AI Article Synopsis
- - The study investigates how changes in specific blood ratios (NLR, PLR, MLR, SII, dNLR) during the first 48 hours of ICU admission can predict the need for invasive mechanical ventilation (IMV) and death in critical COVID-19 patients.
- - Analyzing data from 272 severe COVID-19 patients, the findings show that a change in NLR of more than 2 significantly predicts IMV needs, followed by changes in SII and dNLR.
- - For mortality prediction, an NLR greater than 11 was found to be the strongest indicator, alongside dNLR over 6.93 and SII above 3700.
Article Abstract
Background: Hematological indices can predict disease severity, progression, and death in patients with coronavirus disease-19 (COVID-19).
Objectives: To study the predictive value of the dynamic changes (first 48 h after ICU admission) of the following ratios: neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR), systemic inflammation index (SII), and derived neutrophil-to-lymphocyte (dNLR) for invasive mechanical ventilation (IMV) need and death in critically ill COVID-19 patients.
Methods: Observational, retrospective, and multicentric analysis on 272 patients with severe or critical COVID-19 from two tertiary centers. Hematological indices were adjusted for confounders through multivariate analysis using Cox regression.
Results: Patients comprised 186 males and 86 females with no difference across groups ( > 0.05). ΔNLR > 2 had the best independent predictive value for IMV need (HR = 5.05 (95% CI, 3.06-8.33, < 0.0001)), followed by ΔSII > 340 (HR = 3.56, 95% CI 2.21-5.74, < 0.0001) and ΔdNLR > 1 (HR = 2.61, 95% CI 1.7-4.01, < 0.0001). Death was also best predicted by an NLR > 11 (HR = 2.25, 95% CI: 1.31-3.86, = 0.003) followed by dNLR > 6.93 (HR = 1.89, 95% CI: 1.2-2.98, = 0.005) and SII > 3700 (HR = 1.68, 95% CI: 1.13-2.49, = 0.01).
Conclusions: Dynamic changes of NLR, SII, and dNLR independently predict IMV need and death in critically ill COVID-19 patients.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615772 | PMC |
| http://dx.doi.org/10.3390/biomedicines9111656 | DOI Listing |
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