COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 769830

Comparison between drug therapy-based comorbidity indices and the Charlson Comorbidity Index for the detection of severe multimorbidity in older subjects.

Background: To know burden disease of a patient is a key point for clinical practice and research, especially in the elderly. Charlson's Comorbidity Index (CCI) is the most widely used rating system, but when diagnoses are not available therapy-based comorbidity indices (TBCI) are an alternative. However, their performance is debated. This study compares the relations between Drug Derived Complexity Index (DDCI), Medicines Comorbidity Index (MCI), Chronic Disease Score (CDS), and severe multimorbidity, according to the CCI classification, in the elderly. Methods: Logistic regression and Receiver Operating Characteristic (ROC) analysis were conducted on two samples from Italy: 2579 nursing home residents (Korian sample) and 7505 older adults admitted acutely to geriatric or internal medicine wards (REPOSI sample). Results: The proportion of subjects with severe comorbidity rose with TBCI score increment, but the Area Under the Curve (AUC) for the CDS (Korian: 0.70, REPOSI: 0.79) and MCI (Korian: 0.69, REPOSI: 0.81) were definitely better than the DDCI (Korian: 0.66, REPOSI: 0.74). All TBCIs showed low Positive Predictive Values (maximum: 0.066 in REPOSI and 0.317 in Korian) for the detection of severe multimorbidity. Conclusion: CDS and MCI were better predictors of severe multimorbidity in older adults than DDCI, according to the CCI classification. A high CCI score was related to a high TBCI. However, the opposite is not necessarily true probably because of non-evidence-based prescriptions or physicians' prescribing attitudes. TBCIs did not appear selective for detecting of severe multimorbidity, though they could be used as a measure of disease burden, in the absence of other solutions

Relation between drug therapy-based comorbidity indices, Charlson's comorbidity index, polypharmacy and mortality in three samples of older adults.

Background: Comorbidity indexes were designed in order to measure how the disease burden of a patient is related to different clinical outcomes such as mortality, especially in older and intensively treated people. Charlson's Comorbidity Index (CCI) is the most widely used rating system, based on diagnoses, but when this information is not available therapy-based comorbidity indices (TBCI) are an alternative: among them, Drug Derived Complexity Index (DDCI), Medicines Comorbidity Index (MCI), and Chronic Disease Score (CDS) are available. Aims: This study assessed the predictive power for 1-year mortality of these comorbidity indices and polypharmacy. Methods: Survival analysis and Receiver Operating Characteristic (ROC) analysis were conducted on three Italian cohorts: 2,389 nursing home residents (Korian), 4,765 and 633 older adults admitted acutely to geriatric or internal medicine wards (REPOSI and ELICADHE). Results: Cox's regression indicated that the highest levels of the CCI are associated with an increment of 1-year mortality risk as compared to null score for all the three samples. DDCI and excessive polypharmacy gave similar results but MCI and CDS were not always statistically significant. The predictive power with the ROC curve of each comorbidity index was poor and similar in all settings. Conclusion: On the whole, comorbidity indices did not perform well in our three settings, although the highest level of each index was associated with higher mortality