Literature DB >> 32370577

Risk factors for disease progression in hospitalized patients with COVID-19: a retrospective cohort study.

Wei Hou1, Wei Zhang1, Ronghua Jin1, Lianchun Liang1, Bin Xu1, Zhongjie Hu1.   

Abstract

Background: To investigate the risk factors related to aggravation and clinical outcomes in coronavirus disease 2019 (COVID-19) patients.
Methods: We performed a retrospective study on the risk factors for disease progression of cases with COVID-19. Based on the clinical types, the patients were divided into a progression group and an improvement group. Multivariable logistic regression and ROC curve analysis were performed to explore the risk factors for disease progression.
Results: A total of 101 patients were included in this study; diseases progression occurred in 17 patients, 84 patients improved, 6 were transferred to intensive care unit (ICU), and 5 died. The mean time to obtain negative nucleic acid results was 12.5 ± 5.0 days. Multivariate logistic analysis indicated that age (OR, 0.104; p = .002), C-reactive protein (CRP) (OR, 0.093; p < .001) and lymphocyte count (OR, 3.397; p = .022) were risk factors for disease progression. ROC curve analysis revealed that the AUC of age, CRP and lymphocyte count for disease progression were 0.873, 0.911 and 0.817, respectively.Conclusions: Older age increased CRP and decreased lymphocyte count resulted in potential risk factors for COVID-19 progression. This may be helpful in identifying patients whose condition worsens at an early stage.

Entities:  

Keywords:  COVID-19; risk factors outcome

Mesh:

Substances:

Year:  2020        PMID: 32370577      PMCID: PMC7212540          DOI: 10.1080/23744235.2020.1759817

Source DB:  PubMed          Journal:  Infect Dis (Lond)        ISSN: 2374-4243


Introduction

Since December 2019, unexplained pneumonia, today known as a novel coronavirus (2019-nCoV), has been identified in several patients across multiple Wuhan city hospitals (Hubei Province, China). Within 2 months, the infection spread rapidly to other Countries and regions [1,2]. Today (11 April 2020), the virus has spread to 212 countries around the world, infecting more than 1,607,467 people and causing more than 98,866 deaths [3]. Coronavirus is a large family of viruses that cause illness ranging from the common cold to more severe diseases, such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The novel coronavirus (nCoV) is a new strain that has not been previously identified in humans. On 11 February 2020, the International Committee on Taxonomy of viruses issued a statement stating an official designation for the novel virus: “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2) [3]. Fever, dry cough, and fatigue are the main clinical manifestations of the disease. Many infected patients present with mild flu-like symptoms and recover quickly. Yet, in certain populations, especially the elderly, the virus may cause respiratory failure and even result in death. Therefore, early identification of risk factors for disease progression is helpful for early intervention in severe patients. Previous studies have suggested that the highest temperature, dyspnoea, respiratory rate, white blood cell count, neutrophil count, lymphocyte count, d-dimer, albumin, and procalcitonin are risk factors for ICU care in patients with coronavirus disease 2019 (COVID-19) [4,5]. In this study, we retrospectively examined 101 laboratory-confirmed cases of COVID-19 admitted to our hospital between 21 January 2020 and 9 March 2020, so as to investigate the factors affecting the outcomes with the hope of improving the treatment and reducing mortality.

Methods

Subjects

We performed a retrospective study of clinical characteristics and risk factors for disease progression in laboratory-confirmed cases with COVID-19 hospitalized at Beijing YouAn hospitals (Capital Medical University, Beijing) between 21 January 2020 and 9 March 2020. All patients were evaluated and clinically typed upon admission, according to the “Diagnosis and Treatment Protocol for Novel Coronavirus Infection-Induced Pneumonia version 7 (trial)” [6]. Specific clinical types included: (1) mild: mild clinical manifestation with no unusual imaging data; (2) common: fever, respiratory symptoms, pneumonia performance on X-ray or CT; (3) severe (meet any of the followings): (i) respiratory distress, RR ≥ 30/min; (ii) oxygen saturation ≤ 93% at rest state; (iii) arterial partial pressure of oxygen (PaO2)/fraction of inspiration O2 (FiO2) ≤ 300 mmHg, 1 mmHg = 0.133 kPa; (4) critically ill (meet any of the followings): (i) respiratory failure needs mechanical ventilation; (ii) shock; (iii) combined with other organ failure, patients need ICU monitoring and treatment. All patients were divided into two groups: progression group and improvement group. The definition of the progression group: clinically advanced types; patients admitted to ICU; death during hospitalization. The improvement group: the clinical types remained unchanged or changed to a lighter type, and patients were discharged from the hospital.

