Development and Cross-Validation of the In-Hospital Mortality Prediction in Advanced Cancer Patients Score: A Preliminary Study
Bibliographic record
Abstract
PURPOSE: Acute palliative care units (APCUs) provide intensive symptom support and transition of care for advanced cancer patients. Better understanding of the predictors of in-hospital mortality is needed to facilitate program planning and patient care. In this prospective study, we identified predictors of APCU mortality, and developed a four-item In-hospital Mortality Prediction in Advanced Cancer Patients (IMPACT) predictive model. METHODS: Between April and July 2010, we documented baseline demographics, the Edmonton Symptom Assessment Scale (ESAS), 80 clinical signs including known prognostic factors, and 26 acute complications on admission in consecutive APCU patients. Multivariate logistic regression analysis was used to identify factors for inclusion in a nomogram, which was cross-validated with bootstrap analysis. RESULTS: Among 151 consecutive patients, the median age was 58, 13 (9%) had hematologic malignancies, and 52 (34%) died in the hospital. In multivariate analysis, factors associated with in-hospital mortality were advanced education (odds ration [OR]=11.8, p=0.002), hematologic malignancies (OR=8.6, p=0.02), delirium (OR=4.3, p=0.02), and high ESAS global distress score (OR=20.8, p=0.01). In a nomogram based on these four factors, total scores of 6, 10, 14, 17, and 21 corresponded to a risk of death of 10%, 25%, 50%, 75%, and 90%, respectively. The model has 92% sensitivity and 88% specificity for predicting patients at low/high risk of dying in the hospital, and a receiver-operator characteristic curve concordance index of 83%. CONCLUSIONS: Higher education was associated with increased utilization of the interdisciplinary palliative care unit until at the end of life. Patients with higher symptom burden, delirium, and hematologic malignancies were also more likely to require APCU care until death.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".