1. Introduction
Since December 2019, the coronavirus disease 2019 (COVID-19 ) has been reported worldwide.[ 1 ] Many studies have summarized the characteristics of patients with COVID-19 and identified several factors associated with the viral shedding time (VST).[ 2–7 ] However, few studies have developed predictive models for the VST of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) Delta variant.
In accordance with the Protocol for the Prevention and Control of COVID-19 (version 9), the National Health Commission of the People’s Republic of China recommended that patients whose symptoms have been relieved require 2 consecutive negative results of SARS-COV-2 nucleic acid tests (24 h apart) to confirm the complete elimination of the virus.[ 8 ] In addition, previous studies have shown that asymptomatic patients may not transmit the virus.[ 9 ] Despite this, there is still a possibility of viral transmission. Meanwhile, the rising number of COVID-19 cases has placed a significant burden on healthcare systems. If we can predict the VST of SARS-COV-2, we can develop more effective clinical strategies and improve treatment efficacy.
Therefore, understanding the VST of SARS-CoV-2 is essential for effective disease control. This study aimed to investigate the factors that could prolong the VST of the SARS-CoV-2 Delta variant and develop a nomogram to predict the probability of virus shedding at a given time, determine the appropriate isolation time, and optimize clinical strategies.
2. Methods
2.1. Ethical approval
The study was approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (KYLLSL-20220-106), according to the Declaration of Helsinki, 2013.
2.2. Study design and participants
In this retrospective study, we included 332 patients with COVID-19 treated at Ward 3 of Xi’an Chest Hospital (Shaanxi, China) between December 19, 2021 and February 5, 2022. The patients were confirmed to be positive according to the clinical guidelines for COVID-19 developed by the WHO[ 10 ] and the National Health Committee of China.[ 11 ] Single-sample testing was performed for individuals with a clear epidemiological history, and repeated testing was performed to confirm positive results when a positive report was reported (ie, cycle threshold [Ct] values <40 for N and ORF ). A 10-person sample mixed test was conducted on individuals without a clear epidemiological history. When the mixed sample had a positive result (ie, the Ct value of the N and ORF was <40), the single-sample test was repeated for each person. In addition, viral gene sequencing analysis demonstrating a high degree of similarity to known novel coronavirus was deemed positive.
Patients were excluded if they had (I) severe diseases such as tumors and (II) organ failure.
2.3. Outcomes and data collection
Two consecutive negative results (24 h apart) from reverse-transcription polymerase chain reaction (RT-PCR) of throat swabs or nasopharyngeal specimens indicated the cessation of SARS-CoV-2 shedding. Ct values of the novel coronavirus nucleic acid N and ORF genes ≥40 detected by real-time RT-PCR, in respiratory specimens were defined as negative for people without a clear COVID-19 epidemiological history. Two consecutive nucleic acid tests (24 h apart) with Ct values of N and ORF ≥ 35 were considered negative for people with an epidemiological history of COVID-19 , asymptomatic infected patients, and patients who were cured after diagnosis.
The primary outcome was the SARS-CoV-2 VST. The VST was defined as (1) the duration between the onset of symptoms (before the first positive RT-PCR test) and cessation of SARS-CoV-2 shedding in patients with COVID-19 -related symptoms, or (2) the duration between the first positive RT-PCR test result and the cessation of SARS-CoV-2 shedding in patients without COVID-19 -related symptoms. Presently, there are no standardized criteria for the VST threshold of SARS-COV-2. In our study, prolonged VST was defined as >21 d according to the median time, and patients were separated into 2 groups based on a 21-d cutoff value.
We obtained patient information from the hospital’s electronic medical record system, including demographic information, clinical symptoms, laboratory findings, radiological data (chest computed tomography [CT]), treatment plans, and clinical outcomes. A team of experienced doctors reviewed the data.