Laboratory testing

Patients’ pharyngeal swab specimens were collected for the SARS-CoV-2 viral nucleic acid detection using real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay. Influenza A virus, influenza B virus, respiratory syncytial virus, adenovirus, parainfluenza virus, enterovirus, human metapneumovirus, coxsackievirus, chlamydia, and mycoplasma were detected by collecting body fluid (nasopharyngeal swabs and sputum) samples. Laboratory variables were tested with conventional methods, including (i) routine blood tests: the numbers of leukocytes, lymphocytes, and neutrophil, haemoglobin, platelet; (ii) blood biochemistry: alanine transaminase, aspartate aminotransferase, total bilirubin, albumin, creatinine, creatine kinase, myoglobin, troponin; (iii) partial pressure of oxygen and oxygen saturation of blood; (iv) infection indices: procalcitonin and CRP.

Data collection

We reviewed the electronic medical records, laboratory findings, and chest X-ray data for all patients. Personal data, epidemiological data, comorbidities, clinical data, laboratory data, radiological data, treatment and outcomes data were obtained with standardized data collection forms from electronic medical records. Two researchers also independently reviewed the data collection forms to double-check the collected data.

Statistical analysis

Categorical variables were described as frequency rates and percentages, analyzed using the Chi-squared test or Fisher’s exact test. Continuous variables were described using mean, median, and interquartile range (IQR) values. Means for continuous variables were compared using independent group t-tests when the data were normally distributed; otherwise, the Mann–Whitney U test was used. Univariate and multivariate analyses of prognostic factors were performed using the logistics regression. SPSS software version 20.0 (SPSS Inc., Chicago, IL) was used for statistical analysis. The area under curve (AUC), cut-off value, sensitivity, and specificity of ROC were analyzed by the receiver operator characteristic curve (ROC). p < .05 was considered statistically significant, and all p values were two-tailed.

Ethical approval

This study was conducted in accordance with the Declaration of Helsinki. The requirement for written informed consent was waived, given the context of emerging infectious diseases.

Results

Demographics and case grouping

The study population included 101 hospitalized patients with laboratory-confirmed COVID-19, and 44 (43.6%) were men. The median age was 50.9 ± 20.1 years, and 27 patients were aged ≥65 years (26.7%). Among the 101 patients, there were 4 patients with the mild type (4.0%), 75 patients with the common type (74.3%), 13 patients with the severe type (12.9%), and nine patients with the critically ill type (8.9%) at admission. Among the 101 patients, 8 cases with common type showed progression of the disease, 5 of them developed a severe type, while 3 progressed to critically ill type. Three cases with severe type progressed to critically ill type, and 6 cases with the critically ill type required ICU treatment after admission during hospitalization. A total of 17 patients (16.8%) were included in the progression group and 84 patients (83.2%) in the improvement group. In the improvement group, 4 cases were with mild type, 67 cases with common type, 10 cases with severe type, and 3 cases with critically ill type. All of them were successfully discharged. Changes in conditions and outcomes of 101 patients after admission are shown in Figure 1.
Figure 1.

The change in condition and outcomes of 101 patients after admission. Among the 101 patients, 8 cases with common type showed progression of the disease, 5 of them developed a severe type, while 3 progressed to critically ill type. Three cases with severe type progressed to critically ill type, and 6 cases with the critically ill type required ICU treatment after admission during hospitalization. A total of 17 patients (16.8%) were included in the progression group and 84 patients (83.2%) in the improvement group. ICU: intensive care unit; *included in the progression group.

The change in condition and outcomes of 101 patients after admission. Among the 101 patients, 8 cases with common type showed progression of the disease, 5 of them developed a severe type, while 3 progressed to critically ill type. Three cases with severe type progressed to critically ill type, and 6 cases with the critically ill type required ICU treatment after admission during hospitalization. A total of 17 patients (16.8%) were included in the progression group and 84 patients (83.2%) in the improvement group. ICU: intensive care unit; *included in the progression group.

