Home Medicine Predictive factors of progression to severe COVID-19
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Predictive factors of progression to severe COVID-19

  • Yi-Hong Zhou , Huan Li , Yuan-Yuan Qin , Xiao-Feng Yan , Yan-Qiu Lu , Hong-Lan Liu , Si-Kuan Ye , Yan Wan , Lu Zhang , Vijay Harypursat and Yaokai Chen EMAIL logo
Published/Copyright: August 28, 2020
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Open Medicine
From the journal Open Medicine Volume 15 Issue 1

Abstract

Aim

Early diagnosis and treatment are crucial for the survival of severe Coronavirus Disease 2019 (COVID-19) patients, but data with regard to risk factors for disease progression from milder COVID-19 to severe COVID-19 remain scarce.

Methods

We conducted a retrospective analysis on 116 patients.

Results

Three factors were observed to be independently associated with progression to severe COVID-19 during 14 days after admission: (a) age 65 years or older (hazard ratio [HR] = 8.456; 95% CI: 2.706–26.426); (b) creatine kinase (CK) ≥ 180 U/L (HR = 3.667; 95% CI: 1.253–10.733); and (c) CD4+ T-cell counts <300 cells/µL (HR = 4.695; 95% CI: 1.483–14.856). The difference in rates of severe COVID-19 development was found to be statistically significant between patients aged 65 years or older (46.2%) and those younger than 65 years (90.2%), between patients with CK ≥ 180 U/L (55.6%) and those with CK < 180 U/L (91.5%), and between patients with CD4+ T-cell counts <300 cells/µL (53.8%) and those with CD4+ cell counts ≥300 cells/µL (83.2%).

Conclusions

Age ≥ 65 years, CK ≥ 180 U/L, and CD4+ T-cell counts <300 cells/µL at admission were risk factors independently associated with disease progression to severe COVID-19 during 14 days after admission and are therefore potential markers for disease progression in patients with milder COVID-19.

Keywords: Coronavirus; SARS-CoV-2; COVID-19; predictive; progression

1 Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, the provincial capital of Hubei Province, China, in December 2019 [1]. The virus is known to spread with ease from person to person among close contacts [2] and may cause an acute respiratory illness, which has been named Coronavirus Disease 2019 (COVID-19). Most patients with SARS-CoV-2 infection develop mild to moderate upper respiratory tract symptoms, whereas others may develop severe respiratory distress, systemic sepsis, septic shock, and death [3,4,5]. Some patients present with fever and respiratory symptoms such as cough and shortness of breath, and others may present with gastrointestinal symptoms including diarrhea, vomiting, and abdominal pain [6]. In addition, some atypical symptoms such as altered mental status, symptoms of stroke, and olfactory and gustatory dysfunctions have been described [7,8]. Although patients with mild to moderate COVID-19 usually have a good prognosis, severe COVID-19 is associated with high mortality.

Early diagnosis and treatment are crucial for the survival of severe COVID-19 patients. As most patients who develop severe COVID-19 start with mild symptoms and later progress to severe disease, it is imperative to identify potential risk factors for disease progression in this population. This may help healthcare providers timeously identify patients with disease progression potential, thus facilitating early diagnosis and treatment of severe COVID-19. However, data with regard to potential risk factors for disease progression from mild or moderate COVID-19 to severe COVID-19 outside Wuhan remain scarce in the published literature and therefore warrant further investigation.

Our infectious disease hospital began admitting COVID-19 patients from January 24, 2020, and over 200 COVID-19 patients had been admitted up until February 20, 2020. The majority were diagnosed with mild or moderate COVID-19 at admission. However, a subgroup of these milder COVID-19 patients progressed to severe COVID-19 during their hospital stay, whereas others stabilized and recovered. In the present study, we retrospectively analyzed the clinical data of patients admitted to our hospital with milder COVID-19, including those who progressed to severe disease after admission. Our objective was to investigate the presence of potential risk factors associated with disease severity progression in the natural history of COVID-19.

2 Material and methods

2.1 Ethics, consent, and permissions

This study was approved by the Ethics Committee of Chongqing Public Health Medical Center (2020-003-01-KY). Informed consent was waived as all data were retrospective and were collected anonymously.

2.2 Patient enrollment and data collection

We included all patients aged 18 or older who had a confirmed diagnosis of mild or moderate COVID-19, who were admitted to Chongqing Public Health Medical Center, China, from January 24, 2020, to February 7, 2020. We transcribed demographics, epidemiological information, clinical manifestations, and clinical outcomes of eligible patients from the electronic hospital medical record system onto case record forms. Laboratory test results including blood gas analysis, hematological analysis, C-reactive protein, coagulation tests, myocardial enzymes, clinical chemistry, and lymphocyte subsets were also extracted from the records and recorded.

