Evidence map›Paper›PMID 40248368›Full record

ArticleFrontiers in cellular and infection microbiology2025

A model based on PT-INR and age serves as a promising predictor for evaluating mortality risk in patients with SARS-CoV-2 infection.

Yongjie Xu, Minjie Tang, Zhaopei Guo, Yanping Lin, Hongyan Guo, Fengling Fang, Lin Lin, Yue Shi, Lu Lai, Yan Pan and 7 more

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In one paragraph

Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Yongjie Xu *Department of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Minjie Tang *Department of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Zhaopei Guo *Department of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Yanping Lin *Department of Laboratory Medicine, The Third Hospital of Xiamen, Xiamen, China.
Hongyan GuoDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Fengling FangDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Lin LinDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Yue ShiDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Lu LaiDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Yan PanDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Xiangjun TangDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Weiquan You *Department of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Zishun LiDepartment of Laboratory Medicine, The Third Hospital of Xiamen, Xiamen, China.
Jialin SongMedical Research Center, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, China.
Liang WangDepartment of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Weidong CaiDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Ya FuDepartment of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 caused by the coronavirus SARS-CoV-2 has resulted in a global pandemic. Considering some patients with COVID-19 rapidly develop respiratory distress and hypoxemia, early assessment of the prognosis for COVID-19 patients is important, yet there is currently a lack of research on a comprehensive multi-marker approach for disease prognosis assessment. Here, we utilized a large sample of hospitalized individuals with COVID-19 to systematically compare the clinical characteristics at admission and developed a nomogram model that was used to predict prognosis. In all cases, those with pneumonia, older age, and higher PT-INR had a poor prognosis. Besides, pneumonia patients with older age and higher PT-INR also had a poor prognosis. A nomogram model incorporating presence of pneumonia, age and PT-INR could evaluate the prognosis in all patients with SARS-CoV-2 infections well, while a nomogram model incorporating age and PT-INR could evaluate the prognosis in those with pneumonia well. Together, our study establishes a prognostic prediction model that aids in the timely identification of patients with poor prognosis and helps facilitate the improvement of treatment strategies in clinical practice in the future.

Indexed as

COVID-19AdultAgedAged, 80 and overAge FactorsFemaleHumansMaleMiddle AgedNomogramsPrognosisRisk AssessmentRisk FactorsSARS-CoV-2agemortalitypredictorPT-INRSARS-CoV-2

Identifiers

PMID40248368
PMCPMC12003402

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