Skull Base Meningioma (SBM)
Five post-operative outcomes, from 41 pre-operative variables.
- Neurologic Worsening
- Any Postoperative Complication
- Performance Status Deterioration
- Severe Complication (Ibañez ≥ 3)
- 30-Day Mortality
Many patients, or checking your outcomes? Use a spreadsheet.
Complete every field before calculating.
Risk Assessment Results
For research and investigational use only. Not a medical device.
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Predicted Outcomes
Each estimate is produced by a model trained and validated specifically for that outcome.
Neurologic Worsening
Probability of a new or worsened neurological status after surgery.
Any Postoperative Complication
Probability of any complication occurring in the postoperative period.
Performance Status Deterioration
Probability of functional status (KPS) deterioration at hospital discharge.
Severe Complication (Ibañez ≥ 3)
Probability of life-threatening adverse events requiring treatment in an intensive care unit (ICU) or death as a result of complications.
30-Day Mortality
Probability of death within 30 days of surgery.
Methodology
Predictions draw on a multicentre registry of operated skull-base meningiomas. For every outcome, several model families were trained and compared, and the single best-performing model was frozen for use in this tool.
Structured Input
You enter 41 pre-operative variables through one guided form: demographics, comorbidity, functional status, clinical presentation and radiological characteristics.
Frozen Best Model
Each outcome uses the model that performed best under temporal validation. The operating threshold is the one frozen during that validation.
Probability & Band
The model returns an individual probability, placed into an outcome-specific Low / Moderate / High band shown alongside the cohort base rate.
What the number means
How often this outcome occurred in patients with a similar profile, among the cases the model was built on.
Model & Cohort
Skull Base Meningioma (SBM) was developed on a multicentre registry of operated skull-base meningiomas, using a temporal validation design so that models are evaluated on patients treated after those they were trained on.
Validation Design
Models were trained and internally compared under a temporal split: the earlier patients form the training set and the most recent patients form a held-out temporal test set. Operating thresholds were frozen on a separate validation split before the test set was seen, and are read at runtime by this calculator rather than tuned interactively.
Per-Outcome Performance
| Outcome | Model | AUROC | Threshold |
|---|---|---|---|
| Neurologic Worsening | svc@391015d | 0.805 | 0.114 |
| Any Postoperative Complication | lr@c02ab7c | 0.755 | 0.173 |
| Performance Status Deterioration | rf@5e04b27 | 0.752 | 0.139 |
| Severe Complication (Ibañez ≥ 3) | lr@2651cac | 0.663 | 0.297 |
| 30-Day Mortality | rf@c3a207a | 0.898 | 0.124 |
30-Day Mortality and Severe Complication (Ibañez ≥ 3) were evaluated on very few test events, so their probabilities are unreliable and are presented with a caveat; the associated factors are the more defensible content on those cards.
Risk Bands
Three bands per outcome. The lower boundary is that outcome's frozen operating threshold; the upper is the 90th percentile of predicted probability in the temporal test set. By construction, High is the top ~10% of cases and Low is the below-threshold group; most cases land in Moderate, which is the correct behaviour for these models.
| Outcome | Low | Moderate | High |
|---|---|---|---|
| Neurologic Worsening | < 0.114 | 0.114 – 0.515 | ≥ 0.515 |
| Any Postoperative Complication | < 0.173 | 0.173 – 0.826 | ≥ 0.826 |
| Performance Status Deterioration | < 0.139 | 0.139 – 0.366 | ≥ 0.366 |
| Severe Complication (Ibañez ≥ 3) | < 0.297 | 0.297 – 0.658 | ≥ 0.658 |
| 30-Day Mortality | < 0.124 | 0.124 – 0.303 | ≥ 0.303 |
Ethics & Governance
Ethics approval: CE-AVEC, Bologna (protocol 19029). For research and educational use; not a medical device and not for clinical decision-making.
Contact & Citation
Version
v0.1.5 · models@bdf7266