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Risk of 12-month mortality among hospital inpatients using the surprise question and SPICT criteria: a prospective study


Objectives People with serious life-limiting disease benefit from advance care planning, but require active identification. This study applied the Gold Standards Framework Proactive Identification Guidance (GSF-PIG) to a general hospital population to describe high-risk patients and explore prognostic performance for 12-month mortality.

Methods Prospective cohort study conducted in a metropolitan teaching hospital in Australia. Hospital inpatients on a single day aged 18 years and older were eligible, excluding maternity and neonatal, mental health and day treatment patients. Data sources included medical record and structured questions for medical and nursing staff. High-risk was predefined as positive response to the surprise question (SQ) plus two or more SPICT indicators of general deterioration. Descriptive variables included demographics, frailty and functional measures, treating team, advance care planning documentation and hospital utilisation. Primary outcome for prognostic performance was 12-month mortality.

Results We identified 540 eligible inpatients on the study day and 513 had complete data (mean age 60, 54% male, 30% living alone, 19% elective admissions). Of these, 191 (37%) were high-risk; they were older, frailer, more dependent and had been in hospital longer than low-risk participants. Within 12 months, 92 participants (18%) died (72/191(38%) high-risk versus 20/322(6%) low-risk, P<0.001), providing sensitivity 78%, specificity 72%, positive predictive value 38% and negative predictive value 94%. SQ alone provided higher sensitivity, adding advanced disease indicators improved specificity.

Conclusions The GSF-PIG approach identified a large minority of hospital inpatients who might benefit from advance care planning. Future studies are needed to investigate the feasibility, cost and impact of screening in hospitals.

  • advance care planning
  • death
  • prognosis
  • decision support techniques
  • hospitals

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