key: cord-0962725-qvsd7kqc
authors: Sun, Liping; Liu, Gang; Song, Fengxiang; Shi, Nannan; Liu, Fengjun; Li, Shenyang; Li, Ping; Zhang, Weihan; Jiang, Xiao; Zhang, Yongbin; Sun, Lining; Chen, Xiong; Shi, Yuxin
title: Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19
date: 2020-05-13
journal: J Clin Virol
DOI: 10.1016/j.jcv.2020.104431
sha: df6efc7bcf7d2d619c855e9329824ec11de1adba
doc_id: 962725
cord_uid: qvsd7kqc

BACKGROUND: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. METHODS AND MATERIALS: In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12(th), were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. RESULTS: Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33% and 100% in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. CONCLUSION: The model was robust and effective in predicting the severe/critical COVID cases.

in every 7.4 days [4] . The R0 was estimated to be 2.24-3.58 [5] . In previous study, the most common early clinical symptoms were fever (98%), cough (76%), dyspnea (55%) and myalgia or fatigue (44%). In addition, sputum production (28%) and headache (8%) were also reported [4] . In consistent with this study, fever (91.7%), cough (75.0%), fatigue (75.0%), and gastrointestinal symptoms (39.6%) were the most common clinical manifestations [6] . Laboratory features including leukopenia (25%), lymphopenia (25%) and raised aspartate aminotransferase (37%, including seven of 28 non-ICU patients) was also included. In addition, AST, ALT, γ-GT, LDH and α-HBDH abnormality was reported [5] .

Histopathologic changes and CT features observed [6, 7] .

Clinically, criteria for severe was identified as respiratory distress, more than 30 times/min, SpO3<93% at rest, and PaO2/FiO2 <=300mmHg. Critical was respiratory failure, shock and extra pulmonary organ failure [8] . However, the mild cases may develop into severe or critical. Despite of the effort devoted for CT-based early critical case diagnosis [9] , the performance is still blur. While prediction model for mild case developing into severe or critical is still not reported yet. In this study, it is aimed to identify the initial clinical observations or laboratory features at significantly associated with severe/critical cases, and predict if the disease would develop into severe/critical cases. Machine learning is emphasized for investigating COVID-19 [10] . Clinical Center days after the initial diagnosis, and the clinical and laboratory features were generated from Shanghai Public Heath Clinical Center, and the sample was treated as initial ones. Temperature, heart rate, blood pressor was collected when the patients reached the hospital.

Demographic information, laboratory features and clinical indicators were collected from the electronic record system of Shanghai Public Health Clinical Center and rearranged manually by expert doctors. The accession of the system has been approved by the director of the hospital. The History of hypertension diseases, diabetes, coronary diseases and tuberculosis was collected individually. Severe/critical symptom was defined had one of the following criteria: (a) respiratory frequency ≥30/min; (b) rest pulse oximeter oxygen saturation ≤93% or (c) oxygenation index (PaO2/FiO2) ≤ 300 mm Hg.

Pharyngeal swab specimens were collected from each patient was used for the J o u r n a l P r e -p r o o f triiodothyronine, thyroxine and free thyroxine), and electrolyte balance (Na + , Cl -).

Considering that severe/critical symptom was detected when 10 out of these 26 patients reached the hospital, these features may reflect the character of severe/critical cases, instead of the sign. In other word, these features may be used for diagnosis instead of prediction. Thus, the severe/critical samples were further divided into two groups, one group did not show severe/critical symptom when collecting samples while the other did.

Statistical difference of these 33 features were re-analyzed. Interestingly, none of these features were statistically different between the groups (Table S1 ). This may imply that the various immune cells have participate in the severe/critical disease, and laboratory features have been exhibited before the severe/critical symptom onset. (Table 2) . Thus, the combination was used, and the performance of model was also satisfactory in testing dataset, and AUROC was 0.9757 (Fig. 2b) . Table 2 . The combinations performed best in the training set using SVM models based SVM model is robust and effective in predicting the severe/critical patients. 

