Front. Med. Frontiers in Medicine Front. Med. 2296-858X Frontiers Media S.A. 10.3389/fmed.2025.1500605 Medicine Original Research Warning indicators of COVID-19 severity: a retrospective observational study integrating modern biomarkers and traditional tongue features Jing Zhang 1 2 * Yuntao Liu 3 Danwen Zheng 4 Gangfu Ye 1 Qiumin Chen 5 Jianshan Huang 6 Jiamei Wang 7 Zengming Ma 8 Zhongde Zhang 3 * 1Department of Pulmonary and Critical Care Medicine, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China 2Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China 3Key Laboratory of Infectious Diseases, Guangdong Provincial Bureau of Chinese Medicine, Guangzhou, China 4Emergency Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China 5Department of Traditional Chinese Medicine, Xinglin Branch of the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China 6Department of Pediatrics, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China 7Department of Traditional Chinese Medicine, Xiamen Fifth Hospital, Xiamen, Fujian, China 8Integrated TCM & Western Medicine Department, Xiamen Xianyue Hospital, Xiamen, Fujian, China

Edited by: Elisa Belluzzi, University of Padua, Italy

Reviewed by: Namyata Pathak, University of California, San Francisco, United States

Ryuichiro Araki, Saitama Medical University, Japan

 *Correspondence: Zhang Jing, 1746970426@qq.com; Zhang Zhongde, deshu9966@163.com 15 04 2025 2025 12 1500605 23 09 2024 19 03 2025 Copyright © 2025 Jing, Yuntao, Danwen, Gangfu, Qiumin, Jianshan, Jiamei, Zengming and Zhongde. 2025 Jing, Yuntao, Danwen, Gangfu, Qiumin, Jianshan, Jiamei, Zengming and Zhongde

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 Objective

This study aims to identify early warning indicators of COVID-19 severity by integrating modern medical biomarkers with traditional Chinese medicine (TCM) tongue features.

 Methods

A retrospective observational study was conducted on 409 hospitalized COVID-19 patients from two centers in China. Patients were stratified into severe (n = 50) and non-severe (n = 359) groups based on the 10th edition of China’s diagnostic guidelines. Data included demographics, clinical symptoms, tongue characteristics, and laboratory parameters. Univariate analyses (chi-square/Fisher’s exact tests) and stepwise logistic regression were performed to identify key predictors.

 Results

Age (p < 0.001), fever (p < 0.001), elevated procalcitonin (PCT, p < 0.001), thick tongue fur (p = 0.003), and fat tongue shape (p = 0.002) were significant predictors of severity. The combined model integrating these factors demonstrated superior predictive performance (Nagelkerke R2 = 0.741).

 Conclusion

Integrating TCM tongue features (thick fur and fat shape) with clinical biomarkers (age, fever, and PCT) enhances early identification of severe COVID-19, particularly in resource-limited settings.

 COVID-19 severity prediction traditional Chinese medicine tongue diagnosis biomarkers section-at-acceptance Translational Medicine Introduction

As of 12 July 2024, the World Health Organization (WHO) (1) reports that the global pandemic of novel Coronavirus Disease 2019 (COVID-19) has resulted in over 700 million confirmed cases and 7 million deaths. History teaches us that the next pandemic is a matter of “when,” not “if” (2). Strategies for preventing severe outcomes and improving treatment efficacy remain critical areas requiring ongoing and relentless research in COVID-19 management. In this study, we focus on obtaining warning indicators of severity by using the retrospective observational method.

To date, numerous studies have explored warning indicators for COVID-19. These include baseline characteristics, such as age, diabetes, obesity, hypertension, and genetic risk (3–7), clinical symptoms, such as chest pain, dyspnea, and headache (8), and laboratory markers, such as procalcitonin (PCT), D-dimer (D-D), lactic dehydrogenase (LDH), c-reactive protein (CRP), lymphocyte (LY), tumor necrosis factor-α (TNF-α), and serum cystatin C (sCys C) (9–13). However, the majority of existing research relies on cross-sectional or retrospective designs, lacking comprehensive integration of multidisciplinary approaches.

Traditional Chinese medicine (TCM), akin to other global traditional medical systems, posits that external manifestations (e.g., tongue features) can predict disease progression. For instance, tongue diagnosis has been shown to aid in predicting acute heart failure, classifying diabetes and hypertension, and identifying diagnostic signatures in coronary artery disease with clopidogrel resistance (14–17). These non-invasive diagnostic methods are particularly valuable in resource-limited settings where advanced testing equipment is unavailable. Despite this potential, research on TCM-based warning indicators for COVID-19 severity remains scarce.

In this study, we conducted a retrospective observational analysis combining modern medical indicators (e.g., laboratory markers) and TCM tongue features. These variables were analyzed using one-way ANOVA and logistic regression to identify severity-related warning indicators. This approach aims to bridge the gap between modern and traditional medicine, offering a supplementary tool for early risk stratification in diverse clinical settings.

