A Composite Index Predicts Disease Progression in Early Stages of COVID-19: a Propensity Score-matched Cohort Study

doi:10.21203/rs.3.rs-30635/v1

Download PDF

Research

A Composite Index Predicts Disease Progression in Early Stages of COVID-19: a Propensity Score-matched Cohort Study

https://doi.org/10.21203/rs.3.rs-30635/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Thus far, studies on COVID-19 have focused on the epidemiology of the disease and clinical characteristics of patients (14-19), as well as on the risk factors associated with mortality during hospitalization in critical COVID-19 cases. However, no research has been performed on the prediction of progression in patients in the early stages of the disease. The aim of this work was to identify the early predictors of COVID-19 progression.

Methods: The study included 338 patients with COVID-19 treated at two hospitals in Wuhan, Chian, from December, 2019 to March, 2020. Predictors of the progression of COVID-19 from mild to severe stages were selected by the logistic regression analysis. The predictive accuracy was evaluated further in the propensity score-matched cohort.

Results: COVID-19 progression to severe and critical stages was confirmed in 78(23.1%) patients. The average value of the neutrophil-to-lymphocyte ratio (NLR) was higher in patients in the disease progression group than in the improvement group. Multivariable logistic regression analysis revealed that elevated NLR, LDH, and IL-10, were independent predictors of disease progression. The optimal cut-off value of NLR for predicting the progression of COVID-19 was 3.75. In the propensity score-matched cohort, NLR ≥ 3.75 was still an independent predictor of COVID-19 progression after multivariate analysis.

Conclusions: The performed analysis demonstrates that NLR qualiﬁes as an independent predictor of disease progression in COVID-19 patients at the early stage of the disease. The combined evaluation of NLR and LDH improved the accuracy of the prediction of COVID-19 progression. Assessment of predictors might facilitate early identification of COVID-19 patients at high risk for disease progression and ensure timely administration of appropriate treatment to prevent mild cases from becoming severe.

Infectious Diseases

COVID-19

SARS-CoV-2

Neutrophil-to-lymphocyte ratio

Systemic immune-inflammation index

Propensity score matching

Predictor

The novel coronavirus, named by the World Health Organization (WHO) as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in the city of Wuhan, China, in December 2019 (1). Given the rapid outbreak of the coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in China and its rapid spread of COVID-19 pandemic worldwide, global concerns have emerged (2). The extent and burden of COVID-19 are particularly evident in countries lacking adequate medical resources. As of April 24, 2020, 84312 patients have been diagnosed with COVID-19 in China, and 4642 (5.5%) of them died.

The analysis of 44,672 confirmed cases of COVID-19 in China revealed that the majority of SARS-CoV-2 infections were mild or general type, while 889 (1.2%) were asymptomatic, 13.8% were severe, and 4.7% were critical cases (3). The mortality rate in critical COVID-19 cases is approximately 49% (3). Severe patients usually develop dyspnea or hypoxemia one week after the onset of the disease. Critical COVID-19 patients develop rapidly acute respiratory distress syndrome, septic shock, refractory metabolic acidosis, coagulation disorders, and multi-organ failure (4). In the early days of COVID-19 spread in Wuhan, patients with severe or critical COVID-19 were admitted to a hospital, while patients with mild or general COVID-19 were advised to be isolated at home. With increased awareness of the spread and progression of COVID-19, efforts have been made throughout China to admit and treat all confirmed or suspected COVID-19 patients. It has been reported that patients in Guangzhou, China, had a better prognosis than in Wuhan. Patients in Wuhan were more likely to be admitted to an ICU and had higher mortality (5). This difference highlights the importance of appropriate care and treatment in early mild cases, especially in patients at high risk of disease progression. Early detection of risk factors for progression to severe illness can help to provide adequate supportive care and treatment, decreasing the number of patients that develop the severe condition, reducing mortality, and alleviating the shortages of medical resources.

The neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII), both easily calculated from a routine blood test, are indicators of inflammation and immune response (2, 6). Elevated NLR represents a risk factor for mortality not only from infectious diseases, but also from malignancies, intracerebral hemorrhage, and dermatomyositis (7-9). Similarly, increased SII has a prognostic value in a variety of malignancies, including hepatocellular carcinoma, breast cancer, and esophageal squamous cell carcinoma (10-12). It has been documented that severe and critical COVID-19 cases tend to have higher neutrophil counts and lower lymphocyte counts (1, 13). Whether NLR or SII could be an independent predictor of COVID-19 progression in the early (mild or moderate) stages of the disease remains to be determined.

Thus far, studies on COVID-19 have focused on the epidemiology of the disease and clinical characteristics of patients (14-19), as well as on the risk factors associated with mortality during hospitalization in critical COVID-19 cases. However, no research has been performed on the prediction of progression in patients in the early stages of the disease. To address this lack of data, the present research was designed to determine whether baseline values of NLR and SII in patients with mild or moderate COVID-19 can be used as reliable predictors of the progression of the disease in its early stages.

2.1. Study design and participants

The present investigation was designed as a retrospective study involving a total of 476 COVID-19 patients admitted consecutively to the Union Hospital of Huazhong University of Science and Technology and the Wuhan Asia General Hospital, from December 2019 to March 2020. The clinical outcomes, discharge from hospital, or death in hospital, were recorded up to March 31, 2020. Both hospitals are located in Wuhan, Hubei Province, and were designated by the Chinese government as hospitals responsible for the treatment of COVID-19 patients. The disease was diagnosed according to the WHO guidelines.

Exclusion criteria were as follows: (1) patients that were pregnant or under 18 years old; (2) patients with severe or critical COVID-19 at admission; (3) patients receiving irregular treatment before the admission (Fig. 1). Based on the exclusion criteria, 338 COVID-19 patients were included in the final analyses.

