What is it about?
COVID-19 placed enormous pressure on healthcare systems, especially in low- and middle-income countries such as Peru. During the first pandemic waves, clinicians needed simple and reliable tools to quickly identify hospitalized patients at high risk of death. In this study, we compared four widely used clinical prediction scores (q-CSI, ISARIC-4C, SEIMC, and CALL) in Peruvian patients hospitalized with COVID-19 pneumonia during 2020. We found that the q-CSI score showed the best overall performance for predicting in-hospital mortality. Importantly, q-CSI only requires simple respiratory variables that are rapidly available at admission, making it especially useful in resource-limited settings. Our findings suggest that simpler bedside tools may outperform more complex models when laboratory resources are limited or delayed. These results may help improve triage, risk stratification, and resource allocation during future infectious disease outbreaks.
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Why is it important?
Many prognostic models for COVID-19 were developed in high-income countries and may not perform equally well in Latin American populations. External validation in real-world settings is essential before implementing these tools in clinical practice. This study provides evidence from Peru, a country heavily affected during the pandemic, and highlights the importance of validating prediction models in resource-constrained healthcare systems.
Perspectives
This study was particularly meaningful because it was conducted using real-world data from one of the most critical periods of the pandemic in Peru. During that time, hospitals faced major limitations in resources, laboratory availability, and ICU capacity. Evaluating practical and accessible clinical prediction tools became highly relevant for daily decision-making. One of the most interesting findings was that the simplest score, q-CSI, outperformed more complex models requiring multiple laboratory parameters. This highlights how simple bedside tools can still provide strong clinical value, especially in low-resource settings. We hope this work contributes not only to COVID-19 research, but also to future preparedness strategies for emerging infectious diseases and resource-limited healthcare systems.
Johan Azanero Haro
Universidad Ricardo Palma
Read the Original
This page is a summary of: Comparison of the performance of four clinical prediction rules for mortality in patients with COVID-19, PLOS One, May 2026, PLOS,
DOI: 10.1371/journal.pone.0348683.
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