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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

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The influence of the dynamic context of the pandemic on the predictive performance of mortality predictions over time

Bas F M van Raaij¹, Anum Zahra², Ewout W Steyerberg³

¹Section of Geriatrics and Gerontology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands; LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands.

Journal of Clinical Epidemiology

|December 28, 2024

Summary

The predictive model for COVID-19 mortality in older patients showed decreasing performance over pandemic waves. Continuous monitoring and updates are crucial for pandemic prediction models.

Keywords:

COVID-19 In-hospital mortality Older people Pandemic Prediction model Temporal validation

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Area of Science:

Gerontology
Epidemiology
Medical Informatics

Background:

The COVID-19 pandemic introduced dynamic changes affecting disease outcomes and the predictability of mortality.
Assessing the stability of predictive models in evolving pandemic contexts is essential for clinical decision-making.

Purpose of the Study:

To evaluate the temporal predictive performance of a COVID-19 in-hospital mortality prediction model in older patients across different pandemic waves.
To determine if pandemic dynamics influenced the accuracy of mortality predictions.

Main Methods:

A multicentre cohort study in the Netherlands included 3067 hospitalized COVID-19 patients aged 70 years and older.
A prediction model for in-hospital mortality was developed using LASSO regression on data from the first wave and validated across subsequent waves.

Main Results:

The model, incorporating demographics, frailty, and disease severity, achieved an AUC of 0.80 in internal validation.
Predictive performance declined over time, with AUCs of 0.76, 0.77, and 0.59 in the second, third, and fourth waves, respectively.
A significant decrease in discrimination and calibration was observed, particularly in the fourth wave.

Conclusions:

The predictive model's performance moderately decreased in the second and third pandemic waves and substantially in the fourth.
This underscores the necessity for continuous data collection, performance monitoring, and model recalibration during public health crises like the COVID-19 pandemic.