Patients exhibiting one or more of the following conditions were classified as having severe COVID-19: (a) respiratory distress (≥30 breaths/min); (b) oxygen saturation ≤93% at rest; (c) arterial partial pressure of oxygen (PaO2)/fraction of inspiration O2 (FiO2) ≤300 mmHg (1 mmHg = 0.133 kPa); (d) respiratory failure requiring mechanical ventilation; (e) development of septic shock; and (f) critical organ failure requiring ICU care. Patients not meeting the aforementioned criteria were classified as mild or moderate COVID-19 cases and referred to as “milder” cases as a stratification category to clearly differentiate between milder and severe cases of COVID-19 in our data analysis.

2.3 Statistical analysis

All analyses were performed using Statistical Package for the Social Sciences software, Version 19.0 (IBM SPSS Statistics, Chicago, IL, USA). Categorical variables were described as frequency rates and percentages and compared via the Chi-squared test or the Fisher exact test as appropriate. Continuous variables were described using mean, median, and interquartile range (IQR) values. Mean values for continuous variables were compared using independent group t-tests when the data were normally distributed; otherwise, the Mann–Whitney test was used. Statistical significance was assumed when p-values less than 0.05 were calculated. Furthermore, time to developing severe COVID-19 was analyzed over the duration of 14 days of hospitalization by the Kaplan–Meier method. The hypothesis test was two tailed, with a p ≤ 0.05 indicative of statistical significance. Cox regression was applied to estimate the unadjusted hazard ratios (HRs) of risk factors for disease progression during 14 days of hospitalization, and adjusted HRs were identified by using a forward stepwise approach.

3 Results

3.1 Patient characteristics

A total of 130 patients with mild or moderate COVID-19 were admitted to our hospital from January 24, 2020, to February 7, 2020. After excluding four patients younger than 18 years and ten patients diagnosed with severe COVID-19 at admission, a total of 116 patients were included for the analysis in this study. Of them, 17 patients (14.7%) eventually developed severe disease, while 99 patients (85.3%) did not meet the criteria for diagnosis of severe COVID-19 during 14 days of hospitalization.

As depicted in Table 1, patients who developed severe COVID-19 were significantly older (59 years [IQR, 50–70] vs 41 years [IQR, 35–54], p  < 0.001) compared with those who did not develop severe COVID-19. In the 17 patients who went on to develop severe COVID-19 during 14 days of hospitalization, the median duration from the symptom onset to the diagnosis of severe COVID-19 was 12 days (IQR, 10–15), and the median duration from hospital admission to diagnosis of severe COVID-19 was 6 days (IQR, 4–9).

Table 1

Patient characteristics at hospital admission

CharacteristicsTotal (n = 116)No progression to severe COVID-19 (n = 99)Progression to severe COVID-19 (n = 17)p
Medical age, median (IQR), years46 (36–56)41 (35–54)59 (50–70) <0.001
Gender, n (%)
Male5951 (51.5)8 (47.1)0.734
Female5748 (48.5)9 (52.9)
Married, n (%)
Yes9882 (82.8)16 (94.1)0.409
No1817 (17.2)1 (5.9)
Smoking, n (%)
Yes2117 (17.2)4 (23.5)0.773
No9582 (82.8)13 (76.5)
BMI23.69 ± 2.9223.6 ± 2.9224.11 ± 2.940.527
Symptoms at admission, n (%)
Yes10690 (90.9)16 (94.1)1.000
No109 (9.1)1 (5.9)
History of stay in Wuhan, n (%)
Yes3734 (34.3)3 (17.6)0.172
No7965 (65.7)14 (82.4)
Diabetes, n (%)
Yes97 (7.1)2 (13.3)0.859
No10792 (92.9)15 (86.7)
Hypertension, n (%)
Yes1411 (11.1)3 (17.6)0.718
No10288 (88.9)14 (82.4)
Time from symptom onset to hospital admission, median (IQR), d4 (2–7)4 (2–7)5 (4–7)0.224
Time from symptom onset to severe COVID-19, median (IQR), d——12 (10–15)—
Time from hospital admission to severe COVID-19, median (IQR), d——6 (4–9)—

BMI: body mass index; IQR: interquartile range.

3.2 Comparison of clinical manifestations between the two groups

In our cohort of 116 patients, fever was the most common symptom at illness onset, occurring in 67.2% of patients in our cohort, followed by cough (59.5%), sputum production (37.9%), fatigue (27.6%), and anorexia (23.3%). In addition, some patients presented with atypical symptoms including heart palpitations (0.9%), xerostomia (0.9%), hemoptysis (0.9%), hyposmia (0.9%), and low back pain (1.7%). There were no significant differences in clinical symptoms between patients who developed severe COVID-19 and those who did not during 14 days of hospitalization after admission in our cohort (Table 2).