All patients admitted to the hospital with mild or moderate COVID-19 were divided into the improvement group and the progression group. The improvement group included patients who recovered after admission and were discharged from the hospital. The progression group included patients who after the admission progressed to severe or critical condition, or died in the hospital, regardless whether the final outcome was discharged or death,

The protocol of the study was approved by the Research Ethics Committee of the Tongji Medical College. The data used in the study were anonymized, and the requirement for informed consent was waived by the Committee.

2.2. Data collection

Complete clinical data for all COVID-19 patients were collected from the medical records of the patients. They included demographic, clinical, laboratory, imaging, treatment, and outcome information. The laboratory data and CT scan of the lung were collected corresponded to the results of the first test performed upon admission. Prior to the analysis, the patient information was de-identified and anonymized. The NLR was calculated from the results of neutrophil and lymphocyte counts. SII was calculated according to the results of a routine blood test, according to the formula: SII=N×P/L, where N, P, and L represents the count of neutrophils, platelets, and lymphocytes, respectively (6).

2.3. Laboratory examination

Laboratory confirmation of the infection of SARS-CoV-2 was conducted by local CDC in accordance with the Chinese CDC protocol. Pharyngeal swab samples were collected from all patients and the samples were stored in a viral-transport medium for laboratory testing. Real-time RT-PCR was performed to exclude infection with other respiratory viruses including influenza A virus, coxsackie virus, influenza B virus,respiratory syncytial virus, enterovirus and parainfluenza virus. Automatic biochemical analyser, AU5800(Beckman Coulter, USA), was used for measuring the concentrations of ALT, AST, albumin, and creatinine. Liquid Assayed Multiqual was performed in QC procedures. Automated blood analyser, XE-2100 (Sysmex, Japan), was used for measuring the count of white blood cells, neutrophils, lymphocytes, and platelets. The lymphocyte test kit (Beckman Coulter Inc., FL, USA) was used for lymphocyte subset analysis. Plasma cytokines (IL10, IL6, IL4, IL2, IFN – γ and TNF - α) were detected using the human Th1/2 cytokine kit II (BD Ltd., Franklin lakes, NJ, USA). All laboratory tests were conducted in accordance with the product manual.

2.4. Study definitions

The classification of the severity of COVID-19 was based on the “Diagnosis and Treatment Protocol for COVID-19 (Trial Version 7)” (4). The clinical classifications of illness severity of COVID-19 were as follows: (1) mild type, with mild clinical symptoms and the absence of signs of pneumonia on imaging; (2) moderate type, with fever, respiratory tract symptoms, and signs of pneumonia were identified on imaging; (3) severe type, characterized by one of the following: a) respiratory distress, respiratory rate ≥ 30 breaths/min; b) mean oxygen saturation ≤ 93% in the resting state; c) oxygenation index ≤ 300 mmHg; and (4) critical type, characterized by one of the following: a) shock; b) respiratory failure requiring mechanical ventilation; c) organ failure requiring ICU admission. Fever was defined as an axillary temperature of at least 37.3°C(20). The duration of viral shedding was defined as the time from the onset of illness to the second negative nucleic acid test (4).

2.5. Statistical analysis

Summary statistics of the demographic data, clinical characteristics, laboratory results, and radiographic findings were expressed as median and the interquartile range (IQR) for continuous variables and as frequencies and proportions for categorical variables. If the variance in the improvement group and the progression group was the same, continuous variables were compared by the Student’s t-test; otherwise, the Welch’s t-test was used. Categorical variables were compared by the χ2 test or Fisher's exact test.

The relationship between the NLR treated as a continuous variable, and the progression of COVID-19 was examined first; subsequently, the relationship was evaluated considering NLR as a categorical variable according to the best threshold value. These relationships were examined using univariate and multivariate logistic regression analyses, and odds ratio (OR) and 95% confidence interval (CI) were calculated. After univariate logistic regression analysis, only the variables with a P-value of less than 0.1 were considered for multivariate analysis to identify predictors of progression of COVID-19. To avoid overfitting in multivariate analysis, six variables were selected on the basis of clinical constraints and previous studies. Previous investigations have documented that older age was associated with poor prognosis in COVID-19 patients (20). Recent studies indicated that patients with severe COVID-19 had higher C-reactive protein (CRP) and lactate dehydrogenase (LDH) levels than patients with non-severe COVID-19 (13). Moreover, in comparison with patients with mild COVID-19, the levels of IL-10 and IFNγ in patients with severe COVID-19 were significantly increased in the early stage of the disease, and most detected cytokines peaked in the serum 3-6 days after the onset (21, 22). Therefore, NLR, CRP, LDH, interleukin-10(IL-10), and interferon γ(IFNγ) were selected as the six variables for the multivariable logistic regression analysis.

To further validate the association between elevated NLR and disease progression, propensity score matching (PSM) was used to eliminate confounding bias (23). All COVID-19 patients were divided into two groups, the high NLR (≥3.75) group and the low NLR (<3.75) group, based on the best threshold value of NLR predicting disease progression. The propensity score was calculated using the logistic regression model in which age, gender, hypertension, coronary heart disease, diabetes, chronic obstructive pulmonary disease, fever, and cough were considered. The matching was performed using a 1:1 ratio. By this approach, patients in the high NLR group were matched with patients in the low NLR group having the closest propensity score. Patients selected by PSM were enrolled in a new cohort and subjected to further analysis of the association between NLR elevation and disease progression. The best threshold value of NLR was calculated according to the Youden index. The two-sided P-value of less than 0.05 was considered to indicate a statistically significant difference. SPSS 24.0 software was used to perform PSM and for statistical analysis.

