redasa-covid-data-dataset/s3://pansurg-curation-workflo-kendraqueryresults50d0eb-open-data/redasa1-Q2-3000/docs/0013_pansurg_6736e2d5-4a84-4e3b-a13d-7596ef32b346_1591730933_efc44484071432110517cd562c108bc9b1137aa8.txt

# URL to online version

https://api.elsevier.com/content/article/pii/S0163445320301626
https://doi.org/10.1016/j.jinf.2020.03.035
https://www.ncbi.nlm.nih.gov/pubmed/32277967/
https://www.sciencedirect.com/science/article/pii/S0163445320301626?v=s5


# Title

SAA is a biomarker to distinguish the severity and prognosis of Coronavirus Disease 2019 (COVID-19)


# Abstract

Abstract Background To explore the significance of SAA in evaluating the severity and prognosis of COVID-19. Methods A total of 132 patients with confirmed COVID-19 who were admitted to a designated COVID-19 hospital in Wuhan, China from January 18, 2020 to February 26, 2020 were collected. The dynamic changes of blood SAA, CRP, PCT, WBC, Lymphocyte (L), PLT, CT imaging, and disease progression were studied. All patients completed at least twice laboratory data collection and clinical condition assessment at three time points indicated for this study; The length of hospital stay was longer than 14 days prior to February 26, 2020. Results COVID-19 patients had significantly increased SAA and CRP levels, while L count decreased, and PCT, WBC, and PLT were in the normal range. As disease progressed from mild to critically severe, SAA and CRP gradually increased, while L decreased, and PLT, WBC, and PCT had no significant changes; ROC curve analysis suggests that SAA/L, CRP, SAA, and L count are valuable in evaluating the severity of COVID-19 and distinguishing critically ill patients from mild ones; Patients with SAA consistently trending down during the course of disease have better prognosis, compared with the patients with SAA continuously rising; The initial SAA level is positively correlated with the dynamic changes of the serial CT scans. Patient with higher initial SAA level are more likely to have poor CT imaging. Conclusions SAA and L are sensitive indicators in evaluating the severity and prognosis of COVID-19. Monitoring dynamic changes of SAA, combined with CT imaging could be valuable in diagnosis and treatment of COVID-19.


# Main text

COVID-19, recently broke out in Wuhan, China, has spread rapidly throughout China and other countries. This new type of coronavirus could cause severe acute respiratory syndrome and injuries in other systems as well. The disease progresses rapidly, leading to multiple organ failure and death.1
,
2 Quite a few patients have no specific symptoms/signs or radiological abnormalities at the early stage, with only mild symptoms, making the early diagnosis of disease difficult. Therefore, early identification of the infection and creating effective treatment plan are particularly imperative.3
,
4 A series of inflammation factors, such as serum amyloid A (SAA), C-reactive protein (CRP), procalcitonin (PCT), white blood cells (WBC), lymphocyte (L) and platelet (PLT) have been used in clinic as inflammation indicators. In this research, the authors want to explore if these factors can also assist in the diagnosis of COVID-19 infection and estimate of the disease severity. Thus, in this research, the authors systematically studied the dynamic changes of above inflammation indicators in patients infected with COVID-19, in order to evaluate their clinical values in predicting the severity and prognosis of COVID-19.

We conducted a retrospective study focusing on the significance of SAA in evaluating the severity and prognosis of COVID-19. A total of 132 patients with COVID-19 were collected from Tianyou Hospital of Wuhan University of Science and Technology, from January 18, 2020 to February 26, 2020. Tianyou Hospital, Wuhan University of Science and Technology, located in Wuhan, Hubei Province, the endemic areas of COVID-19, is one of the major tertiary teaching hospitals and is responsible for the treatments for COVID-19 assigned by the government. All patients were tested positive with SARS-CoV-2 and hospitalized. Patients completed at least twice laboratory data collection and clinical condition assessment at three time points indicated for this study. The length of hospital stay was longer than 14 days prior to February 26, 2020. The dynamic changes of blood SAA, CRP, PCT, WBC, Lymphocyte (L), PLT, CT imaging, and disease progression were studied. At the same time, clinical conditions were evaluated and CT scans were obtained. Data were collected at three time points: admission, 3-5 days of hospitalization, and at the composite endpoint. Composite endpoint is February 26, 2020.As of February 26, 2020, the number of hospital discharge, inpatients, and the number of dead were counted.

We extracted the medical records of patients and sent these to the data collection center of Wuhan University of Science and Technology. A team of doctors who had been treating patients with COVID-19 collected and reviewed the data. Because of the urgent need to collect data on this emerging pathogen, the requirement for informed consent was waived. If information was not clear, the working group in Wuhan University of Science and Technology contacted the doctor responsible for the treatment of the patient for clarification. This case series was approved by the Medical ethics Review Board of Wuhan University of Science and Technology (No. 202009).