Table 2

Clinical manifestations of patients with COVID-19 at hospital admission

Signs and symptoms TotalNo progression to severe COVID-19Progression to severe COVID-19p
Fever, n (%)
Yes7867 (67.7)11 (64.7)0.809
No3832 (32.2)6 (35.3)
Rigors, n (%)
Yes77 (7.1)0 (0.0)0.562
No10992 (92.9)17 (100)
Fatigue, n (%)
Yes3228 (28.3)4 (23.5)0.911
No8471 (71.7)13 (76.5)
Cough, n (%)
Yes6958 (58.6)11 (64.7)0.635
No4741 (41.4)6 (35.3)
Dyspnea, n (%)
Yes31 (1)2 (11.7)0.056
No11398 (99.0)15 (88.2)
Sputum, n (%)
Yes4436 (36.4)8 (47.1)0.401
No7263 (63.6)9 (52.9)
Sore throat, n (%)
Yes2017 (17.2)3 (17.6)1.000
No9682 (82.8)14 (82.4)
Xerostomia, n (%)
Yes11 (1.0)0 (0.0)1.000
No11598 (99.0)17 (100.0)
Hemoptysis, n (%)
Yes10 (0.0)1 (5.9)0.147
No11599 (100)16 (94.1)
Palpitations, n (%)
Yes10 (0.0)1 (5.9)0.147
No11599 (100.0)16 (94.1)
Myalgia, n (%)
Yes109 (9.2)1 (6.7)1.000
No10689 (90.8)14 (93.3)
Arthralgia, n (%)
Yes43 (3.0)1 (5.9)0.474
No11296 (97.0)16 (94.1)
Low back pain, n (%)
Yes22 (2.0)0 (0.0)1.000
No11497 (98.0)17 (100.0)
Abdominal pain, n (%)
Yes43 (3.0)1 (5.9)0.474
No11296 (97.0)16 (94.1)
Nausea and vomiting, n (%)
Yes43 (3.0)1 (5.9)0.474
No11296 (97.0)16 (94.1)
Diarrhea, n (%)
Yes109 (9.1)1 (5.9)1.000
No10690 (90.9)16 (94.1)
Anorexia, n (%)
Yes2722 (22.2)5 (29.4)0.736
No8977 (77.8)12 (70.6)
Headache, n (%)
Yes1512 (12.1)3 (17.6)0.813
No10187 (87.9)14 (82.4)
Dizziness, n (%)
Yes1411 (11.1)3 (17.6)0.718
No10288 (88.9)14 (82.4)
Hyposmia, n (%)
Yes11 (1.0)0 (0.0)1.000
No11598 (99.0)17 (100.0)
Asymptomatic, n (%)
Yes109 (9.1)1 (5.9)1.000
No10690 (90.9)16 (94.1)
Moist rales, n (%)
Yes96 (6.1)3(33.3)0.246
No10793 (93.9)14 (82.4)
Median pulse, mean ± SD, beats/min90.06 ± 13.06590.40 ± 13.29388.06 ± 11.8080.465
Median systolic blood pressure, median (IQR), mm Hg127.25 ± 15.148126.69 ± 15.116130.53 ± 15.3710.336

SD: standard deviation.

3.3 Comparison of laboratory test results between the two groups

Compared with patients who had milder COVID-19, those who developed severe COVID-19 after admission had significantly lower lymphocyte counts, platelet counts, estimated glomerular filtration rates (eGFRs), CD4+ T-cell counts, CD8+ T-cell counts, and PaO2/FiO2 ratios, and significantly higher C-reactive protein levels, lactate dehydrogenase levels, aspartate transaminase levels, and beta 2-microglobulin levels (Table 3).