3.1. Patient characteristics

A total of 338 COVID-19 patients, in which the diagnosis was confirmed by the detection of SARS-CoV-2 RNA, were enrolled (Fig. 1). Their demographic, clinical, laboratory, radiographic, and outcomes data are listed in Table 1. Six patients died during hospitalization, and 332 were discharged. The median age of the patients was 57.5 years (IQR 35.0–68.0), and the majority were female (56.8%). Comorbidities were present in 154 (45.6%) patients, with hypertension being the most common comorbidity, followed by diabetes and coronary heart disease. The most frequent symptoms on admission were fever, followed by cough, fatigue, chest distress, and myalgia. In routine blood tests, the median values of neutrophils, lymphocytes, platelet, SII, and NLR were 3.05 (×10^9/L,), 1.30 (×10^9/L,), 205.50 (×10^9/L,), 503.43 (×10^9/L,), 2.31, respectively.

3.2. Relationship between clinical characteristics and COVID-19 progression

Seventy-eight (23.1%) COVID-19 patients developed disease progression after the admission, and 6 (7.7%) of them died while hospitalized (Table 1). Table 1 also indicates that patients in the progression group were 6.5 years older than in the improvement group (62.5 vs. 56.0 years, P = 0.024). There was no significant difference in the ratio of males to females between the two groups (P = 0.548). Compared with patients in the improvement group, patients in the progression group had a higher rate of developing a fever, chest distress, myalgia, nausea or vomiting, and palpitation (all P＜0.05). They also had a higher count of neutrophils (3.34 vs. 2.92 ×109/L, P = 0.014), lower count of lymphocytes (0.93 vs. 1.40 ×109/ L, P＜0.001), and higher NLR (3.90 vs. 2.08, P＜0.001) and SII (807.86 vs. 453.16 ×109/ L, P＜0.001) values. In addition, they had higher levels of creatine kinase, LDH, CRP, ESR, IL-10, and IFNγ (all P＜0.05) on admission. The incidence of lung consolidation detected by imaging was higher in the progression group (74.4% vs. 43.8%, P＜0.001).

3.3. Risk factors associated with COVID-19 progression

To identify the predictors of COVID-19 progression, multiple clinical parameters were evaluated by logistic regression analysis (Table 2). In univariable analysis, the odds of disease progression were higher in patients with fever or cough (Table 2). Age, elevated neutrophil count, reduced lymphocyte count, elevated NLR, SII, creatine kinase, LDH, CRP, ESR, IL-10, and IFNγ were also associated with disease progression. To avoid overfitting in multivariate analysis, age, NLR, CRP, LDH, IL-10, and IFNγ were selected for multivariate regression analysis, as justified in the Methods section. Multivariable logistic regression analysis documented that increased NLR, LDH, and IL-10 were independent predictors of disease progression in COVID-19 patients.

The receiver operating curve (ROC) analysis indicated that the optimal cut-off value of NLR for predicting the progression of COVID-19 was 3.75. The values of the area under the curve, reflecting the accuracy of predicting COVID-19 progression by NLR and SII, were 0.739 (95%CI: 0.605-0.804) and 0.674 (95%CI: 0.603-0.745) (Fig. 2). Therefore, NLR, but not SII, was included in the multivariate regression analysis.

In this analysis, we demonstrate that a NLR of 3.75 is the best cutoff point for predicting disease progression in patients with COVID-19 by ROC curve analysis. The ROC curve analysis revealed that a LDH cutoff point of 213.5 U/L showed the biggest Youden index(Fig 2). The AUC (95%CI) was 0.727(0.670-0.785). Furthermore, we estimated whether a NLR of 3.75 combined with a LDH cutoff of 213.5 U/L would improve the accuracy of prediction of disease progression in patients with COVID-19. Table 3 presents sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV)(95%CI) of these variables in combination or either alone.

3.4. Risk factors associated with COVID-19 progression analyzed after propensity score matching

To further validate the association between elevated NLR and disease progression, PSM was used to reduce confounding bias. The patients were divided into two groups based on the best threshold value of NLR predicting disease progression: the high NLR (≥3.75) group and the low NLR (<3.75) group. The high NLR group had a higher incidence of COVID-19 progression than the low NLR group (P＜0.001). Seventy-four propensity score-matched pairs of COVID-19 patients were selected and enrolled in a new cohort. However, a statistically significant difference in COVID-19 progression continued to be present between the high and low NLR groups (P＜0.001) (Table 4). After univariate analysis, the variables with P<0.1, including age, NLR, LDH, CRP, IL-10, and IFNγ, were selected for multivariate regression analysis. This analysis confirmed that NLR ≥3.75 was still an independent predictor of disease progression in COVID-19 patients(Table 5).

The present study has demonstrated that NLR, measured at the early stage of the disease, is a significant independent predictor for the progression of COVID-19 to severe and critical stages. Moreover, the finding that NLR ≥3.75 is an independent predictor of disease progression was also confirmed in the propensity score-matched cohort. The PPV of NLR is actually quite poor. The combined evaluation of NLR and LDH improved the accuracy of the prediction of COVID-19 progression. The relevance of early adequate treatment to prevent mild or moderate cases from developing into severe ones is well-recognized since the treatment of critical COVID-19 patients requires not only significant medical resources but, most importantly, results in a high mortality rate (20). This current analysis provides a new evidence-based strategy for early identification of patients at high risk of COVID-19 progression.

