Coronavirus disease (COVID-19) has spread globally since 2020, with over 750 million confirmed cases and approximately 7 million fatalities.1 COVID-19 is an infectious illness with a wide spectrum of clinical signs, ranging from asymptomatic to moderately symptomatic and severe forms. This indicates that the host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a significant role.2,3 The COVID-19 pandemic has also negatively affected hospitals’ activity regarding chronic pathology and elective surgery.4,5
Studies have found the majority of infections caused by SARS-CoV-2 to be moderate; 31% were severe (with dyspnea, hypoxia, or more than 50% lung involvement on detection imaging), whereas 5% of patients developed a life-threatening condition with respiratory failure or multiple organ dysfunction.6 The risk of mortality from COVID-19 is heavily influenced by age and medical history. Older individuals are considerably more likely to have catastrophic or fatal illness outcomes, particularly if they have comorbidities such as hypertension, cardiovascular disease, obesity, chronic renal disease, pulmonary disease, and diabetes.3,7,8
Researchers have used specific ratios to identify and analyze several inflammatory disorders in recent years. Numerous investigations have discovered that various combinations of hematological elements of the systemic immune response, such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), were successful indicators of prognosis in patients with an array of malignancies, heart disease, diabetes, acute ischemic stroke, peripheral arterial disease, and chronic kidney disease.9,10,11,12,13,14,15,16,17,18,19 The elements of these simply derived metrics are widely available, affordable, and frequently assessed as part of a complete blood test report in everyday practice. Calculating these hematological components associated with the systemic immune response may offer healthcare specialists an additional helpful tool for clinical risk classification.
Our study aims to provide an updated overview of the current landscape of the role of hematological inflammatory biomarkers (NLR, MLR, and PLR) on the risk of developing major adverse cardiovascular events (MACE) and mortality in patients with COVID-19, focusing on the optimal cut-off value of the biomarkers, their clinical impact, and the possibility of stratification of groups of patients at risk.
Following the analysis of the studies published in the literature, we included 21 articles in this review, reporting on a total number of 7,588 patients. The average age of the patients was 65.36 years, and 56.57% were male. Regarding the clinical data, 57.49% of the patients presented hypertension (15 out of the 21 studies reported hypertensive patients), followed by ischemic heart disease in 33.56% of patients (13 studies) and diabetes in 30.37% of patients (17 studies). In additional, among the usual risk factors, 23.55% of patients presented obesity (7 studies), and 23.02% were active smokers (10 studies). Regarding the hematological inflammatory biomarkers, NLR was analyzed in 21 studies, PLR was investigated in nine studies, and MLR was explored in only six. The rest of the data are shown in Table 1.
