Multidimensional prognostic index (MPI) predicts successful application for disability social benefits in older people.

AIM: The economic recognition of disability is of importance in daily practice, but the tools used in older people are still limited. Therefore, we aimed to investigate the effectiveness of the multidimensional prognostic index (MPI) to identify frail older subjects to be submitted to civil invalidity application for disability benefits including Attendance Allowance (AA) indemnity, Carer's Leave (Law 104) and/or Parking Card for people with disabilities. METHODS: From March 2018 to January 2019, 80 older people were included. The MPI was calculated from comprehensive geriatric assessment information including eight different domains. Civil benefits included attendance allowance (AA) indemnity by the Local Medico-Legal Committee (MLC-NHS) and by the National Institute of Social Security Committee (INPS), Carer's Leave (Law 104), and Parking Card for people with disabilities. RESULTS: MPI values were associated with an increased probability to obtain a 100% civil disability, AA indemnity, Carer's Leave and a parking card for people with disabilities. MPI score showed a very good accuracy in predicting the civil invalidity benefits with a area-under-curve (AUC) of 87.3 (95% CI 80.6-97.4) to predict the release of AA indemnity, 81.3 (95% CI 68.5-91.1) to predict Care's leave and 70.7 (95% CI 59.4-84.7) to predict the Parking Card release. Moreover, data showed that a cut-off score of MPI ≥ 0.75 could identify the 100% of older subjects who successfully obtained the indemnity release. CONCLUSION: MPI is an excellent predictor of social benefits' release by local and national agencies.

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults.

BACKGROUND: Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. METHODS: Community-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method. RESULTS: Of the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p<0.001), respectively. The best model that comprised only robotic parameters (the 20 parameters identified in the final model) achieved a better performance than the clinical parameters alone, but worse than the combination of both clinical and robotic variables (ROC AUC: 0.73, 95% CI 0.63-0.83). CONCLUSION: A multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.

Robotic balance assessment in community-dwelling older people with different grades of impairment of physical performance.

BACKGROUND: Impaired physical performance is common in older adults and has been identified as a major risk factor for falls. To date, there are no conclusive data on the impairment of balance parameters in older subjects with different levels of physical performance. AIMS: The aim of this study was to investigate the relationship between different grades of physical performance, as assessed by the Short Physical Performance Battery (SPPB), and the multidimensional balance control parameters, as measured by means of a robotic system, in community-dwelling older adults. METHODS: This study enrolled subjects aged ≥ 65 years. Balance parameters were assessed by the hunova robot in static and dynamic (unstable and perturbating) conditions, in both standing and seated positions and with the eyes open/closed. RESULTS: The study population consisted of 96 subjects (62 females, mean age 77.2 ± 6.5 years). According to their SPPB scores, subjects were separated into poor performers (SPPB < 8, n = 29), intermediate performers (SPPB = 8-9, n = 29) and good performers (SPPB > 9, n = 38). Poor performers displayed significantly worse balance control, showing impaired trunk control in most of the standing and sitting balance tests, especially in dynamic (both with unstable and perturbating platform/seat) conditions. CONCLUSIONS: For the first time, multidimensional balance parameters, as detected by the hunova robotic system, were significantly correlated with SPPB functional performances in community-dwelling older subjects. In addition, balance parameters in dynamic conditions proved to be more sensitive in detecting balance impairments than static tests.

Development and Validation of a Self-Administered Multidimensional Prognostic Index to Predict Negative Health Outcomes in Community-Dwelling Persons.

The multidimensional prognostic index (MPI) is a comprehensive geriatric assessment (CGA)-based tool that accurately predicts negative health outcomes in older subjects with different diseases and settings. To calculate the MPI several validated tools are assessed by health care professionals according to the CGA, whereas self-reported information by the patients is not available, but it could be of importance for the early identification of frailty. We aimed to develop and validate a self-administered MPI (SELFY-MPI) in community-dwelling subjects. For this reason, we enrolled 167 subjects (mean age = 67.3, range = 20-88 years, 51% = men). All subjects underwent a CGA-based assessment to calculate the MPI and the SELFY-MPI. The SELFY-MPI included the assessment of (1) basic and instrumental activities of daily living, (2) mobility, (3) memory, (4) nutrition, (5) comorbidity, (6) number of medications, and (7) socioeconomic situation. The Bland-Altman methodology was used to measure the agreement between MPI and SELFY-MPI. The mean MPI and SELFY-MPI values were 0.147 and 0.145, respectively. The mean difference was +0.002 ± standard deviation of 0.07. Lower and upper 95% limits of agreement were -0.135 and +0.139, respectively, with only 5 of 167 (3%) of observations outside the limits. Stratified analysis by age provided similar results for younger (≤65 years old, n = 45) and older subjects (>65 years, n = 122). The analysis of variances in subjects subdivided according to different year decades showed no differences of agreement according to age. In conclusion, the SELFY-MPI can be used as a prognostic tool in subjects of different ages.