 Materials and methods Cases

A total of 409 cases of novel coronavirus infection were admitted to sentinel hospitals in Guangzhou, Guangdong, and Xiamen, Fujian, China, from May to October 2021.

 Inclusion and exclusion criteria

According to China’s 10th edition of the Diagnosis and Treatment Guidelines for Novel Coronavirus Infection (Trial) (18), a positive nucleic acid test for the novel coronavirus was the diagnostic criterion. During this period, all patients were infected with the Delta strain, and cases with psychiatric conditions were excluded. The diagnostic criteria for severity are listed in Table 1. A diagnosis could be made if any one of the following criteria was met. Cases meeting these criteria were included in the severe group, while others were placed in the non-severe group.

Diagnostic criteria for severity.

<14 y 14 y~
High fever >3 days; Respiratory rate ≥ 30 breaths/min;
Increased respiratory rate in non-fever and emotionally affected conditions:<2 m, RR ≥ 60 breaths/min;2–12 m, RR ≥ 50;1–5 y, RR ≥ 40;>5 y, RR ≥ 30 Resting-state without oxygen, SpO2 ≤ 93%;
Resting-state without oxygen, SpO2 ≤ 93% Oxygenation index ≤300 mmHg;
Nasal flaring, triple concave sign, wheezing, or stridor Clinical symptoms aggravated, and imaging progression of more than 50% in 24–48 h.
Impaired consciousness or convulsions
Refusal to eat or difficulty in feeding, signs of dehydration.
All ages
Respiratory failure requires mechanical ventilation.
Shock
Other organ failure required intensive care unit (ICU) treatment.
Data collection

Data were collected by clinicians and cross-verified by two independent researchers. Inaccurate data were discarded and treated as missing. Abbreviations are provided in Appendix Table 1.

General information includes gender, age, body mass index (BMI), heavy smoking, hypertension, heart disease, diabetes, chronic lung disease, chronic liver disease, chronic kidney disease, neoplasms, rheumatologic and immunologic diseases, surgery, and vaccination history, totaling 15 items.

Clinical symptoms include fever, cold, headache, sweating, nasal congestion and runny nose, cough, sputum, sore throat, shortness of breath, chest tightness, nausea and vomiting, fatigue, loss of taste or smell, diarrhea, muscular pains, and poor appetite, totaling 18 items. Data for the severe group were collected 4 days before the diagnosis of severity, while data for the non-severe group were collected between 7 and 11 days of disease duration. The aim of the study was to detect critically ill patients as early as possible. Therefore, data from 4 days before the diagnosis of severity were selected for the severe group. In contrast, the non-severe group was pre-measured and found to have worse data between days 7 and 11 of the disease course; hence, this period was selected for comparison.

Tongue features were evaluated according to the tongue assessment criteria (19), with two senior staff members providing their evaluations after careful deliberation. The classification of tongue features is detailed in Table 2. Examples of assessments are provided in Appendix Table 2.

Tongue classification (29 items).

Classification Content
Tongue color Pale white, light red, red, reddish-red, purple, blue, bruise;
Tongue shape Withered, old, tender, fat, thin, punctured, cracked, teeth marks;
Color of tongue fur White, yellow, gray, black;
Quality of tongue fur Scanty, thick, moist, slippery, dry, greasy, rotten, peeling, like peeling.

Testing information was categorized as normal or abnormal based on the reference range provided by the test results. The criteria for judgment are provided in Appendix Table 3. The Tongue classification items are presented in Table 3. As the study objective was to identify early warning indicators rather than precise disease manifestations, abnormal laboratory findings were not subcategorized into elevated or decreased levels. Comprehensive data characterization, including median values and interquartile ranges (IQR), are provided in Appendix Table 4. Data for the severe group were collected 4 days before the diagnosis of severity, while data for the non-severe group were collected between 7 and 11 days of disease duration.

Laboratory markers (22 items).

Classification Items
Blood counts White blood cell (WBC), neutrophil (NE), LY, hemoglobin (HGB), platelet (PLT);
Inflammatory indicators CRP, interleukin 6 (IL-6), PCT;
Coagulation tests Activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (Fib), fibrinogen degradation products (FDP), D-D, international normalized ratio (INR), prothrombin activity (PTA);
Liver and kidney routine Creatine kinase (CK), creatine kinase isoenzyme (CKMB), glutamic-pyruvic transaminase (ALT), glutamic oxaloacetic transaminase (AST), creatinine (Cr), LDH.
Statistical analysis methods

