Pubblicazioni

Autori:

Fratta Pasini, Anna Maria; Stranieri, Chiara; Di Leo, Edoardo Giuseppe; Bertolone, Lorenzo; Aparo, Antonino; Busti, Fabiana; Castagna, Annalisa; Vianello, Alice; Chesini, Fabio; Friso, Simonetta; Girelli, Domenico; Cominacini, Luciano

Titolo:

Identification of Early Biomarkers of Mortality in COVID-19 Hospitalized Patients: A LASSO-Based Cox and Logistic Approach

Anno:

2025

Tipologia prodotto:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Lingua:

Inglese

Formato:

Elettronico

Referee:

Sì

Nome rivista:

VIRUSES

ISSN Rivista:

1999-4915

N° Volume:

17

Numero o Fascicolo:

3

Intervallo pagine:

1-20

Parole chiave:

COVID-19; early biomarkers; interleukin-10; lactate dehydrogenase

Breve descrizione dei contenuti:

This study aimed to identify possible early biomarkers of mortality among clinical and biochemical parameters, iron metabolism parameters, and cytokines detected within 24 h from admission in hospitalized COVID-19 patients. We enrolled 80 hospitalized patients (40 survivors and 40 non-survivors) with COVID-19 pneumonia and acute respiratory failure. The median time from the onset of COVID-19 symptoms to hospital admission was lower in non-survivors than survivors (p < 0.05). Respiratory failure, expressed as the ratio of arterial oxygen partial pressure to the fraction of inspired oxygen (P/F), was more severe in non-survivors than survivors (p < 0.0001). Comorbidities were similar in both groups. Among biochemical parameters and cytokines, eGFR and interleukin (IL)-1β were found to be significantly lower (p < 0.05), while LDH, IL-10, and IL-8 were significantly higher in non-survivors than in survivors (p < 0.0005, p < 0.05 and p < 0.005, respectively). Among other parameters, LDH values distribution showed the most significant difference between study groups (p < 0.0001). LASSO feature selection combined with Cox proportional hazards and logistic regression models was applied to identify features distinguishing between survivors and non-survivors. Both approaches highlighted LDH as the strongest predictor, with IL-22 and creatinine emerging in the Cox model, while IL-10, eGFR, and creatinine were influential in the logistic model (AUC = 0.744 for Cox, 0.723 for logistic regression). In a similar manner, we applied linear regression for predicting LDH levels, identifying the P/F ratio as the top predictor, followed by IL-10 and eGFR (NRMSE = 0.128). Collectively, these findings underscore LDH's critical role in mortality prediction, with P/F and IL-10 as key determinants of LDH increases in this Italian COVID-19 cohort.

Id prodotto:

144945

Handle IRIS:

11562/1158947

ultima modifica:

4 aprile 2025

Citazione bibliografica:

Fratta Pasini, Anna Maria; Stranieri, Chiara; Di Leo, Edoardo Giuseppe; Bertolone, Lorenzo; Aparo, Antonino; Busti, Fabiana; Castagna, Annalisa; Vianello, Alice; Chesini, Fabio; Friso, Simonetta; Girelli, Domenico; Cominacini, Luciano, Identification of Early Biomarkers of Mortality in COVID-19 Hospitalized Patients: A LASSO-Based Cox and Logistic Approach «VIRUSES» , vol. 17 , n. 3 , 2025 , pp. 1-20

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo