key: cord-0945975-rnoz7vdk authors: Guan, Yingchao; Chen, Chaojin; Guo, Anping; Wei, Jingru; Cai, Jiahui; Han, Hua; Hei, Ziqing; Tan, Haizhu; Li, Xiaoyun title: Prolonged symptom onset to admission time is associated with severe Coronavirus disease: A meta combined propensity‐adjusted analysis date: 2021-08-12 journal: J Med Virol DOI: 10.1002/jmv.27253 sha: 290ddbe8c45cd47a1ccd32d61533c371bb80c9c4 doc_id: 945975 cord_uid: rnoz7vdk BACKGROUND: Patients with severe COVID‐19 are more likely to develop adverse outcomes with a huge medical burden. We aimed to investigate whether a shorter symptom onset to admission time (SOAT) could improve outcomes of COVID‐19 patients. METHODS: A single‐center retrospective study combined with a meta‐analysis was performed. The meta‐analysis identified studies published between 1 December 2019 and 15 April 2020. Additionally, clinical data of COVID‐19 patients diagnosed between January 20 and February 20, 2020, at the First Affiliated Hospital of the University of Science and Technology of China were retrospectively analyzed. SOAT and severity of illness in patients with COVID‐19 were used as effect measures. The random‐effects model was used to analyze the heterogeneity across studies. Propensity score matching was applied to adjust for confounding factors in the retrospective study. Categorical data were compared using Fisher's exact test. We compared the differences in laboratory characteristic varied times using a two‐way nonparametric, Scheirer–Ray–Hare test. RESULTS: In a meta‐analysis, we found that patients with adverse outcomes had a longer SOAT (I (2) = 39%, mean difference 0.88, 95% confidence interval = 0.47–1.30). After adjusting for confounding factors, such as age, complications, and treatment options, the retrospective analysis results also showed that severe patients had longer SOAT (mean difference 1.13 [1.00, 1.27], p = 0.046). Besides, most biochemical marker levels improved as the hospitalization time lengthened without the effect of disease severity or associated treatment (p < 0.001). CONCLUSION: Shortening the SOAT may help reduce the possibility of mild patients with COVID‐19 progressing to severe illness. Severe acute respiratory syndrome coronavirus 2 has caused a worldwide pandemic. As of June 1, 2021, the COVID-19 brings the cumulative numbers to over 170 million reported cases and over 3.5 million deaths globally, and in some countries and areas, such as India, the epidemic seems to have a frantic recurrence. 1 It was reported that 19% of cases of COVID-19 were severe and critical, with mortality rates as high as 49%; these comprised 2% of the total number of infected patients. 2 Moreover, patients with severe COVID-19 are more likely to develop adverse outcomes, including acute respiratory distress syndrome, shock, significant organ injuries, and admission to the intensive care unit (ICU), and this is associated with a huge burden on the medical system. [3] [4] [5] Although advanced age, hypertension, diabetes, and coronary heart disease are potential risk factors for severe and fatal outcomes of COVID-19, 6-8 these inherent factors cannot be changed in a short term. Implementing better methods to reduce the progression of cases with mild COVID-19 to those with severe disease may play a crucial role in containing the outbreak. After the implementation of a strict quarantine policy, which mandated isolation or hospitalization of patients as soon as they were diagnosed, not only the incidence of new cases but also adverse outcomes (such as critical illness and mortality) in patients with COVID-19 were declining. [9] [10] [11] [12] However, to the best of our knowledge, no study has been reported on the effect of reducing symptom onset to admission time (SOAT) on the patients' prognoses. Therefore, in the present study, we aimed to investigate the relationship between SOAT and the severity of COVID-19 through a systematic review and meta-analysis combined with a retrospective analysis using data on patients from the First Affiliated Hospital of the University of Science and Technology of China. Our results may provide a reference for supporting COVID-19 control strategies. This report follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses-Individual Patient Data (PRISMA)-IPD guidelines for the registration of the protocol, trial identification, data collection and integrity, assessment of bias, and sensitivity analyses. 13 This meta-analysis was registered with PROSPERO (CRD42020189946). The terms "COVID-19" and "clinical study" were used to conduct a comprehensive literature search in the PubMed, Embase, Cochrane, and Chinese (including Zhiwang, Wanfang, and Weipu) databases for articles published up to April 15, 2020. The inclusion criteria were (1) clinical studies, (2) studies with patient conditions likely to be associated with COVID-19 adverse outcomes, including death, admission to the ICU, or diagnosis of severe and critical illness, (3) studies including SOAT, and (4) studies with a Jadad score of 3 points or more. 14 All enrolled patients with COVID-19 were divided into Group A (adverse outcomes group, including outcomes, such as severe or critical illness, admission to ICU, and death) and Group C (control group). A standardized form was used to extract data, including authors' information, journals, publication dates, language, Jadad scores, sample sizes, average age, coexisting complications, and SOAT, from published articles. A fixed-effect meta-analysis was conducted by