General characteristics of the studies included in the analysis
Fois |
119 | 72 | 77 (64.7%) | – | – | 25 (21%) | 27 (22.69%) | 36 (30.25%) | NLR, MLR, and PLR |
Abrishami |
100 | 55.5 | 68 (68%) | 33 (33%) | 21 (21%) | 21 (21%) | 25 (25%) | – | NLR and PLR |
Pakos |
242 | 66.03 | 208 (85.95%) | 180 (74%) | – | 118 (49%) | – | – | NLR |
Allahverdiyev |
455 | 56 | 217 (47.7%) | 170 (37.4%) | 88 (19.3%) | 128 (28.1%) | – | – | NLR |
Zeng |
352 | >60 years 133 (37.78%) | 190 (53.97%) | – | – | – | – | 57 (16.19%) | NLR |
<60 years 219 (62.22%) | |||||||||
Moradi |
219 | – | 137 (62.6%) | 85 (38.8%) | 46 (21%) | 83 (38%) | – | 23 (10.5%) | NLR |
Yildiz |
198 | Derivation group 64.4 | 110 (55%) | 101 (51%) | 107 (54%) | 49 (25%) | – | 8 (4%) | NLR |
Validation group 65 | 65 (64%) | 52 (51.5%) | 45 (44.6%) | 22 (21.8%) | – | 2 (2%) | |||
Karaaslan |
191 | 54.32 | 94 (49.2%) | 72 (37.7%) | – | 44 (23%) | – | – | NLR and PLR |
Kudlinski |
285 | 62 | 189 (66.3%) | 153 (55.2%) | 26 (9.4%) | 57 (20.7%) | 134 (47.7%) | 20 (7%) | NLR |
Rose |
454 | – | 291 (64.1%) | 225 (49.6%) | 137 (30.2%) | 119 (26.2%) | 103 (22.7%) | – | NLR and PLR |
Halmaciu |
267 | 71.19 | 159 (59.55%) | 167 (62.55%) | 145 (54.31%) | 116 (43.45%) | 69 (25.84%) | 99 (37.08%) | NLR and MLR |
Arbănași |
510 | 69.6 | 247 (62.37%) | 228 (57.78%) | 138 (34.85%) | 150 (37.88%) | 114 (28.79%) | 134 (33.84%) | NLR, MLR, and PLR |
Mureșan |
889 | 70.5 | 474 (53.32%) | 735 (82.67%) | 513 (57.70%) | 268 (30.14%) | 146 (16.42%) | 256 (28.79%) | NLR, MLR, and PLR |
Citu |
108 | 63.31 | 56 (51.9%) | 76 (70.4%) | 51 (47.2%) | 50 (46.3%) | – | – | NLR, MLR, and PLR |
Ghobadi |
1,792 | Elderly 76.29 | 988 (55.13%) | – | – | 522 (29.12%) | – | – | NLR, MLR, and PLR |
Non-elderly 48.35 | |||||||||
Regolo |
411 | 72 | 237 (57.7%) | 244 (59.4%) | 70 (17.1%) | 111 (27%) | – | – | NLR |
Seyfi |
312 | – | – | – | – | – | – | – | NLR |
Strazzulla |
184 | – | 103 (55.97%) | – | – | – | – | – | NLR and PLR |
Zhan |
159 | – | 73 (45.91%) | 72 (45.28%) | 15 (9.43%) | 33 (20.75%) | – | 53 (33.33%) | NLR |
Predenciuc |
130 | 71 | 86 (66.2%) | 117 (90%) | 106 (81.5) | 39 (30%) | – | – | NLR |
Khorvash |
211 | 66.28 | 110 (52.13%) | 126 (59.7%) | 53 (25.1%) | 103 (48.8%) | – | – | NLR |
Regarding mortality, we identified an average NLR of 9.24 (range 5.00–17.70) in the group of patients with negative outcomes, much higher than in the control group, in which the average NLR was 4.86 (range 2.14–12.29). In addition, in 14 studies, the authors identified an optimal cut-off value of 7.16 (range 2.70–15.20) using receiver operating characteristic (ROC) analysis. The area under the curve (AUC) analysis yielded an average value of 0.77 (range 0.63–0.87), with an average sensitivity of 72.54% and a specificity of 72.31% (Figure 1 and Table 2). When analyzing the prognostic role of NLR in MACE, we found an optimal average cut-off value of 9.43 (range 5.40–13.67), with an average AUC of 0.830, a sensitivity of 76.87%, and a specificity of 82.2% (Table 2).
