Hindawi BioMed Research International Volume 2020, Article ID 6159720, 10 pages https://doi.org/10.1155/2020/6159720 Research Article D-Dimer and Prothrombin Time Are the Significant Indicators of Severe COVID-19 and Poor Prognosis Hui Long,1 Lan Nie,2 Xiaochen Xiang,2 Huan Li,1 Xiaoli Zhang,1 Xiaozhi Fu,1 Hongwei Ren,1 Wanxin Liu,2 Qiang Wang ,2 and Qingming Wu 1,2 1Internal Medicine of Tianyou Hospital, Wuhan University of Science and Technology, Wuhan 430064, China 2Institute of Infection, Immunology and Tumor Microenvironment, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China Correspondence should be addressed to Qiang Wang; [email protected] and Qingming Wu; [email protected] Received 22 April 2020; Accepted 19 May 2020; Published 17 June 2020 Academic Editor: Frederick D. Quinn Copyright © 2020 Hui Long et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. To investigate the value of coagulation indicators D-dimer (DD), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (Fg) in predicting the severity and prognosis of COVID-19. Methods. A total of 115 patients with confirmed COVID-19, who were admitted to Tianyou Hospital of Wuhan University of Science and Technology between January 18, 2020, and March 5, 2020, were included. The dynamic changes of DD, PT, APTT, and Fg were tested, and the correlation with CT imaging, clinical classifications, and prognosis was studied. Results. Coagulation disorder occurred at the early stage of COVID-19 infection, with 50 (43.5%) patients having DD increased and 74 (64.3%) patients having Fg increased. The levels of DD and Fg were correlated with clinical classification. Among 23 patients who deceased, 18 had DD increased at the first lab test, 22 had DD increased at the second and third lab tests, and 18 had prolonged PT at the third test. The results from ROC analyses for mortality risk showed that the AUCs of DD were 0.742, 0.818, and 0.851 in three times of test, respectively; PT was 0.643, 0.824, and 0.937. In addition, with the progression of the disease, the change of CT imaging was closely related to the increase of the DD value (P <0:01). Conclusions. Coagulation dysfunction is more likely to occur in severe and critically ill patients. DD and PT could be used as the significant indicators in predicting the mortality of COVID-19. 1. Introduction and fibrinogen (Fg), could sensitively reflect the clotting state of the body. COVID-19 which emerged in Wuhan, Hubei Province, The aim of the report is to investigate role of the dynamic China, is caused by severe acute respiratory syndrome coro- changes of DD, PT, APTT, TT, and Fg in predicting the navirus 2 (SARS-CoV-2). It is typically spread via respira- severity and prognosis in patients with COVID-19. tory droplets and during close contact. The main clinical manifestation is lung injury[1, 2]. Most of the patients have 2. Materials and Methods a favorable prognosis, but some rapidly progress to severe and critical cases with respiratory distress syndrome, coag- 2.1. Source of Patients and Diagnosis Criteria. The informa- ulation dysfunction, multiple organ failure, etc.[3, 4]. tion of a total of 115 patients with confirmed COVID-19 Therefore, early identification of the severity is very impor- who were admitted to Tianyou Hospital affiliated to the tant to the clinical diagnosis of and treatment for COVID- Wuhan University of Science and Technology between Janu- 19. Commonly used clinical laboratory coagulation indexes, ary 18, 2020, and March 5, 2020, was collected. The con- such as D-dimer (DD), prothrombin time (PT), activated firmed patients had a positive result of the nucleic acid test partial thromboplastin time (APTT), thrombin time (TT), of SARS-CoV-2 by real-time fluorescence RT-PCR. Three 2 BioMed Research International clinical disease assessments were conducted using labora- sures directly. No patients were asked to advise on the inter- tory data collected. Cases of hospital discharge, death, and pretation or writing up of results. under treatment with a duration of hospitalization longer than 14 days prior to March 5, 2020, were studied. Cases 3. Results and Discussion with incomplete laboratory data or with a duration of hos- pitalization shorter than 14 days prior to March 5, 2020, 3.1. Demographic Characteristics. Among 115 patients with were excluded. This study was approved by the Medical COVID-19, the median ages were 63:55 ± 13:86 (27-96) Ethics Review Board of Wuhan University of Science and years old, male were 66 (57.4%) cases, female were 49 Technology (No. 202009). (42.6%) cases, and over 60 years old were 78 (67.8%) cases. At the time of admission, mild and