independent researchers different from those who performed data screening and entry. procalcitonin, D-dimer, prothrombin times, prothrombin activation times, fibrinogen, total bilirubin, direct bilirubin, alanine aminotransferase, and creatinine), comorbidities (including hypertension, diabetes, chronic obstructive pulmonary disorder, asthma, cardiovascular disease, chronic kidney disease, chronic liver disease, malignant tumors, central nervous system diseases, and immune system diseases), and treatment measures (oxygen inhalation, hormone therapy, traditional Chinese medicine therapy, and immunoglobulin therapy) were collected and retrospectively reviewed. To minimize bias, two experienced researchers who were unaware of the purpose of the study reviewed, abstracted, crosschecked, and consolidated the data from the electronic medical records. The records of all patients were collected retrospectively by two independent physicians, and the professionals who performed statistical and meta-analyses were unaware of the purpose of the study. All statistical analyses were performed using R version 3.6.3 (Mathsoft of Parametric Technology Corporation). The data represented as medians and interquartile ranges were converted into means and standard deviations (Refer to Methods in the Supporting Information). For continuous data, mean differences (MD) and 95% confidence intervals (CI) were used for the effect size analyses. The random-effects model was used for the analysis of heterogeneity across studies. For the retrospective clinical research, MDs between groups were used for continuous variables. Categorical data were compared using Fisher's exact test. The continuous variable counts, mean values, and 95% CI and count data were expressed as the number of occurrences. Outliers were identified by the multivariate outlier detection method using the Mahalanobis distance. Collinearity was handled by calculating the variance inflation factor. From the results of analyses, the baseline characteristics of patients between the two groups were different. We applied inverse probability of treatment weighting (IPTW) along with the propensity score matching (PSM) method to eliminate confounding variables by weighting samples. PSM was used to reduce or eliminate the effects of multiple confounding variables so that we did not require larger sample sizes. To compare the difference of laboratory characteristics at a different time point, we applied a two-way nonparametric, Scheirer-Ray-Hare test, to examine whether the laboratory characteristics were affected by two factors, such as time after hospital admission (time) and the severity of illness (group), and the odds ratio (OR) was calculated. p < 0.05 was considered statistically significant. For the meta-analysis, a total of 1652 articles were retrieved. After excluding articles that did not meet the inclusion criteria, 11 were included and analyzed as shown in the PRISMA flow diagram ( Figure S1 ). A total of 2503 patients, including 1009 males and 1044 females, were included in the final analysis (Table S1 ). In all, 500 patients (24.35%) with adverse outcomes, including 245 deaths, 36 ICU admissions, and 219 severe diseases, were enrolled. The SOATs in patients with adverse outcomes (Group A) and in those without adverse outcomes (Group C) were significantly different (I 2 = 39%, MD = 0.88, 95% CI = 0.47-1.30; the fixed-effect model was used; Figure 1 ). On performing sensitivity analysis, we found that after omitting one of the studies, the results were still significantly different, which meant that our analysis was stable and robust ( Figure S2 ). Heterogeneity analysis showed that I 2 decreased from 39% to 2% after omitting the study by Yan Deng (Table S2 ). The reason for heterogeneity was that their study used a different grouping method that divided the cohort into the death and recovery groups. The recovery group involved some severe cases with rehabilitation. To better confirm the preliminary results in the meta-analysis, we enrolled 84 patients with COVID-19 in our retrospective study; of these, 25 (29.76%) had severe COVID-19, and 23 (27.4%) needed intensive care. However, there were no statistically significant differences in the SOATs between the severe and mild groups (7.60 ± 6.13 vs. 5.69 ± 4.70 days, p = 0.1549) in this study (Table 1) . Furthermore, we found that patients in the severe group were older than those in the mild group (57.48 ± 17.49 years vs. 40.76 ± 14.30 years, p < 0.001; Table 1 ) and had more complications at admission (cardiovascular disease, 20% vs. 1.68%, p = 0.007; hypertension, 32% vs. 11.86%, p = 0.058). The occurrence of dyspnea and death was rare (2.38%, n = 2% and 1.2%, n = 1, respectively) and only occurred in the severe group. In addition, patients in the severe F I G U R E 1 Impact of SOAT on patients' prognoses. Group A: Patients who were diagnosed as severely or critically ill, were transferred to the intensive care unit (ICU), or those who died. Group C: Patients who were not diagnosed as severely or critically ill, were not transferred to the ICU, or those who recovered from the disease. CI, confidence interval; MD, mean difference; SD, standard deviation; SOAT, symptom onset to admission time group had higher neutrophil percentages, and lower lymphocyte and platelet counts at admission than those of patients in the mild group at admission (all p < 0.05; Table 2 ). In terms of treatment, more patients in the severe group required oxygen inhalation treatment (96% vs. 