Multidimensional Prognostic Index (MPI) is useful as a prognostic tool in hospitalized older patients, but our knowledge is derived from retrospective studies. We therefore aimed to evaluate in a multicenter, longitudinal, cohort study whether the MPI at hospital admission is useful to identify groups with different mortality risk and whether MPI at discharge may predict institutionalization, rehospitalization, and use of home care services during 12 months. This longitudinal study, carried out between February 2015 and August 2017, included nine public hospitals in Europe and Australia. A standardized comprehensive geriatric assessment including information on functional, nutritional, cognitive status, risk of pressure sores, comorbidities, medications, and cohabitation status was used to calculate the MPI and to categorize participants in low, moderate, and severe risk of mortality. Data regarding mortality, institutionalization, rehospitalization, and use of home care services were recorded through administrative information. Altogether, 1,140 hospitalized patients (mean age 84.1 years, women = 60.8%) were included. In the multivariable analysis, compared to patients with low risk group at admission, patients in moderate (odds ratio [OR] = 3.32; 95% CI: 1.79–6.17; p < .001) and severe risk (OR = 10.72, 95% CI: 5.70–20.18, p < .0001) groups were at higher risk of overall mortality. Among the 984 older patients with follow-up data available, those in the severe-risk group experienced a higher risk of overall mortality, institutionalization, rehospitalization, and access to home care services. In this cohort of hospitalized older adults, higher MPI values are associated with higher mortality and other negative outcomes. Multidimensional assessment of older people admitted to hospital may facilitate appropriate clinical and postdischarge management.

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. 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. 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). 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.