Prognostic value of neutrophil-to-lymphocyte ratio, lactate dehydrogenase, D-dimer and CT score in patients with COVID-19

To explore the signicance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19). Patients with laboratory conrmed COVID-19 were retrospectively enrolled. The baseline data, laboratory ndings, chest computed tomography (CT) results evaluating by CT score on admission, and clinical outcomes were collected and compared. The logistic regression was used to assess the independent relationship between the baseline level of four indicators (NLR, LDH, D-dimer, CT score) and the severity of COVID-19.


Background
Since December 2019, coronavirus disease-2019 (COVID-19) caused by a novel coronavirus (SARS-CoV-2) spread rapidly all over the world and has caused a major public health issue [1]. By the morning of May 11, Beijing time, the number of patients has exceeded 4.1 million [2]. It is obviously a huge challenge to the global healthcare system [3]. And the mortality of COVID-19 patients is related to the health-care burden [4].Therefore, the reasonable distribution of medical resources is particularly important. Early identi cation of critical patients is critical to the rational allocation of resources and the improvement of patients' prognosis.
Meanwhile, according to reports, compared with non-severe patients, the hematological changes of severe patients are more prominent [5]. Neutrophil-to-Lymphocyte Ratio (NLR), lactate dehydrogenase (LDH), and D-dimer are close with the poor prognosis of COVID-19 [6,7].Without other clinical parameters, the computed tomography (CT) evaluation is an independent prognostic factor in patients with COVID-19 [8]. However, there is little data about the comparison of the above four indicators. Therefore, in this study, we aimed to compare the prediction e ciency of four indicators and evaluate the signi cance of optimum cutoff. Subsequently, combined diagnosis analysis was performed to evaluate whether it can improve the diagnosis e ciency.

Study Design and Participants
From January 20, 2020, to March 30, 2020, a total of 432 patients con rmed COVID-19 by the laboratory Commission [9]: the mild and moderate types were classi ed as non-severe group and the severe and critical were included into severe group. The disease is classi ed as severe if one of the following items is met: 1) shortness of breath, respiratory rate ≥ 30 beats per min; 2) the oxygen saturation ≤ 93% in a resting state; 3) arterial partial pressure of oxygen (PaO 2 ) / fraction of inspiration O 2 (FiO 2 ) ≤ 300 mmHg (1 mmHg = 0.133 kPa); 4) pulmonary images show that the lesions progressed more than 50% within 24-48 h. The critical should meet one of the following conditions: 1) respiratory failure and need mechanical ventilation; 2) shock; and 3) other organ failures need ICU monitoring and treatment.

Statistical Analysis
According to the different data distribution, continuous variables were described as mean ± standard or median (Inter-quartile range, IQR), and groups were compared by student's t-test or Mann-Whitney U test based on the data distribution. Categorical variables were presented as n (%) and analyzed by Pearson's chi-square. Receiver operator characteristic (ROC) was used to evaluate the e cacy of NLR, LDH, D-dimer and CT score and get the optimum cutoff. Logistic regression was used to access the predictive value for disease risk. The statistical software needed is SPSS version 21 and Medcalc (version 19.1). A value of p < 0.05 was considered statistically signi cant.
Predictive value of NLR, LDH, D-dimer and CT score As Table 1 showed, NLR, LDH, D-dimer, and CT score were statistically signi cantly higher in the severe group. On the basis of receiver operating characteristic (ROC), the area under curve (AUC) was 0.716 for NLR, 0.740 for LDH, 0.650 for D-dimer, and 0.612 for CT score, indicating certain diagnostic value for the severity of disease ( Fig. 1 and Table 2). In addition, the optimum cutoff from ROC was 3.82, 246 U/L, 0.83 µg/ml, and 7 for NLR, LDH, D-dimer, and CT score, respectively ( Abbreviation: ROC= receiver operator characteristic, NLR=neutrophil-to-lymphocyte ratio, LDH=lactate dehydrogenase. Table 2 Area under ROC curve and optimum cutoff We assumed that when the level of NLR, LDH, D-dimer, and CT score on admission exceeded the optimum cutoff, the patients were prone to develop severe or critical types. According to optimum cutoff, the patients were divided into different subgroups. As   (Table 4). Abbreviation: NLR = neutrophil -to-lymphocyte ratio, LDH = lactate dehydrogenase, COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein.

Evaluation of multi-parameter model
According to the logistic regression, NLR > 3.82 and LDH > 246U/L were statistically signi cant risk factors (Table 4). And as Table 2 showed, the sensitivity of NLR > 3.82 and LDH > 246U/L in predicting the severity of COVID-19 were 50.40% and 59.20%, respectively. Then, further evaluation was made to judge whether the combined diagnosis model of two indexes can improve the sensitivity of prediction. The

