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Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving
Andreas Daniel Meid1, Ana Isabel Gonzalez-Gonzalez2,3, Truc Sophia Dinh4
1Department of Clinical Pharmacology & Pharmacoepidemiology, Heidelberg University, Heidelberg, Baden-Württemberg, Germany andreas.meid@med.uni-heidelberg.de gonzalezgonzalez@allgemeinmedizin.uni-frankfurt.de.
BMJ Open
|August 5, 2021
Summary
Predicting hospital admissions (HAs) in older patients is challenging due to varied predictor effects and baseline risks. Understanding these factors improves external validation of prognostic models for better healthcare planning.
Area of Science:
- Geriatric Medicine
- Health Services Research
- Biostatistics
Background:
- Developing accurate prognostic models for hospital admissions (HAs) in complex older patients is crucial for effective healthcare management.
- External validation of these models is essential to ensure their generalizability and reliability across different populations and settings.
- Factors influencing the performance of prognostic models during external validation require thorough investigation.
Purpose of the Study:
- To investigate factors impacting the external validation performance of a prognostic model for hospital admissions (HAs).
- To develop and validate a prognostic model predicting HAs in complex older general practice patients.
- To explore the influence of heterogeneity on model performance across different studies.
Main Methods:
- Individual participant data from four cluster-randomised trials were used.
- Logistic regression was employed to develop a prognostic model for all-cause HAs within a 6-month follow-up period.
- Internal validation and internal-external cross-validation (IECV) were performed, using a stratified intercept to address heterogeneity.
Main Results:
- The final model included prior HAs, physical components of the comorbidity index, and medication-related variables.
- Internal bootstrap validation indicated moderate discriminatory performance but a significant risk of overfitting.
- IECV revealed highly variable calibration, even after accounting for between-study heterogeneity, confirming issues with baseline risk and predictor effects.
Conclusions:
- Heterogeneity in predictor effects and differing baseline risks are key limitations to the external performance of HA prediction models.
- Recognizing these drivers allows for more purposeful adjustments during external validation, such as intercept recalibration or complete updating.
- Improved external validation strategies are needed for reliable prognostic models in complex older populations.
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