2.4. Diagnostic criteria
The severity of COVID-19 was categorized into non-severe group (mild and moderate) and severe group (severe and critically ill) based on trial version 8.0 of the guidelines issued by the National Health Commission of the People’s Republic of China.[ 11 ]
2.5. Statistical analysis
We presented categorical variables as frequencies and percentages. The chi-square or Fisher exact test was used to determine the significance of categorical variables. Continuous variables were presented as mean and standard deviation (SD) or median and interquartile range from 1 to 3 (Q1, Q3). The Student t test or the Mann-Whitney U test was used to determine the significance of continuous variables. A linear regression model was used to identify factors related to VST. The collinear screening was performed for the factors associated with VST. The variance inflation factor is an index for measuring data collinearity. If the variance inflation factor is >5, there is collinearity among the data, resulting in erroneous statistical conclusions. Before conducting the multivariate analysis, it was necessary to eliminate all data with a variance inflation factor >5. The gradual screening method of variance inflation factors calculated the variance inflation factors for each variable in the univariate model with P < 0.05. If the maximum variance inflation factor was ≥5, the maximum variance inflation factor was eliminated. The remaining variables with P < 0.05 were then included in the multivariate model to identify significant factors.
Based on the results of the multivariate analysis, we developed a nomogram to predict the probability of VST for each patient with COVID-19 . The concordance index (C-index) was used to evaluate the predictive accuracy of the nomogram. The area under the curve (AUC) and its confidence interval (95% confidence interval [CI]) from the receiver operating characteristic (ROC) curves were used to evaluate the discriminative ability. A calibration curve was used to assess the coincidence between the actual and predicted probabilities. The model was validated internally using the bootstrap method (1000 resamples). Empower 3.0 and R software version 3.6.3 was used for all data analysis. A 2-tailed P < 0.05 was considered to be statistically significant.
3. Results
3.1. Clinical characteristics
Table 1 shows the clinical characteristics. We included 332 patients with COVID-19 in this study, and the median patient age was 33 years (24, 48); 169 (50.90%) patients were men, and 31 (9.34%) had at least 1 comorbidity. Of the 332 patients, 121 (36.45%) had mild symptoms, 203 (61.14%) had moderate symptoms, and 8 (2.41%) had severe symptoms. None of the patients were critically ill or died. The mean VST for SARS-CoV-2 was (21 ± 7) d. The patients were divided into 2 groups: VST <21 d group (n = 169, 50.90%) and the VST ≥21 d group (n = 163, 49.10%). Age, clinical type, comorbidity, number of vaccinations, white blood cell (WBC) count, chest CT findings, and treatment plan were significantly different between the 2 groups [Table 1 ].
Table 1 -
Clinical characteristics of patients with coronavirus disease 2019
Variables
Total (n = 332)
VST < 21 d (n = 169)
VST ≥ 21 d (n = 163)