Previous studies have documented that older age is a significant independent predictor of mortality in MERS and SARS (24, 25). The current investigation has found that older age is also associated with increased odds of in-hospital death in COVID-19 patients (26). However, age is not an independent predictor of the progression of COVID-19 to severe or critical stages. COVID-19 is commonly susceptible in the population, and there is a risk of progression to severe disease (4). A possible explanation of this apparent discrepancy might be that older patients with severe or critical COVID-19 have a higher risk of death. Since patients with severe or critical COVID-19 on admission were not included in the study, the fatality rate among the analyzed population was lower than that documented in previous reports (26).

In agreement with previous research (4), the present analysis has shown that an increased level of LDH at the early stage of COVID-19 was also a significant independent predictor for disease progression. Moreover, COVID-19 patients in the progression group had higher levels of IL-10 and IL-6 at the early stage of the disease, although differences in IL-6 between the groups were not statistically significant. However, IL-10 at the early stage was also a significant independent predictor for COVID-19 progression. The identification of elevated levels of inflammatory factors in COVID-19 patients is consistent with previous studies (27). The current work documented that, except for IL-6, the serum concentration of cytokines peaked at 3-6 days after the onset of the disease in critically ill COVID-19 patients; these cytokines included IL-10, IL-2, IL-4, TNFα, and IFNγ (27). In addition, levels of IL-10 and IL-6 continued to increase in patients with severe COVID-19, and with the levels of IL-6 beginning to decrease after 16 days (27).

It is currently well-recognized that COVID-19 is highly contagious in the general population, and caused a global pandemic in a short period of time. The treatment of severe and critically ill patients is imperative, but it requires the consumption of a great deal of valuable medical resources, especially the ventilators. The lack of adequate medical resources became already evident in some developed countries and is more dramatic in the developing ones. It has been reported that in Guangzhou, China, where all patients with mild and moderate COVID-19 receive appropriate care and treatment at an early stage of the illness, their prognosis is better than that for patients in Wuhan, where they are more likely to be admitted to ICU and succumb to the disease (5). The difference between these two cities underscores the importance of appropriate care and treatment for early mild cases, particularly for patients at high risk of disease progression. Timely detection of risk factors for disease progression might help to provide necessary supportive care and treatment to prevent mild cases from becoming severe, reduce mortality, and alleviate medical resource shortages.

Since there is no specific drug to treat COVID-19, the treatment consists mostly of symptomatic support therapy. The Chinese management guidelines for COVID-19 (version 7.0) recommend that the general treatment for mild or moderate COVID-19 patients includes 1) bed rest, intensive supportive treatment, adequate supply of energy; maintenance of internal environmental stability; 2) monitoring of blood indicators and chest images; 3) timely delivery of effective oxygen therapy in time; 4) administration of antiviral therapy, such as α-interferon, lopinavir/ritonavir, or abidor; 5) antimicrobial treatment (4). The care for patients with the severe or critical stage of COVID-19 relies on active prevention and treatment of complications, management of comorbidities, prevention of secondary infection, and supporting organ function on the basis of symptomatic treatment (4). Surprisingly, the value of traditional Chinese medicine represented by lotus qingwen capsule in the treatment of COVID-19 has been reflected in the guidelines (4).

It has been reported that a cytokine storm might be associated with the severity of COVID-19 (28). Immunoregulatory therapy downregulating the cytokine storms might be an effective strategy for the treatment of COVID-19. A study of 201 COVID-19 patients documented that treatment with methylprednisolone reduced the risk of death in patients with acute respiratory distress syndrome (29). The Diagnosis and Treatment Protocol for COVID-19 (Version 7) (4) recommends corticosteroids only in certain critical COVID-19 patients, and at low-to-moderate doses for a short duration. It was reported that elevated serum levels of IL-6 might be predictive of mortality in COVID-19 patients (30). The treatment with tocilizumab, a specific monoclonal antibody that blocks IL-6, led to a reduction in fever and lung lesion opacity and normalized the percentage of lymphocytes in peripheral blood in the COVID-19 patients with upregulated IL-6 (31). Moreover, the use of tocilizumab has been recommended by management guidelines (4) in COVID-19 patients with extensive lesions in both lungs and confirmed rising levels of IL-6.

Some limitations of the present work should be acknowledged. First, this investigation was designed as a retrospective study, and relatively few cases of progressing COVID-19 were included in the analyses. Thus, large-scale multicenter prospective cohort studies are necessary to confirm the results and strengthen the conclusions reached. Second, mild or general cases were treated before they progressed to severe cases. As stated earlier, patients with mild COVID-19 are at risk of progressing to the severe stage, and once they become severe, they have a high mortality rate. Therefore, it would be unreasonable not to treat the patients already admitted to the hospital. Although they were treated at different sites, the two designated hospitals followed the same guidelines for treating mild or moderate COVID-19 specified in the Diagnosis and Treatment Protocol for COVID-19 issued by the National Health Commission of China. Thus, the use of the data from two different centers did not affect the results of the study, since similar treatment strategies were employed.

The performed analysis demonstrates that NLR qualiﬁes as an independent predictor of disease progression in COVID-19 patients at the early stage of the disease. The combined evaluation of NLR and LDH improved the accuracy of the prediction of COVID-19 progression. Assessment of predictors might facilitate early identification of COVID-19 patients at high risk for disease progression and ensure timely administration of appropriate treatment to prevent mild cases from becoming severe.

WHO: World Health Organization; SARS-CoV-2: acute respiratory syndrome coronavirus 2; COVID-19: coronavirus disease 2019; NLR : neutrophil-to-lymphocyte ratio; SII: systemic immune-inflammation index; IQR: interquartile range; OR: odds ratio; CI: confidence interval; CRP: C-reactive protein; LDH: lactate dehydrogenase; IL: interleukin; IFNγ: interferon γ; PSM: propensity score matching; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; ESR: erythrocyte sedimentation rate; TNFα: tumor necrosis factor α; NPV: negative predictive value; PPV: positive predictive value.