Clinical characteristics

Among these patients, 43 (42.6%) were in Hubei province during the virus outbreak, 78 (77.2%) clustering onset. The median duration from the first symptoms to diagnosis was 4.8 ± 2.9 days. Of the 101 patients, 30 (29.7%) had one or more comorbidities. Hypertension [21(20.8%)], cardiovascular disease [11(10.9%)], diabetes [6(5.9%)], and malignancy [5 (5.0%)] were the most common comorbidities. The most common symptoms were fever [74 (73.3%)] and dry cough [63 (62.4%)]. The median Sequential Organ Failure Assessment (SOFA) score was 1 (IQR, 0–1). The patients in the progression group were significantly older than those in the improvement group (72.4 ± 13.9 versus 46.6 ± 18.3). There was no significant difference in sex between the two groups (p > .05). Compared with the improvement group, the patients in the progression group were more likely to have underlying comorbidities (58.8% versus 23.8%, χ = 8.301, p = .004). Besides, the progression group had a significantly higher proportion of patients with hypertension than the improvement group (47.1% versus 15.5%, χ = 6.753, p = .009). There was no significant difference in other comorbidities. The maximum body temperature, heart rate, mean arterial pressure, and blood oxygen saturation did not differ between patients in the progression and improvement group. The respiratory rate in the progression group was significantly higher than in the improvement group (21.0 ± 2.0 breaths/min versus 20.0 ± 1.7 breaths/min, t = –2.072, p = .041). The median SOFA scores in the progression group were significantly higher than those in the improvement group [3(1.5, 4) versus 3(0, 1), U = 6.175, p < .001]. Demographic data and clinical characteristics of 101 patients with COVID-19 are shown in Table 1.
Table 1.

Demographic data and clinical characteristics of 101 patients with COVID-19.

 Totaln = 101Improvementn = 84Progressionn = 17t/U/χ2 valuep Value
Age (years, mean ± S.D.)50.9 ± 20.146.6 ± 18.372.4 ± 13.9−5.496 <.001
Gender (male/female)44/5734/5010/71.936.164
History of exposure to Hubei province (n, %)43 (42.6)37 (44.0)6 (35.3)0.443.506
Cluster (n, %)78 (77.2)63 (75.0)15 (88.2)0.756.385
Duration from first symptoms to diagnosis (d, mean ± S.D.)4.8 ± 2.94.7 ± 2.75.2 ± 3.6−0.600.550
Comorbidities (n, %)30 (29.7)20 (23.8)10 (58.8)8.301.004
 Hypertension21 (20.8)13 (15.5)8 (47.1)6.753.009
 Coronary artery disease11 (10.9)7 (8.3)4 (23.5)1.981.159
 Diabetes mellitus6 (5.9)4 (4.8)2 (11.8)0.304.581
 Cerebrovascular3 (3.0)2 (2.4)1 (5.9)0.595.428
 Chronic obstructive pulmonary disease4 (4.0)2 (2.4)2 (11.8)3.241.131
 Malignancy5 (5.0)4 (4.8)1 (5.9)0.038.846
Symptoms (n, %)
 Fever74 (73.3)59 (70.2)15 (88.2)1.510.219
 Dry cough63 (62.4)53 (63.1)10 (58.8)0.110.740
Temperature max (°C, mean ± S.D.)38.5 ± 0.638.5 ± 0.638.6 ± 0.5–0.849.399
Respiratory rate (breaths/min, mean ± S.D.)20.2 ± 1.820.0 ± 1.721.0 ± 2.0−2.072.041
Heart rate (beats/min, mean ± S.D.)85.5 ± 12.885.6 ± 12.584.9 ± 14.50.188.851
Mean arterial pressure (mmHg, mean ± S.D.)89.7 ± 13.890.8 ± 10.484.7 ± 24.41.678.097
Blood oxygen saturation (%, mean ± S.D.)96.5 ± 4.096.8 ± 3.095.3 ± 7.11.376.172
SOFA (median, IQR)1.0 (0.0,1.0)0.0 (0.0,1.0)3.0 (1.5,4.0)6.157<.001

SOFA: Sequential Organ Failure Assessment.

Demographic data and clinical characteristics of 101 patients with COVID-19. SOFA: Sequential Organ Failure Assessment.

Laboratory findings and imaging characteristics

Laboratory indices of the 101 patients with COVID-19 were determined at the time of admission. There were numerous differences in laboratory data between patients in the progression group and the improvement group (Table 2). White blood cell count and neutrophil count were significantly elevated, and lymphocyte count significantly decreased in the progression group compared to the improvement group. Albumin was significantly decreased in the progression group compared to the improvement group. In addition, the levels of estimated glomerular filtration rate, creatine kinase isoenzymes, myoglobin, troponin, C-reactive protein, procalcitonin, and lactic acid were significantly increased in the progression group compared to the improvement group. Eighty-seven out of 101 enrolled patients showed bilateral involvement of chest CT scan, and 10 patients showed unilateral involvement. There were no significant differences in imaging findings between the two groups (Table 2).
Table 2.

