Developing an electronic Surprise Question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions

Singh, Baldev; Kumari-Dewat, Nisha; Ryder, Adam; Klaire, Vijay; Jennens, Hannah; Ahmed, Kamran; Sidhu, Mona; Viswanath, Ananth; Parry, Emma

Abstract

Objective
Determining the accuracy of a method calculating the Gold Standards Framework Surprise Question (GSFSQ) equivalent end-of-life prognosis amongst hospital inpatients.
Design
A prospective cohort study with regression calculated 1-year mortality probability. Probability cut points triaged unknown prognosis into the GSFSQ equivalent “Yes” or “No” survival categories (> or < 1-year respectively), with subsidiary classification of “No”. Prediction was tested against prospective mortality.
Setting
An acute NHS hospital.
Participants
18,838 acute medical admissions.
Interventions
Allocation of mortality probability by binary logistic regression model (X2=6650.2, p<0.001, r2 = 0.43) and stepwise algorithmic risk-stratification.
Main outcome measure
Prospective mortality at 1-year.
Results
End-of-life prognosis was unknown in 67.9%. The algorithm's prognosis allocation (100% vs baseline 32.1%) yielded cohorts of GSFSQ-Yes 15,264 (81%), GSFSQ-No Green 1,771 (9.4%) and GSFSQ-No Amber or Red 1,803 (9.6%). There were 5,043 (26.8%) deaths at 1-year. In Cox's survival, model allocated cohorts were discrete for mortality (GSFSQ-Yes 16.4% v GSFSQ-No 71.0% (p<0.001). For the GSFSQ-No classification, the mortality Odds Ratio was 12.4 (11.4 – 13.5) (p<0.001) vs GSFSQ-Yes (c-statistic 0.72 (0.70 – 0.73), p<0.001; accuracy, positive and negative predictive values 81.2%, 83.6%, 83.6%, respectively). Had the tool been utilised at the time of admission, the potential to reduce possibly avoidable subsequent hospital admissions, death-in-hospital, and bed days was significant (p<0.001).
Conclusion
This study has unique in methodology with prospectively evidenced outcomes. The model algorithm allocated GSFSQ equivalent EOL prognosis universally to a cohort of acutely admitted patients with statistical accuracy validated against prospective mortality outcomes.

Citation

Singh, B., Kumari-Dewat, N., Ryder, A., Klaire, V., Jennens, H., Ahmed, K., Sidhu, M., Viswanath, A., & Parry, E. (2025). Developing an electronic Surprise Question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions. Clinical Medicine, Article 100292. https://doi.org/10.1016/j.clinme.2025.100292

• Journal Article Type: Article
• Acceptance Date: Feb 3, 2025
• Online Publication Date: Feb 6, 2025
• Publication Date: 2025-02
• Deposit Date: Feb 10, 2025
• Journal: Clinical Medicine
• Print ISSN: 1470-2118
• Electronic ISSN: 1473-4893
• Publisher: Royal College of Physicians
• Peer Reviewed: Peer Reviewed
• Article Number: 100292
• DOI: https://doi.org/10.1016/j.clinme.2025.100292
• Keywords: Emergency care (D004632), Algorithms (D000465), Decision Making (D003657), Advance Care Planning (D032722), Mortality (D009026), Health informatics (D008490)
• Public URL: https://keele-repository.worktribe.com/output/1073407
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1470211825000107?via%3Dihub