The “surprise question” for predicting death in seriously ill patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The surprise question — “Would I be surprised if this patient died in the next 12 months?” — has been used to identify patients at high risk of death who might benefit from palliative care services. Our objective was to systematically review the performance characteristics of the surprise question in predicting death. METHODS: We searched multiple electronic databases from inception to 2016 to identify studies that prospectively screened patients with the surprise question and reported on death at 6 to 18 months. We constructed models of hierarchical summary receiver operating characteristics (sROCs) to determine prognostic performance. RESULTS: Sixteen studies (17 cohorts, 11 621 patients) met the selection criteria. For the outcome of death at 6 to 18 months, the pooled prognostic characteristics were sensitivity 67.0% (95% confidence interval [CI] 55.7%–76.7%), specificity 80.2% (73.3%–85.6%), positive likelihood ratio 3.4 (95% CI 2.8–4.1), negative likelihood ratio 0.41 (95% CI 0.32–0.54), positive predictive value 37.1% (95% CI 30.2%–44.6%) and negative predictive value 93.1% (95% CI 91.0%–94.8%). The surprise question had worse discrimination in patients with noncancer illness (area under sROC curve 0.77 [95% CI 0.73–0.81]) than in patients with cancer (area under sROC curve 0.83 [95% CI 0.79–0.87; p = 0.02 for difference]). Most studies had a moderate to high risk of bias, often because they had a low or unknown participation rate or had missing data. INTERPRETATION: The surprise question performs poorly to modestly as a predictive tool for death, with worse performance in noncancer illness. Further studies are needed to develop accurate tools to identify patients with palliative care needs and to assess the surprise question for this purpose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".