors to help predict caregiver burden among elderly patients with cancer. MATERIALS AND METHOD: The study evaluated 249 patients newly diagnosed with cancer, aged 70years and above, who attended the geriatric oncology clinic at the National Cancer Centre Singapore between 2007 and 2010. RESULTS: Out of 249 patients, 244 patients had information available on family caregiver burden and were analysed. On univariate analysis, ADL dependence, lower IADL scores, ECOG performance status of 3-4, higher fall risk, lower scores in dominant hand grip strength test and mini mental state examination, polypharmacy, higher nutritional risk, haemoglobin <12g/dL and presence of geriatric syndromes were significantly associated with mild to severe caregiver burden. On multivariate analysis, only ECOG performance status of 3-4 (odds ratio [OR], 4.47; 95% confidence interval [CI], 2.27-8.80) and haemoglobin <12g/dL (OR, 2.38; 95% CI, 1.14-4.99) were associated with an increased probability of mild to severe caregiver burden. The model achieved a good fit (Hosmer-Lemeshow's p=0.196) and discrimination (area under the curve [AUC]=0.742; bias-corrected AUC=0.737). Based on this, patients were stratified into 3 risk groups with different proportion of patients with increased caregiver burden (low risk: 3.9% vs intermediate risk: 18.8% vs high risk: 39.6%; p<0.001). CONCLUSION: ECOG performance status and haemoglobin were associated with increased caregiver burden among elderly patients with cancer. Using these two factors in the clinic may help clinicians identify caregivers at risk and take preventive action to mitigate that.
Authors: Lee A Kehoe; Huiwen Xu; Paul Duberstein; Kah Poh Loh; Eva Culakova; Beverly Canin; Arti Hurria; William Dale; Megan Wells; Nikesha Gilmore; Amber S Kleckner; Jennifer Lund; Charles Kamen; Marie Flannery; Mike Hoerger; Judith O Hopkins; Jane Jijun Liu; Jodi Geer; Ron Epstein; Supriya G Mohile Journal: J Am Geriatr Soc Date: 2019-03-29 Impact factor: 5.562
Authors: Supriya G Mohile; William Dale; Mark R Somerfield; Mara A Schonberg; Cynthia M Boyd; Peggy S Burhenn; Beverly Canin; Harvey Jay Cohen; Holly M Holmes; Judith O Hopkins; Michelle C Janelsins; Alok A Khorana; Heidi D Klepin; Stuart M Lichtman; Karen M Mustian; William P Tew; Arti Hurria Journal: J Clin Oncol Date: 2018-05-21 Impact factor: 44.544
Authors: Wagahta Semere; Andrew D Althouse; Ann-Marie Rosland; Douglas White; Robert Arnold; Edward Chu; Thomas J Smith; Yael Schenker Journal: J Geriatr Oncol Date: 2021-01-18 Impact factor: 3.929
Authors: Alberto Sardella; Vittorio Lenzo; Angela Alibrandi; Antonino Catalano; Francesco Corica; Maria C Quattropani; Giorgio Basile Journal: Int J Environ Res Public Health Date: 2021-03-25 Impact factor: 3.390