Laboratory results and imaging findings of 101 patients with COVID-19.

 Totaln = 101Improvementn = 84Progressionn = 17t/U/χ2 valuep Value
Laboratory results (mean ± S.D. or median, IQR)
 WBC (× 109/L)4.3 (3.5,5.8)4.1 (3.5,5.6)6.4 (3.7,8.2)2.360.018
 N (× 109/L)2.6 (1.8,3.8)2.3 (1.8,3.3)4.9 (2.7,7.3)3.556<.001
 L (× 109/L)1.2 ± 0.81.3 ± 0.80.7 ± 0.33.205.002
 PLT (× 109/L)192.1 (156.2,245.8)192.2 (158.8,248.8)193.5 (111.4,218.5)−1.002.316
 ALT (U/L)28.3 (20.0,46.70)28.6 (20.5,45.3)36.2 (18.4,63.1)0.743.458
 AST (U/L)30.3 (22.5,45.6)30.5 (22.6,42.8)37.8 (22.5,76.4)1.569.117
 TBIL (μmol/L)9.2 (6.6,12.4)8.9 (6.4,12.0)12.1 (7.6,15.6)1.633.102
 ALB (g/L)35.8 ± 5.836.5 ± 5.232.3 ± 7.22.793.006
 Cr (umol/L)64.3 (53.5,77.8)62.9 (53.1,74.8)77.3 (54.5,88.1)1.680.093
 eGFR99.5 (89.0,114.7)105.3 (94.0,118.8)91.2 (66.7,96.9)−3.512<.001
 CK (U/L)80.5 (46.0,130.3)74.5 (45.8,122.3)96.0 (59.7,161.3)1.105.269
 CKMB (U/L)0.3 (.02,0.9)0.3 (0.2,0.6)1.1 (0.3,2.5)2.919.004
 MYO (ng/ml)44.5 (30.0,67.5)36.5 (28.2,59.4)97.4 (52.3,198.5)3.755<.001
 TNI (ng/ml)0.01 (0.01,0.02)0.01 (0.01,0.02)0.04 (0.01,0.08)3.306.001
 PT (s)12.6 (11.9,13.1)12.6 (12.0,13.1)12.8 (11.6,13.6)0.164.869
 INR1.1 (1.1,1.2)1.1 (1.1,1.2)1.1 (1.0,1.2)0.052.959
 CRP (mg/L)19.3 (3.6,59.1)13.9 (2.6,26.9)86.0 (66.7,122.5)5.312<.001
 PCT (ng/ml)0.1 (0.1,0.1)0.1 (0.1,0.1)0.1 (0.1,0.2)2.944.003
 LAC (mmol/L)1.3 ± 0.61.3 ± 0.51.7 ± 0.8−2.946.004
Imaging findings (n, %)
 Normal4 (4.0)4 (4.8)0 (0)0.056.813
 Unilateral involvement10 (10.0)10 (11.9)0 (0)1.110.292
 Bilateral involvement87 (86.1)70 (83.3)17 (100.0)2.041.153

WBC: white blood cell count; N: neutrophil count; L: lymphocyte count; PLT: platelet count; ALT: alanine transaminase; AST: aspartate transaminase; TBIL: total bilirubin; ALB: albumin; Cr: creatinine; CK: creatinine kinase; CKMB: creatine kinase isoenzymes; MYO: myoglobin; TNI: troponin; PT: prothrombin time; INR: international normalized ratio; CRP: C-reactive protein; PCT: procalcitonin; LAC: lactic acid.

Laboratory results and imaging findings of 101 patients with COVID-19. WBC: white blood cell count; N: neutrophil count; L: lymphocyte count; PLT: platelet count; ALT: alanine transaminase; AST: aspartate transaminase; TBIL: total bilirubin; ALB: albumin; Cr: creatinine; CK: creatinine kinase; CKMB: creatine kinase isoenzymes; MYO: myoglobin; TNI: troponin; PT: prothrombin time; INR: international normalized ratio; CRP: C-reactive protein; PCT: procalcitonin; LAC: lactic acid.

Treatments, complications and outcomes

The main treatments, complications, and outcomes are shown in Table 3. Of the 101 patients, 35 (34.7) patients received antiviral therapy. The proportion of systemic corticosteroid treatment and human-immunoglobulin treatment in the progression group was significantly higher than that in the improvement group (82.3% versus 21.4%; 35.3% versus 1.2%). The proportion of patients in the progression group who required ICU care, continuous renal replacement therapy (CRRT), invasive mechanical ventilation, and extracorporeal membrane oxygenation (ECMO) treatment were 58.8%, 29.4%, 41.2%, and 23.5%, respectively.
Table 3.