Ethics approval and consent to participate: The protocol of the study was approved by the Research Ethics Committee of the Tongji Medical College (NO: IORG0003571). The data used in the study were anonymized, and the requirement for informed consent was waived by the Committee.

Consent for publication: All authors familiar with the contents of the final draft and Agreed to the publication of the article.

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests: All authors indicated no potential conflicts of interest.

Research funding: This work was supported by the Natural Science Foundation of Hubei Province (No. 2019CFB729), the National Natural Science Foundation of China (No.81903173, No. 81874231).

Author contributions: Jianjun Xu, Shaobo Hu, Yang Gao and Qichang Zheng researched literature and conceived the study. Suzhen Li, Weimin Wang, Xing Zhou, Yuzhe Wu, Zhe Su and Xiang Cheng were involved in gaining ethical approval, patient recruitment, protocol development and data analysis. Jianjun Xu and Shaobo Hu wrote the first draft of the manuscript. All authors edited and reviewed the manuscript and approved the final version of the manuscript.

Acknowledgements: This work was supported by the Natural Science Foundation of Hubei Province (No. 2019CFB729), the National Natural Science Foundation of China (No.81903173, No. 81874231).

Huang Y, Tu M, Wang S, Chen S, Zhou W, Chen D, et al. Clinical characteristics of laboratory confirmed positive cases of SARS-CoV-2 infection in Wuhan, China: A retrospective single center analysis. Travel Med Infect Dis. 2020; doi: 10.1016/j.tmaid.2020.101606.
Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HHX, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J Infect. 2020; doi: 10.1016/j.jinf.2020.04.002.
The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) — China, 2020. China CDC Weekly, 2020, 2(8): 113-22. http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51.
National Health Commission of the People’s Republic of China. Chinese management guideline for COVID-19 (Trial Version 7). Mar 03, 2020. http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml. (accessed Mar 03, 2020; in Chinese).
Lei Z, Cao H, Jie Y, Huang Z, Guo X, Chen J, et al. A cross-sectional comparison of epidemiological and clinical features of patients with coronavirus disease (COVID-19) in Wuhan and outside Wuhan, China. Travel Med Infect Dis. 2020; doi: 10.1016/j.tmaid.2020.101664.
Zhang K, Hua YQ, Wang D, Chen LY, Wu CJ, Chen Z, et al. Systemic immune-inflammation index predicts prognosis of patients with advanced pancreatic cancer. J Transl Med. 2019;17(1):30.
Guthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol. 2013;88(1):218-30.
Giede-Jeppe A, Bobinger T, Gerner ST, Sembill JA, Sprugel MI, Beuscher VD, et al. Neutrophil-to-Lymphocyte Ratio Is an Independent Predictor for In-Hospital Mortality in Spontaneous Intracerebral Hemorrhage. Cerebrovasc Dis. 2017;44(1-2):26-34.
Ha YJ, Hur J, Go DJ, Kang EH, Park JK, Lee EY, et al. Baseline peripheral blood neutrophil-to-lymphocyte ratio could predict survival in patients with adult polymyositis and dermatomyositis: A retrospective observational study. PLoS One. 2018;13(1):e0190411.
Zhao LY, Yang DD, Ma XK, Liu MM, Wu DH, Zhang XP, et al. The Prognostic Value of aspartate aminotransferase to lymphocyte ratio and systemic immune-inflammation index for Overall Survival of Hepatocellular Carcinoma Patients Treated with palliative Treatments. J Cancer. 2019;10(10):2299-311.
Sun Y, Li W, Li AJ, Su H, Yue J, Yu J. Increased systemic immune-inflammation index independently predicts poor survival for hormone receptor-negative, HER2-positive breast cancer patients. Cancer Manag Res. 2019;11:3153-62.
Gao Y, Guo W, Cai S, Zhang F, Shao F, Zhang G, et al. Systemic immune-inflammation index (SII) is useful to predict survival outcomes in patients with surgically resected esophageal squamous cell carcinoma. J Cancer. 2019;10(14):3188-96.
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020; doi: 10.1056/NEJMoa2002032.
Al-Tawfiq JA. Asymptomatic coronavirus infection: MERS-CoV and SARS-CoV-2 (COVID-19). Travel Med Infect Dis. 2020; doi: 10.1016/j.tmaid.2020.101608.
Li X, Zeng W, Li X, Chen H, Shi L, Li X, et al. CT imaging changes of corona virus disease 2019(COVID-19): a multi-center study in Southwest China. J Transl Med. 2020;18(1):154.
Schlagenhauf P, Grobusch MP, Maier JD, Gautret P. Repurposing antimalarials and other drugs for COVID-19. Travel Med Infect Dis. 2020; doi: 10.1016/j.tmaid.2020.101658.
Song R, Han B, Song M, Wang L, Conlon CP, Dong T, et al. Clinical and epidemiological features of COVID-19 family clusters in Beijing, China. J Infect. 2020; doi: 10.1016/j.jinf.2020.04.018.
Tomlins J, Hamilton F, Gunning S, Sheehy C, Moran E, Macgowan A. Clinical features of 95 sequential hospitalised patients with novel coronavirus 2019 disease (COVID-19), the first UK cohort. J Infect. 2020; doi: 10.1016/j.jinf.2020.04.020.
Wu J, Liang J, Zhou H, Peng F, Wang B, Jiang W, et al. Clinical Features and Outcomes of Asymptomatic Cases of SARS-CoV-2 Infection. J Infect. 2020; doi: 10.1016/j.jinf.2020.04.027.
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020; doi: 10.1016/s2213-2600(20)30079-5.
Moore BJB, June CH. Cytokine release syndrome in severe COVID-19. Science. 2020; doi: 10.1126/science.abb8925.
Zhao M. Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies. Int J Antimicrob Agents. 2020; doi: 10.1016/j.ijantimicag.2020.105982.
Kuss O, Blettner M, Borgermann J. Propensity Score: an Alternative Method of Analyzing Treatment Effects. Dtsch Arztebl Int. 2016;113(35-36):597-603.
Choi KW, Chau TN, Tsang O, Tso E, Chiu MC, Tong WL, et al. Outcomes and prognostic factors in 267 patients with severe acute respiratory syndrome in Hong Kong. Ann Intern Med. 2003;139(9):715-23.
Hong KH, Choi JP, Hong SH, Lee J, Kwon JS, Kim SM, et al. Predictors of mortality in Middle East respiratory syndrome (MERS). Thorax. 2018;73(3):286-9.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62.
Liu J, Li S, Liu J, Liang B, Wang X, Wang H, et al. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine. 2020; doi: 10.1016/j.ebiom.2020.102763.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506.
Zha L, Li S, Pan L, Tefsen B, Li Y, French N, et al. Corticosteroid treatment of patients with coronavirus disease 2019 (COVID-19). Med J Aust. 2020 May;212(9):416-20.
Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020; doi: 10.1007/s00134-020-05991-x.
Zhang C, Wu Z, Li JW, Zhao H, Wang GQ. The cytokine release syndrome (CRS) of severe COVID-19 and Interleukin-6 receptor (IL-6R) antagonist Tocilizumab may be the key to reduce the mortality. Int J Antimicrob Agents. 2020; doi: 10.1016/j.ijantimicag.2020.105954.

