Autopsy and Informatics Analysis Evidence Coagulopathy Progress in COVID-19 Patients

Background The outbreak of COVID-19 around the world resulted in more than 480 thousand deaths. Objective To clarify the thrombotic phenomena with coagulation progress in COVID-19 patients based on epidemiological statistics combining the autopsy and informatics analysis. Methods Using 9 autopsy results with COVID-19 pneumonia and the medical records of 407 patients including 39 deceased ones whose discharge status was certain, time-sequential changes of 11 coagulation relevant indices within mild, severe and critical infection throughout hospitalization according to NHC guidelines were evaluated. Informatics tools were applied to calculate the importance and correlation between them and the progression of thrombosis. Results At the beginning of the hospitalization, PLT had a signicant decrease in critically ill patients. GLU, PT, APTT, and D-dimer in critical patients were higher than those in mild and severe during the whole admission period. The ISTH DIC score also showed the continuous overt DIC in critical patients. At the late stage of non-survivors, the dynamic proles of PLT, PT, and D-dimer were signicantly different from survivors. A random forest model indicated that the most important feature was PT, followed by D-dimer, indicating their crucial roles for the progression of disease. around the combining autopsy data, time-sequential proles and informatics to the dynamic changes of coagulation relevant indices throughout the of disease deterioration, which helps guide the and detect the prognosis in different level of


Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was rst identi ed in Wuhan, China and associated with an outbreak of coronavirus disease 2019 (COVID-19) that affected more than 9 million patients with more than 480 thousand deaths globally. [1][2][3][4] The latest published researches remind us of COVID-19 patients with severe hypercoagulability and venous thrombosis, which cannot be ignored. [5][6][7] The coagulation changes in the SARS-CoV-2 infected patients are now described as the COVID-19 associated coagulopathy (CAC) 8 . A few clinical reports have been published on patients with COVID-19 who undergo complete autopsy [9][10][11][12][13][14][15][16] . Notably, several cases nd a high incidence of venous thromboses and embolisms as the direct cause of death. [17][18][19] CAC correlated thrombotic phenomena in COVID-19 deaths have been suggested its signi cant poor prognostic features. 20 Besides, many critical patients with COVID-19 also exhibit abnormal coagulation, most of whom have venous or arterial thromboembolic complications and microvascular thrombosis similar to other systemic coagulopathy associated with severe infections, such as disseminated intravascular coagulation (DIC) or thrombotic microangiopathy 12,21−23 . However, these studies are based on relatively small sample sizes and lack time-sequential properties from admission to discharge, and little knowledge is known about the progress of coagulation that leads to death in COVID-19 patients and the combination of the autopsy with the complete coagulation parameters in con rmed discharge status (decease or discharge without COVID- 19) were not well studied.
Here we reported the pathological features of COVID-19 patients with thrombotic phenomena from autopsy and the coagulation disease progress among mild, severe, and critical patients from one hospital in China. Moreover, we also investigated the difference between survivors and non-survivors in patients with critical infection. Last but not least, we evaluated the correlation and contribution of those features regarding the severity of patients by informatic tools.

Patients
We collected autopsy data from 9 deceased patients and other clinical data from 407 patients in one hospital in China. All patients were con rmed COVID-19 pneumonia. Patients' medical records contain the essential information and values of detection indices during the whole process from admission to discharge (or decease).

Autopsy and histological examination
We performed full-body autopsies on deceased persons with SARS-CoV-2 positivity as soon as possible after taking proper safety precautions at the biosafety level 3 (BSL-3) following guidelines from the industrial standards of public safety of the People's Republic of China. Tissue samples for histopathologic examination were xed in buffered 4% formaldehyde and processed via standard procedure to slides stained with hematoxylin-eosin (H&E stain). All the hematological indices were collected for the last testing before decease.