NLR studies and predictive values for clinical outcomes
Fois |
2020 | Italy | NLR | 9.17 | 5 | 15.2 | 0.697 | 38% | 97% | Mortality |
Abrishami |
2020 | Iran | NLR | 5.02 | 3.02 | 3.65 | 0.678 | 62.5% | 60% | Mortality |
Pakos |
2020 | USA | NLR | 6.4 | 4.5 | – | – | – | – | Mortality |
Allahverdiyev |
2020 | Turkey | NLR | 12.1 | 3.2 | 3 | 0.842 | 92% | 53% | Mortality |
Zeng |
2021 | China | NLR | 5.33 | 2.14 | 2.6937 | 0.828 | 92.9% | 63.9% | Mortality |
Moradi |
2021 | Iran | NLR | 5 | 4.1 | 3.3 | – | – | – | Mortality |
Yildiz |
2021 | Belgium | NLR | – | – | 5.94 | 0.665 | 62% | 64% | Mortality |
Karaaslan |
2022 | Turkey | NLR | 9.27 | 2.73 | 4.21 | 0.810 | 77.1% | 73.7% | Mortality |
Kudlinski |
2022 | Poland | NLR | 17.7 | 12.29 | 11.57 | 0.629 | 63% | 60.5% | Mortality |
Rose |
2022 | Switzerland | NLR | 8.2 | 5.0 | – | – | – | – | Mortality |
Halmaciu |
2022 | Romania | NLR | 11.04 | 3.73 | 6.97 | 0.869 | 80.5% | 85.4% | Mortality |
Arbănași |
2022 | Romania | NLR | 8.45 | 3.01 | 4.57 | 0.845 | 86.6% | 72% | Mortality |
Mureșan |
2022 | Romania | NLR | 9.74 | 5.38 | 9.4 | 0.868 | 81.8% | 74.4% | Mortality |
Citu |
2022 | Romania | NLR | 13.83 | 8.31 | 9.1 | 0.689 | 70% | 67% | Mortality |
Ghobadi |
2022 | Iran | NLR | 6.07 | 4.7 | 9.38 | 0.817 | 73.3% | 86.5% | Mortality |
Regolo |
2022 | Italy | NLR | – | – | 11.38 | 0.772 | 72.9% | 71.9% | Mortality |
Seyfi |
2023 | Iran | NLR | 11.3 | 5.8 | 7.02 | 0.760 | 63% | 83% | Mortality |
Strazzulla |
2021 | France | NLR | 7.5 | 3.2 | – | – | – | – | Acute pulmonary embolism |
Zhan |
2021 | China | NLR | 16.28 | 4.75 | 10.14 | 0.803 | 81.2 | 82.6 | MACE |
Arbănași |
2022 | Romania | NLR | – | – | 8.34 | 0.882 | 81.6% | 87.4% | Acute limb ischemia |
Mureșan |
2022 | Romania | NLR | – | – | 9.63 | 0.836 | 77% | 77.8% | Deep vein thrombosis |
Mureșan |
2022 | Romania | NLR | – | – | 13.67 | 0.801 | 67.7% | 81% | Acute pulmonary embolism |
Predenciuc |
2022 | Republic of Moldova | NLR | 11.1 | 6.3 | 5.4 | – | – | – | Major amputation or mortality |
Khorvash |
2022 | Iran | NLR | 13.9 | 8.03 | – | – | – | – | Acute ischemic stroke |
Abrishami
The association between NLR and clinical outcomes: ORs, HRs, and survival analyses
Fois |
NLR | 1.02 | 0.99 | 1.06 | 0.10 | Mortality | In-hospital mortality based on cut-off value | <0.001 |
Abrishami |
NLR | 1.124 | 1.01 | 1.25 | 0.036 | Mortality | – | – |
Pakos |
NLR | 1.038 | 1.003 | 1.074 | 0.031 | Mortality | – | – |
Allahverdiyev |
NLR | 1.261 | 1.054 | 1.509 | 0.011 | Mortality | – | – |
Zeng |
NLR | 5.4 | 2.6 | 11.1 | <0.001 | Mortality | Disease deterioration based on cut-off value | <0.001 |
21.2 | 2.8 | 161.3 | ||||||
19.8 | 2.6 | 151.4 | ||||||
Moradi |
NLR | 1.03 | 1.003 | 1.07 | 0.03 | Mortality | One-month mortality based on cut-off value | 0.16 |
Rose |
NLR | 1.82 | 1.14 | 2.95 | 0.013 | Mortality | – | – |
Halmaciu |
NLR | 24.13 | 12.2 | 47.73 | <0.001 | Mortality | – | – |
Arbănași |
NLR | 16.32 | 9.09 | 29.3 | <0.001 | Mortality | – | – |
Mureșan |
NLR | 13.07 | 8.29 | 20.62 | <0.001 | Mortality | – | – |