Table 3

Laboratory findings of patients with COVID-19 at hospital admission

Laboratory valuesNumber of patientsTotalNo progression to severe COVID-19Progression to severe COVID-19p
White blood cell count (×109/L)1154.84 (3.87–5.84)4.81 (3.85–6.05)4.96 (3.89–5.54)0.741
Neutrophil count (×109/L)1142.87 (2.00–3.95)2.77 (1.98–3.95)3.41 (2.55–4.12)0.310
Lymphocyte count (×109/L)1151.32 (1.060–1.76)1.45 (1.18–1.78)1.03 (0.74–1.28)0.002
Platelet count (×109/L)115163 (128–212)169.5 (132.25–214.00)136 (96–174)0.029
Hemoglobin (g/L)115136 (125–147)137.5 (124.75–148)128 (122–139)0.117
C-reactive protein (mg/L)1129.36 (2.96–26.26)7.99 (2.87–21.57)25.55 (10.03–70.17)0.002
D-dimer (mg/L)1120.16 (0.10–0.26)0.14 (0.09–0.25)0.21 (0.12–0.3)0.157
CK (U/L)11274.5 (49.5–128)70.5 (47.5–108.25)149 (53.25–299)0.088
LDH (U/L)116195 (164–251)190 (163–237)273 (185.5–310.5)0.003
ALT (U/L)11621 (13.25–31.75)21 (13–31)21 (15–52)0.290
AST (U/L)11624 (19–31)22 (18–28)33 (25–46.5)0.002
Albumin (g/L)11642.236 ± 3.65742.273 ± 3.54440.613 ± 3.6490.095
Total bilirubin (µmol/L)11613.05 (9.625–19.150)12.7 (9.6–17.8)17.6 (11.2–22.6)0.102
Creatinine (µmol/L)11570.1 (59.3–84)69.65 (58.08–83.55)70.1 (61–89.35)0.555
Beta 2-microglobulin (mg/L)1142.57 (2.15–3.05)2.51 (2.14–2.95)3.34 (2.53–3.79)0.004
eGFR11598.36 ± 18.83100.51 ± 18.17783.94 ± 18.1640.006
CD4+ T-cell counts (cells/µL)85424 (264–594)478 (322–606)243.5 (223–290.75)<0.001
CD8+ T-cell counts (cells/µL)83316 (207–459)359 (231–490)159.5 (126–313.25)0.003
CD4+ T-cell counts/CD8+ cell counts831.31 (0.98–1.75)1.36 (1.01–1.73)1.13 (0.76–1.97)0.403
PaO2/FiO2 (mmHg)74414.28 (365.48–455.54)419 (376.19–471.42)366.67 (321.67–413.14)0.005

LDH: lactate dehydrogenase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; eGFR: estimated glomerular filtration rate; PaO2: partial pressure of oxygen; PaO2/FiO2: partial arterial oxygen concentration/inspired oxygen faction.

3.4 Comparison of therapeutic interventions between the two groups

The proportion of antibiotic use in patients who developed severe COVID-19 was significantly higher than in patients who did not develop severe COVID-19 (35.3% vs 10.1%, p = 0.016) during 14 days of hospitalization. However, we found no statistical correlation in the relative use of lopinavir/ritonavir, ribavirin, and traditional Chinese medicine between the two groups of patients, as presented in Table 4.

Table 4

Treatment of COVID-19 patients within 14 days after admission

CharacteristicsTotalNo progression to severe COVID-19Progression to severe COVID-19P
LPV/r, n (%)
Yes8874 (74.7)14 (82.4)0.711
No2825 (25.3)3 (17.6)
Ribavirin, n (%)
Yes4642 (42.4)4 (23.5)0.141
No7057 (57.6)13 (76.5)
Antibiotics, n (%)
Yes1610 (10.1)6 (35.3)0.016
No10089 (89.9)11 (64.7)
TCM, n (%)
Yes3026 (26.3)4 (23.5)1.000
No8673 (73.7)13 (76.5)

LPV/r: lopinavir/ritonavir; TCM: traditional Chinese medicine.

3.5 Independent risk factors for progression to severe COVID-19

All variables with a p ≤ 0.1 in the univariate analysis, other than lymphocyte counts, were included in a Cox proportional hazards model and adjusted for symptoms at admission, ribavirin use, lopinavir/ritonavir use, comorbid diabetes, and comorbid hypertension. We did not include lymphocyte counts in this model to avoid the possible multicollinearity effect on CD4+ T-cell counts.

Three factors were found to be independently associated with progression to severe COVID-19 (Table 5) during 14 days of hospitalization after admission, and these factors are as follows: (a) age 65 years or older (HR = 8.456; 95% CI: 2.706–26.426; p < 0.001); (b) creatine kinase (CK) ≥ 180 U/L (HR = 3.667; 95% CI: 1.253–10.733; p = 0.018); and (c) CD4+ T-cell counts <300 cells/µL (HR = 4.695; 95% CI: 1.483–14.856; p = 0.009).