40.7%; p < 0.001) compared to those in the mild group. There were no statistical differences with respect to the need for antiviral treatment, antibiotic treatment, or traditional Chinese medicine treatment between the two groups. However, patients in the severe group were more likely to require immunoglobulin therapy (52% vs. 10.2%; p < 0.001; Table S3 ). During the course of hospitalization, patients in the severe group were more likely to experience shortness of breath after activity (16.9% vs. 2.2%; p = 0.003; Table S3 ). There were many confounding factors, such as age, complications, and treatment options, which might have affected the severity of COVID-19. Some potential biomarkers, such as counts of WBC, neutrophils, lymphocytes, and platelets and levels of C-reactive protein, procalcitonin, D-dimer, and fibrinogen at admission, which predicted disease severity in patients with COVID-19 were also changed. To explore the effect of SOAT on the severity of COVID-19, we applied IPTW of the PSM method to reduce or eliminate the effects of multiple confounding variables at admission. Eighty patients were included in the final analysis after the 20 Liang et al. 21 Therefore, it may be too late and too challenging to effectively treat patients with severe COVID-19 using oxygen supplementation, after the infection has already progressed. 23 To address this therapeutic dilemma, we focused on early intervention during COVID-19 infection before it manifested into severe disease. SOAT represented the initial disease development process in patients with COVID-19. During this period, patients mostly experienced mild symptoms and did not seek medical care. The results of our meta-analysis indicated that longer SOAT was associated with adverse outcomes, including severe disease, requirement for intensive care, and increased mortality in patients with COVID-19. But for the nature of meta-analysis, we could not obtain detailed data to exclude some factors of explicit effects on disease severity, including advanced age, comorbidities, and poor conditions of patients. So combined with this retrospective study, by performing IPTW and PSM, we found that there was a significant difference in the SOATs between the mild and severe groups, and these results were consistent with those from our meta-analysis. All the results indicated that timely isolation and treatment might prevent the cases with mild COVID-19 from progressing into those with severe disease. In China, after the outbreak of COVID-19, the whole country adopted relatively strict quarantine policies. Patients who were in contact with confirmed COVID-19 patients were quickly isolated in mobile cabin hospitals, and this has been proven to result in a milder disease course. 24 We believe that the shorter SOAT was an important factor that prevented the mild cases to progress to severe ones in China. Most patients with COVID-19 have a fever with elevated heart and respiratory rates, which result in excessive oxygen consumption. Additionally, physical activity increases oxygen consumption, which causes a large imbalance in oxygen supply and demand in the diseased lungs, further deteriorating the condition of the patient. 7, 25 Elderly patients with poor immune function are more likely to experience multiorgan failure and eventual death; however, even in young people with good immune function, subsequent hypoxia due to insufficient oxygen supply and optimum rest in early disease could result in irreversible and severe outcomes. Our study also showed that patients with severe infection were more likely to experience shortness of breath after activity and needed more oxygen inhalation treatment. Shortening SOAT would mean timely oxygen inhalation therapy and reduced physical activity, which would help to avoid the oxygen supply and demand imbalance. 26 To better explore the effect of shortening SOAT and early hospitalization without treatment factors in patients with COVID-19, we compared biochemical markers on Days 1, 7, and 14 after admission in these patients. The results showed that without the effects of severity and their interaction, the longer duration of hospitalization Prolonged SOAT is associated with severe outcomes in patients with COVID-19. Shortening the SOAT helps reduce the progression of patients with COVID-19 to severe illness by ensuring timely isolation and treatment. We would like to thank Yang Qintai, Vice-President of the Third The authors declare that there are no conflict of interests. Our retrospective case-control study was approved by the Research Ethics Commission of the First Affiliated Hospital of the University of Science and Technology of China (approval no. 2020-P-018). The requirement for informed consent was waived because this was a retrospective study, and the patients could not be identified. Yingchao Guan, Jingru Wei, and Chaojin Chen contributed to the data screening and entry of the meta-analysis. Anping Guo and Hua Han collected the data for the retrospective study. Jiahui Cai and Haizhu Tan contributed to the analysis and interpretation of data for the meta-analysis and the retrospective study. Yingchao Guan, Chaojin Chen, and Xiaoyun Li wrote the first draft and revised the manuscript. Xiaoyun Li and Ziqing Hei contributed to the article design. All authors had full access to all the data in the study and accept responsibility for the integrity of the data, accuracy of the data analysis, and submission for publication. 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