Human TSH (Thyroid Stimulating Hormone) ELISA Kit

Background Older patients with cancer require specific and individualized management. The 3-group Multidimensional Prognostic Index (MPI) based on the Comprehensive Geriatric Assessment (CGA) has shown a predictive interest in terms of mortality. The objective of our study was to assess the prognostic value of MPI for 1-year mortality in an external prospective French cohort of elderly patients with cancer. Methods From March 2015 to March 2017 a prospective single-center cohort study enrolled all patients with cancer, aged 75 years and older referred to the geriatric oncology clinic. We used a proportional hazard model for 1-year mortality adjusted for age, sex, tumor sites and metastatic status. C-statistics were used to assess the incremental predictive value of MPI index to these risk factors. Results overall, 433 patients underwent CGA with MPI (women 42%; mean age 82.8 +/- 4.8 years). The most common tumor sites were prostate (23%), skin (17%), colorectum (15%) and breast (12%); 29% of patients had a metastatic disease; 231 patients (53%) belonged to the "MPI-1" group, 172 (40%) to the "MPI-2" group and 30 patients were classified in the "MPI-3" group. One-year mortality rate was 32% (23% in MPI-1, 41% in MPI-2 and 53% in MPI-3,p = 0.024). All domains of MPI except cognition and living status were significantly associated with mortality at one-year, as well as tumor sites and metastatic status. Higher MPI was associated with a higher mortality risk (adjusted HR 1.56 [95%CI 1.70-2.09] and 1.72 [1.33-2.22] for MPI groups 2 and 3 compared to 1;p < 0.0001). Conclusions In addition to established risk factors, MPI improves risk prediction of 1-year mortality. This practical prognostic tool may help to optimize management of these vulnerable patients.

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to create therapy plans that improve their quality and quantity of life. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim of automatizing these parts of the CGA, we have designed and developed CLARC (smart CLinic Assistant Robot for CGA), a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Using CLARC to conduct geriatric tests will reduce the time medical professionals have to spend on purely mechanical tasks, giving them more time to develop individualised care plans for their patients. In fact, ideally, CLARC will perform these tests on its own. In parallel with the effort to correctly address the functional aspects, i.e., the development of the robot tasks, the design of CLARC must also deal with non-functional properties such as the degree of interaction or the performance. We argue that satisfying user preferences can be a good way to improve the acceptance of the robot by the patients. This paper describes the integration into the software architecture of the CLARC robot of the modules that allow these properties to be monitored at run-time, providing information on the quality of its service. Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot's activity and its interaction with the patient covered by the non-functional properties that have been considered.