Table 5

Independent risk factors for progression to severe COVID-19

TotalProgression to severe COVID-19pHR95% CIpAdjusted HR 95% CI
Age
<65 years1031011
≥65 years137<0.0018.2263.102–21.817<0.0018.4562.706–26.426
Symptoms at admission, n
No1011
Yes 106160.637 1.6280.216–12.274
BMI
<245561
≥2442100.093 2.3820.865–6.557
Unknown1910.480 0.4660.056–3.873
Ribavirin, n
Yes4641
No70130.141 2.3230.757–7.125
LPV/r, n
Yes88141
No2830.476 1.5740.452–5.477
Antibiotics, n
No100111
Yes 1660.0063.9961.476–10.821
Dyspnea, n
No113151
Yes320.0049.0152.027–40.092
Diabetes, n
Yes921
No107150.477 0.5850.134–2.561
Hypertension, n
Yes1431
No102140.409 0.5910.170–2.058
Platelet count
≥100 × 109/L104131
<100 × 109/L1140.0383.2731.066–10.051
Unknown100.98800
C-reactive protein
<20 mg/L7461
≥20 mg/L38110.005 4.1661.540–11.274
Unknown400.985 00
CK
<180 U/L94811
≥180 U/L188<0.0016.5752.458–17.5900.018 3.6671.253–10.733
Unknown410.311 2.9270.366–23.4090.666 1.6930.156–18.427
LDH
<250 U/L8771
≥250 U/L29100.001 5.061.925–13.305
AST
<40 U/L100121
≥40 U/L1650.0522.8100.989–7.981
Albumin
≥40 g/L8791
<40 g/L2980.0262.9541.139–7.660
Beta-2 microglobulin
<28 mg/L6851
≥28 mg/L46120.0093.9871.404–11.326
Unknown200.9830.0000.000
eGFR
≥1005940.264
<10056130.0203.7931.236–11.638
Unknown100.98800
PaO2/FiO2
≥400 mmHg4131
<400 mmHg33100.0154.9411.359–17.968
Unknown4240.7471.2800.286–5.719
CD4+ T-cell counts
≥300/µL59411
<300/µL2612<0.0018.7782.825–27.2750.0094.6951.483–14.856
Unknown3110.005 0.4670.052–4.1770.4610.3970.034–4.626
CD8+ T-cell counts
≥238/µL567
<238/µL2790.0283.0221.125–8.118
Unknown3310.170 0.2300.028–1.872

BMI: body mass index; LPV/r: lopinavir/ritonavir; CK: creatine kinase; LDH: lactate dehydrogenase; AST: aspartate aminotransferase; PaO2/FiO2: partial arterial oxygen concentration/inspired oxygen faction; eGFR: estimated glomerular filtration rate.

3.6 Relationship between the number of risk factors considered (age > 65, CK ≥ 180, CD4+ T-cell counts <300) and progression to severe COVID-19

The number of risk factors considered (age > 65, CK ≥ 180, and CD4+ cell counts <300) was included in a Cox proportional hazards model and adjusted for symptoms at admission, including body mass index, ribavirin use, lopinavir/ritonavir use, antibiotic use, dyspnea, comorbid diabetes, comorbid hypertension, platelet count, C-reactive protein, lactate dehydrogenase, aspartate aminotransferase, albumin, Beta-2 microglobulin, eGFR, PaO2/FiO2, and CD8+ T-cell counts.

The consideration of one to two of our observed risk factors (HR = 10.644; 95% CI: 2.305–49.159; p = 0.002) and all three risk factors (HR = 252.368; 95% CI: 24.390–2611.295; p < 0.001) were found to be independently associated with progression to severe COVID-19 (Table 6) during 14 days of hospitalization after admission.

Table 6

Independent risk factors for progression to severe COVID-19

TotalProgression to severe COVID-19pHR95% CIpAdjusted HR95% CI
Number of factors (age > 65, CK ≥ 180, CD4+ cell counts <300)
050211
1–231110.0210.9672.428–49.5320.00210.6442.305–49.159
322<0.001110.00713.298–910.009<0.001252.36824.390–2611.295
Unknown3320.6771.5170.214–10.7710.4902.0060.277–14.497

CK: creatine kinase; adjustment variables: symptoms at admission, body mass index, ribavirin use, lopinavir/ritonavir use, antibiotics use, dyspnea, comorbid diabetes, comorbid hypertension, platelet count, C-reactive protein, lactate dehydrogenase, aspartate aminotransferase, albumin, beta-2 microglobulin, estimated glomerular filtration rate, partial arterial oxygen concentration/inspired oxygen faction, CD8+ cell counts.

3.7 Fourteen-day cumulative survival without developing severe COVID-19

We analyzed the period from admission to developing severe COVID-19 over the duration of 14 days by the Kaplan–Meier method. We found that in patients aged 65 years or older, the rate of not progressing to severe COVID-19 at the end of 14 days was 46.2%, whereas in patients younger than 65 years, the rate of not progressing to severe COVID-19 at the end of 14 days was 90.2%, and the calculated difference in the rates of severe COVID-19 development between the two groups of patients was found to be significant in the statistical analysis.

The following findings were also observed in our analysis. In patients with a CK ≥ 180 U/L, the rate of not progressing to severe COVID-19 at the end of 14 days was 55.6%, whereas in patients with a CK < 180 U/L, the rate of not progressing to severe COVID-19 at the end of 14 days was 91.5%, and again, there was a significant statistically calculated difference between these two rates of progression. In patients with CD4+ T-cell counts <300 cells/µL, the rate of not progressing to severe COVID-19 at the end of 14 days was 53.8%, whereas in patients with CD4+ T-cell counts ≥300 cells/µL, the rate of not progressing to severe COVID-19 at the end of 14 days was 83.2%, and the statistical difference between these two groups of patients was, again, computed to be significant (Figure 1).