Citu |
NLR | 3.85 | 1.35 | 10.95 | 0.01 | Mortality | In-hospital mortality based on cut-off value | <0.001 |
Ghobadi |
NLR | 3.57 | 2.859 | 4.458 | <0.0001 | Mortality | In-hospital mortality based on cut-off value for non-elderly and elderly | <0.001 / <0.001 |
Regolo |
NLR | 1.62 | – | – | <0.0001 | Mortality | In-hospital mortality based on tertiles | <0.0001 |
Seyfi |
NLR | 1.121 | 1.072 | 1.179 | <0.0001 | Mortality | – | – |
Zhan |
NLR | 2.24 | 1.49 | 4.47 | <0.001 | MACE | 6-month MACE based on cut-off value | 0.010 |
Arbănași |
NLR | 30.28 | 13.97 | 65.6 | <0.001 | Acute limb ischemia | – | – |
Mureșan |
NLR | 11.7 | 7.99 | 17.13 | <0.001 | Deep vein thrombosis | – | – |
Mureșan |
NLR | 10.5 | 5.86 | 18.8 | <0.001 | Acute pulmonary embolism | – | – |
Predenciuc |
NLR | 2.46 | 1.0 | 6.03 | 0.04 | Major amputation or mortality | – | – |
The MLR, derived from the absolute monocyte and lymphocyte counts, is another inflammatory biomarker with a prognostic role in the negative evolution of patients with numerous pathologies. According to studies done in Italy and Iran by Fois
We found a cut-off value of 0.516 (range 0.26–0.83), with an AUC of 0.71 (range 0.62–0.83), sensitivity of 66.75% (range 58.00–74.4%) and specificity of 70.50% (range 57.00–81.60%) in terms of mortality (Figure 2 and Table 4).
MLR studies and predictive values for clinical outcomes
Fois |
2020 | Italy | 119 | MLR | 0.429 | 0.333 | 0.364 | 0.617 | 69% | 57% | Mortality |
Halmaciu |
2022 | Romania | 267 | MLR | 0.75 | 0.33 | 0.54 | 0.826 | 74.4% | 81.6% | Mortality |
Arbănași |
2022 | Romania | 510 | MLR | 0.62 | 0.32 | 0.45 | 0.758 | 68.4% | 74% | Mortality |
Mureșan |
2022 | Romania | 889 | MLR | 1.14 | 0.47 | 0.78 | 0.794 | 71.3% | 74% | Mortality |
Citu |
2022 | Romania | 108 | MLR | 0.83 | 0.53 | 0.69 | 0.661 | 58% | 74% | Mortality |
Ghobadi |
2022 | Iran | 1,792 | MLR | 0.20 | 0.16 | 0.26 | 0.628 | 59.4% | 62.4% | Mortality |
Arbănași |
2022 | Romania | 510 | MLR | – | – | 0.49 | 0.787 | 71.4% | 71.6% | Acute limb ischemia |
Mureșan |
2022 | Romania | 889 | MLR | – | – | 0.78 | 0.824 | 77% | 76.2% | Deep vein thrombosis |
Mureșan |
2022 | Romania | 889 | MLR | – | – | 0.81 | 0.766 | 71% | 72.1% | Acute pulmonary embolism |
Regarding mortality, Halmaciu
The association between MLR and clinical outcomes: ORs, HRs, and survival analyses
Fois |
MLR | 1.60 | 0.62 | 4.09 | 0.32 | Mortality | In-hospital mortality based on cut-off value | 0.006 |
Halmaciu |
MLR | 6.49 | 2.51 | 22.24 | <0.001 | Mortality | – | – |
Arbănași |
MLR | 5.51 | 3.50 | 8.67 | <0.001 | Mortality | – | – |
Mureșan |
MLR | 6.89 | 4.64 | 10.23 | <0.001 | Mortality | – | – |
Citu |
MLR | 3.05 | 1.16 | 8.05 | 0.02 | Mortality | In-hospital mortality based on cut-off value | <0.001 |
Ghobadi |
MLR | 1.502 | 1.212 | 1.86 | <0.0001 | Mortality | In-hospital mortality based on cut-off value for non-elderly and elderly | <0.001 |
Arbănași |
MLR | 6.82 | 3.51 | 13.28 | <0.001 | Acute limb ischemia | – | – |
Mureșan |
MLR | 11.19 | 7.68 | 16.29 | <0.001 | Deep vein thrombosis | – | – |
