“How Long Have I Got?”—A Prospective Cohort Study Comparing Validated Prognostic Factors for Use in Patients with Advanced Cancer

© AlphaMed Press 2019 Background: The optimal prognostic factors in patients with advanced cancer are not known, as a comparison of these is lacking. The aim of the present study was to determine the optimal prognostic factors by comparing validated factors. Materials and Methods: A multicenter, prospective observational cohort study recruited patients over 18 years with advanced cancer. The following were assessed: clinician-predicted survival (CPS), Eastern Cooperative Oncology Group performance status (ECOG-PS), patient reported outcome measures (anorexia, cognitive impairment, dyspnea, global health), metastatic disease, weight loss, modified Glasgow Prognostic Score (mGPS) based on C-reactive protein and albumin, lactate dehydrogenase (LDH), and white (WCC), neutrophil (NC), and lymphocyte cell counts. Survival at 1 and 3 months was assessed using area under the receiver operating curve and logistic regression analysis. Results: Data were available on 478 patients, and the median survival was 4.27 (1.86–7.03) months. On univariate analysis, the following factors predicted death at 1 and 3 months: CPS, ECOG-PS, mGPS, WCC, NC (all

"How long have I got?” – a prospective cohort study comparing validated prognostic factors for use in patients with advanced cancer

Background. The optimal prognostic factors in patients with advanced cancer are not known, as a comparison of these is lacking. The aim of the present study was to determine the optimal prognostic factors by comparing validated factors. Materials and Methods. A multicenter, prospective observational cohort study recruited patients over 18 years with advanced cancer. The following were assessed: clinician‐predicted survival (CPS), Eastern Cooperative Oncology Group performance status (ECOG‐PS), patient reported outcome measures (anorexia, cognitive impairment, dyspnea, global health), metastatic disease, weight loss, modified Glasgow Prognostic Score (mGPS) based on C‐reactive protein and albumin, lactate dehydrogenase (LDH), and white (WCC), neutrophil (NC), and lymphocyte cell counts. Survival at 1 and 3 months was assessed using area under the receiver operating curve and logistic regression analysis. Results. Data were available on 478 patients, and the median survival was 4.27 (1.86–7.03) months. On univariate analysis, the following factors predicted death at 1 and 3 months: CPS, ECOG‐PS, mGPS, WCC, NC (all p < .001), dyspnea, global health (both p ≤ .001), cognitive impairment, anorexia, LDH (all p < .01), and weight loss (p < .05). On multivariate analysis ECOG‐PS, mGPS, and NC were independent predictors of survival at 1 and 3 months (all p < .01). Conclusion. The simple combination of ECOG‐PS and mGPS is an important novel prognostic framework which can alert clinicians to patients with good performance status who are at increased risk of having a higher symptom burden and dying at 3 months. From the recent literature it is likely that this framework will also be useful in referral for early palliative care with 6–24 months survival

COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study

Background Patients with cancer are purported to have poor COVID-19 outcomes. However, cancer is a heterogeneous group of diseases, encompassing a spectrum of tumour subtypes. The aim of this study was to investigate COVID-19 risk according to tumour subtype and patient demographics in patients with cancer in the UK. Methods We compared adult patients with cancer enrolled in the UK Coronavirus Cancer Monitoring Project (UKCCMP) cohort between March 18 and May 8, 2020, with a parallel non-COVID-19 UK cancer control population from the UK Office for National Statistics (2017 data). The primary outcome of the study was the effect of primary tumour subtype, age, and sex and on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence and the case–fatality rate during hospital admission. We analysed the effect of tumour subtype and patient demographics (age and sex) on prevalence and mortality from COVID-19 using univariable and multivariable models. Findings 319 (30·6%) of 1044 patients in the UKCCMP cohort died, 295 (92·5%) of whom had a cause of death recorded as due to COVID-19. The all-cause case–fatality rate in patients with cancer after SARS-CoV-2 infection was significantly associated with increasing age, rising from 0·10 in patients aged 40–49 years to 0·48 in those aged 80 years and older. Patients with haematological malignancies (leukaemia, lymphoma, and myeloma) had a more severe COVID-19 trajectory compared with patients with solid organ tumours (odds ratio [OR] 1·57, 95% CI 1·15–2·15; p<0·0043). Compared with the rest of the UKCCMP cohort, patients with leukaemia showed a significantly increased case–fatality rate (2·25, 1·13–4·57; p=0·023). After correction for age and sex, patients with haematological malignancies who had recent chemotherapy had an increased risk of death during COVID-19-associated hospital admission (OR 2·09, 95% CI 1·09–4·08; p=0·028). Interpretation Patients with cancer with different tumour types have differing susceptibility to SARS-CoV-2 infection and COVID-19 phenotypes. We generated individualised risk tables for patients with cancer, considering age, sex, and tumour subtype. Our results could be useful to assist physicians in informed risk–benefit discussions to explain COVID-19 risk and enable an evidenced-based approach to national social isolation policies