P value
VST (d, mean ± SD),
21 ± 7
16 ± 4
26 ± 5
<0.001
Age [years, M (Q1, Q3)]
33 (24, 48)
31 (21, 41)
36(28, 52)
<0.001
Age groups [years, n (%)]
0.001
<18
61 (18.37)
41 (24.26)
20 (12.27)
≥18, <65
244 (73.49)
121 (71.60)
123 (75.46)
≥65
27 (8.13)
7 (4.14)
20 (12.27)
Male [n (%)]
169 (50.90)
87 (51.48)
82 (50.31)
0.831
Comorbidity [n (%)]
31 (9.34)
9 (5.33)
22 (13.50)
0.011
Cardiac-cerebral vascular disease [n (%)]
20 (6.02)
5 (2.96)
15 (9.20)
0.017
Diabetes [n (%)]
5 (1.51)
0 (0)
5 (3.07)
0.022
Chronic respiratory disease [n (%)]
3 (0.90)
1 (0.59)
2 (1.23)
0.541
Number of vaccinations [n (%)]
0.034
≤1
32 (9.64)
22 (13.02)
10 (6.13)
≥2
300 (90.36)
147 (86.98)
153 (93.87)
Onset of symptom to diagnosis [d, M (Q1, Q3)]
1 (0, 3)
1 (0, 3)
1 (0, 4)
0.213
Clinical symptom [n (%)]
260 (78.31)
127 (75.15)
133 (81.60)
0.154
Fever
98 (29.52)
55 (32.54)
43 (26.38)
0.218
Cough
142 (42.77)
67 (39.64)
75 (46.01)
0.241
Sore throat
81 (24.40)
35 (20.71)
46 (28.22)
0.111
Hyposmia or hypogeusia
11 (3.31)
4 (2.37)
7 (4.29)
0.327
Other symptom
74 (22.29)
37 (21.89)
37 (22.70)
0.860
Clinical type [n (%)]
<0.001
Mild
121 (36.45)
76 (44.97)
45 (27.61)
Moderate
203 (61.14)
92 (54.44)
111 (68.10)
Severe
8 (2.41)
1 (0.59)
7 (4.29)
IgG [g/L, M (Q1, Q3)]
7.34 (1.73, 8.66)
7.00 (1.22, 8.66)
7.73 (2.85, 8.66)
0.145
IgM [g/L, M (Q1, Q3)]
1.32 (0.08, 6.39)
0.58 (0.07, 5.46)
2.09 (0.08, 6.79)
0.644
WBC [×109 /L, n (%)]
0.010
3.500–9.500
274 (82.53)
150 (88.76)
124 (76.07)
<3.500
27 (8.13)
9 (5.33)
18 (11.04)
>9.500
31 (9.34)
10 (5.92)
21 (12.88)
Neutrophils count [×109 /L, n (%)]
0.895
1.800–6.300
296 (89.16)
152 (89.94)
144 (88.34)
<1.800
15 (4.52)
7 (4.14)
8 (4.91)
>6.300
21 (6.33)
10 (5.92)
11 (6.75)
Lymphocyte count [×109 /L, n (%)]
0.814
1.100–3.200
221 (66.57)
114 (67.46)
107 (65.64)
<1.100
91 (27.41)
44 (26.04)
47 (28.83)
>3.200
20 (6.02)
11 (6.51)
9 (5.52)
d -dimer [mg/L, n (%)]
0.767
≤1
303 (91.27)
155 (91.72)
148 (90.80)
>1
29 (8.73)
14 (8.28)
15 (9.20)
ESR [mm/h, n (%)]
0.898
≤20
215 (64.76)
110 (65.09)
105 (64.42)
>20
117 (35.24)
59 (34.91)
58 (35.58)
CRP [mg/L, n (%)]
0.838
≤10
240 (72.29)
123 (72.78)
117 (71.78)
>10
92 (27.71)
46 (27.22)
46 (28.22)
PCT [ng/mL, n (%)]
0.210
≤0.05
287 (86.45)
150 (88.76)
137 (84.05)
>0.05
45 (13.55)
19 (11.24)
26 (15.95)
Scope of pneumonia [n (%)]a
0.009
None
87 (27.97)
55 (35.95)
32 (20.25)
Unilateral lung
84 (27.01)
37 (24.18)
47 (29.75)
Bilateral lungs
140 (45.02)
61 (39.87)
79 (50.00)
Ground-glass opacity [n (%)]a
138 (44.37)
57 (37.25)
81 (51.27)
0.013
Pulmonary consolidation or multiple patchy [n (%)]a
105 (33.76)
44 (28.76)
61 (38.61)
0.066
Other lung lesions [n (%)]a
53 (17.04)
20 (13.07)
33 (20.89)
0.067
Prone ventilation [n (%)]
270 (81.33)
127 (75.15)
143 (87.73)
0.003
Anticoagulant [n (%)]
202 (60.84)
87 (51.48)
115 (70.55)
<0.001
Chinese herbs [n (%)]
271 (81.63)
128 (75.74)
143 (87.73)
0.005
Nutrition support [n (%)]
61 (18.37)
41 (24.26)
20 (12.27)
0.005
Clinical outcome [n (%)]a
1.000
Virus nucleic acid retest negative
332 (100.00)
169 (100.00)
160 (100.00)
Died
0 (0)
0 (0)
0 (0)
P values indicate differences between VST < 21 d and VST ≥ 21 d patients group. P < 0.05 was considered statistically significant.
a indicates that the data are partially missing.