Mureșan |
MLR | 8.96 | 5.11 | 15.69 | <0.001 | Acute pulmonary embolism | – | – |
We found eight studies that analyzed the prognostic role of PLR regarding mortality. The average value of PLR was 254.82 (range 168.00–363.16) in the case of patients with a negative outcome and 166.95 (range 128.22–215.50) for the control group. In addition, eight studies presented the results of the ROC analysis, in which we identified an average AUC value of 0.66 (range 0.56–0.82) and an optimal calculated cut-off value of 220.78 (range 177.51–266.90), with a sensitivity of 63.00% (range 52.60–72.00%) and a specificity of 69.92% (range 58.00–81.10%) (Table 6). Also, Strazzulla
PLR studies and predictive values for clinical outcomes
Fois |
2020 | Italy | 119 | PLR | 265 | 214 | 240 | 0.572 | 59% | 58% | Mortality |
Abrishami |
2020 | Iran | 100 | PLR | 202 | 160.8 | – | 0.559 | – | – | Mortality |
Karaaslan |
2022 | Turkey | 191 | PLR | 287.5 | 139.94 | 189.5 | – | – | – | Mortality |
Rose |
2022 | Switzerland | 454 | PLR | 268.3 | 215.5 | – | – | – | – | Mortality |
Arbănași |
2022 | Romania | 510 | PLR | 229.83 | 128.22 | 177.51 | 0.775 | 68.4% | 77.5% | Mortality |
Mureșan |
2022 | Romania | 889 | PLR | 363.16 | 156.22 | 266.9 | 0.819 | 72% | 81.1% | Mortality |
Citu |
2022 | Romania | 108 | PLR | 345 | 324 | – | – | – | – | Mortality |
Ghobadi |
2022 | Iran | 1,792 | PLR | 168 | 154 | 230 | 0.585 | 52.6% | 63.1% | Mortality |
Strazzulla |
2021 | France | 184 | PLR | 259 | 204 | – | – | – | – | Acute pulmonary embolism |
Arbănași |
2022 | Romania | 510 | PLR | – | – | 178.99 | 0.858 | 81.6% | 73.1% | Acute limb ischemia |
Mureșan |
2022 | Romania | 889 | PLR | – | – | 230.67 | 0.802 | 72.8% | 76.8% | Deep vein thrombosis |
Mureșan |
2022 | Romania | 889 | PLR | – | – | 207.06 | 0.734 | 74.2% | 61.3% | Acute pulmonary embolism |
Regarding the predictive role of PLR in clinical outcomes, Fois
The association between PLR and clinical outcomes: ORs, HRs, and survival analyses
Fois |
PLR | 1.0006 | 1.00 | 1.0013 | 0.058 | Mortality | In-hospital mortality based on cut-off value | 0.13 |
Rose |
PLR | 1.37 | 0.79 | 2.46 | 0.27 | Mortality | – | – |
Arbănași |
PLR | 7.47 | 4.71 | 11.83 | <0.001 | Mortality | ||
Mureșan |
PLR | 11.04 | 7.34 | 16.62 | <0.001 | Mortality | ||
Ghobadi |
PLR | 1.451 | 1.17 | 1.799 | <0.0001 | Mortality | In-hospital mortality based on cut-off value for non-elderly and elderly | <0.001 / 0.10 |
Arbănași |
PLR | 12.07 | 7.71 | 21.77 | <0.001 | Acute limb ischemia | – | – |
Mureșan |
PLR | 8.36 | 5.82 | 12.02 | <0.001 | Deep vein thrombosis | – | – |
Mureșan |
PLR | 6.26 | 3.54 | 11.07 | <0.001 | Acute pulmonary embolism | – | – |
Based on the results of our state-of-the-art review, we can conclude that NLR, MLR, and PLR have good predictive values regarding the risk of MACE and mortality in patients with COVID-19. The evaluation of hematological inflammatory biomarkers at admission, in the case of patients with viral or septic infections, could help in the stratification of risk groups for better management.