Sputum and throat swab specimens collected from all patients at admission were tested by real time polymerase chain reaction for SARS-Cov-2 RNA within three hours. Laboratory confirmation of the virus was performed using real time reverse transcription polymerase chain reaction. Virus detection was repeated twice every 24 hours. Laboratory tests were conducted at admission, including a complete blood count, serum biochemistry, and identification of other respiratory pathogens such as influenza A virus (H1N1, H3N2, H7N9), influenza B virus, respiratory syncytial virus, parainfluenza virus, and adenovirus. According to the COVID-19 Diagnosis and Treatment Plan issued by National Health Committee of China, patients received supportive oxygen therapy, antiviral medication, and other supportive treatments.

1) Clinical classification

According to COVID-19 Diagnosis and Treatment Plan issued by National Health Committee of China, clinical conditions are classified into four types: mild, moderate, severe, and critically severe.

Mild: mild clinical symptoms, no radiological changes

Moderate: fever, respiratory distress, CT scan indicating pneumonia signs

Severe: Meet any of the following(1)Shortness of breath, RR>30 times per minute;(2)At room air, SpO2 lower than 93%;(3)The partial pressure of Arterial blood oxygen (PaO2)/the fraction of inspired oxygen (FiO2) ≤ 300mmHg;(4)CT chest imaging shows that lung damage develops significantly within 24 to 48 hours.


Critically severe: Meet any of the following(1)Respiratory failure requiring mechanical ventilation;(2)Signs of septic shock;(3)Multiple organ failure requiring ICU admission.


2) CT imaging classification: the imaging was classified into four types of normal, mild, progressive, and severe, with scored at 0, 1, 2, and 3, respectively.(1)Mild: the main manifestations are ground-glass opacities and consolidation. Some cases show very thin, small patchy subpleural ground-glass opacities or ground-glass nodules. Lesions can be single or multiple, and both lung lobes can be involved. Lesions are more common in the middle and lower lobes, and mostly distributed in the outer zones of the lung and subpleural areas.(2)Progressive: large lesions can be seen and multiple lung lobes in both lungs can be involved. Consolidation and fibrosis of varying sizes are often seen within the lesions. Some cases may be accompanied by bronchial retraction, bronchiectasis, and interlobular pleural thickening. However, pleural effusion and enlarged mediastinal lymph nodes are rare in this type.(3)Severe: Lesions are diffuse in both lungs and uneven in density. Large areas of consolidation and ground-glass opacities can be seen. The sign of “white lung” can be seen due to large areas of the lung are involved. The interlobular pleura and bilateral pleura are usually thickened, and pleural effusion can be seen.


3) Outcome of illness: According to clinical progression, cases were divided into four types: fully recovered, improved, exacerbation, and death.

Statistical analysis was performed using SPSS 25.0 software. Kruskal-Wallis H-test and independent sample chi-square test were used to analyze differences between groups. Due to unequal variance, Tamhane ’s T2 statistical method was a fair measure to perform multiple comparisons among groups of mild, moderate, severe, and critical severe patients for the value of SAA/L. Two-tailed P value less than 0.05 was considered statistically significant. The Receiver Operating Characteristic curve (ROC curve) was used to calculate the area under the curve (AUC) of SAA, CRP, L, and SAA / L in order to evaluate the sensitivity and specificity of these factors. Spearman correlation coefficient was utilized to measure the degree of correlation between the hierarchically ordered variables in this study.

This was a retrospective case series study and no patients were involved in the study design, setting the research questions, or the outcome measures directly. No patients were asked to advise on interpretation or writing up of results.

From January 18, 2020 to February 26, 2020, 693 patients with COVID-19 was treated in Tianyou Hospital, and 132 patients met the requirements of this study. The patients were between 33-89 years old, with an average age of 62 years. And 87 of 132 patients were over 60 years, accounting for 65.9%. Of these patients, 75 were males, accounting for 56.8%, and 57 were females, accounting for 43.2%. (Table 1
)

At the time of admission, 60 patients had mild or moderate symptoms, accounting for 45.5%; and 56 patients had severe symptoms, accounting for 42.4%. 16 of 132 patients were critically severe, accounting for 12.1%. In this study, more patients were male and more patients were more than 60 years, consistently with previous literature reports [3]. (Table 1)

(1) The relationship between the levels of SAA, CRP, WBC, L, PCT and clinical classification at admission.

According to the results showed in Table 2
, with disease progressing from mild to critically severe, SAA and CRP gradually increased, while L gradually decreased (P<0.05). However, PLT, WBC, and PCT were all within the normal ranges, suggesting that SAA, CRP, and L are closely related to disease classification, while WBC, PCT, and PLT are of little significance.

Due to unequal variance, Tamhane ’s T2 statistical method was a fair measure to perform multiple comparisons among groups of mild, moderate, severe, and critical severe patients for the value of SAA/L. The SAA/L of severe/critically severe patients was significantly higher than that of mild/moderate ones, and p<0.01 indicates significant difference. (Fig 1
).