VST: Viral shedding time; SD: Standard deviation; M (Q1, Q3): Median (interquartile 1, interquartile 3); WBC: White blood cell count; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; PCT: Procalcitonin.
3.2. Factors associated with the VST of SARS-CoV-2 Delta variant
A linear regression model analysis for the VST of SARS-CoV-2 Delta variant is shown in Table 2 . Age, comorbidity, clinical type, clinical symptoms, time from onset of symptoms to diagnosis, chest CT findings, abnormal WBC count, and treatment plan were associated with the VST of SARS-COV-2 in univariate analysis (all P ’s < 0.05). After collinearity screening using the gradual screening method, the age groups, anticoagulant use, Chinese herbs, and nutritional support were dropped. The remaining variables associated with VST were included in the multivariate analysis. Multivariate analysis identified that age (β = 0.070, 95% CI = 0.010–0.130, P = 0.024), clinical type classified as severe (β = 6.600, 95% CI = 0.400–12.790, P = 0.038), time from onset of symptoms to diagnosis (β = 0.190, 95% CI = 0.030–0.360, P = 0.019), sore throat (β = 2.350, 95% CI = 0.620–4.080, P = 0.008), WBC < 3.5 × 109 /L (β = 3.010, 95% CI = 0.590–5.430, P = 0.015), and WBC > 9.5 × 109 /L (β = 2.940, 95% CI = 0.430–5.450, P = 0.022) were independent risk factors for prolonged VST of SARS-CoV-2 Delta variation.
Table 2 -
Risk factors associated with the VST of SARS-CoV-2
Delta variant
Variables
Univariate analysis
Multivariate analysis
β (95%CI)
P value
β (95%CI)
P value
Sex (female vs. male)
0 (−1.420, 1.420)
0.999
Age (years)
0.100 (0.060, 0.130)
<0.001
0.070 (0.010, 0.130)
0.024
Age groups (years)
<18
Reference
≥18, <65
2.070 (0.280, 3.850)
0.024
≥65
7.610 (4.720, 10.500)
<0.001
Comorbidity
4.160 (1.750, 6.560)
<0.001
2.130 (−1.950, 6.220)
0.307
Cardiac-cerebral vascular disease
4.780 (1.830, 7.730)
0.002
−0.420 (−5.330, 4.500)
0.868
Diabetes
6.490 (0.690, 12.300)
0.029
−0.740 (−6.840, 5.360)
0.812
Chronic respiratory disease
3.002 (−4.500, 10.540)
0.432
Clinical type
Mild
Reference
Reference
Moderate
2.980 (1.580, 4.380)
<0.001
−0.790 (−4.720, 3.140)
0.694
Severe
13.130 (8.670, 17.590)
<0.001
6.600 (0.400, 12.790)
0.038
Number of vaccinations (times, ≥2 vs. ≤1)
2.150 (−0.250, 4.550)
0.080
Onset of symptom to diagnosis (d)
0.190 (0.030, 0.340)
0.022
0.190 (0.030, 0.360)
0.019
Clinical symptom
3.520 (1.830, 5.200)
<0.001
0.120 (−2.010, 2.240)
0.915
Fever
−0.270 (−1.830, 1.290)
0.736
Cough
1.760 (0.330, 3.190)
0.016
0.960 (−0.620, 2.540)
0.236
Sore throat
1.710 (0.060, 3.350)
0.043
2.350 (0.620, 4.080)
0.008
Hyposmia or hypogeusia
0.930 (−3.040, 4.910)
0.646
Other clinical symptom
0.290 (−1.420, 2.000)
0.742
Scope of pneumonia
None
Reference
Reference
Unilateral lung
2.300 (0.390, 4.210)
0.019
0.620 (−2.920, 4.170)
0.730
Bilateral lungs
3.510 (1.800, 5.210)
<0.001
1.380 (−2.520, 5.290)
0.488
Ground-glass opacity
2.180 (0.740, 3.620)
0.003
0.620 (−1.220, 2.450)
0.510
Pulmonary consolidation or multiple patchy
2.830 (1.330, 4.330)
<0.001
1.810 (0, 3.620)
0.051
Other lung lesions
1.000 (−0.920, 2.930)
0.307
WBC (×109 /L)
3.500–9.500
Reference
Reference
<3.500
4.260 (1.700, 6.830)
0.001
3.010 (0.590, 5.430)
0.015
>9.500
2.720 (0.310, 5.130)
0.028
2.940 (0.430, 5.450)
