Clinical Prediction Models
Summary (1 min read)
Summary
- The number of articles presenting a neurosurgical prediction model is rapidly increasing [1].
- Nowadays, with the rise of so many prediction models, the authors should be able to make firm conclusions which model to use on their patients.
- Clinical prediction models aim to predict an outcome of interest, for example, survival in high-grade glioma (HGG) patients or intraventricular hemorrhage in preterm infants, by combining two or more patient-related variables.
- The development and evaluation of clinical prediction models involve multiple methodological steps.
- Utilizing invalid prediction models may jeopardize adequate decisionmaking in their daily clinical practice.
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Citations
2,156 citations
920 citations
Cites methods from "Clinical Prediction Models"
...Methods for prediction model updating Several methods for updating prediction models have been proposed and evaluated (table 1).(3) 11 12 Most often, differences are seen in the outcome or event frequency between the development and new validation sample....
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885 citations
Cites background or methods from "Clinical Prediction Models"
...The traditional approach to develop these models involves the use of regression models, for example, logistic regression (LR) to predict disease presence (diagnosis) or disease outcomes (prognosis) [1]....
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...Another limitation is that many studies had a fairly limited number of events per considered predictor, a common problem despite repeated warnings [1,17,99,103,109]....
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662 citations
Cites background from "Clinical Prediction Models"
...For logistic regression models, for instance, there are several well-recognised approaches [4-9]....
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635 citations
Cites background or methods from "Clinical Prediction Models"
...The concepts explained in this section are illustrated in Additional file 1, with the validation of the Risk of Ovarian Malignancy Algorithm (ROMA) for the diagnosis of ovarian malignancy in women with an ovarian tumor selected for surgical removal [28]; further details can be found elsewhere [1, 4, 29]....
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...When we find poorly calibrated predictions at validation, algorithm updating should be considered to provide more accurate predictions for new patients from the validation setting [1, 20]....
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...miscalibration, and suffers from low statistical power [1, 4]....
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References
2,156 citations
920 citations
885 citations
662 citations
635 citations
Related Papers (5)
Frequently Asked Questions (7)
Q2. What is the purpose of a prediction model?
Clinical prediction models aim to predict an outcome of interest, for example, survival in high-grade glioma (HGG) patients or intraventricular hemorrhage in preterm infants, by combining two or more patient-related variables.
Q3. What are the main features of the prediction models?
The obtained predictions of these models can then be used for medical and shared decision-making such as initiating surgical treatment, and for example for guidance in planning future lifestyle.
Q4. What is the main point of the article?
As a result of overfitting, prediction models tend to have a too optimistic predictive performance in terms of discrimination and calibration [2].
Q5. What is the author's opinion on the article?
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Q6. What is the important aspect of clinical prediction models?
with the rise of so many prediction models, the authors should be able to make firm conclusions which model to use on their patients.
Q7. What is the author's intent for the article?
If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.