Data Collection and Procedures
We reviewed the electronic clinical charts, examination records, and laboratory ndings for 407 COVID-19 patients (including 39 deceased patients). During the whole process from admission to discharge (or death), time-sequential hematological investigations including 11 indices i.e. platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), brinogen (FIB), D-dimer, blood glucose (GLU), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were extracted for subsequent analyses. Other clinical characteristics including age, gender, major comorbidities (coronary artery disease [CAD], hypertension [HTN], and diabetes mellitus [DM]), discharge status (survive or die) and the hospitalization time were also analyzed in our study. Following data extraction, those patients were divided into 3 groups (mild, severe or critical) according to the Chinese National Health Commission (NHC) guidelines (7th trial edition) for COVID-19 pneumonia. 24 Furthermore, we focused on the dynamic pro les of these indices between 39 non-survivors and 42 survivors in the critical group to assess the coagulation process of disease deterioration. In addition, correlation using Pearson method and a random forest model was calculated for patient classi cation for evaluate the importance of 11 indicator for the process of COVID-19.
For DIC analysis, the International Society on Thrombosis and Haemostasis (ISTH) diagnostic criteria were applied to all the patients. 5,25 Case De nitions Mild infection, severe infection, and critical infection were characterized throughout the entire hospitalization according to NHC's guidelines. 24 Brie y, mild infection is characterized with mild symptoms, fever, respiratory symptoms, and imaging ndings of pneumonia. Severe infection is with any of the following appears: shortness of breath (RR > 30 times/min), oxygen saturation ≤ 93%, PaO 2 /FiO 2 ≤ 300 mmHg. Critical infection is with any of the following appears: respiratory failure requires mechanical ventilation, shock, other organ failure requires ICU monitoring treatment.

Pearson Correlation Coe cient and Random Forest Model Analysis
We labeled the male as 1 and female as 0 in the correlation and random forest model. Due to the limitations of our detection system, the reportable range of D-dimer and TT were 0.22-21 µg/mL and 13-240 s, respectively. Therefore, when it was reported out of this level (e.g. >21 µg/mL for D-dimer), we corrected those values the barrier of the reportable range (e.g. >21 µg/mL for D-dimer as 21 µg/mL). We also labeled the severity of patients as 1 for mild syndrome, 2 for severe syndrome, and 3 for critical syndrome. Then all data were put into one le to calculate the Pearson correlation coe cient (R, 3.6.1, package 'gpairs') and random forest model (Python 3.7) according to previous reports. 26

Statistical Analysis
For categorical variables and baseline indices, median and interquartile range (IQR) were applied in the form of counts and percentages. Mean and standard error were also used to display the line charts of indices changes. Proportions for categorical variables were compared using the χ 2 test. Continuous variables were compared using Wilcoxon rank sum test. These statistical analyses were performed using R (version 3.6.1) and the graphs were drawn using GraphPad prism (version 8.0.2).

Role of funding source
None of the funders had any role in the study design, data collection, analysis, interpretation or in the writing of the article and the decision to submit it for publication. Independence from funders and sponsors were con rmed by the researchers.

