Deutsch
Nederlands
The model predicts 14-day mortality in nursing home residents with dementia and pneumonia who are treated with antibiotics.
Research authors: Simone P. Rauh, Martijn W. Heymans, Tessa van der Maaden, David R. Mehr, Robin L. Kruse, Henrica C.W. de Vet, Jenny T. van der Steen
Details
Formula
Study characteristics
Files & References
★★★
Model author
Model ID
1640
Version
1.18
Revision date
2019-05-13
Specialty
MeSH terms
Model type
Logistic regression
(Calculation)
| Formula | No Formula defined yet |
|---|
| Condition | Formula |
|---|
Additional information
Data were used from the PneuMonitor study (Netherlands trial register number NTR5071). The PneuMonitor study prospectively included 464 pneumonia episodes in 429 patients in 32 nursing homes between January 2012 and May 2015. The decision whether or not to treat the pneumonia with antibiotics was at the discretion of the physician.A total of 380 episodes were eligible for analysis to develop the current prediction model.
Study Population
Total population size: 380Males: {{ model.numberOfMales }}
Females: {{ model.numberOfFemales }}
Continuous characteristics
| Name | Mean | SD | Unit |
|---|---|---|---|
| Age | 84.2 | 7.3 | years |
| Dementia severity (BANS-S Score) | 15.7 | 4.5 | score |
| Respiratory rate | 25.4 | 8.0 | breaths per minute |
| Pulse rate | 90.9 | 17.3 | beats per minute |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| 14-day mortality | Alive | 325 |
| Died | 55 | |
| Respiratory difficulty | No | 172 |
| Yes | 208 | |
| Decreased alertness | No | 269 |
| Yes | 111 | |
| Fluid intake | Sufficient | 192 |
| Insufficient | 188 | |
| Eating dependency | Independent | 78 |
| Need for assistance | 130 | |
| Fully dependent | 155 | |
| Pressure sores | No | 349 |
| Yes | 31 | |
| Undernutrition | No | 253 |
| Yes | 127 | |
| Undernutrition | (Severely) cachectic | 88 |
| Weight loss | 47 | |
| BMI <18.5 kg/m2 | 69 | |
| Dehydration | No | 268 |
| Yes | 112 | |
| Increase in eating dependency during the 2 weeks before diagnosis | No | 257 |
| Yes | 123 | |
| Dressing dependency | Independent | 13 |
| Need for assistance | 169 | |
| Fully dependent | 173 | |
| Mobility dependency | Independent | 117 |
| Need for assistance | 104 | |
| Fully dependent | 134 | |
| Bedfast | No | 351 |
| Yes | 29 | |
| Coughing | No | 93 |
| Yes | 287 | |
| Aspiration | No | 330 |
| Yes | 50 | |
| Cardiovascular history | No | 203 |
| Yes | 177 | |
| COPD | No | 295 |
| Yes | 85 | |
| Bowel incontinence | No | 159 |
| Yes | 221 |
|
Predicting Mortality in Nursing Home Residents with Dementia and Pneumonia Treated with Antibiotics |
|
V-1.18-1640.19.05.13 |
 |
Refer to Intended Use for instructions before use |
 |
Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands |
Related files
No related files available
Supporting Publications
| Title or description | Tags |
|---|---|
| Predicting Mortality in Nursing Home Residents with Dementia and Pneumonia Treated with Antibiotics: Validation of a Prediction Model in a More Recent Population | Internal validation Paper Peer review |
The estimated 14-day mortality risk 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 estimated 14-day mortality risk is:
{{ resultSubheader }}
{{ chart.title }}
{{ chart.title }}
{{ chart.title }}
{{ table.title }}
| {{ row }} |
{{ chart.title }}
Outcome stratification
Result interval
{{ additionalResult.min }} to
{{ additionalResult.max }}
Conditional information
Result interpretation
The model showed good discrimination on internal validation (AUC = 0.80) and calibration remained adequate (Hosmer-Lemeshow statistic: p = 0.67)
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 Geriatrics Models
Recent Infectious disease 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 }}