Table 1: Baseline characteristics of COVID-19 patients (n=338)

Covariate	Total (n=338)	Progressed group (n=78)	Improved group (n=260)	p value
Demographics and clinical characteristics
Age, years	57.5(35-68)	62.5(41-70)	56.0(34-67)	0.024
Gender				0.548
Female	192(56.8)	42(53.8)	150(57.7)
Male	146(43.2)	36(46.2)	110(42.3)
Comorbidity	154(45.6)	38(48.7)	116(44.6)	0.523
Hypertension	90(26.6)	24(30.8)	66(25.4)	0.345
Coronary heart disease	32(9.5)	6(7.7)	26(10.0)	0.541
Diabetes	36(10.7)	8(10.3)	28(10.8)	0.898
Chronic obstructive lung disease	14(4.1)	4(5.1)	10(3.8)	0.618
Other	82(24.3)	20(25.6)	62(23.8)	0.746
Fever (temperature ≥37·3°C)	258(76.3)	72(92.3)	186(71.5)	＜0.001
Sore-throat	68(20.1)	14(17.9)	54(20.8)	0.586
Nasal congestion	24(7.1)	6(7.7)	18(6.9)	0.817
Cough	208(61.5)	58(74.4)	150(57.7)	0.008
Chest distress	132(39.1)	50(64.1)	82(31.5)	＜0.001
Fatigue	180(53.3)	48(61.5)	132(50.8)	0.095
Myalgia	118(34.9)	36(46.2)	82(31.5)	0.018
Headache	70(20.7)	20(25.6)	50(19.2)	0.220
Nausea or vomiting	28(8.3)	16(20.5)	12(4.6)	＜0.001
Diarrhoea	94(27.8)	28(35.9)	66(25.4)	0.069
Palpitate	38(11.2)	22(28.2)	16(6.2)	＜0.001
Chest pain	14(4.1)	2(2.6)	12(4.6)	0.425
Laboratory findings on admission
White blood cell count, ×10^9/L	5.14(3.90-6.66)	4.90(3.76-6.87)	5.17(4.11-6.56)	0.431
<4, ×10^9/L	85(25.1)	23(29.5)	62(23.8)	0.314
>10, ×10^9/L	10(3.0)	2(2.6)	8(3.1)	0.815
Neutrophil count, ×10^9/L	3.05(2.23-4.28)	3.34(2.52-5.12)	2.92(2.17-4.19)	0.014
Lymphocyte count, ×10^9/L	1.30(0.97-1.63)	0.93(0.68-1.21)	1.40(1.09-1.80)	＜0.001
NLR	2.31(1.52-3.82)	3.90(2.27-5.56)	2.08(1.43-3.27)	＜0.001
Platelet count, ×10^9/L	205.50(161.75- 269.75)	198.50(157.50-272.50)	206.00(163.00-268.75)	0.656
SII, ×10^9/L	503.43(289.26-878.25)	807.86(409.93-1478.95)	453.16(261.10-772.36)	＜0.001
Haemoglobin, g/ dl	12.70(11.50-13.60)	12.80(11.10-13.60)	12.70(11.60-13.60)	0.348
ALT, U/L	23(16.75-36)	27(20-38)	21(16-33)	0.530
AST, U/L	23 (19-33)	29 (23-39)	21.50(17-29)	0.003
Albumin, g/L	37.30(33.08-40.80)	35.40(28.90-40.20)	37.75(33.70-41.00)	＜0.001
Creatinine, μmol/L	64.70(56.20-78.13)	70.00(58.30-82.60)	63.50(54.40-77.40)	0.904
BUN, mmol/L	3.93(3.15-4.93)	3.90(3.00-5.29)	3.94(3.21-4.75)	0.898
Creatine kinase, U/L	47(28.25-73.25)	66 (42.00-109)	44 (1.60-66)	＜0.001
Lactate dehydrogenase, U/L	213 (173.75-261.25)	243 (216-332)	193 (160-245)	＜0.001
C-reactive protein, mg/L	28.69(8.39-30.38)	28.69(10.50-57.30)	28.69(7.81-28.69)	＜0.001
ESR, mm/h	35(14-51)	35(25-60)	27 (12-45)	0.002
IL-2, pg/mL	2.70(2.53-2.81)	2.70(2.50-2.74)	2.70(2.53-2.81)	0.829
IL-4, pg/mL	2.14(1.92-2.31)	2.14(2.10-2.26)	2.14(1.90-2.31)	0.385
IL-6, pg/mL	13.74(4.97-16.71)	16.71(7.93-16.71)	12.28(4.44-16.71)	0.062
IL-10, pg/mL	4.20(3.44-4.33)	4.20(4.20-5.14)	4.11(3.34-4.21)	＜0.001
TNFα, pg/mL	3.32(2.21-4.27)	2.48(2.16-4.27)	3.41(2.22-4.27)	0.173
IFNγ, pg/mL	2.34(1.98-2.44)	2.34(2.21-2.50)	2.34(1.96-2.43)	0.001
CD3, ×10⁹ per L	0.92(0.69-1.22)	0.66(0.49-0.86)	1.01(0.77-1.32)	＜0.001
CD4, ×10⁹ per L	0.55(0.42-0.74)	0.42(0.30-0.52)	0.60(0.47-0.87)	＜0.001
CD8, ×10⁹ per L	0.29(0.21-0.42)	0.24(0.13-0.31)	0.32(0.22-0.45)	＜0.001
CD4/CD8	1.85(1.39-2.59)	1.75(1.26-2.48)	1.85(1.40-2.61)	0.751
Duration of viral shedding after COVID-19 onset, days	20(17-26)	20.50(18-25)	20 (17-26.75)	0.912
Imaging features
Consolidation	172(50.9)	58(74.4)	114(43.8)	＜0.001
Ground-glass opacity	251(74.3)	61(78.2)	190(73.1)	0.364
Bilateral patchy shadowing	261(77.2)	66(84.6)	195(75.0)	0.076
Outcomes				NS
Death	6/338(1.8)	6/78(7.7)	0/260(0)
Discharge	332/338(98.2)	72/78(92.3)	260/260(100)

