Frailty index and adverse outcomes in older patients in haemodialysis.

BACKGROUND: In older people, the prevalence frailty is inversely proportional to renal function, therefore it is supposed to be the highest in haemodialysis patients. However, frailty and its association with adverse outcomes have been scarcely investigated in this population. The aim of the present study was to characterize the frailty status and explore its association with hospitalization and mortality in a cohort of older patients undergoing chronic haemodialysis. MATERIALS AND METHODS: This is a retrospective longitudinal study based on data from 105 older patients undergoing haemodialysis for at least 3 months. We computed a 24-item frailty index (FI) based on sociodemographic, clinical and biological data collected at baseline. During the follow-up, death and hospitalizations events were recorded. Unadjusted and adjusted Cox proportional hazard models were performed to test the association of frailty with hospitalization and death. RESULTS: Mean age of the patients was 79.1 (SD 7.6) years, and their mean FI was 0.23 (SD 0.10). About 55% of patients were classified as frail (i.e., FI≥ 0.25). Patients were observed for 21 (interquartile range [IQR] 8-32) months. Overall, during the follow-up, 75% of patients required hospitalization and 28% died. Frail subjects where at higher risk of hospitalization (HR 1.60, 95% CI 1.00-2.57, p = 0.05) and of all-cause mortality (HR 2.52, 95% CI 1.10-5.80, p = 0.03) CONCLUSIONS: : Frailty is highly prevalent among older people undergoing haemodialysis. Frail individuals present a higher risk of hospitalizations and mortality. The FI is a reliable tool to study vulnerability in this population.

Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies.

Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables' effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient's response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients.