key: cord-0820895-80c342fm authors: Chen, Yuhong; Zhi, Haijun; Zhang, Kun; Zhu, Guijun; Liu, Lixia; Yan, Xixin; Cai, Zhigang; Zhao, Congcong; Hu, Zhenjie title: Combined predictive performance of age and neutrophilic percentage on admission for severe novel coronavirus disease 2019 date: 2021-04-29 journal: Int J Clin Pract DOI: 10.1111/ijcp.14257 sha: 3bae2c3c384b1d6fdd131031e0c607ce5efe0844 doc_id: 820895 cord_uid: 80c342fm BACKGROUND: Novel coronavirus disease 2019 (COVID‐19) poses a huge threat to the global public health. This study aimed to identify predictive indicators of severe COVID‐19. METHODS: We retrospectively collected clinical data on hospital admission of all patients with severe COVID‐19 and a control cohort (1:1) of gender‐ and hospital‐matched patients with mild disease from 13 designated hospitals in the Hebei Province between 22 January and 15 April 2020. RESULTS: A total of 104 patients (52 with severe COVID‐19 and 52 with mild disease) were included. Only age, fever, duration from symptom onset to confirmation, respiratory rate, percutaneous oxygen saturation (SpO(2)) and neutrophilic percentage were independent predictors of severe COVID‐19. Age and neutrophilic percentage performed best in predicting severe COVID‐19, followed by SpO(2). ‘Age + neutrophilic percentage’ (the sum of age and neutrophilic percentage) (area under the curve [AUC] 0.900, 95% confidence interval [CI] 0.825‐0.950, P < .001) and ‘age and neutrophilic percentage’ (the prediction probability of age and neutrophilic percentage for severe type obtained by logistic regression analysis) (AUC 0.899, 95% CI 0.824‐0.949, P < .001) had excellent predictive performance for severe type. The optimal cut‐off for ‘age + neutrophilic percentage’ was >119.1 (sensitivity, 86.5%; specificity, 84.6%; Youden index, 0.712). CONCLUSION: The combination of age and neutrophil percentage could effectively predict severe COVID‐19. The sum of age and neutrophil percentage was recommended for clinical application because of its excellent predictive value and practicability. TRAIL REGISTRATION: China Clinical Trial Registry, number ChiCTR2000030226. Registered 26 February 2020‐Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=49855 of patients develop critical pneumonia leading to multiple organ dysfunction or even death. 3, 4 The treatment of severe cases has become a major challenge, and the early recognition of severe forms of COVID-19 is essential for timely triaging of patients. However, there are no reliable indicators to predict disease severity. The objective of this study was to identify predictive indicators of severe COVID-19. Data were collected by accessing clinical medical records, nursing records and laboratory and radiological examination records. In the case of missing or uncertain data, we obtained them by direct communication with the managing physician. The data were reviewed by a trained team of physicians. The recorded information included demographic characteristics, exposure history, chronic medical history, surgical history, symptoms from onset to hospital admission, vital signs on hospital admission, chest-computed tomography (CT) findings on admission, laboratory findings on admission, treatment during the illness course, extrapulmonary comorbidities during the illness course, duration of hospital stay, and mortality. All analyses were performed using SPSS version 26.0 (IBM, USA). Categorical data were presented as numbers and percentages and compared using Pearson's chi-square test or Fisher's exact probability test. The normality of continuous variables was examined using the Kolmogorov-Smirnov test. Continuous variables without and with a normal distribution were compared using non-parametric tests and independent-sample t tests, respectively. 6 Binary logistic regression was performed to identify the independent predictors of severe COVID-19. The prediction probability of the combined predictors for severe type was obtained by logistic regression analysis. The predictive performance of the independent predictors for severe type was analysed using receiver operator characteristic (ROC) curves. Delong's test was used to compare area under the curves • Increasing studies showed that most patients with COVID-19 were asymptomatic or mild, about 20% of patients develop critical pneumonia, multiple organ dysfunction or even death. • The early recognition of severe forms of COVID-19 is absolutely essential for timely triaging of patients. • Age, comorbidities and inflammatory indicators were associated with the severity of COVID-19. • We analysed the predictive value of a variety of indicators that were easily accessible to patients upon admission for severe COVID-19 and found that the sum of age and neutrophil percentage was an excellent predictive and clinical practicable indicator for severe COVID-19. (AUCs) between each predictor using MedCalc version 18.2.1 (MedCalc Software Ltd, Ostend, Belgium). P < .05 was considered statistically significant. 