0.022
Neutrophils (×109 /L)
1.800–6.300
Reference
<1.800
2.660 (−0.770, 6.080)
0.130
>6.300
1.190 (−1.730, 4.110)
0.426
Lymphocyte (×109 /L)
1.100–3.200
Reference
<1.100
1.000 (−0.610, 2.610)
0.224
>3.200
−1.900 (−4.910, 1.120)
0.219
d -dimer (mg/L, >1 vs . ≤1)
0.450 (−2.070, 2.970)
0.729
ESR (mm/h, >20 vs. ≤20)
−0.380 (−1.870, 1.110)
0.619
CRP (mg/L, >10 vs. ≤10)
0.070 (−1.520, 1.660)
0.934
PCT (ng/mL, >0.05 vs . ≤0.05)
0.940 (−1.130, 3.020)
0.374
IgG (g/L)
0.080 (−0.120, 0.280)
0.442
IgM (g/L)
−0.010 (−0.040, 0.030)
0.757
Prone ventilation
2.700 (0.900, 4.510)
0.004
−2.300 (−4.980, 0.380)
0.094
Anticoagulant
3.150 (1.730, 4.560)
<0.001
Chinese herbs
2.620 (0.800, 4.440)
0.005
Nutrition support
−2.620 (−4.440, −0.800)
0.005
Multivariate analysis adjusted for age, sex, cardiac-cerebral vascular disease, diabetes, chronic respiratory disease, and the number of vaccination. P < 0.05 was considered statistically significant.
VST: Viral shedding time; SARS-COV-2: Severe acute respiratory syndrome coronavirus 2; WBC: White blood cell count; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; PCT: Procalcitonin; CI: Confidence interval.
3.3. Development and evaluation of nomogram
A nomogram for predicting the probability of virus shedding was developed based on the 5 risk factors independently related to VST [Figure 1 ]. Internal bootstrap validation was used to evaluate the model. The C-index of this nomogram was 0.762 (95% CI = 0.756–0.768), and the adjusted C-index was 0.762 (95% CI = 0.755–0.769) after internal validation using bootstrapping (1000 resamples). The discriminative ability of the nomogram was measured using the ROC. The AUC of this nomogram was 0.965 (95% CI = 0.958–0.972), indicating a high discriminative ability for predicting the VST of the SARS-COV-2 Delta variant [Figure 2 ].
Figure 1: The nomogram for predicting the probability of viral shedding time of severe acute respiratory syndrome coronavirus 2 Delta variant within 10, 20, and 30 d. Total points are the sum of the values of points of the 5 variables, and the probability corresponding to total points is the probability of virus shedding predicted by a nomogram. The area in the blue square corresponds to the population density in this study. Take a patient, for example, red dots represent individual points, diamonds represents total point, and triangles represent the probability of virus shedding within 10, 20, and 30 d. *P <0.05, *P <0.01, ***P <0.001.
Figure 2: Receiver operating characteristic curve (ROC) of the nomogram for predicting the viral shedding time of the severe acute respiratory syndrome coronavirus 2 Delta variant. The area under the curve (AUC) shows the discriminative ability of the nomogram.
The coincidence between virus carrier status and predicted probabilities was assessed using a calibration curve [Figure 3 ]. The Brier scores on day 10, 20, and 30 were 0.015 (95% CI = 0.005–0.025), 0.071 (95% CI = 0.049–0.093), and 0.101 (95% CI = 0.070–0.132), respectively. The calibration curves for predicting overall virus shedding on days 10, 20, and 30 [Figure 3 ] were in good agreement with the standard curve, indicating a good coincidence between the actual virus carrier status and the predicted probabilities.
