The role of geriatric syndromes in predicting unplanned hospitalizations: a population-based study using Minimum Data Set for Home Care

Abstract

Abstract Background The predictive accuracies of screening instruments for identifying home-dwelling old people at risk of hospitalization have ranged from poor to moderate, particularly among the oldest persons. This study aimed to identify variables that could improve the accuracy of a Minimum Data Set for Home Care (MDS-HC) based algorithm, the Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT) Scale, in classifying home care clients’ risk for unplanned hospitalization. Methods In this register-based retrospective study, factors associated with hospitalization among home care clients aged ≥ 80 years in the City of Tampere, Finland, were analyzed by linking MDS-HC assessments with hospital discharge records. MDS-HC determinants associated with hospitalization within 180 days after the assessment were analyzed for clients at low (DIVERT 1), moderate (DIVERT 2–3) and high (DIVERT 4–6) risk of hospitalization. Then, two new variables were selected to supplement the DIVERT algorithm. Finally, area under curve (AUC) values of the original and modified DIVERT scales were determined using the data of MDS-HC assessments of all home care clients in the City of Tampere to examine if addition of the variables related to the oldest age groups improved the accuracy of DIVERT. Results Of home care clients aged ≥ 80 years, 1,291 (65.4%) were hospitalized at least once during the two-year study period. Unplanned hospitalization occurred following 15.9%, 22.8%, and 33.9% MDS-HC assessments with DIVERT group 1, 2–3 and 4–6, respectively. Infectious diseases were the most common diagnosis within each DIVERT groups. Many MDS-HC variables not included in the DIVERT algorithm were associated with hospitalization, including e.g. poor self-rated health and old fracture (other than hip fracture) (p 0.001) in DIVERT 1; impaired cognition and decision-making, urinary incontinence, unstable walking and fear of falling (p < 0.001) in DIVERT 2–3; and urinary incontinence, poor self-rated health (p < 0.001), and decreased social interaction (p 0.001) in DIVERT 4–6. Adding impaired cognition and urinary incontinence to the DIVERT algorithm improved sensitivity but not accuracy (AUC 0.64 (95% CI 0.62–0.65) vs. 0.62 (0.60–0.64) of the original DIVERT). More admissions occurred among the clients with higher scores in the modified than in the original DIVERT scale. Conclusions Certain geriatric syndromes and diagnosis groups were associated with unplanned hospitalization among home care clients at low or moderate risk level of hospitalization. However, the predictive accuracy of the DIVERT could not be improved. In a complex clinical context of home care clients, more important than existence of a set of risk factors related to an algorithm may be the various individual combinations of risk factors.