key: cord-0953672-8tfjdijp
authors: Dong, Yalan; Zhou, Haifeng; Li, Mingyue; Zhang, Zili; Guo, Weina; Yu, Ting; Gui, Yang; Wang, Quansheng; Zhao, Lei; Luo, Shanshan; Fan, Heng; Hu, Desheng
title: A novel simple scoring model for predicting severity of patients with SARS‐CoV‐2 infection
date: 2020-05-29
journal: Transbound Emerg Dis
DOI: 10.1111/tbed.13651
sha: b9bd10e5da4a914cb13529959b95b6d9419d9783
doc_id: 953672
cord_uid: 8tfjdijp

An outbreak of pneumonia caused by a novel coronavirus (COVID‐19) began in Wuhan, China in December 2019 and quickly spread throughout the country and world. An efficient and convenient method based on clinical characteristics was needed to evaluate the potential deterioration in patients. We aimed to develop a simple and practical risk scoring system to predict the severity of COVID‐19 patients on admission. We retrospectively investigated the clinical information of confirmed COVID‐19 patients from February 10, 2020 to February 29, 2020 in Wuhan Union Hospital. Predictors of severity were identified by univariate and multivariate logistic regression analysis. A total of 147 patients with confirmed SARS‐CoV‐2 infection were grouped into non‐severe (94 patients) and severe (53 patients) groups. We found that an increased level of white blood cells (WBC), neutrophils, D‐dimer, fibrinogen (FIB), IL‐6, C‐reactive protein (CRP), erythrocyte sedimentation rate (ESR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), α‐hydroxybutyrate dehydrogenase (HBDH), serum amyloid A (SAA) and a decreased level of lymphocytes were important risk factors associated with severity. Furthermore, three variables were used to formulate a clinical risk scoring system named COVID‐19 index = 3×D‐dimer (µg/L)+2×lgESR (mm/h)‐4×lymphocyte (×10(9)/L)+8. The area under the receiver operating characteristic (ROC) curve was 0.843 (95% CI, 0.771‐0.914). We propose an effective scoring system to predict the severity of COVID‐19 patients. This simple prediction model may provide health‐care workers with a practical method and could positively impact decision‐making with regard to deteriorating patients.

In December 2019, an outbreak of pneumonia occurred in Wuhan, China, which was confirmed to be caused by a new coronavirus different from the severe acute respiratory syndrome coronavirus 

This article is protected by copyright. All rights reserved regression, to predict the severity of the patients at the initial stage on admission and guide the treatment.

We conducted a retrospective study of 147 patients treated in Wuhan Union Hospital from February 10, 2020 to February 29, 2020. All the patients included were confirmed COVID-19 patients with a positive throat swab test for SARS-CoV-2 nucleic acid and ground-glass CT changes in the lung. Each patient received active treatment and laboratory testing of multiple parameters during hospitalization, and all medical records could be traced. Patients were divided into non-severe and severe groups as described below according to the Guidelines of the Diagnosis 

Demographic characteristics (age, gender, height and weight), basic information (pre-existing disease, time from onset to admission), symptoms and signs were extracted from medical records on admission. Computed tomographic (CT) scans and the laboratory examination results of patients on the first day of admission  including blood routine, biochemical, inflammation and immune-related indicators  were collected for later analysis.

This article is protected by copyright. All rights reserved All statistical comparisons were performed between non-severe and severe cases. For continuous variables, Mann-Whitney U test analysis was performed; and for categorical variables, Pearson's chi-square test or Fisher's exact test were applied as appropriate. Univariate logistic regression analysis was performed to screen the risk factors, and multivariate logistic regression analysis was used to assess the impact of different risk factors on the patient's condition and a scoring system was developed. The predictive value of the scoring system was evaluated by the ROC curve, and the cut-off value that can predict the severity of disease was subsequently determined. All analyses were performed using SPSS 20.0 software. P <0.05 was considered a significant difference.

A total of 147 patients with confirmed SARS-CoV-2 infection were grouped into 94 non-severe patients and 53 severe patients. Of the 94 non-severe patients, there were 34 males and 60 females with a median age of 40. For the 53 severe patients, there were 29 males and 24 females, with a median age of 60. When taking into account age and gender, severe cases were significantly older with a greater proportion of males, which is consistent with other reports that older men are a susceptible group and more likely to have severe symptoms (Guan et al., 2020). In terms of comorbidity, severe patients suffered from more pre-existing diseases. No difference was observed in BMI between the severe and non-severe groups. The clinical characteristics of the patients are shown in Table 1 , from which we found that the most frequent symptoms were fever, cough, fatigue, and the frequency of all symptoms that appeared at admission was not significantly different between the two groups. In addition, the time from symptom onset to admission was longer in the severe group compared to the non-severe group.