Figure 1 Kaplan–Meier curves for rates of not developing severe COVID-19 during 14 days after hospital admission. (a) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients aged 65 years or older compared to patients younger than 65 years (p < 0.001, two sided); (b) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients with CK ≥180 U/L compared to patients with CK <180 U/L (p < 0.001, two sided); (c) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients with CD4+ T-cell counts <300 cells/µL compared to patients with CD4+ T-cell counts ≥300 cells/µL (p < 0.001, two sided).
Figure 1

Kaplan–Meier curves for rates of not developing severe COVID-19 during 14 days after hospital admission. (a) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients aged 65 years or older compared to patients younger than 65 years (p < 0.001, two sided); (b) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients with CK ≥180 U/L compared to patients with CK <180 U/L (p < 0.001, two sided); (c) Kaplan–Meier curves showed worse progression-free survival rates for COVID-19 patients with CD4+ T-cell counts <300 cells/µL compared to patients with CD4+ T-cell counts ≥300 cells/µL (p < 0.001, two sided).

4 Discussion

This study investigated the risk factors for progression to severe COVID-19 in patients diagnosed as mild or moderate COVID-19. The median age of patients developing severe COVID-19 during the hospitalized period of 14 days was significantly higher than that of those not developing severe COVID-19 (59 years [IQR, 50–70] vs 41 years [IQR, 35–54], p  < 0 .001), which concurs with the study results published previously [9].

In our study cohort of patients with milder COVID-19, we failed to observe a significant association between the presence of chronic diseases and the risk of disease progression, suggesting that the presence of chronic diseases may not necessarily contribute significantly to disease severity progression in such patients. Previous studies, however, have observed that some underlying chronic diseases, including hypertension and diabetes, may be risk factors for poor prognosis of COVID-19 [10,11,12]. The poor correlation of our results compared to that of other studies may be secondary to dissimilar study populations, differing sample sizes, and results obtained at different stages and locations of the COVID-19 outbreak, and warrants further investigation.

As outlined in Table 2, we did not observe any association between clinical symptoms and risk of disease progression. Frequently reported symptoms in our cohort of patients included fever, cough, sputum production, and fatigue. There were no significant differences in the proportion of patients progressing to severe COVID-19 between patients who exhibited the aforementioned symptoms and those who did not, suggesting that these symptoms were not sensitive indicators for disease progression in milder COVID-19 patients.

Compared with patients not developing severe COVID-19 during the period of 14 days after admission, patients progressing to severe COVID-19 during this period were more likely to have been administered antibiotics (10.1% vs 35.3%, p  = 0.016). Antibiotics were used in this subgroup of patients to prevent or treat secondary nosocomial bacterial infections. Our result indicates that antibiotic use may not be useful in arresting disease progression in the natural history of COVID-19.

In the present study, we found that age ≥65 years, CK ≥ 180 U/L, and CD4+ cell counts <300 cells/µL at admission were associated with disease progression during 14 days after hospital admission in patients with milder COVID-19. This result concurs with the previous study results in patients with severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19, in which older age was also found to be a risk factor for progression to severe disease [13,14,15,16,17]. Similarly, a recent study from Wuhan also found that laboratory cardiac injury diagnostic parameters, including CK, were associated with poor prognosis in COVID-19 patients [18]. This has also been observed in patients developing severe SARS and MERS, who also tended to have significantly higher CK levels (≥180 U/L) [19,20]. Secondary systemic myositis as a direct consequence of coronavirus infection may be a reasonable explanation for this increase in CK levels [21]. In addition, a decline in CK levels has been significantly associated with COVID-19 mRNA clearance ratios, which may indicate that this may be a good indicator for recovery of COVID-19 infection [22]. Wong et al. reported that T-lymphocyte subsets may be depleted early in the course of SARS and that low levels of CD4+ T-cell and CD8+ T-cell counts may be associated with poor clinical outcomes [23]. In the present study, we also observed that a CD4+ T-cell count <300 cells/µL was an independent risk factor for progression to severe COVID-19, suggesting that patients with milder COVID-19 develop CD4+ T-cell count depletion before significant disease progression, which was similar to the study conducted in Shanghai [17].

Our study has limitations. First, as a retrospective, observational study, it is inevitable that some data were incomplete, and this could possibly have led to biased effect estimate results. Second, the study period for data observation was only 14 days, which may not have been a long enough period to reflect actual disease progression during the course of the natural history of COVID-19. Third, the number of different factors included in our study for univariate and multivariate analyses may not have been comprehensive enough, and some potential risk factors may have been missed. Despite these limitations, our results may nevertheless be useful to indicate potential markers for possible disease progression in patients with mild to moderate COVID-19.