(3) SAA/L,CRP,SAA,L and clinical classification

To detect if SAA/L is more sensitive in predicting the severity of disease, the authors used ROC curve analysis to calculate the area under the curve (AUC), regarding mild/moderate type as negative whereas severe/critical severe type as positive. The results showed that AUC from high to low was SAA1/L1 > CRP1 > SAA1 > L1, with the specific value at 0.748, 0.744, 0.718, and 0.700, respectively. Next, the authors used the method of Jordan Index to calculate the critical values of SAA/L that could be utilized as the reference for patient clinical classification (Fig 2
).

The dynamic changes of SAA, CRP, L, and SAA/L reflected the change of patient condition at 3-5 day-hospitalization. Patients with decreased SAA2, CRP2, SAA2/L2 and elevated L2 were more likely to have improved conditions (Table 3
).

To detect if SAA2/L2 is more sensitive in predicting the progression of disease, the authors used ROC curve analysis to calculate the AUC of SAA2, CRP2, L2, and SAA2/L2, with the criteria of recovering as negative whereas exacerbation as positive. The results showed that AUC from high to low was SAA2/L2 > L2 > SAA2 > CRP2, with the specific value of 0.933, 0.909, 0.856, and 0.850, respectively. Next, the authors used the method of Jordan index to calculate the optimal critical values of them and obtained 199, 159, 24.65, and 0.87 for SAA2/L2, SAA2, CRP2, and L2, respectively (Fig 3
).

The changes of SAA, CRP, L and SAA/L between the first and second time point. Patients with increased SAA2, CRP2, SAA2/L2 and decreased L2 were more likely to have a worsening condition.Patients with decreased SAA2, CRP2, SAA2/L2 and elevated L2 were more likely to have improved conditions (Table 4
).

To detect if the dynamic changes of studied inflammation factors were valuable in predicting the progression of disease, the authors used ROC curve analysis to calculate the AUC of the changes of SAA, CRP, L and SAA/L between the first and second time point, regarding recovering as negative whereas exacerbation as positive. The results showed that AUC from high to low were SAA2/L2-SAA1/L1>L1-L2>SAA2-SAA1>CRP2-CRP1, with the specific value at 0.880, 0.846, 0.832, 0.622, respectively. Next, the authors used the method of Jordan index to calculate the optimal critical values according to sensitivity and specificity.

ROC curve analysis results at three time points indicated SAA/L was more sensitive in predicting the progression of disease, especially SAA2/L2 at the second time point, with the sensitivity high at 0.933 (Fig 4
).

The authors performed a multivariate analysis of variance for SAA levels that were measured at three time points. The results showed a significant correlation of SAA dynamics and patient outcome. Specifically, SAA3, CRP3, and SAA3/L3 levels continued to increase whereas L consistently decreased in exacerbating and deceased patients. While, the levels of SAA3, CRP3, and L3 were within normal ranges in recovered patients (Table 5
)

In patients who were discharged/well recovered, from the first time point at admission to the third time point at composite endpoint, the levels of SAA, CRP, and SAA/L consistently decreased, but L consistently increased. While, in exacerbating and deceased patients, the levels of SAA, CRP, and SAA/L consistently increased, whereas L decreased (Table 6
).

To detect if SAA and L dynamic changes are valuable in predicting the patient outcome, the authors used ROC curve analysis to calculate the AUC of the difference of SAA, L, and SAA/L between the first and third time point, with the criteria of patient discharge as negative whereas patient death as positive. The results showed that AUC from high to low were L1-L3>SAA3-SAA1>SAA3/L3-SAA1/L1, with the specific value of 0.925, 0.884, and 0.863, respectively(Fig 5
).


Fig 6, Fig 7
.

The CT imaging features were evaluated and scored as follows: normal (0 points), mild (1 point), progressive (2 points), and severe (3 points). Of the CT scans performed at admission, 98.5% (130/132) showed abnormalities, and only two cases were normal.

Of ten exacerbating patients, seven of them had the second CT scan showing the worsening of disease; Of six deceased patients, five of them had the second CT imaging showing the worsening of disease; Of six exacerbating or deceased patients, five of six had the third CT scan showing the worsening of disease. Additionally, all of deceased patients had CT scans showing the worsening of disease (Table 7
).

According to the correlation analysis showed in Table 8
, the first SAA level at admission was correlated with the dynamic changes of the first, second, and third CT scans. Specifically, for the patients with higher level of SAA1 at admission, the CT classification tended towards severe. The correlation of the first SAA with the second CT result was higher than that with the first CT result, suggesting the significance of SAA in estimating the progression of disease was higher than CT scan alone (Table 8).

One limitation of this study lies in it was performed in a single medical facility, lacking the control group design due to the emergent situation of COVID-19 breakout. In the future, the researchers will collaborate with a few medical facilities in the area and design the control group to improve the reliability of the study.

Based on the study results, SAA alone or combined with L (SAA/L) could be used as a significant marker to indicate and track inflammation conditions in COVID-19 infected patients. SAA and L are sensitive indicators in evaluating the severity and prognosis of COVID-19. Monitoring dynamic changes of SAA, combined with CT imaging could be a valuable strategy in the diagnosis and treatment of COVID-19.

The authors declare that they have no competing interests.