Discussion
A total of 432 patients with COVID-19 were included in this retrospective study. In the univariate analysis, we found that the high level of NLR, LDH, D-dimer, and CT score have signi cant correlation with the severity of COVID-19. While after adjusting other statistically signi cant indexes, the predictive value of NLR > 3.82, LDH > 246U/L were persisted. This indicated that when NLR exceeded the cutoff point, the risk of serious disease increased by 2.163 times. And the risk of LDH over optimum cutoff increased by 2.298 times. While the value of D-dimer > 0.83 µg/ml and CT score > 7 in predicting the severity of disease was weak and could not be recommended as independent predictors. In addition, the risk of severity was also closely related to fatigue, chest tightness, hypertension and CRP. Meanwhile, combining NLR > 3.82 and LDH > 246U/L can improve the sensitivity of disease risk prediction.
Immune dysfunction plays an important role in the severity of COVID-19 [12]. Recent studies have elucidated that neutropenia and lower lymphopenia could be found in the severe group of COVID-19 [13,14]. NLR took lymphocyte and neutrophil into account at the same time. Several studies have shown the predictive value of NLR in distinguishing COVID-19 patients with severe and critical types. In a study of the dynamic changes of lymphocyte subsets and cytokine pro les in patients with COVID-19, NLR can be used as a prognostic factor for early identi cation of severe cases [15]. A cohort of patients with COVID-19 also proved that, after adjustment of confounding factors, each unit increase in NLR, the risk of inhospital mortality increases by 8% [16]. Another study conducted by Yang X et. al, [6] in 93 patients with COVID-19 demonstrated that NLR can be used as independent indicators for poor clinical outcome and the largest AUC for NLR were 0.841,with speci city (63.6%) and sensitivity (88%). However, limited by sample diversity, the outcome needs further evaluation. In the present study, the predictive value of NLR is consistent with abovementioned studies. Meanwhile, the sample size and diversity were enriched by collecting data from two clinical centers, which will strengthen the reliability of conclusions. The optimum cutoff for NLR was 3.82 and AUC was 0.716. And the sensitivity and speci city of NLR > 3.82 were 50.40% and 84.04%, respectively. Meanwhile, in multivariate logistic regression, NLR > 3.82 can be used as an independent predictor for disease risk (OR = 2.163;95%CI = 1.162-4.026; p = 0.015).
The elevation of LDH was one of the most common laboratory abnormalities in patients with COVID-19. Acute lung injury was highly related to LDH [17]. A systematic literature review and meta-analysis had shown that LDH > 245U/L can predict the progress of COVID-19 [7]. In a study of the risk factors for death in cancer patients with COVID-19, the elevated LDH was closely related to the increase of mortality [18].
Furthermore, in another retrospective analysis of 120 patients with COVID-19, comparing with mild patients, the severe patients have higher LDH levels (mean 200.8 U/L for mild vs mean 342.8 U/L for severe) [19]. The predictive value of LDH is further con rmed by our study. Meanwhile, ROC analysis showed that the AUC for LDH was 0.74 and the optimum cutoff was 246 U/L. Moreover, the sensitivity, speci city, and AUC for NLR and LDH are not relatively high enough. Due to the different admission time of patients with COVID-19 and the acute aggravation of some patients in a period of time after admission, the value of admission indicators may be underestimated. However, compared with other articles [6,15,20], the sample size and diversity of patients with COVID-19 increase the reliability of the results in this study. Meanwhile, more importantly, the optimum cutoff can indicate the risk of acute aggravation of patients with COVID-19 in the present study. Furthermore, it provides more evidence for the establishment of multi-parameter diagnosis model.
Coagulation disorders are more common in severe patients than in light patients [21,22]. A study conducted by Zhang L et al. [23] had proved that the level of D-dimer ≥ 2.0 µg/mL (fourfold increase) could effectively predict the mortality of patients with COVID-19. While after balancing the confounding factors, the logistic regression showed that D-dimer > 0.83 µg/ml could not be used as an independent predictor of disease risk in this study (OR = 1.209; 95%CI = 0.626-2.334; p = 0.571). In a dynamic study of hematological parameters in patients with COVID-19, the D-dimer of the severe group was higher than the non-severe group on days 1, 7 and 14 (p < 0.05) [24]. This suggests that due to different admission times, the ability of D-dimer to predict disease risk may be weakened. In another response to the prognostic COVID-19 patients have lung involvement with imaging changes [10,26]. In different stages of the disease, the CT manifestations are different, which are important to the diagnosis and staging of patients [27]. With the same semi-quantitative scoring system, a multi-center paired cohort study conducted by J. Liu et al. [28] showed that CT changes are obvious in acute exacerbation of COVID-19, accompanied by an increase of CT score. This indicated that elevated CT score may predict the poor outcome. Another retrospective single-center study indicated that the CT score has a high diagnostic value in patients with severe COVID-19. ROC analysis showed that AUC for CT score was 0.918. The optimum cutoff of CT score was 7.5. The sensitivity was 82.6% and the speci city was 100% [11]. However, the study only analyzes imaging, without combined with clinical data. And signi cant differences in the number of patients between severe-critical patients and non-severe groups also affect the accuracy of the results.
While in present study, after combining the clinical data, the CT score can't be used as an independent predictor of disease risk (OR = 1.519; 95%CI = 0.71-3.247; p = 0.281). A study by Zhang B et. al[29], demonstrated that the severity of lung abnormalities evaluated by CT score might be associated with laboratory parameters. Therefore, due to correlation between CT score and laboratory parameters, the ability to independently predict disease risk of CT score may be attenuated. Additional investigations are warranted to assess whether CT score can be an independent predictor of disease risk.
There are some limitations in this study. First, owing to different severity of patients and different medical resources, the time from onset to admission might not be representative, which might affect the level of four parameters on admission. Meanwhile, the representativeness of CT score and D-dimer may also be affected by different admission times. Second, other clinical data and test results are not included in the analysis, which may cause bias, weakening the reliability of the results. Third, to a certain degree, the CT score as a semi-quantitative evaluation method was subjective.

Conclusion
As independent factors, the levels of NLR, LDH in serum have a signi cant correlation with the severity of COVID-19. We suggested that NLR and LDH could be recommended as predictors for evaluating the severity of COVID-19. Availability of data and material: CT score).