Cancer mortality and competing causes of death in older adults with cancer: A prospective, multicentre cohort study (ELCAPA-19)

BACKGROUND

In older patients with cancer, comorbidities compete with cancer for cause of death. The objectives were to evaluate cancer mortality and factors associated, according to metastatic status.

METHODS

Between 2007 and 2014, patients with cancer aged ≥70 referred for pre-therapeutic geriatric assessment (GA) were included through the ELCAPA prospective cohort study. The underlying cause of death was defined according to the International Classification of Diseases, 10th Revision. The World Health Organisation definition was used to categorise the cause of death as cancer versus another disease (e.g. cardiovascular disease, infectious disease, etc.) Competing risk models were used.

RESULTS

Mean (SD) age of the 1445 included patients was 80.2 (5.8) and 48% were women. Most common tumour sites were colorectal (19%), breast (17%) and urinary (15%); 773 patients (49%) had metastases. After a 34-month median follow-up, 706 cancer deaths were observed among 843 deaths. The 6-month and 3-year cancer mortality rates (95% CI) were 12% (9-15) and 34% (29-38) for non-metastatic patients and 43% (39-47) and 79% (75-82) for metastatic patients, respectively. Dependency in activities of daily living and comorbidities were associated with 6-month and 3-year cancer mortality in non-metastatic (adjusted subhazard ratio [aSHR] = 1.68 [0.99-2.85] and 1.69 [1.16-2.45]; and 1.98 [1.08-3.63] and 3.38 [1.47-7.76], respectively) and metastatic patients (aSHR = 2.81 [2.01-3.93] and 2.95 [2.14-4.07]; and 1.63 [1.18-2.25] and 2.06 [1.39-3.05], respectively). Impaired Timed-Get-Up-and-Go test was associated with 6-month and 3-year cancer mortality in metastatic patients (aSHR = 1.5 [1.06-2.12] and 1.38 [1.06-1.81], respectively). Obesity was negatively associated with 3-year cancer death in non-metastatic (aSHR = 0.53 [0.29-0.97]) and metastatic patients (aSHR = 0.71 [0.51-1.00]).

CONCLUSIONS

The majority of older adults with cancer referred for pre-therapeutic GA die from cancer. Geriatric parameters are independently associated with cancer mortality and should be considered for prognosis assessment, decision-making and care.

Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).