Laboratory examinations were performed to dynamically monitor the course of patients' conditions. For each parameter, the values on admission were chosen to compare differences between the two groups. As shown in Table 2 (Table 3) Table 4) .

To predict the possibility of deterioration of the disease on admission, a simple and efficient

This article is protected by copyright. All rights reserved clinical scoring model, the COVID-19 index, was established based on laboratory parameters. The formula consisted of key risk factors including the lymphocytes counts, the serum level of D-dimer and ESR. Based on the β value presented in Table 4 , the contributions of the risk factors were identified. The formula was constructed from the multivariate logistical regression model and presented as COVID-19 index = 3×D-dimer (µg/L)+2×lgESR (mm/h)-4×lymphocyte (×10 9 /L)+8.

To assess the performance of the predictive scoring system, we performed an ROC analysis. rarely develop to severe cases. When the patients' admission scores were higher than 9, the proportion of severe patients was 61.5%, which was significantly different when compared to those with scores <7. Our findings might suggest that the optimal cut-off value of COVID-19 index was 9, and the sensitivity, specificity, Youden index were 0.8, 0.79, 0.59, respectively. This means that SARS-CoV-2 infected patients with a COVID-19 index higher than 9 were much more likely to progress to severe illness.

The purpose of this study was to establish a scoring system to predict the patient's progression in advance. After identifying the three key risk factors of lymphocytes, D-dimer and ESR, we developed a scoring system based on their contribution to patient severity. Our study provides a simple and effective method for predicting the severity of a patient's condition.

This article is protected by copyright. All rights reserved COVID-19 is a disease caused by SARS-CoV-2 infection, which is a novel virus that belongs to the beta coronavirus and is highly pathogenic. Since the first COVID-19 patient was confirmed, it has spread throughout the China quickly. At present, COVID-19 has become a pandemic. To April 20, 2020, the cumulative number of confirmed diagnoses has exceeded 2,300,000 and 160,120 people have died in 213 coutries, areas or territories (WHO, 2020). Infection with SARS-CoV-2 usually present as flu-like symptoms, such as fever, cough, and shortness of breath. However, a considerable number of patients undergo rapid deterioration and die suddenly. Therefore, being able to timely and effectively predict the progression of disease is important for clinicians to implement beneficial interventions, which would promote the efficient use of health-care resources and save more lives.

It has been reported that age and pre-existing diseases are considered to be the risk factors for Zhang, W., 2020). Importantly, the clinical acquisition of these indicators is quick and easy. Therefore, in this study, we tried to identify key risk factors from these laboratory test parameters to establish a predictive model. 

This article is protected by copyright. All rights reserved related to the more severe inflammation, hypercoagulable state and poorer body condition. Hence, we used these three variables to assemble a score formula named COVID-19 index. Although many variables are related to the patient's disease trend, a scoring system that incorporates multiple variables is more predictive. In addition, as a newly discovered coronavirus, COVID-19 caused by SARA-CoV-2 has not been thoroughly studied, and there is no accepted clinical grading and treatment system. Therefore, the disease prediction scoring system established in this study fills this gap and has relevance. For newly admitted inpatients with SARS-CoV-2 infection, the risk of the disease can be evaluated and the treatment can be guided by quickly improving the corresponding examination, reducing the probability of the patient developing serious illness, and increasing the timeliness of treatment for severe patients.

Currently, predicted risk factors associated with a severe outcome have been identified in some studies. Chen's group proposed a nomogram to predict the prognosis of patients with COVID-19, including age, dyspnea, coronary heart disease, cerebrovascular disease, elevated procalcitonin ). In addition, Li and his group revealed that older age, hypertension, high LDH and D-dimer level were risk factors for severe cases (Li, X. et al., 2020). Compared to these investigations, our study establish a simple scoring formula to early identify patients who will progress to severe COVID-19. Although the model includes only 3 parameters, it is able to efficiently distinguish patients at higher risk to progress to severe cases. As far as we know, it is the first predictive formula relevant to severity of COVID-19 patients.

As a simple and practical method for predicting the progression of COVID-19, this model provides a valuable approach for clinicians to stratify patients and then implement prompt and efficient therapy as soon as possible. ROC curve analysis showed that our scoring formula was 

This article is protected by copyright. All rights reserved receiver operating characteristic. 

This article is protected by copyright. All rights reserved 

This article is protected by copyright. All rights reserved   Hospital admission HBDH, α-Hydroxybutyrate Dehydrogenase.

a P values indicate differences between non-severe and severe patients. P <0.05 was considered statistically significant. a P values indicate differences between non-severe and severe patients. P <0.05 was considered statistically

Serum Amyloid A (mg/dL)