3.1 | Epidemiological characteristics and symptoms from onset to hospital admission By 15 April 2020, 327 patients with COVID-19 were diagnosed, including 57 (17.43%) severe-type patients. Finally, 52 severe-type patients and 52 gender-and hospital-matched mild-type patients were included in this study. The main reasons for exclusion of severe-type patients were lack of clinical data (n = 2) and death within 24 hours (n = 3) (Figure 1 ). Severe-type patients were older than mild-type patients (P < .05). In the severe type, there were fewer cases of exposure to confirmed patients (P = .001), more cases with Wuhan contact history (P = .070), and more patients had chronic medical history (hypertension, diabetes, cardiovascular disease and pulmonary disease), surgical history, fever and expectoration symptoms (P < .05) than in the mild type (Table 1 ). The duration from symptom onset to confirmation in severe-type patients was longer than in mild-type patients (P < .001) ( Table 1 ). In addition, cases with exposure to confirmed patients had a longer duration from symptom onset to confirmation than those who had no exposure (P < .05). The respiratory rate was higher, and SpO 2 was lower in severetype patients than in mild-type patients (P < .05). More severe-type patients received mechanical ventilation than mild-type patients (P < .05) ( Table 2 ). In severe-type patients, neutrophil percentage, neutrophil count and C-reactive protein were markedly higher, but the lymphocyte percentage and lymphocyte count were lower than in mild-type patients (P < .05) ( Table 2) . Moreover, the white blood cell counts was higher in severe-type patients than in mild-type patients, but the difference was not significant (P = .063) ( Table 2) . Additionally, severe-type patients had lower albumin levels and higher blood urea nitrogen (BUN) than mild-type patients (P < .001) ( Table 2) . Chest CT imaging on admission showed more bilateral infiltrates, ground-glass opacity and reticular pattern in severe-type patients than in mild-type patients (P < .05) ( Table 2 ). Nearly all patients received antiviral agents and traditional Chinese medicine in both groups. Antibiotic therapy, glucocorticoid treatment and vasoactive drug administration were more common in severe-type patients than in mild-type patients (P < .001). Moreover, severe-type patients received more antifungal therapy than mildtype patients, but the difference was not significant (P = .126) ( Table 3 ). All severe-type patients had acute respiratory distress syndrome, and 19 (36.5%), 11 (21.2%) and two (3.8%) of them received mechanical ventilation, prone position ventilation and extracorporeal membrane oxygenation therapy, respectively (Table 3 ). More severe-type patients had extrapulmonary comorbidities than mild-type patients (P < .001). No mild-type patient had cardiac injury, acute kidney injury or gastrointestinal bleeding. Additionally, two (3.8%) mild-type and four (7.7%) severe-type patients had liver dysfunction ( Table 3 ). The clinical outcome was worse in severe-type patients than in mild-type patients with three (5.8%) deaths among severe-type patients and none among mild-type patients. Furthermore, the length of hospital stay was F I G U R E 1 Study flow diagram longer in severe-type patients than in mild-type patients (P < .001) ( Table 3 ). In binary logistic regression, the significant predictors of severe type were age (P < .001), fever (P = .013), duration from symptom onset to confirmation (P = .004), respiratory rate (P = .016), SpO 2 (P = .023) and neutrophilic percentage (P = .002) ( Table 4 ). Age (AUC 0.815, 95% confidence interval [CI] 0.727-0.884, P < .001) and neutrophilic percentage (AUC 0.814, 95% CI 0.726-0.884, P < .001) had the best predictive value with high specificity for severe type, followed by SpO 2 (AUC 0.811, 95% CI 0.723-0.881, P < .001). The performance of fever, duration from symptom onset to confirmation and respiratory rate in predicting severe type were poor (Table 5 and Figure 2 ). Given the good performance of age and neutrophilic percentage, we combined these two indicators as 'age and neutrophilic percentage' The optimal cut-off for 'age + neutrophilic percentage' was >119.1 (sensitivity, 86.5%; specificity, 84.6%; Youden index, 0.712) ( Table 5 and Figure 2 ). Note: Variables entered on step 1: age, exposure, chronic medical illness, surgery history, fever, expectoration, duration from symptom onset to confirmation, respiratory rate, SpO 2 , neutrophilic percentage, lymphocyte percentage, C-reactive protein, albumin, blood urea nitrogen, bilateral involvement, ground-glass opacity, and reticular pattern. Abbreviations: CI, confidence interval; COVID-19, novel coronavirus disease 2019; OR, odds ratio. of lung injury. The delay in confirmation hinders early treatment of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that may lead to deterioration of the condition. Consistent with the results of other studies, fever was associated with the development of severe type. 7, 8 However, the difference in patient temperature on hospital admission between the groups was small, which may be related to the use of antipyretic drugs before admission. Moreover, the performance of fever, duration from symptom onset to confirmation, and respiratory rate in predicting severe COVID-19 was poor and that of SpO 2 was good. Older age and neutrophilic percentage performed best in predicting severe COVID-19. SARS-CoV-2 binds to the ACE2 receptor TA B L E 5 ROC curves for age, fever, duration from symptom onset to confirmation, respiratory rate, SpO 2 , neutrophilic percentage and combined indicators as predictors of severe COVID-19 to play important roles in disease severity. 