ordinary patients were 2.2. Clinical Classifications 39 (33.9%) cases, severe patients were 48 (41.7%) cases, and critical patients were 28 (24.3%) cases (Table 1). In this study, 2.2.1. Case Identification. According to the Guidance for more patients were male and more patients were more than Corona Virus Disease 2019: Prevention, Control, Diagnosis, 60 years, consistently with previous literature report [1]. and Management edited by the National Health Commission of the People’s Republic of China, all cases were identified 3.2. The Relationship between the Levels of DD1, PT1, APTT1, into four categories of mild cases, ordinary cases, severe Fg1, and Clinical Classification. There are significant differ- cases, and critical cases. (1) Mild cases had mild clinical ences in DD1 between different clinical classifications symptoms and no pneumonia manifestation in imaging. (2) (P <0:05). The severity of the disease increased as DD1 Ordinary cases had symptoms like fever and respiratory tract increased. 81 (70.4%) patients had Fg1 increased (Table 2). symptoms, and pneumonia manifestation can be seen in imaging. (3) Severe cases met any of the following: respira- 3.3. Relationship between the Dynamics Changes of DD, PT, fi tory distress, RR ≥ 30 breaths/min; the oxygen saturation is APTT, TT, Fg, and the Prognosis of COVID-19. Signi cant ff P : less than 93% at a rest state; or arterial partial pressure di erence ( <005) and positive correlation were found of oxygen ðPaO Þ/oxygen concentration ðFiO Þ ≤ 300 mmHg between DD, PT, and outcomes at composite endpoints. 2 2 fi (1mmHg=0:133 kPa). Patients with >50% lesion progression Correlation in third detection was stronger than that in rst within 24 to 48 hours in pulmonary imaging were treated as and second detection. severe cases. (4) Critical cases met any of the following: respi- Among 23 patients who died, 18 (78.3%) cases had DD1 ratory failure occurs, and mechanical ventilation is required; increased, 12 of 18 had DD1 two times higher (>1.10 mg/L), shock occurs; or complicated with other organ failure that 22 cases had DD2 and DD3 increased, 21 of 22 had DD2 and requires monitoring and treatment in ICU. DD3 two times higher (>1.10 mg/L). Eight cases in exacer- bated patients occurred increased DD2 and DD3 all higher 2.2.2. Outcome of Illness. According to clinical progression, (1.10 mg/L) (Table 3). outcomes in endpoints were divided into four types: hospital 3.4. Analysis of DD and PT in Predicting Hospital Discharge discharge, improved, exacerbation, and death. and Mortality of COVID-19. We used the ROC curve analysis to evaluate the diagnostic value of hospital discharge and 2.3. Data Collection. The laboratory data were collected at mortality in 115 patients. The AUCs of DD1, DD2, and three time points: admission, 3-5 days of hospitalization, DD3 to predict hospital discharge and mortality were 0.742, and at the composite endpoint. DD, PT, APTT, and Fg were 0.818, and 0.851, respectively (Figure 1(a)). The AUCs of obtained and labeled as DD1-3, PT1-3, APTT1-3, TT1-3, and fi PT1, PT2, and PT3 to predict hospital discharge and mortal- Fg1-3, respectively. Meanwhile, case identi cation, imaging ity were 0.643, 0.824, and 0.937, respectively (Figure 1(b)). identification, and outcome of illness were defined. 3.5. Dynamic Changes of Chest CT Imaging, DD and CTA in 2.4. Statistical Methods. Statistical analysis was conducted COVID-19 Patients. At the early stage of the disease, the cor- using the SPSS 25.0 software. Descriptive statistics included relation between CT imaging changes and DD value was not means and standard deviations. The Kruskal-Wallis H-test obvious; however, with the progression of the disease, the and independent sample chi-square test were used to analyze change of CT was closely related to the increase of DD value, differences between groups. The Receiver Operating Charac- and there was a significant statistical difference (Table 4). teristic curve (ROC curve) was used to calculate the area The clinical observation showed that the abnormal coag- under the curve (AUC) of DD and PT in order to evaluate ulation factor was consistent with the CT imaging results. In the sensitivity and specificity of these factors in predicting this paper, a typical patient was taken as an example. The mortality and hospital discharge. Spearman’s rank correla- dynamic changes of chest CT imaging and DD were consis- tion analysis was utilized to measure the degree of correlation tent (Figure 2(a)). Increased DD was associated with pulmo- between the hierarchically ordered variables in this study. A nary embolism, which was confirmed by CTA (Figure 2(b)).