Claudia Martinez-Tapia

Background The Geriatric Core Dataset (G-CODE) was developed to standardise geriatric assessment (GA) in older adults with cancer. This study aimed to develop and externally validate a mortality prediction score based on the G-CODE at 6-month (main endpoint), 12- and 24-month (secondary endpoints), in older patients with cancer. Methods Data came from two prospective French cohorts of cancer patients aged ≥70 referred for GA: the Elderly Cancer Patients (ELCAPA) cohort (training set) and the Geriatric Oncology Marseille (GOM) cohort (validation set). G-CODE items were evaluated using multivariate Cox regression analyses, with Akaike information criterion selection. Discrimination was assessed using time-dependent area under the receiver operator curve (tAUC) for 6-, 12-, and 24-month mortality. Results We included 950 and 766 patients in the training and validation sets (mean ± sd age: 83 ± 6 years/81 ± 6 years; men: 55%/59%; 6-month mortality: 27%/22%). The final G-CODE model highlighted five strong predictors of 6-month mortality: not having a caregiver, weight loss, comorbidities, an impaired 4-instrumental activities of daily living score, and an impaired timed-up-and-go test. The tAUCs of the model, including the G-CODE, age, cancer type, metastases, and treatment, were 82%, 78% and 80%. Conclusions The final G-CODE score (range: 0–17) accurately predicts 6-, 12-, and 24-month mortality. Clinical trial registration NCT02884375 (ClinicalTrials.gov).

Une évaluation gériatrique (EG) est recommandée pour tous les patients âgés atteints de cancer pour identifier d’éventuels problèmes de santé et ainsi optimiser la stratégie thérapeutique. Néanmoins, elle est très consommatrice de temps et de moyens. Plusieurs outils de dépistage ont été développés mais a) aucun ne dispose de propriétés diagnostiques adéquates en pratique clinique, b) ils ont le plus souvent été développés sur la base d’opinions d’experts sans développement statistique spécifique, et c) peu de données sont disponibles pour apprécier leur robustesse au changement de gold standard pour définir le concept de « fragilité ». Par conséquent, notre objectif était 1) de construire et valider un outil de dépistage performant de la fragilité et de le comparer à l’outil de dépistage G8 actuellement utilisé en oncogériatrie, 2) d’en tester la robustesse vis-à-vis de 6 définitions de référence de la fragilité et 3) d’en évaluer la valeur pronostique pour la mortalité à 1 et 3 ans. Les données étaient issues de deux cohortes prospectives de patients âgés atteints de cancer : ELCAPA (2007–2012: n=729 [développement]), et ONCODAGE (2008–2010: n=1304 [validation externe]). L’outil G8 modifié final (aire sous la courbe ROC [AUROC] : 91,6% ; Sensibilité=89% ; Spécificité=79%) comprenait 6 items indépendants : perte de poids, problèmes neuropsychologiques, statut fonctionnel, état de santé perçu, poly-prescription et existence parmi les antécédents d’une insuffisance cardiaque ou coronaropathie. Les travaux accomplis dans le cadre de cette thèse ont de plus permis de confirmer ses bonnes propriétés diagnostiques en validation externe (AUROC : 84,6% ; Sensibilité=82% ; Spécificité=69%), sa robustesse au changement de gold standard et sa valeur pronostique forte vis-à-vis de la mortalité. L’utilisation du G8 et G8 modifié devrait être encouragée en oncologie gériatrique.

