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Application of clinical prediction modeling in pediatric neurosurgery: a case study
Hendrik-Jan Mijderwijk1, Thomas Beez2, Daniel Hänggi2
1Medical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany. Hendrik-Jan.Mijderwijk@med.uni-duesseldorf.de.
Insights
This study explains clinical prediction models for pediatric neurosurgery. While helpful for decision-making, the presented model needs external validation before clinical use.
Area of Science:
- Neurosurgery
- Medical Informatics
- Biostatistics
Background:
- Increasing interest in clinical prediction models (CPMs) within pediatric neurosurgery.
- CPMs combine risk factors to estimate patient outcomes.
- These tools can aid medical decision-making and shared decision-making with patients and families.
Purpose of the Study:
- To explain the methodology of developing and applying CPMs.
- To tailor this methodology specifically for pediatric neurosurgery.
- To illustrate the process with a case study using institutional pediatric data.
Main Methods:
- Explanation of CPM methodology.
- Application of methodology to pediatric neurosurgery.
- Case study using original pediatric data for illustration.
Main Results:
- A CPM was developed using institutional pediatric data.
- The developed model is not externally validated.
- Clinical impact of the model has not been assessed.
Conclusions:
- A basic understanding of CPM methodology is essential for development and application.
- The presented model requires further validation and assessment before clinical recommendation.
- This paper provides a methodological overview relevant to pediatric neurosurgery CPMs.
Abstract:
There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual's risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.