We observed that age ≥65 years, CK ≥ 180 U/L, and CD4+ T-cell counts <300 cells/µL at admission were risk factors associated with disease progression to severe COVID-19 during 14 days after admission. These factors may represent potential markers for possible disease progression in patients with milder COVID-19. Affording due attention to these risk factors may facilitate early identification of patients with the potential for progression to severe COVID-19 in the mild and moderate COVID-19 patient population. Patients with these risk factors will require close monitoring for potential COVID-19 disease progression during their hospital admission.

Abbreviations

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

BMI

Body mass index

CK

Creatine kinase

COVID-19

Coronavirus disease 2019

eGFR

Estimated glomerular filtration rate

HR

Hazard ratios

ICU

Intensive care unit

IQR

Interquartile range

LDH

Lactate dehydrogenase

LPV/r

Lopinavir/Ritonavir

MERS

Middle east respiratory syndrome

PaO2

Partial pressure of oxygen

PaO2/FiO2

Partial arterial oxygen concentration/inspired oxygen faction

SARS

Severe acute respiratory syndrome

SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2

SD

Standard deviation

TCM

Traditional Chinese medicine

Acknowledgements

This work was supported by the Chongqing Special Research Project for Novel Coronavirus Pneumonia Prevention and Control (No. cstc2020jscx-fyzxX0005). The funding body had no role in the collection, analysis and interpretation of data, the writing of the report, and the decision to submit for publication.

  1. Author contributions: Y-KC, Y-HZ, and Y-YQ designed and executed this analysis. Y-KC and Y-HZ contributed to revising and finalizing the manuscript. HL, X-FY, and VH helped to revise the protocol. Y-QL, H-LL, S-KY, YW, and LZ contributed to data collection and management. All authors contributed to the refinement of the study protocol and have read and approved the final manuscript.

  2. Conflict of interest: The authors declare no conflicts of interest.

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Received: 2020-05-13
Revised: 2020-06-11
Accepted: 2020-06-14
Published Online: 2020-08-28