Results
Complete autopsies of 9 deceased COVID-19 patients (5 males and 4 females) with 15 median hospitalization days (IQR, 10-22) before death were performed ( Table 1). The median of ages was 67 years old (IQR, 63-78). Except for the missing comorbidity records of 2 cases (cases 6 and 9), the other 7 cases all had comorbidities. To be speci c, 7/7 cases had the comorbidity of hypertension, 2/7 (cases 1 and 4) of cerebral infarction, 2/7 (cases 5 and 8) of coronary artery disease, and 1/7 (case 7) of gout. Of note, one case (case 5) had not only hypertension and coronary artery disease but also renal dysfunction, lacunar infarction, and chronic bronchitis with emphysema. 8/9 cases (88.9%) died mainly due to the respiratory failure with multiple organ failure and the other 1/9 (11.1%) died due to sudden cardiac death from acute coronary heart disease. Besides the diffuse alveolar damage in the lung, the predominant histological ndings were hyaline thrombi among all the 9 deceased patients (Fig. 1). To be speci c, 9/9 cases showed microthrombi in hilar arteriole, alveolar wall capillary and interstitial vascular lumen of the lung, 4/9 (1, 2, 3, and 5) in the subarachnoid arteriole and parenchymal small endovascular lumen of the brain, 4/9 (1, 2, 3, and 5) in the small vascular lumen of the spleen, 2/9 (cases 2 and 9) within the kidney, and 1/9 (case 4) in coronary artery lumen together with hemorrhage. To evaluate the coagulation state before death, we also extracted the last hematological indices relevant to coagulation, i.e. platelet, prothrombin time, activated partial thromboplastin time, thrombin time, brinogen, and D-dimer in these cases when they were alive (Table 1). Although all the medians of these indices were within the normal range, the ISTH DIC scores in 8/9 cases matched the grade of overt DIC (≥ 5 points).
The autopsy results of thrombi in the major organs of the body and their overt-DIC before death strongly indicated coagulation abnormalities in COVID-19 patients (Table 1, Fig. 1). Together with previous reports showing the high relevance of blood glucose, total cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein with coagulation, 27-30 we then included those indices in our clinical data analyses. The clinical time-sequential data included 407 hospitalized patients with con rmed COVID-19. Their demographic and clinical characteristics were shown in Table 2. The median (IQR) age was 62.0 years (51.0-58.0) with the overall range from 6 years to 92 years; 51.8% of the patients were from 40 to 65 years of age.       (Table S1). Furthermore, though the critical patients showed no statistical difference in duration of hospitalization between the severe ones (P = 0.2806), the median days of critical patients were shorter than that of severe ones. This led us to think about whether the non-survivors displayed any difference in the critical group. In addition, those commodities percentages of commodities and hospital duration in survivors were more similar to the severe group than those in nonsurvivors from the critical group (Table 2).
To determine the major hematological features that appeared during COVID-19 thrombogenic progression, the dynamic pro les of 11 clinical laboratory indices, including platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), brinogen (FIB), D-dimer, blood glucose (GLU), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL), were tracked on admission until outcome (Table S3). All the 407 patients with de nite discharge status were analyzed and displayed using the line chart (Fig. 2). During hospitalization, most patients had increased D-dimer, and those with critical infection stay signi cantly higher D-dimer after admission until the outcome. Intriguingly, PLT in the critical patients showed a marked down on admission, kept the counts low until day 14 (both P < 0.05 compared to mild and severe patients [ Table S3]), and then gradually increased. In addition, the indices, e.g. PT, and GLU in critical patients showed continuous prolonged time, higher score or higher level during the hospitalization than those in severe or mild patients, while others, e.g. TC and HDL in the critical patients were lower at the initial stage and stayed a relatively low level until the outcome. On the other hand, indices e.g. LDL exhibited their changes at the late stage and TT was intermittently prolonged after admission in the critical patients while APTT, FIB, and TG had no discernable difference among patients with different levels of severity during hospitalization. We also found that the continuous overt-DIC in critical patients during the whole hospitalization stay by counting the ISTH DIC scoring system.
To evaluate the severity of the coagulation state in patients with different disease levels, we also ought to evaluate the percentages of 11 indices' peak value in every individual that ever reached out of normal range during hospitalization. Table S4 and Table S5   Our autopsy results and 81 critical patients strongly suggested the difference existing between survivors and non-survivors, so the progression analyses of laboratory hematological indices were also taken to evaluate the severity of coagulation. Since the destinations of all patients were con rmed either discharged with SARS-CoV-2 negativity or deceased, we de ned the date of discharge or decease as day 1 before outcome and the previous dates increased backward (Fig. 3, Table S6) (Table S7, Table S8), strikingly when it came close to the destination date (P = 0.0027 and P = 0.0051 for PLT and PT at day 11 before outcome, respectively; P = 0.0063 and P = 0.0193 for D-dimer at day 12 before outcome) (Fig. 3, Table S6). Notably, while no obvious change could be found when divided all patients into 3 groups (Fig. 2), the subgroup of nonsurvivors manifested a signi cantly higher level of brinogen than that of survivors at days 7 and 9 before outcome (Fig. 3, Table S6).
To further explore the underlying correlation between these groups, the heat map was applied to visualize the Pearson correlation coe cient between each clinical feature or laboratory indices (Fig. 4). "Label" in the heatmap indicated the severity of COVID-19, i.e. mild, severe, and critical classi cations. As We further applied those data to the normal distribution curve to estimate those features' relationship with the severity (Fig. 4). Unlike age-severity distribution with the critical group's mean between severe group and mild group (Fig. 4), coagulation indices-severity distributions including PT, APTT, and D-dimer all complied with the mild-severe-critical distribution positively and other indices such as TC, HDL, and LDL negatively ( Figure  S1). To explore which indices played an indispensable role, a random forest model was constructed according to patient classi cations. The best accuracy of the model is 83.8%, the maximum depth of the tree is 9, and the number of classi ers is 50 ( Figure S1). Then the model showed us the importance of each feature (Fig. 4, Figure S1). The most important feature was PT, followed by D-dimer. These two features contributed to the 40% importance of total. The red dotted line together with the black one separated the features that totaled 90% importance. Taken together, those data suggested the important role of coagulation and hematological indices during the deterioration of COVID-19 progress.