PURPOSE: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA). MATERIALS AND METHODS: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010). Candidate predictors included baseline oncologic and geriatric factors and routine biomarkers. We built predictive models using Cox regression, single decision tree (DT), and random survival forest (RSF) methods, comparing their predictive performance for 3-, 6-, and 12-month mortalities by computing time-dependent area under the receiver operator curve (tAUC). RESULTS: A total of 2,012 and 1,397 patients were included in the training and validation set, respectively (mean age: 81 ± 6 years/78 ± 5 years; women: 47%/70%; metastatic cancer: 50%/34%; 12-month mortality: 43%/16%). Tumor site/metastatic status, cancer treatment, weight loss, ≥five prescription drugs, impaired functional status and mobility, abnormal G-8 score, low creatinine clearance, and elevated C-reactive protein (CRP)/albumin were identified as relevant predictors in the Cox model. DT and RSF identified more complex combinations of features, with G-8 score, tumor site/metastatic status, and CRP/albumin ratio contributing most to the predictions. The RSF approach gave the highest tAUC (12 months: 0.87 [RSF], 0.82 [Cox], 0.82 [DT]) and was retained as the final model. CONCLUSION: The GCSS on the basis of a machine learning approach applied to two large French cohorts gave an accurate externally validated mortality prediction. The GCSS might improve decision making and counseling in older patients with cancer referred for pretherapeutic GA. GCSS's generalizability must now be confirmed in an international setting.

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.

Older cancer patients have an elevated risk of sarcopenia. The aim was to estimate the prevalence of four criteria for sarcopenia case finding, assessment, diagnosis, and severity determination: abnormal strength, assistance with walking, rising from a chair, climbing stairs, and falls (SARC-F), low hand-grip strength (HGS), low arm circumference (AC, a muscle mass proxy), and low physical performance (PP). Sarcopenia (low HGS and AC) and severe sarcopenia (low HGS, AC, and PP) and their predictive values for 6-month mortality were estimated in the whole population and by metastatic status. We analyzed data from the NutriAgeCancer French nationwide study of cancer patients aged ≥70 referred for geriatric assessment before anti-cancer treatment. We performed Cox proportional hazards analysis for each criterion separately and all criteria combined. Overall, 781 patients from 41 geriatric oncology clinics were included (mean age: 83.1; females: 53%; main cancer types: digestive (29%) and breast (17%); metastases: 42%). The prevalence of abnormal SARC-F, low HGS, a low AC, low PP, sarcopenia, and severe sarcopenia were, respectively, 35.5%, 44.6%, 44.7%, 35.2%, 24.5%, and 11.7%. An abnormal SARC-F and/or low HGS, sarcopenia, and severe sarcopenia were associated with 6-month mortality in patients with metastases (adjusted hazard ratios [95% confidence interval]: 2.72 [1.34–5.49], 3.16 [1.48–6.75] and 6.41 [2.5–16.5], respectively). Sarcopenia was strongly predictive of 6-month mortality in patients with metastatic cancer.

Screening tools have been developed to identify patients warranting a complete geriatric assessment (GA). However, GA lacks standardization and does not capture important aspects of geriatric oncology practice. We measured and compared the diagnostic performance of screening tools G8 and modified G8 according to multiple clinically relevant reference standards. We included 1136 cancer patients ≥ 70 years old referred for GA (ELCAPA cohort; median age, 80 years; males, 52%; main locations: digestive (36.3%), breast (16%), and urinary tract (14.8%); metastases, 43.5%). Area under the receiver operating characteristic curve (AUROC) estimates were compared between both tools against: (1) the detection of ≥1 or (2) ≥2 GA impairments, (3) the prescription of ≥1 geriatric intervention and the identification of an unfit profile according to (4) a latent class typology, expert-based classifications from (5) Balducci, (6) the International Society of Geriatric Oncology task force (SIOG), or using (7) a GA frailty index according to the Rockwood accumulation of deficits principle. AUROC values were ≥0.80 for both tools under all tested definitions. They were statistically significantly higher for the modified G8 for six reference standards: ≥1 GA impairment (0.93 vs. 0.89), ≥2 GA impairments (0.90 vs. 0.87), ≥1 geriatric intervention (0.85 vs. 0.81), unfit according to Balducci (0.86 vs. 0.80) and SIOG classifications (0.88 vs. 0.83), and according to the GA frailty index (0.86 vs. 0.84). Our findings demonstrate the robustness of both screening tools against different reference standards, with evidence of better diagnostic performance of the modified G8.