Treatments, complications and outcomes of 101 patients with COVID-19.

 Totaln = 101Improvementn = 84Progressionn = 17t/U/χ2 valuep Value
Treatments (n, %)
 Antiviral treatment35 (34.7)29 (34.5)6 (35.3)0.004.951
 Systemic corticosteroid treatment32 (31.7)18 (21.4)14 (82.3)24.246<.001
 Human-immunoglobulin7 (6.9)1 (1.2)6 (35.3)20.480<.001
 ICU care10 (10.0)0 (0)10 (58.8)48.446 <.001
 CRRT5 (5.0)0 (0)5 (29.4)20.118<.001
 Oxygen support
  Nasal cannula63 (62.4)46 (54.8)17 (100.0)12.329<.001
  Non-invasive ventilation or high-flow nasal cannula3 (3.0)0 (0)3 (17.6)9.768.002
  Invasive mechanical ventilation7 (6.9)0 (0)7 (41.2)31.054<.001
  Invasive mechanical ventilation and ECMO4 (4.0)0 (0)4 (23.5)14.858.001
Complications (n, %)
 Acute Respiratory Distress Syndrome7 (6.9)0 (0)7 (41.2)31.054 <.001
 Sepsis7 (6.9)1 (1.2)6 (35.3)20.480 <.001
 Septic shock6 (5.9)0 (0)6 (35.3)25.520 <.001
 Cardiogenic shock3 (3.0)0 (0)3 (17.6)9.768.002
 Arrhythmia7 (6.9)0 (0)7 (41.2)31.054 <.001
 Myocardial injury24 (23.8)10 (11.9)14 (82.3)34.942 <.001
 Acute kidney injury12 (11.9)6 (7.1)6 (35.3)8.182.004
 Liver injury38 (37.6)28 (33.3)10 (58.8)3.914.048
 Secondary infection9 (8.9)1 (1.2)8 (47.1)31.242 <.001
Outcomes (n, % or mean ± S.D.)
 Time taken for temperature normalization (d)8.6 ± 4.38.0 ± 3.811.3 ± 5.4−2.798.006
 Time required to obtain negative nucleic acid results (d)12.5 ± 5.011.7 ± 4.417.2 ± 5.9−4.246 <.001
 Time taken for imaging improvement (d)14.9 ± 5.414.0 ± 3.222.8 ± 11.3−2.319.048
 Improved and discharged (n)93 (92.1)84 (100.0)9 (52.9)26.827 <.001
  Death (n)5 (5.0)0 (0)5 (29.4)20.118 <.001
 Length of hospital stay [d, (median, IQR)]13.0 (10.0, 17.0)13.0 (10.0, 16.0)20.5 (15.3, 22.3)3.065.002

ICU: intensive care unit; ECMO: extracorporeal membrane oxygenation; CRRT: continuous renal replacement therapy.

Treatments, complications and outcomes of 101 patients with COVID-19. ICU: intensive care unit; ECMO: extracorporeal membrane oxygenation; CRRT: continuous renal replacement therapy. Seven out of 101 patients (6.9%) developed acute respiratory distress syndrome (ARDS). Seven patients were complicated with sepsis; six of them developed into septic shock. Cardiogenic shock and arrhythmia were found in 3 patients and seven patients, respectively. The proportion of myocardial injury, acute renal injury, and liver injury was 23.8%, 11.9%, and 37.6%, respectively. Nine patients developed a secondary bacterial infection. The rate of secondary bacterial infection in the progression group was 47.1%, which was significantly higher than that in the improvement group (1.2%). The mean time for the body temperature to decrease was 8.6 ± 4.3 days, the mean time to obtain negative nucleic acid result was 12.5 ± 5.0 days, and the mean time for imaging improvement was 14.9 ± 5.4 days. Compared with the improvement group, the body temperature decreased more slowly, and negative nucleic acid results and imaging improvements were achieved later in patients from progression group (11.3 ± 5.4 versus 8.0 ± 3.8; 17.2 ± 5.9 versus 11.7 ± 4.4; 22.8 ± 11.3 versus 14.0 ± 3.2 days). The mortality in these patients was 5%, and the median hospital stay was 13 days (IQR, 10.0–17.0).