Figure 3: The calibration curves for predicting overall SARS-COV-2 shedding on day 10 (A), day 20 (B), and day 30 (C). The x-axis shows the nomogram-predicted probability of total SARS-COV-2 shedding, and the y-axis shows the actual overall SARS-COV-2 shedding. An ideal calibration model would have a solid line along the dashed line, indicating that the predicted probabilities closely match the actual probabilities. SARS-COV-2: Severe acute respiratory syndrome coronavirus 2.
Finally, we calculated the fitting score of the nomogram model for each patient and divided them into 2 groups based on the median fitting score. The probability of viral shedding between the 2 patient groups was compared using the Kaplan-Meier curve and log-rank test [Figure 4 ]. The results showed that the group with a lower fitting score of the nomogram model had a higher probability of longer VST (χ2 =286, log-rank P < 0.001).
Figure 4: Kaplan-Meier curves comparing the probability of viral shedding between the different patient groups with coronavirus disease 2019. Patients were divided into 2 groups by the median fitting score of the nomogram, and Kaplan-Meier curves showed the differences in viral shedding time between the 2 groups. The significance of the difference was calculated by a log-rank test.
4. Discussion
In most countries around the world, the number of people infected with SARS-COV-2 is increasing rapidly. The duration of VST is the essential determinant of disease transmission. Therefore, determining the VST for SARS-COV-2 is crucial for disease control.
In this retrospective study, we developed and validated a nomogram for predicting the VST of SARS-CoV-2 based on 5 variables. The nomogram model performed exceptionally well in predicting the probability of viral shedding probability on days 10, 20, and 30. Furthermore, the internal validation of the nomogram demonstrated high predictive discriminability and accuracy. A lower score for the model’s fit indicated a high probability of a longer VST. These findings revealed that the nomogram accurately predicted SARS-COV-2 Delta variant virus shedding in patients with COVID-19 .
A meta-analysis of 3229 participants from 43 studies revealed that the median VST of SARS-COV-2 in the upper respiratory tract was 17 d.[ 12 ] A study of 536 Italian patients reported that the median time between the onset of symptoms and virus shedding was 18 d.[ 13 ] Another review of 28 studies reported a median VST of 18 d for nasopharyngeal swabs.[ 14 ] In our study, the SARS-COV-2 Delta variant had a mean VST of 21 d, which was longer than that reported in a previous study. This disparity can be attributed to several factors. First, there were differences in age, comorbidity, and severity among the study populations, which were identified as risk factors associated with the VST of SARS-COV-2. Second, the patients in this study were infected with the Delta variant, and the differences in virus strains might have led to differences in VST.
A study reported that immunosuppression (odds ratio [OR] = 7.160, 95% CI = 1.390–36.900), high IL-6 level (OR = 3.700, 95% CI = 1.210–11.350), the duration from onset of symptoms to diagnosis (OR = 0.770, 95% CI = 0.660–0.900), and the need for mechanical ventilation (OR = 4.950, 95% CI = 1.507–15.590) were risk factors for longer VST.[ 5 ] A prospective study reported that older age, longer duration from symptom onset to hospitalization, and women prolonged the SARS-COV-2 VST.[ 15 ] A retrospective investigation of 113 patients found that men, older age, hypertension, delayed hospitalization, severe symptoms, invasive mechanical ventilation, and corticosteroid therapy were risk factors for VST of SARS-COV-2.[ 16 ] Our study revealed that age (β = 0.070, 95% CI = 0.010–0.130, P = 0.024), duration from symptom onset to diagnosis (β = 0.190, 95% CI = 0.030–0.360, P = 0.019), and clinical type classified as severe (β = 6.600, 95% CI = 0.400–12.790, P = 0.038) were risk factors for VST of the SARS-COV-2 Delta variant.
