Autopsy and Informatics Analysis Evidence Disturbed Haemostasis Progress in COVID-19: Medical Records from 407 Patients


 Background: The progress of coagulation in COVID-19 patients with confirmed discharge status (decease or discharge) and the combination of the autopsy with the complete coagulation parameters were not well studied.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 relevant indices within mild, severe and critical infection throughout hospitalization according to the Chinese National Health Commission (NHC) guidelines were evaluated. Informatics tools were applied to calculate the importance of 11 indices and the correlation between those indices and the severities of COVID-19.Results: At the beginning of the hospitalization, platelet (PLT) had a significant decrease in critically ill patients. Blood glucose (GLU), prothrombin time (PT), activated partial thromboplastin time (APTT), and D-dimer in critical patients were higher than those in mild and severe during the whole admission period. The International Society on Thrombosis and Haemostasis (ISTH) disseminated intravascular coagulation (DIC) score also showed the high DIC level in critical patients. At the relatively late stage of non-survivors, the temporal changes of PLT count, PT, and D-dimer were significantly different from survivors. A random forest model indicated that the most important feature was PT, followed by D-dimer, indicating their positive associations for the severities of disease. Autopsy data from 9 deceased patients also showed the DIC phenomena with prolonged PT, APTT, less PLT count and thrombosis in multiple organs.Conclusions: Combining autopsy data, time-sequential changes and informatics methods to explore the coagulation relevant indices among the different severities of the disease, helps guide the therapy and detect the prognosis in COVID-19 infection.

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 Wuhan, 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 clinical data from 407 patients in one hospital in Wuhan. All patients were con rmed COVID-19 pneumonia according to the Chinese National Health Commission (NHC) guidelines (7 th trial edition) for COVID-19 pneumonia. Brie y, the patients with SARS-CoV-2 positive using RT-PCR were further con rmed pneumonia using computed tomography (CT) scanning. 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 9 deceased persons with SARS-CoV-2 positivity as soon as possible after taking proper safety precautions at the bio-safety 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 investigations including 11 indices, six of which were haemostasis related indices detected by the kits from Simons (German) using Sysmex 5100 (Japan) --platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), brinogen (FIB), D-dimer, and ve of which were lab parameters detected by the kits from Leadman (China) using Hitachi Automatic Analyzer 7600 (Japan) ---blood glucose (GLU), total cholesterol (TC), triglyceride (TG), highdensity lipoprotein (HDL) and low-density lipoprotein (LDL) were extracted for subsequent analyses 25 . 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 NHC guidelines for COVID-19 pneumonia. 26 Furthermore, we focused on the temporal changes 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. 5,27 Case De nitions Mild infection, severe infection, and critical infection were characterized throughout the entire hospitalization according to NHC's guidelines. 26 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 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. >21μg/mL for D-dimer), we corrected those values the barrier of the reportable range (e.g. >21μg/mL for D-dimer as 21μg/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. 28,29 Brie y, when constructing the model, the decision tree is enerated by CART algorithm using Gini index (also identi ed as importance index in our study). Gini index represents the impurity level of the model and the Gini index is smaller, the lower purity. In the classi cation problem, assuming there are K categories, the probability of the kth category is pk, the Gini index formula is as follows: See formula 1 in the supplementary les section.
We then used Gini index gain as the basis of selecting feature of decision tree, as the following formula: See formula 2 in the supplementary les section.
We choose the maximum value of Gini index gain as the splitting characteristic, the node is used as the split condition.

Statistical Analysis
Descriptive analyses of categorical variables and baseline indices were expressed as median [interquartile range (IQR)], or number (%). 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 died mainly due to the respiratory failure with multiple organ failure and the other 1/9 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 ( Figure 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). The ISTH DIC scores in 8/9 cases matched the grade of DIC (≥5 points).
The autopsy results of thrombi in the major organs of the body and their DIC before death strongly indicated coagulation abnormalities in COVID-19 patients (Table 1, Figure 1). Together with previous reports showing the high relevance of blood glucose, total cholesterol, triglyceride, highdensity lipoprotein, and low-density lipoprotein with coagulation, [30][31][32][33] 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 Table 2, Table S1).
However, those 81 critical patients showed slightly close features of the underlying comorbidities to the mild ones (39 [48.1%] in critical ones, P = 0.1339) and no signi cant difference of hypertension proportion between them (31 [38.3%] in critical ones, P = 0.1894) (Table S1). Furthermore, despite no statistically signi cant difference (P = 0.2806), the median hospitalization days of critical patients were slightly shorter than that of severe ones (19.  ; P < 0.0001). In addition, those comorbidities percentages of comorbidities and hospital duration in survivors were more similar to the severe group than those in non-survivors from the critical group (Table 2).
To determine the major hematological features that appeared during COVID-19 thrombogenic progression, the temporal changes 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 ( Figure 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 persistent 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 the persistent high DIC score in the critical patients during whole hospitalization using 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 summarized   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 ( Figure 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)( Figure 3, Table S6). Notably, while no obvious change could be found when divided all patients into 3 groups (Figure 2), the subgroup of non-survivors manifested a signi cantly higher level of brinogen than that of survivors at days 7 and 9 before outcome ( Figure 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 (Figure 4). "Severity" in the heatmap indicated the severities of COVID-19, i.e. mild, severe, and showed a signi cantly negative correlation with patient classi cations. We further applied those data to the normal distribution curve to estimate those features' relationship with the severity (Figure 4). Unlike age-severity distribution with the critical group's mean between severe group and mild group (Figure 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 (Figure 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 Ddimer 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. 5,18 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 haemostasis-related indices such as PLT, PT, and D-dimer 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 ( Figure 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 ( Figure 2, Figure 3). In concert with the previous study 5 , levels of D-dimer showed two marked peaks during hospitalization in non-survivors (Figure 3), suggesting the coagulation activation 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 (Figure 3). This phenomenon of DIC together with the observation of thrombotic from autopsy histological results showed different disturbed haemostasis state among different levels of severity in COVID-19 patients.
Other haemostasis 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. 35 Since the protective effect of HDL through inhibiting blood vessel aggregation, in ammation, oxidation, endothelial damage and thrombosis in several hematological diseases 36 , 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 37 , 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) 38 , especially induces a cytokine storm that might lead to the activation of the coagulation cascade in severe cases. 39 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. 40 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 comorbidities 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 haemostasis relevant indices during thrombosis. Those results might help guide the therapy and detect the prognosis in different

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.