Validation of a Supportive and Palliative Care Indicator Tool Among Patients Hospitalized Due to Heart Failure

Background: Palliative care, including symptom alleviation and advance-care planning, is relevant for patients with heart failure (HF). The Supportive and Palliative Care Indicator Tool (SPICT) is a tool for identifying patients who may benefit from palliative-care assistance but has not been validated in patients hospitalized due to HF. Methods and Results: Clinical backgrounds, symptom burdens and outcomes were evaluated using the SPICT as assessed on admission in consecutive hospitalized patients with HF. SPICT-positive was defined when 2 or more general indicators and a New York Heart Association class >_ III were present. Of 601 patients hospitalized due to HF (mean age: 79 f 12 years; male, 314 [52%]; and mean left ventricular ejection fraction: 44 f 18%), 100 (17%) patients were SPICT-positive. SPICT-positive patients were older (85 f 9 vs 78 f 12 years; P < 0.001) and had higher clinical frailty scales (6 f 1 vs 4 f 1 points; P < 0.001), whereas symptom burdens assessed by the Integrated Palliative care Outcome Scale were not different (17 [13, 28] vs 20 [11, 26] points; P = 0.97) when compared with patients who were SPICT-negative. During the median follow-up period of 518 days, 178 patients (30%) died. Being SPICT-positive was independently associated with higher all-cause mortality (hazard ratio: 3.49, 95% confidence interval: 2.41-5.05; P < 0.001) after adjusting for age, sex, New York Heart Association class IV, Get-With-The-Guideline risk score, N-terminal pro B-type natriuretic peptide levels, and left ventricular ejection fractions. Conclusions: In patients admitted for HF, being SPICT-positive was significantly associated with higher all-cause mortality rates, suggesting the utility of the SPICT as an indicator to initiate advance-care planning for end-of-life care among patients hospitalized due to HF. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.