Background: Although oldest-old, those aged 85 years and older, patients are the fastest growing segment, clinical evidences regarding the acute care of oldest-old patients are still lacking. Because acute medical conditions requiring emergent hospitalization is frequently followed by high rate of progressive physical decline and increased mortality after discharge in oldest-old patients, prognostic information collected during hospitalization can provide the basis for discussion about the goals of care and therapy. The aim of our study was to identify predictive factors for postdischarge mortality in oldest-old patients. Methods: The study included 403 oldest-old patients discharged from the acute care setting of a general hospital, who were followed-up for 1 year. Predictive values of the patients’ characteristics collected during their hospitalization for 1-year mortality were identified utilizing Cox proportional hazard regression analysis. Results: During 1-year follow-up, 104 patients (25.8%) died. The variables independently associated with 1-year mortality in multivariate analysis were the Charlson Comorbidity Index equal or greater than 2 [HR (hazard ratio) 4.71, 95%CI (confidence interval) 1.09–20.42], six or more prescribed medications at discharge (HR 3.12, 95% CI 1.39–6.99), benzodiazepines use (HR 1.64, 95% CI 1.04–2.60), nonsteroidal anti-inflammatory drugs use (HR 1.70, 95% CI 1.10–2.63), albumin less than or equaling 3.4 g/dl (HR 2.16, 95% CI 1.13–4.14), hemoglobin 10–12 g/dl (HR 2.32, 95% CI 1.22–3.56), hemoglobin less than 10 g/dl (HR 2.67, 95% CI 1.43–4.95), the presence of pressure sores (HR 1.84, 95% CI 1.14–2.97), and a history of delirium (HR 2.24, 95% CI 1.32–3.79). Functional impairment assessed by the Katz Index was only weakly associated with mortality (HR 1.24, 95% CI 0.53–2.91). Conclusion: Although often underappreciated, polypharmacy, particular medication use, anemia, the presence of pressure sores, and a history of delirium were important predictors for postdischarge mortality in oldest-old patients.
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