Risk factors for disease progression

The results of the univariate logistic analysis found that age, comorbidities, neutrophil count, lymphocyte count, albumin, myoglobin, and CRP were significantly associated with the disease progression. Furthermore, the multivariate logistic analysis indicated that age (OR, 0.104; 95% CI: 0.024–0.449; p = .002), CRP (OR, 0.093; 95% CI: 0.025–0.314; p < .001) and lymphocyte count (OR, 3.397; 95% CI: 1.195–9.565; p = .022) were risk factors for disease progression (Table 4). ROC curve analysis revealed that the AUC of age, CRP and lymphocyte count for disease progression were 0.873 (95% CI: 0.786–0.961, p < .001), 0.911 (95% CI: 0.843–0.978, p < .001), 0.817 (95% CI: 0.711–0.923, p < .001), respectively (Figure 2). Age > 62.5 years, CRP > 51.4 mg/L and lymphocyte count < 0.945 × 109/L indicated the progression of the disease, where the sensitivity was 82.4%, 94.1.2% and 94.1%, and the specificity was 79.3%, 85.4% and 68.3%.
Table 4.

Logistic analysis results of risk factors for disease progression (n = 101).

 BORp Value95% CI
Age (years)
  <60 1 (ref)  
 ≥60−2.2670.104.0020.024–0.449
CRP (mg/L)
  <60 1 (ref)  
 ≥60−2.3730.093<.0010.025–0.341
Lymphocyte count (× 109/L)
  <1.1 1 (ref)  
 ≥1.11.2233.397.0221.195–9.565

OR: odds ratio; CRP: C-reactive protein.

Figure 2.

ROC curve of independent risk factors for disease progression in patients with COVID-19.

ROC curve of independent risk factors for disease progression in patients with COVID-19. Logistic analysis results of risk factors for disease progression (n = 101). OR: odds ratio; CRP: C-reactive protein.

Discussion

In this retrospective cohort study, 17 patients (16.8%) developed disease progression after admission; 6 were transferred to ICU, and five (4.9%) patients died. Similar data were reported in the recent meta-analysis that examined 50466 cases with SARS-CoV-2 [7]. This study identified several risk factors for disease progression in patients who were hospitalized with COVID-19 in Beijing. In particular, older age, higher CRP, and lower lymphocyte count on admission were associated with higher odds of in-hospital disease progression. Additionally, more comorbidities, elevated levels of white blood cell count, myoglobin, troponin, and decreased levels of albumin were more commonly seen in patients with disease progression. In addition, the time to obtain negative nucleic acid results was significantly longer in patients with disease progression compared to those who showed improvement. Coronaviruses are a large family of viruses found in several domestic animals, pets, and humans, causing a variety of acute and chronic diseases [8]. Currently, the pathogenesis of the COVID-19 still remains unclear, and there is no precise and effective treatment. Recent clinical research suggests that some patients have mild symptoms and can recover quickly, while others may develop rapid disease progression, which often leads to respiratory failure and even death. Besides, advanced age at presentation has shown to be very useful in predicting the mortality in SARS [9] and MERS [10] cases. Elderly patients tend to develop a greater number of comorbidities, potentially leading to poor outcomes [11]. Besides, studies have shown that older macaques have stronger host innate responses to virus infection than younger adults, which promotes inflammation [12]. The current study confirmed that increased age was associated with disease progression in patients with COVID-19. CRP is an important inflammatory index and a significant predictor of disease progression. Previous studies on SARS have suggested the initial CRP level as a predictive factor of death [13]. Rapid elevation of inflammatory cytokines-IL-6, IL-8, and TNF-alpha might have a role in the development of SARS-related ARDS. The timing of elevations in inflammatory cytokines and CRP is correlated with the progression of pulmonary infiltrates of SARS patients [14]. In addition, studies have found that CRP is one of the risk factors for disease progression in Wuhan patients with COVID-19 [15]. Moreover, lymphocyte count is another predictor of disease progression. The most common laboratory abnormalities observed across several studies were depressed total lymphocytes [16]. Recently, it has been found that the number of CD4 and CD8 T cells in the peripheral blood of patients with COVID-19 is significantly reduced, but the state is over-activated, which shows the increase of Th17 and the high cytotoxicity of CD8 T cells thus suggesting serious damage of immune system in these patients [16]. Our study confirmed that CRP and lymphocytopenia were independent risk factors for disease progression. Our study also found that patients with disease progression were more likely to have various complications, especially cardiac complications. The incidence of cardiogenic shock and arrhythmia in patients with disease progression was significantly higher than that in the improvement group. Moreover, the incidence of myocardial injury, including an abnormal increase of creatine kinase isoenzymes (CKMB) and/or troponin (TnI) was significantly higher than that of the improvement group. The previous study reported an incidence of acute cardiac injury of 7.2–12% in Wuhan patients with COVID-19 [17,18], which was lower than our results (23.8%). In addition, ARDS, secondary infection, sepsis and septic shock, kidney injury, and liver injury also resulted as related to disease progression. Compared with the improvement group, the body temperature decreased more slowly in patients from the progression group (11.3 ± 5.4 versus 8.0 ± 3.8). Besides, detectable SARS-CoV-2 RNA persisted for a mean of 17.2 ± 5.9 days in the progression group, which was significantly longer compared to the disease improvement group (11.7 ± 4.4 days). Therefore, it is critical for these patients to receive effective antiviral treatment as early as possible [19]. Currently, there are no FDA-approved treatments for human CoV infection; however, there are agents that have shown to be effective for SARS and MERS epidemics, such as Lopinavir and Ritonavir [20,21]. Our study has few limitations. First, our sample size was relatively small, which may lead to biased results. Consequently, a multi-center large-scale study is required. Second, not all patients underwent laboratory testing, such as detecting lactate dehydrogenase and cytokine levels, so they were not included in the risk factor analysis. In conclusion, patients with COVID-19 showed similar clinical manifestations to those with MERS or SARS. Older age, CRP, and lymphocyte count at admission were identified as risk factors for disease progression in patients with COVID-19 and may be useful for identifying an early-stage disease. Effective antiviral therapy may improve the patients’ outcomes.

