Early palliative care: The surprise question and the palliative care screening tool-better together

Yung Feng Yen, Ya Ling Lee, Hsiao Yun Hu, Wen Jung Sun, Ming Chung Ko, Chu Chieh Chen, Weng Kee Wong, Donald E. Morisky, Sheng Jean Huang, Dachen Chu

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Objective The surprise question (SQ) and the palliative care screening tool (PCST) are the common assessment tools in the early identification of patients requiring palliative care. However, the comparison of their prognostic accuracies has not been extensively studied. This study aimed to compare the prognostic accuracy of SQ and PCST in terms of recognising patients nearing end of life (EOL) and those appropriate for palliative care. Methods This prospective study used both the SQ and PCST to predict patients' 12-month mortality and identified those appropriate for palliative care. All adult patients admitted to Taipei City Hospital in 2015 were included in this cohort study. The c-statistic value was calculated to indicate the predictive accuracies of the SQ and PCST. Results Out of 21 109 patients, with a mean age of 62.8 years, 12.4% and 11.1% had a SQ response of â € no' and a PCST score of ≥4, respectively. After controlling for other covariates, an SQ response of â € no' and a PCST score of ≥4 were the independent predictors of 12-month mortality. The c-statistic values of the SQ and PCST at recognising patients in their last year of life were 0.680 and 0.689, respectively. When using a combination of both SQ and PCST in predicting patients' 12-month mortality risk, the predictive value of the c-statistic increased to 0.739 and was significantly higher than either one in isolation (p<0.001). Conclusion A combination of the SQ with PCST has better prognostic accuracy than either one in isolation.

Original languageEnglish
Pages (from-to)211-217
Number of pages7
JournalBMJ Supportive and Palliative Care
Volume12
Issue number2
DOIs
Publication statusPublished - 2022 Jun 1

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Oncology(nursing)
  • Medical–Surgical

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