Nederlands
The current multivariable model showed clinical stage, biopsy tumor grade, tumor architecture, and hemoglobin level were independently associated with nonorgan-confined upper-tract urothelial carcinoma NOC-UTUC.
Research authors: Firas G. Petros, Wei Qiao, Nirmish Singla, Timothy N. Clinton, Haley Robyak, Jay D. Raman, Vitaly Margulis, Surena F Matin
Details
Custom formula
Study characteristics
Files & References
★★★
Model author
Model ID
1704
Version
1.9
Revision date
2019-01-28
MeSH terms
Model type
Logistic regression
(Calculation)
| Formula | No Formula defined yet |
|---|
| Condition | Formula |
|---|
Additional information
Retrospective evaluation of 699 patients undergoing radical nephroureterectomy at 3 academic centers. Multiplex preoperative patient, imaging, endoscopic, and laboratory values were evaluated. Model derivation and validation were based on a split-sample method. Patients were divided randomly into a development (training) cohort (70% of patients) and validation (test) cohort (30% of patients). Univariate and multivariate logistic regression addressed the prediction of NOC disease (pT3/pT4 and/or pN+) based on training cohort. A backward stepdown selection process achieved the most informative nomogram. The ROC analysis identified a cut-off point predicting high-risk disease. The test cohort served as “external” validation to verify the findings based on the training cohort. Bootstrap resampling was conducted for both internal and “external” validation to evaluate the model fitting.Study Population
Total population size: 566Males: {{ model.numberOfMales }}
Females: {{ model.numberOfFemales }}
Continuous characteristics
| Name | Mean | SD | Unit |
|---|---|---|---|
| Age | 69.7 | 10.7 | years |
| Tumor size | 3.63 | 2.4 | cm |
| N:L ratio | 3.9 | 3.88 | ratio |
| Serum Na | 139.9 | 3.32 | mEq/L |
| eGFR | 60.6 | 22.4 | ml/min/1.73m2 |
| Hgb | 12.8 | 1.76 | g/dL |
| Albumin | 4.1 | 0.42 | g/dL |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| Cohort | Development | 396 |
| Validation | 170 | |
| Race | Caucasian | 462 |
| Non-caucasian | 79 | |
| Symptoms | None | 134 |
| Local (gross hematuria/flank pain) | 345 | |
| Systemic | 17 | |
| ECOG performance status | 0-1 | 459 |
| 2-3 | 67 | |
| CCI | 0, 1, 2, 3 | 151 |
| >3 | 295 | |
| Year of surgery | <1999 | 57 |
| 2000-2009 | 245 | |
| ≥2010 | 264 | |
| Hydronephrosis | None/mild | 352 |
| Moderate/severe | 163 | |
| Tumor location | Renal pelvis/calyces | 259 |
| Ureter | 247 | |
| Both | 51 | |
| Focality | Unifocal | 336 |
| Multifocal | 109 | |
| Infiltrative component | Absent | 370 |
| Present | 46 | |
| Clinical stage | cTx, cTa, cTis | 290 |
| cT1-cT2 | 31 | |
| cT3 | 46 | |
| Biopsy tumor grade | Low | 140 |
| High | 224 | |
| Tumor architecture | Papillary | 433 |
| Sessile | 52 |
Related files
| Preview | Name | Tags |
|---|---|---|
|
862.75 kB | Figure (results-page) |
Supporting Publications
| Title or description | Tags |
|---|---|
| Preoperative multiplex nomogram for prediction of high-risk nonorgan-confined upper-tract urothelial carcinoma. | Internal validation Peer review |
Probability of stage pT3/pT4 and pN+ is: ...
{{ resultSubheader }}
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Result
Note
Notes are only visible in the result download and will not be saved by Evidencio
Probability of stage pT3/pT4 and pN+ is:
{{ resultSubheader }}
Outcome stratification
Result interval
{{ additionalResult.min }} to {{ additionalResult.max }}
Conditional information
Result interpretation
This preoperative nomogram can be used to more optimally select patients for preoperative systemic chemotherapy, and facilitate clinical trial enrollment.
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Comments
Rating
Comment
Please enter a comment of rating
Comments are visible to anyone
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} Comments
{{ comment.content }}
Recent Urology Models
Recent Oncology Models
Sign In
Sign Up
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
Please sign in to enable Evidencio print features
In order to use the Evidencio print features, you need to be logged in.
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
Printed results - Examples {{ new Date().toLocaleString() }}
Evidencio Community Account Benefits
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
{{ variable.title }}