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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Real-time imputation of missing predictor values in clinical practice.

Steven W J Nijman¹, Jeroen Hoogland¹, T Katrien J Groenhof¹

¹Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.

European Heart Journal. Digital Health

|January 30, 2023

Summary

This study introduces joint modelling imputation (JMI) to handle missing predictor values in clinical prediction models. JMI, especially with auxiliary variables, improves model performance over mean imputation for real-time use.

Keywords:

Computerized decision support system Electronic health records Joint modelling imputation Missing data Prediction Real-time imputation

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Published on: May 27, 2022

Area of Science:

Biostatistics
Clinical Epidemiology
Health Informatics

Background:

Clinical prediction models are essential but often require complete data, which is challenging in practice.
Missing predictor values can hinder the accurate application of these models in real-world settings.
Existing methods for handling missing data may not be optimal for real-time use.

Purpose of the Study:

To describe and compare two methods for real-time handling of missing predictor values in clinical prediction models.
To evaluate the performance of joint modelling imputation (JMI) against mean imputation (M-imp).
To assess the impact of auxiliary variables on imputation accuracy.

Main Methods:

Comparison of mean imputation (M-imp) with joint modelling imputation (JMI) using a joint multivariate normal model.
Evaluation of imputation methods on two cardiovascular cohorts using metrics like mean squared error (MSE), c-index, and calibration.
Incorporation of auxiliary variables alongside prediction model variables in JMI.

Main Results:

JMI significantly improved prediction model performance compared to M-imp, showing lower MSE (0.10 vs. 0.13) and better discrimination (c-index: 0.70 vs. 0.68).
JMI enhanced calibration (intercept and slope) and net benefit, particularly when auxiliary variables were included.
Using an external cohort for imputation led to calibration deterioration but maintained similar discrimination.

Conclusions:

Joint modelling imputation (JMI) with auxiliary variables is recommended for real-time imputation of missing values in prediction models.
Updating imputation models is advised when implementing them in new settings or subpopulations.
This approach enhances the practical utility and accuracy of clinical prediction models.