Author contributions

Wei Hou wrote the article; participated in designing the concept of the study, the definition of intellectual content, and data acquisition. Wei Zhang, Lianchun Liang and Bin Xu contributed to data acquisition. Ronghua Jin and Zhongjie Hu designed and reviewed the manuscript.
  17 in total

1.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China.

Authors:  Dawei Wang; Bo Hu; Chang Hu; Fangfang Zhu; Xing Liu; Jing Zhang; Binbin Wang; Hui Xiang; Zhenshun Cheng; Yong Xiong; Yan Zhao; Yirong Li; Xinghuan Wang; Zhiyong Peng
Journal:  JAMA       Date:  2020-03-17       Impact factor: 56.272

2.  Predictors of mortality in Middle East respiratory syndrome (MERS).

Authors:  Ki-Ho Hong; Jae-Phil Choi; Seon-Hui Hong; Jeewon Lee; Ji-Soo Kwon; Sun-Mi Kim; Se Yoon Park; Ji-Young Rhee; Baek-Nam Kim; Hee Jung Choi; Eui-Cheol Shin; Hyunjoo Pai; Su-Hyung Park; Sung-Han Kim
Journal:  Thorax       Date:  2017-07-19       Impact factor: 9.139

3.  Exacerbated innate host response to SARS-CoV in aged non-human primates.

Authors:  Saskia L Smits; Anna de Lang; Judith M A van den Brand; Lonneke M Leijten; Wilfred F van IJcken; Marinus J C Eijkemans; Geert van Amerongen; Thijs Kuiken; Arno C Andeweg; Albert D M E Osterhaus; Bart L Haagmans
Journal:  PLoS Pathog       Date:  2010-02-05       Impact factor: 6.823

4.  A systematic review of lopinavir therapy for SARS coronavirus and MERS coronavirus-A possible reference for coronavirus disease-19 treatment option.

Authors:  Tian-Tian Yao; Jian-Dan Qian; Wen-Yan Zhu; Yan Wang; Gui-Qiang Wang
Journal:  J Med Virol       Date:  2020-03-12       Impact factor: 2.327

5.  Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.

Authors:  Wei Liu; Zhao-Wu Tao; Lei Wang; Ming-Li Yuan; Kui Liu; Ling Zhou; Shuang Wei; Yan Deng; Jing Liu; Hui-Guo Liu; Ming Yang; Yi Hu
Journal:  Chin Med J (Engl)       Date:  2020-05-05       Impact factor: 2.628

6.  Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up.

Authors:  Lang Wang; Wenbo He; Xiaomei Yu; Dalong Hu; Mingwei Bao; Huafen Liu; Jiali Zhou; Hong Jiang
Journal:  J Infect       Date:  2020-03-30       Impact factor: 6.072

7.  The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China.

Authors:  David S Hui; Esam I Azhar; Tariq A Madani; Francine Ntoumi; Richard Kock; Osman Dar; Giuseppe Ippolito; Timothy D Mchugh; Ziad A Memish; Christian Drosten; Alimuddin Zumla; Eskild Petersen
Journal:  Int J Infect Dis       Date:  2020-01-14       Impact factor: 3.623

Review 8.  Emerging coronaviruses: Genome structure, replication, and pathogenesis.