Our study found a correlation between an abnormal WBC count and the extension of VST. When the WBC count was <3.500 × 109 /L, VST was prolonged by 3.010 d for each 1 × 109 /L decrease (95% CI = 0.590–5.430, P = 0.015), and when WBC count was >9.500 × 109 /L, VST was prolonged by 2.940 d for each 1 × 109 /L increase (95% CI = 0.430–5.450, P = 0.022). WBCs are essential to the body’s immune system because they can resist and eliminate invading pathogens. Bin Zhu confirmed that when the WBC count is abnormal, the body’s resistance is decreased, leading to a longer VST.[ 17 ] A meta-analysis of 29 studies, including 4911 individuals, found that patients with increased WBC counts had poorer outcomes.[ 18 ]
In our study, sore throat (β = 2.350, 95% CI = 0.620–4.080, P = 0.008) was a risk factor for VST of the SARS-COV-2 Delta variant, which had not previously been reported. A meta-analysis revealed that sore throat is uncommon in patients with COVID-19 .[ 19 ] Sun et al.[ 20 ] conducted a study in response to this result, which proved that sore throats are common. Similarly, 24.40% of the participants in our study had sore throats, which is common. Another study reported that sore throat was more common in the Alpha variant of SARS-CoV-2 than in the wild-type SARS-CoV-2.[ 21 ] The high rate of patients with sore throat in this study may be due to the Delta variant. We confirmed that a sore throat increases the likelihood of the virus invading the lower respiratory tract, which may account for the longer VST in patients with sore throat.
Previous studies have demonstrated that comorbidities, including hypertension, diabetes, and chronic respiratory diseases, are risk factors for VST.[ 16,22–24 ] In this study, comorbidity was not independently associated with viral shedding . Previous studies have reported that fever is associated with a long VST.[ 24,25 ] In our investigation, fever was not an independent risk factor for prolonged VST. Several potential explanations exist for this discrepancy. First, the proportion of clinical disease classifications in the above study differed from that in our study. Furthermore, in this study, the health of the general population was better, and the probability of comorbidity and fever was low, which might have contributed to the differences in the results.
Although previous studies have reported the risk factors related to the VST of SARS-COV-2, there are few available predictive models. The nomogram appears to be user-friendly and straightforward among the few available prediction models. One study successfully used a nomogram to predict the probability of severe pneumonia in patients with SARS-COV-2 accurately.[ 26 ] In this study, we developed a nomogram prediction model for VST to predict the probability of SARS-CoV-2 shedding within 10, 20, and 30 d. Our nomogram prediction model is based on easily accessible factors; therefore, the model can be easily implemented in situations where healthcare resources are scarce, or systems are overburdened.
There are limitations to this study. First, SARS-COV-2 shedding in the upper respiratory tract may not always indicate actual viral shedding , as SARS-COV-2 virus has been detected in stool samples from individuals with negative nasopharyngeal swab testing.[ 27 ] Second, this was a single-center study that only included a subset of patients from the current outbreak in Xi’an, which does not accurately reflect the situation of the COVID-19 Delta variant in this region. Third, because this is a retrospective study, it was not possible to collect certain indicators, and other factors affecting VST may have been excluded from the analysis.
In conclusion, the results of our study should be interpreted with caution. Further studies are needed to evaluate the effectiveness of the model in forecasting the VST of SARS-COV-2.
Acknowledgments
We thank all the staff who participated in the data collection. Our great thanks also extend to the First Affiliated Hospital of Xi’an Jiaotong University for supporting us to conduct this research.
Funding
This study was supported by the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China (XJTU1AF-CRF-2018-025).
Author Contributions
Tian Yang and Mingwei Chen contributed to the conception and design. Lin Fan and Tian Yang have full access to all of the data in the study and take responsibility for the integrity of the data. Jiejun Zhou and Duo Li analyzed the data. Jiejun Zhou wrote the paper’s first draft, and all authors revised the manuscript and approved the final manuscript.
Conflicts of Interest
None.
Data Availability Statement
The data sets generated and/or analyzed during current study are available from the corresponding author on reasonable request.
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