The performance of the SVM model was further analyzed by comparing the survival analysis. Since only three death cases were enrolled in this study, the "event" was selected as the time clinical severe/critical symptom observed. Using the aforementioned cut-off value, 0.0667, the samples were divided in to two groups, named Low-risk and High-risk groups. Since the sample number with severe/critical symptom is limited, the training set and validation set was combined for further analyses. As expected, the High-risk group has a higher severe rate than the Low-risk group (Fig. 3a, p<1e-16) . Since a proportion of J o u r n a l P r e -p r o o f cases were detected severe/critical symptom, which may bring bias in analysis. Thus, these samples were excluded for "survival" analysis. In consistent with previous results, the severe/critical symptom rate of High-risk groups was also significantly higher than the Low-risk groups (Fig. 3b, p<1e-16 ).

In addition to survival analyses, the prediction risk value was compared between severe/critical cases. As expected, the risk value of severe/critical cases is significantly higher than that of mild cases (Fig. 3c) . Cox multivariate regression was analyzed, and the results showed that the features used in the model,, GSH, total protein and CD3

percentage were not statistically significant, except for age, while the risk value is (Table   3 ). It is notable that despite that age is statistically significant, but the hazard ration is much low than the risk model (33 vs 1.04) indicating that model is more informative than these features. immune cells and immune products, including CD3, CD4, CD19, CRP, high-sensitive CRP, leukomonocytes and neutrophils. In consistent with this, previous study claimed that severe cases have significantly more leukocytes count and CRP [6] . In combination of these clues, we suspect that the acute immune response has been start several days before severe/critical symptom begins.

The lack of prediction model makes the early detection difficult. Despite that models for COVID-19 diagnosis and prognosis was developed, and at least 27 studies and 31

prediction model was developed [11] . Among these models, 10 were for survival risk while only two models were aimed to predict progression to a severe or critical state. A new study revealed that one demographic and six serological indicators (age, serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width (RDW), blood urea nitrogen, albumin, direct bilirubin were associated with severe symptoms, which is consistent with our study [12] . The model developed has sensitivity of 77.5 % and specificity of 78.4%. in the validation cohort. Since the laboratory indicators of this study is limited, the sensitivity and specificity are not satisfactory. Another study collected data from 133 patients with mild symptom in Wuhan, and used multivariate logistic regression for predicting the patients who will developed into severe symptom using AI, and the best AUC achieved was 0.954. However, the sample number is the major concern [13] . Compared with the models, our model used over 220 clinical indicators, and the model developed achieved a better performance and this model was further was validated.

It is also noticed triiodothyronine (T3), free triiodothyronine, thyroxine (T4) and free J o u r n a l P r e -p r o o f thyroxine was significantly lower in severe/critical patients. The AUROC of triiodothyronine reached 0.96. Despite that correlation between thyroxine and severe/critical symptom was not reported in COVID-19 or MERS, relationship between critical symptom and thyroxine has been reported, and could be used for prognosis. Also, some SARS infected patients have decreased T3 and T4 [14] , which may be caused by necrosis of thyroid [15] .

The utilization of the model: develop an SVM model using the existing data, consisting of clinical outcome (severe/critical symptom) and features (age, GSH, total protein and CD3 percentage), input the corresponding data of each individual, and the likelihood of the patient develop into S/C symptom will be generated. If the value is high than the cutoff (0.0067), The patient is predicted to progress into SC, and vice versa. The limitation of this study is the relatively small sample size (N=336). Due to the relative advanced treatment technology in Shanghai region, the critical/severe symptom rate is lower, which result in the limited number of severe/critical cases. In addition, among the patients with severe/mild symptom, some had observed critical/severe symptom when the samples were collected.

In the future work, we will collect and analyze more samples from the other regions to further validate our model. 

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