Deutsch
Nederlands
This model calculates the preoperative probability of developping a surgical site infection (SSI) after liver resection. SSI may either be an incisional infection or an intra-abdominal infection. Additional measures may be taken in patients with increased risk.
The model was created in a Dutch multicenter cohort study, and is based on logistic regression. The study included 982 liver resections, both benign and malignant indications.
The model was created in a Dutch multicenter cohort study, and is based on logistic regression. The study included 982 liver resections, both benign and malignant indications.
Research authors: G.W. de Klein, M.J. Bruins, M.S.L. Liem, H. Eker, J.M. Klaase, V.B. Nieuwenhuijs
Details
Formula
Study characteristics
Files & References
★★
Model author
Model ID
2006
Version
1.3
Revision date
2020-01-16
Specialty
MeSH terms
Model type
Logistic regression
(Calculation)
| Formula | No Formula defined yet |
|---|
| Condition | Formula |
|---|
Additional information
This cohort study used data from prospectively held databases from four high-volume HPB centers, including one academic center. All patients who underwent liver resection (primary and repeat) between 2013 and 2017 for both malignant and benign indications were included. Patients with trauma-related resections, donor hepatectomy, and patients who only underwent radiofrequency ablation (RFA) were excluded.Stepwise logistic regression was used to detect independent predictors for SSI. The predictors that were known preoperatively were used to create a nomogram to indicate the probability of the development of a SSI of individuals
Study Population
Total population size: 982Males: {{ model.numberOfMales }}
Females: {{ model.numberOfFemales }}
Continuous characteristics
| Name | Mean | SD | Unit |
|---|---|---|---|
| Age | 73 | 10 | year |
| BMI | 26.5 | 4.5 | kg/m² |
| Name | LL | Q1 | Median | Q3 | UL | Unit |
|---|---|---|---|---|---|---|
| Operative time | 120 | 228 | 449 | minutes | ||
| Blood loss | 200 | 400 | 775 | mL |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| SSI | Yes | 80 |
| No | 902 | |
| Type resection | Major | 289 |
| Minor | 693 | |
| Simultaneous liver and colorectal resection | Yes | 154 |
| No | 828 | |
| Concomitant RFA | No | 869 |
| Yes | 113 | |
| Indication | Colorectal metastasis | 693 |
| Primary liver tumor | 80 | |
| Metastasis non-colorectal | 56 | |
| Benign | 122 | |
| Approach | Open | 836 |
| Laparoscopy | 146 | |
| Present liver disease | No | 956 |
| Hepatitis | 17 | |
| Cirrhosis | 9 | |
| Abdominal comorbidity | No | 891 |
| Yes | 91 | |
| Physical status classification | ASA I | 208 |
| ASA II | 613 | |
| ASA III | 156 | |
| ASA IV | 4 | |
| Diatabetes | No | 882 |
| Yes | 100 |
Related files
| Preview | Name | Tags |
|---|---|---|
|
381.97 kB | Figure |
Supporting Publications
| Title or description | Tags |
|---|---|
|
A nomogram predicting surgical site infection before liver resection, a multicenter cohort study
DOI:
Prepublication
|
Internal validation Paper |
The pre-operative risk of surgical site infection after liver resection is ...
{{ resultSubheader }}
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Result
Download report
Generating report
Note
Notes are only visible in the result download and will not be saved by Evidencio
The pre-operative risk of surgical site infection after liver resection is
{{ resultSubheader }}
{{ chart.title }}
{{ chart.title }}
{{ chart.title }}
Outcome stratification
Result interval
{{ additionalResult.min }} to
{{ additionalResult.max }}
Conditional information
Result interpretation
Precautionary measures in high risk patients might include an extended antimicrobial prophylaxis, for instance based on rectal cultures that may be obtained preoperatively to detect colonization of resistant microbes and adjust antibiotic prophylaxis. Furthermore, a more extensive antiseptic regime for instance with chlorhexidine body wash preoperatively, the use of antimicrobial coated sutures, and the use of wound protectors or negative pressure wound therapy. Finally, enhanced nutritional support, and physical prehabilitation to optimize the immuno-nutritional status, might reduce the risk of SSI. See also the WHO Global guidelines on the prevention of surgical site infection.
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
Recent Surgery 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 }}