A prognostic index for 1-year mortality can also predict in-hospital mortality of elderly medical patients

Elderly patients admitted to the hospital are at increased risk for both in-hospital and post-discharge mortality. Risk assessment models (RAMs) for in-hospital mortality are based mainly on physiological variables and a few laboratory data, whereas RAMs for late mortality usually include other domains such as disability and comorbidities. We aim to evaluate if a previous validated model for 1-year mortality (the Walter Score) would also work well in predicting in-hospital mortality. We retrospectively revised the medical records of patients admitted on our ward, from April to December, 2013. Data regarding gender, activities of daily living (ADLs), comorbidities, and routine laboratory tests were used to calculate a Modified Walter Score (MoWS). The main outcome measure was all cause, in-hospital mortality. The analysis involved 1,004 patients. Of these, 888 were discharged alive, and 116 (11.5 %) died during the hospitalization. The mean MoWS was 4.9 (±3.6) in the whole sample. Stratification into risk classes parallels with in-hospital mortality (Chi square for trend p < 0.001). When dichotomized, MoWS has a sensitivity of 97.4 % (95 % CI 92.1–99.3), and a specificity of 48.2 % (95 % CI 44.9–51.5) with a good prognostic accuracy (area under the ROC = 0.81; 95 % CI 0.78, 0.84). Subgroup analysis according to different age groups gives similar results. A simple RAM based on multiple domains, previously validated for predicting mortality of older adults within 1 year from the index hospitalization, can be useful at the moment of admission to Internal Medicine wards to accurately identify patients at low risk of in-hospital mortality.