Data are median (IQR), n (%), or n/N (%). P values were calculated by t-test, Welch’s t-test, χ² test, or Fisher’s exact test, as appropriate. NLR: neutrophil-to-lymphocyte ratio; SII: systemic immune-inflammation index; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; ESR: erythrocyte sedimentation rate; IL: interleukin; TNFα: tumor necrosis factor α; IFNγ: interferon γ.

Table 2: Risk factors associated with COVID-19 progression

Covariate	Univariable OR (95% CI)	p value	Multivariable OR (95% CI)	p value
Demographics and clinical characteristics
Age, years	1.017(1.002-1.033)	0.025	0.995(0.978-1.014)	0.616
Gender	0.856(0.514-1.423)	0.548
Hypertension	1.306(0.749-2.278)	0.346
Coronary heart disease	0.750(0.297-1.894)	0.543
Diabetes	0.947(0.413-2.172)	0.898
Chronic obstructive lung disease	1.351(0.412-4.434)	0.619
Fever (temperature ≥37·3°C)	4.774(1.990-11.456)	＜0.001
Cough	2.127(1.209-3.740)	0.009
Laboratory findings on admission
White blood cell count, ×10^9/L	0.946(0.825-1.085)	0.430
<4, ×10^9/L	1.335(0.760-2.348)	0.315
>10, ×10^9/L	0.829(0.172-3.987)	0.815
Neutrophil count, ×10^9/L	1.205(1.037-1.400)	0.015
Lymphocyte count, ×10^9/L	0.072(0.033-0.157)	＜0.001
NLR	1.299(1.153-1.464)	＜0.001	1.183(1.045-1.339)	0.008
Platelet count, ×10⁹ per L	0.999(0.996-1.002)	0.656
SII, ×10^9/L	1.001(1.000-1.001)	＜0.001
Haemoglobin, g/dl	0.992(0.976-1.008)	0.348
ALT, U/L	1.003(0.993-1.013)	0.530
AST, U/L	1.019(1.005-1.034)	0.006
Albumin, g/L	0.902(0.857-0.950)	＜0.001
Creatinine, μmol/L	1.000(0.992-1.007)	0.904
BUN, mmol/L	1.008(0.887-1.146)	0.898
Creatine kinase, U/L	1.009(1.005-1.014)	＜0.001
Lactate dehydrogenase, U/L	1.009(1.005-1.012)	＜0.001	1.007(1.003-1.010)	＜0.001
C-reactive protein, mg/L	1.016(1.007-1.025)	0.001	1.003(0.992-1.014)	0.610
ESR, mm/h	1.016(1.006-1.026)	0.002
IL-6, pg/mL	1.010(0.999-1.021)	0.077
IL-10, pg/mL	1.487(1.229-1.799)	＜0.001	1.344(1.080-1.672)	0.008
TNFα, pg/mL	0.932(0.841-1.033)	0.180
IFNγ, pg/mL	1.699(1.130-2.556)	0.011	0.869(0.495-1.524)	0.623
CD3, ×10⁹ per L	0.034(0.012-0.092)	＜0.001
CD4, ×10⁹ per L	0.008(0.002-0.039)	＜0.001
CD8, ×10⁹ per L	0.006(0.001-0.050)	＜0.001
CD4/CD8	1.022(0.892-1.172)	0.751

OR: odds ratio. NLR: neutrophil-to-lymphocyte ratio; SII: systemic immune-inflammation index; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; ESR: erythrocyte sedimentation rate; IL: interleukin; TNFα: tumor necrosis factor α; IFNγ: interferon γ.

