Nederlands
| Formula | No Formula defined yet |
|---|
| Condition | Formula |
|---|
Additional information
Aims:The aim of this study was to investigate the predictive ability of screening tools regarding the occurrence of major postoperative complications in onco-geriatric surgical patients and to propose a scoring system.
Methods:
328 patients 70 years undergoing surgery for solid tumors were prospectively recruited. Preoperatively, twelve screening tools were administered. Primary endpoint was the incidence of major complications within 30 days. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using logistic regression. A scoring system was derived from multivariate logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was applied to evaluate model performance.
Results:
At a median age of 76 years, 61 patients (18.6%) experienced major complications. In multivariate analysis, Timed Up and Go (TUG), ASA-classification and Nutritional Risk Screening (NRS) were predictors of major complications (TUG>20 OR 3.1, 95% CI 1.1-8.6; ASA3 OR 2.8, 95% CI 1.2-6.3; NRSimpaired OR 3.3, 95% CI 1.6-6.8). The scoring system, including TUG, ASA, NRS, gender and type of surgery, showed good accuracy (AUC: 0.81, 95% CI 0.75e0.86). The negative predictive value with a cut-off point >8 was 93.8% and the positive predictive value was 40.3%.
Conclusions:
A substantial number of patients experience major postoperative complications. TUG, ASA and NRS are screening tools predictive of the occurrence of major postoperative complications and, together with gender and type of surgery, compose a good scoring system.
Source:
Huisman MG, Audisio RA, Ugolini G, et al. Screening for predictors of adverse outcome in onco-geriatric surgical patients: A multicenter prospective cohort study. 2015;41(7):844-51.
Study Population
Total population size: 328Continuous characteristics
| Name | LL | Q1 | Median | Q3 | UL | Unit |
|---|---|---|---|---|---|---|
| Age | 70 | 76 | 96 | years |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| Living situation | Independent/family | 323 |
| Resedential care/nursing home | 2 | |
| Hemoglobin level | ≥ 12 g/dl | 198 |
| < 12 g/dl | 110 | |
| Surgery | Minor | 105 |
| Major | 223 | |
| Cancer site | Breast | 81 |
| Colorectal | 121 | |
| Gastric | 22 | |
| Gynaecological | 19 | |
| Pancreas and biliary tract | 34 | |
| Renal and bladder | 23 | |
| Soft tissue and skin | 18 | |
| Remaining | 12 | |
| Tumor stage | Stage 0 / benign | 19 |
| Stage 1 | 75 | |
| Stage 2 | 83 | |
| Stage 3 | 65 | |
| Stage 4 | 53 | |
| Unknown | 33 | |
| Age | 70-74 | 120 |
| 75-79 | 103 | |
| 80-84 | 72 | |
| ≥ 85 | 33 |
Related files
| Preview | Name | Tags |
|---|---|---|
|
8.87 kB | Institute Logo |
|
201.17 kB | Internal validation Paper Peer review |
.png)
|
Variables comprising PREOP scoring system (Huisman et al, 2015).png 62.63 kB |
Figure Model coefficients |
Supporting Publications
| Title or description | Tags |
|---|---|
| Huisman MG, Audisio RA, Ugolini G, et al. Screening for predictors of adverse outcome in onco-geriatric surgical patients: A multicenter prospective cohort study. 2015;41(7):844-51. | Internal validation Paper Peer review |
Calculated PREOP score: ... points
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Calculated PREOP score: points
Outcome stratification
Conditional information
Result interpretation
How this model should be used:
The PREOP scoring system can easily be implemented into daily practice as a screening measure, to support the judgment of the clinician. The high negative predictive value indicates that the scoring system can exclude the fit elderly from further evaluation, whilst a positive score might indicate that a more comprehensive assessment by a geriatrician or by means of a multidisciplinary meeting is indicated.
Strenghts and weaknesses:
A strength of the PREOP-study is its prospective and comprehensive design. A large number of medical centers participated, which further enhances the generalisability of the results. Selection bias is a limitation of the underlying study as inclusion of a consecutive series of patients cannot be guaranteed. Furthermore, cultural differences could have influenced the reporting of results and answers to questionnaires.
Assessement of model performance:
The AUC for this individual risk score was 0.81 (95% CI: 0.75-0.86). Based on the ROC, a cut-off point was set at >8, with a sensitivity of 78.7% and a specificity of 73.4%. Positive and negatieve predictive values were 40.3% and 93.8%, respectively.
Source:
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} CommentsSign 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
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.
{{ variable.title }}