9,10 SARS-CoV-2 might mainly act on lymphocytes, especially T lymphocytes, resulting in a significant decrease in the number of T cells, which is further hampered in severe cases. 9 The significant decrease in the number and percentage of peripheral lymphocytes in patients with COVID-19 may be related to the redistribution and increased consumption of lymphocytes and defective haematopoiesis. 11, 12 Neutrophils are the main source of chemokines and cytokines. In addition, reduced lymphocyte levels and impaired immune cell function in patients with severe COVID-19 may make them more sensitive to bacterial infection, 13, 14 which leads to a significant increase in neutrophil count. The neutrophil percentage had the best predictive performance for severe COVID-19, possibly because it reflects both lymphocytic decline and neutrophil elevation. Older age was associated with both severity and death. 15 Besides older age is associated with reduced immune competence, 16 elderly patients often have coexisting medical conditions, which were associated with severe COVID-19. 2 The combined parameters of age and neutrophil percentage performed better in predicting severe COVID-19 than these parameters alone and significantly better than single indicators, possibly because age and neutrophil percentage reflect the severity of inflammation and susceptibility of the population, respectively. In this study, we explored two ways in which age and neutrophilic percentage can be combined. 'Age and neutrophilic percentage' were derived from logistic regression analysis, which may be the best method of combination but was complex and clinically impractical. 'Age + neutrophilic percentage', which was the sum of age and neutrophil percentage, was recommended for clinical application because of its excellent predictive value for severe COVID-19 and practicability. In addition, there were fewer cases of exposure to confirmed patients, and cases with exposure to confirmed patients had shorter duration from symptom onset to confirmation in the severe type, which may be related to the timely follow-up of close contacts of confirmed patients so that their contacts can receive timely diagnosis and treatment. In terms of treatment, antibiotic therapy, glucocorticoid treatment and vasoactive drug administration were more common in the severe type, which is associated with a more intense inflammatory response, more severe haemodynamic disorders and more severe immune impairment in severe-type patients than in mild type. Thus, the clinical outcome was worse in the severe type with more extrapulmonary comorbidities and longer length of hospital stay than in the mild type. Our study has several potential weaknesses. First, it was a retrospective study, and the number of patients in this study was small. To reduce research bias, the cases in our study were from 13 hospitals in Hebei Province rather than a single centre, and the cases were matched according to the hospitals they were admitted to and their gender. Thus, to some extent, the results of this study may give clinicians a hint for early screening of patients with a tendency to progress to severe disease. Second, the main indicators analysed in this study were those on hospital admission; therefore, many parameters, such as arterial blood gas, erythrocyte sedimentation rate and procalcitonin, were not included in the analysis because of missing data. Nevertheless, our results provide a moderate and important insight on this topic. The combination of age and neutrophil percentage could effectively predict severe COVID-19. The sum of age and neutrophil percentage was recommended for clinical application because of its excellent predictive value for severe COVID-19 and practicability. Authors wish to acknowledge all patients and their families involved in this study. We are grateful to the Expert Panel of the COVID-19 of Hebei Province and all health care workers around the word. We also thank Health Care Committee of Hebei Province for data support, and the Department of Science and Technology of Hebei Province for financial support. The authors declare that there are no conflicts of interests regarding the publication of this paper. The Ethics Commission of the Fourth Clinical Medical College of Hebei Medical University approved this study(2020KS002). After publication, we could share the data to other researchers, and a statement will be needed for evaluating the reasonability and validity. With agreement of the corresponding author and designated hospitals to treat patients with COVID-19, raw data will be provided. 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