Table 3: Sensitivity, Specificity, and Negative and Positive Predictive Values of a NLR of 3.75 and LDH of 213.5 U/L in the prediction of COVID-19 progression

Variable	Sensitivity(%)	Specificity(%)	NPV(%)	PPV(%)
NLR ≥ 3.75	0.55(0.43-0.66)	0.83(0.77-0.87)	0.86(0.81-0.90)	0.49(0.39-0.60)
LDH ≥ 213.5 U/L	0.79(0.69-0.87)	0.61(0.55-0.67)	0.91(0.85-0.94)	0.38(0.30-0.46)
NLR ≥ 3.75 or LDH ≥ 213.5 U/L	0.88(0.79-0.94)	0.52(0.46-0.58)	0.94(0.88-0.97)	0.36(0.29-0.43)
NLR ≥ 3.75 and LDH ≥ 213.5 U/L	0.46(0.35-0.58)	0.92(0.87-0.95)	0.85(0.80-0.89)	0.62(0.48-0.74)

NLR: neutrophil-to-lymphocyte ratio; LDH: lactate dehydrogenase; NPV: negative predictive value; PPV: positive predictive value.

Table 4: Comparison of clinical parameters between the low and high NLR groups

	Unmatched cohort			Propensity score-matched cohort
Covariate	NLR<3.75 (n=251 )	NLR≥3.75 (n= 87)	p value	NLR<3.75 (n= 74)	NLR≥3.75 (n=74 )	p value
Age	53(35-66)	67(42-75)	＜0.001	63.5(39-67)	62.5(39.3-70)	0.741
Gender			0.002			0.742
Female	155(61.8)	37(42.5)		36(48.6)	34(45.9)
Male	96(38.2)	50(57.5)		38(51.4)	40(54.1)
Hypertension	61(24.3)	29(33.3)	0.101	22(29.7)	25(33.8)	0.596
Coronary heart disease	23(9.2)	9(10.3)	0.746	8(10.8)	9(12.2)	0.797
Diabetes	20(8)	16(18.4)	0.007	11(14.9)	9(12.2)	0.631
Chronic obstructive lung disease	10(4)	4(4.6)	0.804	4(5.4)	2(2.7)	0.405
Fever	185(73.7)	73(83.9)	0.054	65(87.8)	61(82.4)	0.355
Cough	158(62.2)	52(59.8)	0.694	46(62.2)	44(59.5)	0.736
COVID-19 progression	35(13.9)	43(49.4)	＜0.001	11(14.9)	35(47.3)	＜0.001

Data are median (IQR) or n (%). P values were calculated by t-test, Welch’s t-test, χ² test, or Fisher’s exact test, as appropriate. NLR: neutrophil-to-lymphocyte ratio.

Table 5: Risk factors associated with COVID-19 progression after propensity score matching

Covariate	Univariable OR (95% CI)	p value	Multivariable OR (95% CI)	p value
Demographics and clinical characteristics
Age, years	1.022(1.000-1.044	0.048	1.008(0.982-1.034)	0.569
Gender	0.618(0.304-1.255)	0.183
Hypertension	1.222(0.583-2.558)	0.596
Coronary heart disease	0.652 (0.201-2.120)	0.477
Diabetes	0.349(0.097-1.256)	0.107
Chronic obstructive lung disease	1.114(0.197-6.308)	0.903
Fever (temperature ≥37·3°C)	2.250(0.716-7.070)	0.165
Cough	1.312(0.637-2.706)	0.461
Laboratory findings
White blood cell count, ×10^9/L	0.914(0.762-1.097)	0.334
<4, ×10^9/L	1.132(0.446-2.870)	0.795
>10, ×10^9/L	0.302(0.036-2.525)	0.269
Neutrophil count, ×10^9/L	1.061(0.854-1.318)	0.592
Lymphocyte count, ×10^9/L	0.162(0.060-0.439)	＜0.001
High NLR vs. Low NLR	5.140(2.341-11.285)	＜0.001	4.783(2.102-10.883)	＜0.001
Platelet count, ×10^9/L	1.001(0.997-1.005)	0.563
SII, ×10^9/L	1.000(1.000-1.001)	0.073
ALT, U/L	1.012(0.997-1.028)	0.129
AST, U/L	1.036(1.010-1.063)	0.006
Albumin, g/L	0.911(0.849-0.977)	0.009
Creatinine, μmol/L	0.999(0.990-1.008)	0.895
BUN, mmol/L	0.954(0.812-1.122)	0.572
Creatine kinase, U/L	1.010(1.004-1.017)	0.002
Lactate dehydrogenase, U/L	1.008(1.004-1.012)	＜0.001	1.007(1.003-1.012)	0.001
C-reactive protein, mg/L	1.014(1.002-1.026)	0.023	1.006(0.992-1.021)	0.379
ESR, mm/h	1.013(0.999-1.027)	0.063
IL-6, pg/mL	1.021(0.994-1.049)	0.130
IL-10, pg/mL	1.541(1.090-2.179)	0.014	1.228(0.790-1.911)	0.362
TNFα, pg/mL	0.966(0.888-1.050)	0.414
IFNγ, pg/mL	2.153(0.912-5.082)	0.080	1.904(0.740-4.900)	0.182

OR: odds ratio; NLR: neutrophil-to-lymphocyte ratio; SII: systemic immune-inflammation index; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; ESR: erythrocyte sedimentation rate; IL: interleukin; TNFα: tumor necrosis factor α; IFNγ: interferon γ.

Download PDF

Version 1

posted

You are reading this latest preprint version

A Composite Index Predicts Disease Progression in Early Stages of COVID-19: a Propensity Score-matched Cohort Study

Status:

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Status:

Version 1