Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods

Aug 5, 2026

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COVID-19 shows substantial clinical and biological heterogeneity, making phenotype identification valuable for explaining disease trajectories and improving care and trial design. Adult patients treated at Xinhua Hospital from Dec 15, 2022 to Feb 15, 2023 were clustered with k-prototypes using 50 clinical variables to derive phenotypes. Two subphenotypes emerged: hypo-inflammatory and hyper-inflammatory, differing in age, sex distribution, mortality, and organ dysfunction. AdaBoost best predicted subphenotypes and in-hospital mortality, with key biomarkers including CRP, IL-2R, D-dimer, ST2, BUN, and NT-proBNP.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Background

Methods

Results

Subphenotype characterization

Conclusion

Keywords

Introduction

Journal of Inflammation Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Journal of Inflammation Research Open Access Full Text Article ORIGINAL RESEARCH Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods Xiaojing Ji, Yiran Guo , Lujia Tang , Chengjin Gao Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China Correspondence: Lujia Tang; Chengjin Gao, Email [tanglujia@xinhuamed.com.cn](tanglujia@xinhuamed.com.cn); [gaochengjin@xinhuamed.com.cn](gaochengjin@xinhuamed.com.cn) | Background: COVID-19 exhibits complex pathophysiological manifestations, characterized by significant clinical and biological heterogeneity. Identifying phenotypes may enhance our understanding of the disease’s diverse trajectories, benefiting clinical practice and trials.
Methods: This study included adult patients with COVID-19 from Xinhua Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, between December 15, 2022, and February 15, 2023. The k-prototypes clustering method was employed using 50 clinical variables to identify phenotypes. Machine learning algorithms were then applied to select key classifier variables for phenotype recognition.
Results: A total of 1376 patients met the inclusion criteria. K-prototypes clustering revealed two distinct subphenotypes: Hypoinflammatory subphenotype (824 [59.9%]) and Hyper-inflammatory subphenotype (552 [40.1%]) . Patients in Hypo-inflammatory subphenotype were younger, predominantly female, with low mortality and shorter hospital stays. In contrast, Hyper-inflammatory subphenotype patients were older, predominantly male, exhibiting a hyperinflammatory state with higher mortality and rates of organ dysfunction. The AdaBoost model performed best for subphenotype prediction (Accuracy: 0.975, Precision: 0.968, Recall: 0.976, F1: 0.972, AUROC: 0.975) .“CRP”, “IL-2R”,“D-dimer”, “ST2”,“BUN”,“NT-proBNP”, “neutrophil percentage”, and “lymphocyte count” were identified as the top-ranked variables in the AdaBoost model.
Conclusion: This analysis identified two phenotypes based on COVID-19 symptoms and comorbidities. These phenotypes can be accurately recognized using machine learning models, with the AdaBoost model being optimal for predicting in-hospital mortality. The variables “CRP”, “IL-2R”, “D-dimer”, “ST2”, “BUN”, “NT-proBNP”, “neutrophil percentage”, and “lymphocyte count” playa significant role in the prediction of subphenotypes. Use the identified subphenotypes for risk stratification in clinical practice. Hyperinflammatory subphenotypes can be closely monitored, and preventive measures such as early admission to the intensive care unit or prophylactic anticoagulation can be taken.
Keywords: COVID-19, subphenotypes, K-prototypes clustering, machine learning, mortality prediction | | --- | | Introduction
Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite the implementation of preventive measures. This highly contagious viral infection has rapidly spread worldwide, raising significant health concerns.1 As of March 12, 2024, there have been 704 million confirmed COVID-19 cases globally, resulting in 7 million reported deaths. On December 7, 2022, the Chinese government introduced the New 10 epidemic prevention policy, effectively ending the dynamic zero-COVID strategy.2 This change triggered a new wave of the omicron variant, leading to a sudden increase in disease burden that presents substantial challenges to clinical practice. | Received: 22 November 2024 Accepted: 18 February 2025 Published: 27 February 2025 Journal of Inflammation Research 2025:18 3009–3024 3009 © 2025 Ji et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this li

FAQs

How were COVID-19 clinical phenotypes identified in this study?

Adult COVID-19 patients from Xinhua Hospital were clustered using k-prototypes based on 50 clinical variables. Machine learning models were then used to select key classifier variables for phenotype recognition.

What distinguishes the hypo-inflammatory and hyper-inflammatory subphenotypes?

Hypo-inflammatory patients were younger, predominantly female, with lower mortality and shorter hospital stays. Hyper-inflammatory patients were older, predominantly male, showed a hyperinflammatory state with higher mortality and more organ dysfunction.

Which model performed best and what biomarkers were most important?

AdaBoost performed best for subphenotype prediction, including high accuracy and AUROC. Top-ranked variables included CRP, IL-2R, D-dimer, ST2, BUN, NT-proBNP, neutrophil percentage, and lymphocyte count.

Aug 5, 2026

Upload by Miles