Evidence map›Paper›PMID 40071734›Full record

ArticleImmunity, inflammation and disease2025

Serum Lactate Dehydrogenase Levels Reflect the Lung Injury Extension in COVID-19 Patients at Hospital Admission.

Maria Sofia Bertilacchi, Giulia Vannucci, Rebecca Piccarducci, Lorenzo Germelli, Chiara Giacomelli, Chiara Romei, Brian Bartholmai, Greta Barbieri, Claudia Martini, Michela Baccini

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Article in Immunity, inflammation and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Maria Sofia BertilacchiDepartment of Pharmacy, University of Pisa, Pisa, Italy.
Giulia VannucciDepartment of Electrical and Information Technology DIETI, University of Naples Federico II, Napoli, Italy.
Rebecca PiccarducciDepartment of Pharmacy, University of Pisa, Pisa, Italy.
Lorenzo GermelliDepartment of Pharmacy, University of Pisa, Pisa, Italy.
Chiara GiacomelliDepartment of Pharmacy, University of Pisa, Pisa, Italy.ORCID 0000-0002-6244-602X
Chiara RomeiDepartment of Radiology, Pisa University Hospital, Pisa, Italy.ORCID 0000-0003-3023-8784
Brian BartholmaiDivision of Radiology, Mayo Clinic Rochester, Rochester, Minnesota, USA.
Greta BarbieriDepartment of Emergency Medicine Department, Pisa University Hospital, Pisa, Italy.
Claudia MartiniDepartment of Pharmacy, University of Pisa, Pisa, Italy.
Michela BacciniDepartment of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.

Funding

This research project is funded by the Tuscany Region "Bando ricerca covid-19" OPTIMISED and supported by the Optimised Study Group (Supplementary Material_Optimised_Group).
6 · The paper itself

Abstract

backgroundSeveral hematological and biochemical parameters have been related to the COVID-19 infection severity and outcomes. However, less is known about clinical indicators reflecting lung involvement of COVID-19 patients at hospital admission. Computed tomography (CT) represents an established imaging tool for the detection of lung injury, and the quantitative analysis software CALIPER has been used to assess lung involvement in COVID-19 patients. Herein, the relationship between the lung involvement expressed by CALIPER interstitial lung disease (ILD) percentage and a set of blood parameters related to tissue oxygenation and damage in COVID-19 patients at hospital admission was evaluated.

methodsWe performed a retrospective and a prospective study involving 321 and 75, respectively, COVID-19-positive patients recruited from Pisa University Hospital. The association between CALIPER ILD percentages and selected blood parameters was investigated by a regression tree approach, after multiple imputations of the dataset missing values.

resultsHigh serum lactate dehydrogenase (LDH) values appeared to be predictive of high CALIPER ILD percentages at hospital admission in both retrospective and prospective datasets, even if the predictive performance of the algorithm was not optimal.

conclusionsLDH levels could be evaluated as a tool for early identification of COVID-19 patients at risk of extensive lung injury, as well as in fast screening procedures before hospitalization.

Indexed as

COVID-19L-Lactate DehydrogenaseLung Diseases, InterstitialLung InjuryAdultAgedBiomarkersFemaleHospitalizationHumansMaleMiddle AgedProspective StudiesRetrospective StudiesSARS-CoV-2Tomography, X-Ray ComputedBiomarkersL-Lactate Dehydrogenaseblood parametersCALIPER ILDchest computed tomographyCOVID‐19lactate dehydrogenaselung injury

Identifiers

PMID40071734
PMCPMC11898011

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.