Discussion
This study combined 9 autopsy results with the epidemiological and clinical characteristics of 407 COVID-19 patients to explore the dynamic changes in coagulation function pro les during the entire hospitalization. Based on the evaluation of 11 hematological indices on admission to discharge, we found several interesting phenomena that were not reported before. These hematological indices such as PT, APTT, PLT, and D-dimer showed signi cant changes among different types of patients. Notably, in our study, deceased patients were categorized in critical patients. Mortality among critically ill patients was as high as 48.1%. Moreover, a high level of FIB in the non-survivors at days 5-10 before the outcome was found in our study, which was different from previous reports. 5,17 Considering the same critical patients as the control group and the intact period of hospitalization, our data were more likely to elucidate the underlying coagulation process. In the same period when FIB was higher in the non-survivors, the other coagulation related indices such as PLT, PT, and Ddimer were all deviated from the normal range, indicating hypercoagulation state in the non-survivors. Of note, PLT was signi cantly lower in the critical group, and then gradually went up at the late stage of hospitalization (Fig. 2). However, when separating the critical group into subgroups, we found that there were not so many critical changes in the survivors and we could reason that the decline of PLT was the result of non-survivors' thrombocytopenia (Fig. 2, Fig. 3). In concert with the previous study 5 , levels of D-dimer showed a marked elevation twice after admission and before death in non-survivors (Fig. 3), suggesting the coagulation activation and secondary hyper brinolysis condition during thrombosis. Considering so many coagulation related abnormalities, we also calculated the ISTH DIC score to evaluate the DIC state in all patients along the time axis. Despite the increased level of FIB in non-survivors, the DIC score showed the critical patient reaching the limit nearly all the time with no signi cant difference in its survivor or non-survivor subgroup (Fig. 3). This phenomenon of overt DIC together with the observation of thrombotic from autopsy histological results showed different coagulopathy among different levels of severity in COVID-19 patients.
Other coagulopathy relevant indices are LDH and HDL. Surprisingly, our observation along the hospitalization showed a signi cant decrease in these 2 indices in critical patients instead of an increase in the previous report. 31 Since the protective effect of HDL through inhibiting blood vessel aggregation, in ammation, oxidation, endothelial damage and thrombosis in several hematological diseases 32 , the low-level HDL and LDL in our observation in critical patients indicated the disturb hematological system, which might contribute to the disease deterioration. Although GLU exhibited much higher in critical group and diabetes mellitus has been found the risk factor of COVID-19 progression especially for deaths in previous and our studies 33 , we should still be careful giving suggestions between diabetes and COVID-19 unless more de nite conclusions are made through detail researches. Previous studies have shown that several COVID-19 patients have increased concentrations of proin ammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin (IL) 34 , especially induces a cytokine storm that might lead to the activation of the coagulation cascade in severe cases. 35 Besides, diabetes can also affect vascular abnormalities and promote the increased synthesis of glycosylation end products (AGEs) and pro-in ammatory cytokines, oxidative stress to mediate in ammation. 36 Taken together, all these showed us the complex CAC progresses in COVID-19 patients with thrombotic complications.

Limitations
Our study has notable limitations. First, the number of patients included in our study is still not large sample, especially for deceased patients. This may bias the proportion of commodities and other observations. It would be better to include more patients over the world and among different countries. Second, indices are still not enough to evaluate the comprehensive aspects of thrombogenesis since thrombogenesis is a complex complication especially when a patient is infected with a virus of high infectivity. Third, we start the records from the admission instead of the onset of illness, which might lose part of the coagulation information.

Conclusions
In summary, our study provides the full spectrum of coagulation progress with the de nite discharge status and also shows the existence and dynamic changes of DIC along with this progress. Importantly, we combined the autopsy histology and informatics analysis to reveal the signi cance of coagulopathy relevant indices during thrombosis. Those results might help guide the therapy and detect the prognosis in different levels of COVID-19 infections. contributed to the critical revision of the data. All authors contributed to data acquisition, data analysis, or data interpretation, and reviewed and approved the nal version.

Data sharing
After the publication of the study ndings, the data that support the ndings of this study will be available for others from the corresponding author based on reasonable request. We will provide an email address for communication once the data are approved to be shared with others under the supervision of the corresponding author and the NHC's guidelines.

Declaration of interests
These authors declare no competing interests.    Table S4.   Table S6. Median, interquartile and statistical test results across different hospitalization days between survivors and non-survivors at different days before outcome related to Figure 3. Table S7. laboratory indices among survivors and nonsurvivors of 81 critically ill patients with COVID-19.

Supplementary Files
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