A prediction algorithm to improve the accuracy of the Gold Standard Framework Surprise Question end-of-life prognostic categories in an acute hospital admission cohort-controlled study. The Proactive Risk-Based and Data-Driven Assessment of Patients at the End of Life (PRADA)

Singh, Baldev; Kumari-Dewat, Nisha; Ryder, Adam; Klaire, Vijay; Bennion, Gemma; Jennens, Hannah; Matthews, Dawn; Rayner, Sophie; Ritzenthaler, Benoit; Shears, Jean; Ahmed, Kamran; Sidhu, Mona; Viswanath, Ananth; Warren, Kate; Parry, Emma

Abstract

Objective To determine the accuracy of a clinical data algorithm allocated end-of-life prognosis amongst hospital inpatients.

Method The model allocated a predicted Gold Standard Framework end-of-life prognosis to all acute medical patients admitted over a 2-year period. Mortality was determined at 1 year.

Results Of 18,838 patients, end-of-life prognosis was unknown in 67.9%. A binary logistic regression model calculated 1-year mortality probability (X2=6650.2, p<0.001, r2 = 0.43). Probability cut off points were used to triage those with unknown prognosis using the GSF Surprise Question “Yes” or “No” survival categories (> or < 1 year respectively), with subsidiary classification of “No” to Green (months), Amber (weeks) or Red (days). This digitally driven 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 of 0.71 (0.70 – 0.73), p<0.001; accuracy, positive and negative predictive values of 81.2%, 83.6%, 83.6% respectively. If this tool had been utilised at the time of admission, the potential to reduce subsequent hospital admissions, death-in-hospital, and bed days was all p<0.001.

Conclusions The defined model successfully allocated end-of-life prognosis in cohorts of hospitalised patients with strong performance metrics for prospective 1 year mortality, yielding the potential to provide anticipatory care and improve outcomes.

Citation

Singh, B., Kumari-Dewat, N., Ryder, A., Klaire, V., Bennion, G., Jennens, H., …Parry, E. (2023). A prediction algorithm to improve the accuracy of the Gold Standard Framework Surprise Question end-of-life prognostic categories in an acute hospital admission cohort-controlled study. The Proactive Risk-Based and Data-Driven Assessment of Patients at the End of Life (PRADA). [preprint]

• Other Type: Other
• Online Publication Date: Sep 8, 2023
• Publication Date: 2023-09
• Deposit Date: Jan 22, 2024
• DOI: https://doi.org/10.1101/2023.09.07.23295196
• Related Public URLs: https://www.medrxiv.org/content/10.1101/2023.09.07.23295196v1