© 2020 Yi-Hong Zhou et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/med-2020-0184
Keywords for this article
Coronavirus; SARS-CoV-2; COVID-19; predictive; progression
Creative Commons
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  76. miR-101-3p sensitizes non-small cell lung cancer cells to irradiation
  77. Distinct functions and prognostic values of RORs in gastric cancer
  78. Clinical impact of post-mortem genetic testing in cardiac death and cardiomyopathy
  79. Efficacy of pembrolizumab for advanced/metastatic melanoma: a meta-analysis
  80. Review Article
  81. The role of osteoprotegerin in the development, progression and management of abdominal aortic aneurysms
  82. Research Article
  83. Identification of key microRNAs of plasma extracellular vesicles and their diagnostic and prognostic significance in melanoma
  84. miR-30a-3p participates in the development of asthma by targeting CCR3
  85. microRNA-491-5p protects against atherosclerosis by targeting matrix metallopeptidase-9
  86. Bladder-embedded ectopic intrauterine device with calculus
  87. Case Report
  88. Mycobacterial identification on homogenised biopsy facilitates the early diagnosis and treatment of laryngeal tuberculosis
  89. Research Article
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  93. Case Report
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  97. Electromyography as an intraoperative test to assess the quality of nerve anastomosis – experimental study on rats
  98. Case Report
  99. CT findings of severe novel coronavirus disease (COVID-19): A case report of Heilongjiang Province, China
  100. Commentary
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  104. Extracorporeal shock wave therapy for the treatment of chronic pelvic pain syndrome
  105. Plasma microRNAs in human left ventricular reverse remodelling
  106. Bevacizumab for non-small cell lung cancer patients with brain metastasis: A meta-analysis
  107. Risk factors for cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage
  108. Problems and solutions of personal protective equipment doffing in COVID-19
  109. Evaluation of COVID-19 based on ACE2 expression in normal and cancer patients
  110. Review Article
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  114. A novel age-biomarker-clinical history prognostic index for heart failure with reduced left ventricular ejection fraction
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  120. CCTs as new biomarkers for the prognosis of head and neck squamous cancer
  121. Effect of glucagon-like peptide-1 receptor agonists on adipokine level of nonalcoholic fatty liver disease in rats fed high-fat diet
  122. 72 hour Holter monitoring, 7 day Holter monitoring, and 30 day intermittent patient-activated heart rhythm recording in detecting arrhythmias in cryptogenic stroke patients free from arrhythmia in a screening 24 h Holter
  123. FOXK2 downregulation suppresses EMT in hepatocellular carcinoma
  124. Case Report
  125. Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  126. Research Article
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  128. Case Report
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  132. Review Article
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  134. Research Article
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  137. Circulating irisin is linked to bone mineral density in geriatric Chinese men
  138. Case Report
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  142. Case Report
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  144. Research Article
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  146. Assessment of knowledge of use of electronic cigarette and its harmful effects among young adults
  147. Predictive factors of progression to severe COVID-19
  148. Procedural sedation and analgesia for percutaneous trans-hepatic biliary drainage: Randomized clinical trial for comparison of two different concepts
  149. Acute chemoradiotherapy toxicity in cervical cancer patients
  150. IGF-1 regulates the growth of fibroblasts and extracellular matrix deposition in pelvic organ prolapse
  151. NANOG regulates the proliferation of PCSCs via the TGF-β1/SMAD pathway
  152. An immune-relevant signature of nine genes as a prognostic biomarker in patients with gastric carcinoma
  153. Computer-aided diagnosis of skin cancer based on soft computing techniques
  154. MiR-1225-5p acts as tumor suppressor in glioblastoma via targeting FNDC3B
  155. miR-300/FA2H affects gastric cancer cell proliferation and apoptosis
  156. Hybrid treatment of fibroadipose vascular anomaly: A case report
  157. Surgical treatment for common hepatic aneurysm. Original one-step technique
  158. Neuropsychiatric symptoms, quality of life and caregivers’ burden in dementia
  159. Predictor of postoperative dyspnea for Pierre Robin Sequence infants
  160. Long non-coding RNA FOXD2-AS1 promotes cell proliferation, metastasis and EMT in glioma by sponging miR-506-5p
  161. Analysis of expression and prognosis of KLK7 in ovarian cancer
  162. Circular RNA circ_SETD2 represses breast cancer progression via modulating the miR-155-5p/SCUBE2 axis
  163. Glial cell induced neural differentiation of bone marrow stromal cells
  164. Case Report
  165. Moraxella lacunata infection accompanied by acute glomerulonephritis
  166. Research Article
  167. Diagnosis of complication in lung transplantation by TBLB + ROSE + mNGS
  168. Case Report
  169. Endometrial cancer in a renal transplant recipient: A case report
  170. Research Article
  171. Downregulation of lncRNA FGF12-AS2 suppresses the tumorigenesis of NSCLC via sponging miR-188-3p
  172. Case Report
  173. Splenic abscess caused by Streptococcus anginosus bacteremia secondary to urinary tract infection: a case report and literature review
  174. Research Article
  175. Advances in the role of miRNAs in the occurrence and development of osteosarcoma
  176. Rheumatoid arthritis increases the risk of pleural empyema
  177. Effect of miRNA-200b on the proliferation and apoptosis of cervical cancer cells by targeting RhoA
  178. LncRNA NEAT1 promotes gastric cancer progression via miR-1294/AKT1 axis
  179. Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis
  180. Comparison of low-molecular-weight heparins in thromboprophylaxis of major orthopaedic surgery – randomized, prospective pilot study
  181. Case Report
  182. A case of SLE with COVID-19 and multiple infections
  183. Research Article
  184. Circular RNA hsa_circ_0007121 regulates proliferation, migration, invasion, and epithelial–mesenchymal transition of trophoblast cells by miR-182-5p/PGF axis in preeclampsia
  185. SRPX2 boosts pancreatic cancer chemoresistance by activating PI3K/AKT axis
  186. Case Report
  187. A case report of cervical pregnancy after in vitro fertilization complicated by tuberculosis and a literature review
  188. Review Article
  189. Serrated lesions of the colon and rectum: Emergent epidemiological data and molecular pathways
  190. Research Article
  191. Biological properties and therapeutic effects of plant-derived nanovesicles
  192. Case Report
  193. Clinical characterization of chromosome 5q21.1–21.3 microduplication: A case report
  194. Research Article
  195. Serum calcium levels correlates with coronary artery disease outcomes
  196. Rapunzel syndrome with cholangitis and pancreatitis – A rare case report
  197. Review Article
  198. A review of current progress in triple-negative breast cancer therapy
  199. Case Report
  200. Peritoneal-cutaneous fistula successfully treated at home: A case report and literature review
  201. Research Article
  202. Trim24 prompts tumor progression via inducing EMT in renal cell carcinoma
  203. Degradation of connexin 50 protein causes waterclefts in human lens
  204. GABRD promotes progression and predicts poor prognosis in colorectal cancer
  205. The lncRNA UBE2R2-AS1 suppresses cervical cancer cell growth in vitro
  206. LncRNA FOXD3-AS1/miR-135a-5p function in nasopharyngeal carcinoma cells
  207. MicroRNA-182-5p relieves murine allergic rhinitis via TLR4/NF-κB pathway
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