Authors:  Yu Chen; Qianyun Liu; Deyin Guo
Journal:  J Med Virol       Date:  2020-02-07       Impact factor: 2.327

9.  Pathological findings of COVID-19 associated with acute respiratory distress syndrome.

Authors:  Zhe Xu; Lei Shi; Yijin Wang; Jiyuan Zhang; Lei Huang; Chao Zhang; Shuhong Liu; Peng Zhao; Hongxia Liu; Li Zhu; Yanhong Tai; Changqing Bai; Tingting Gao; Jinwen Song; Peng Xia; Jinghui Dong; Jingmin Zhao; Fu-Sheng Wang
Journal:  Lancet Respir Med       Date:  2020-02-18       Impact factor: 30.700

10.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.

Authors:  Fei Zhou; Ting Yu; Ronghui Du; Guohui Fan; Ying Liu; Zhibo Liu; Jie Xiang; Yeming Wang; Bin Song; Xiaoying Gu; Lulu Guan; Yuan Wei; Hui Li; Xudong Wu; Jiuyang Xu; Shengjin Tu; Yi Zhang; Hua Chen; Bin Cao
Journal:  Lancet       Date:  2020-03-11       Impact factor: 79.321

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  24 in total

1.  National early warning score on admission as risk factor for invasive mechanical ventilation in COVID-19 patients: A STROBE-compliant study.

Authors:  Min Cheol Chang; Tae Uk Kim; Donghwi Park
Journal:  Medicine (Baltimore)       Date:  2021-05-14       Impact factor: 1.889

Review 2.  Acute Kidney Injury in COVID-19: The Chinese Experience.

Authors:  Xizi Zheng; Youlu Zhao; Li Yang
Journal:  Semin Nephrol       Date:  2020-09-04       Impact factor: 5.299

3.  Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis.

Authors:  Farha Musharrat Noor; Md Momin Islam
Journal:  J Community Health       Date:  2020-12

4.  Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.

Authors:  David N Fisman; Amy L Greer; Michael Hillmer; R Tuite
Journal:  Open Forum Infect Dis       Date:  2020-10-05       Impact factor: 3.835

5.  Understanding factors influencing the length of hospital stay among non-severe COVID-19 patients: A retrospective cohort study in a Fangcang shelter hospital.

Authors:  Shishi Wu; Lanping Xue; Helena Legido-Quigley; Mishal Khan; Hua Wu; Xiaoxiang Peng; Xuewen Li; Ping Li
Journal:  PLoS One       Date:  2020-10-21       Impact factor: 3.240

6.  Shorter incubation period is associated with severe disease progression in patients with COVID-19.

Authors:  Changxiang Lai; Rentao Yu; Mingbo Wang; Wenjie Xian; Xin Zhao; Qiyuan Tang; Ruikun Chen; Xuan Zhou; Xuan Li; Zhiyu Li; Zhiwei Li; Guohong Deng; Fang Wang
Journal:  Virulence       Date:  2020-12       Impact factor: 5.882

7.  Acute kidney injury and kidney replacement therapy in COVID-19: a systematic review and meta-analysis.

Authors:  Edouard L Fu; Roemer J Janse; Ype de Jong; Vera H W van der Endt; Jet Milders; Esmee M van der Willik; Esther N M de Rooij; Olaf M Dekkers; Joris I Rotmans; Merel van Diepen
Journal:  Clin Kidney J       Date:  2020-09-02

8.  Cardiac manifestations in COVID-19 patients-A systematic review.

Authors:  Ahmed M A Shafi; Safwan A Shaikh; Manasi M Shirke; Sashini Iddawela; Amer Harky
Journal:  J Card Surg       Date:  2020-07-11       Impact factor: 1.620

9.  Novel Use of Home Pulse Oximetry Monitoring in COVID-19 Patients Discharged From the Emergency Department Identifies Need for Hospitalization.

Authors:  Sonia Shah; Kaushal Majmudar; Amy Stein; Nita Gupta; Spencer Suppes; Marina Karamanis; Joseph Capannari; Sanjay Sethi; Christine Patte
Journal:  Acad Emerg Med       Date:  2020-07-23       Impact factor: 5.221

10.  Peripheral biomarkers' panel for severe COVID-19 patients.

Authors:  Miriana d'Alessandro; Laura Bergantini; Paolo Cameli; Giuseppe Curatola; Lorenzo Remediani; Piersante Sestini; Elena Bargagli
Journal:  J Med Virol       Date:  2020-10-10       Impact factor: 20.693

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