Excel was used for data entry, and SPSS Statistics for Windows, version 18.0 (SPSS Inc., Chicago, Ill., USA) was used for statistical analysis. Continuous variables were checked for maximum and minimum values, and histograms and quartiles were used to examine the data. Categorical variables were assessed using frequency tables, which were sorted to screen for and exclude erroneous data. Measured data were described using the mean ± standard deviation for conformity to a normal distribution, otherwise median and interquartile spacing median were used (P25, P75). Frequency and percentage were used to express the count data. General information, clinical symptoms, tongue features, and testing information were categorized as count data. Univariate analysis of the count data between groups was performed using the chi-square test or Fisher’s exact probability test. Frequency indicators with a severe positive rate of <3% and secondary indicators with obvious correlations were discarded based on medical and testing principles. The resulting positive indicators were analyzed using binary logistic regression. A p < 0.05 was considered statistically significant.

 Results

A total of 409 cases with novel coronavirus infection were included: 50 cases in the severe group and 359 cases in the non-severe group.

Univariate analysis

In the univariate analysis of general information, indicators with a positive rate of <3% for severity (such as heavy smoking, heart disease, diabetes, chronic lung disease, chronic liver disease, chronic kidney disease, tumors, rheumatologic and immunologic diseases, and surgery) were discarded. Using p < 0.05 as the inclusion criterion, four indicators, namely age (p < 0.001), BMI (p = 0.036), hypertension (p < 0.001), and vaccination (p < 0.001), were included, as shown in Table 4.

Univariate analysis of general information.

General information n Category Non-severe group Severe group Positivity rate for severity χ2 P
n % n % n %
Sex 409 Female 195 88.64 25 11.36 50 100.00 0.329 0.566
Male 164 86.77 25 13.23
Age 409 <14 46 100.00 0 0.00 50 100.00 71.944 <0.001#,a
14~ 183 96.32 7 3.68
45~ 97 82.91 20 17.09
65~ 28 65.12 15 34.88
80~ 5 38.46 8 61.54
BMI 322 <18.5 44 100.00 0 0.00 27 54.00 0.017*,a
18.5~ 163 92.61 13 7.39
24~ 69 87.34 10 12.66
28~ 19 82.61 4 17.39
Heavy smoking 409 N 354 88.06 48 11.94 2 0.49 0.206a
Y 5 71.43 2 28.57
Hypertension 409 N 321 90.93 32 9.07 18 4.40 23.989 <0.001#
Y 38 67.86 18 32.14
Heart disease 409 N 354 88.94 44 11.06 6 1.47 15.032 <0.001#
Y 5 45.45 6 54.55
Diabetes 409 N 347 89.43 41 10.57 9 2.20 16.465 <0.001#
Y 12 57.14 9 42.86
Chronic lung disease 409 N 335 88.16 45 11.84 5 1.22 0.315 0.547
Y 24 82.76 5 17.24
Chronic liver disease 409 N 356 87.68 50 12.32 0 0.00 1.000a
Y 3 100.00 0 0.00
Chronic kidney disease 409 N 357 88.15 48 11.85 2 0.49 0.075a
Y 2 50.00 2 50.00
Neoplasms 409 N 355 88.09 48 11.91 2 0.49 0.159a
Y 4 66.67 2 33.33
rheumatologic and immunologic diseases 409 N 357 87.71 50 12.29 0 0.00 1.000a
Y 2 100.00 0 0.00
Surgery 409 N 331 88.50 43 11.50 7 1.71 1.437 0.231
Y 28 80.00 7 20.00
Vaccination history 397 N 124 78.48 34 21.52 14 3.53 21.950 <0.001#
Y 225 94.14 14 5.86

N, No; Y, Yes. aFisher’s exact probability method, *P < 0.05, **P < 0.01, #P < 0.001.

In the univariate analysis of clinical symptoms, indicators with a positive rate of <3% for severity (such as cold, headache, sweating, nasal congestion and runny nose, sore throat, chest tightness, nausea and vomiting, loss of taste or smell, diarrhea, and muscle pain) were discarded. Finally, six indicators, namely fever (p < 0.001), cough (p < 0.001), sputum (p < 0.001), shortness of breath (p < 0.001), fatigue (p < 0.001), and poor appetite (p < 0.001), were included, as shown in Table 5.

Univariate analysis of clinical symptoms.

Symptom n = 409 Non-severe group Severe group Positivity rate for severity χ2 P
n % n % n %
Fever N 277 97.9 6 2.1 44 10.76 87.413 <0.001#
Y 82 65.1 44 34.9
Cold N 357 87.9 49 12.1 1 0.24 0.324a
Y 2 58.8 1 41.2
Headache N 349 89.0 43 11.0 7 1.71 11.183 <0.001#
Y 10 88.24 7 11.76
Sweating N 358 87.7 50 12.3 0 0.00 0.878a
Y 1 100.0 0 0.0
Nasal congestion and runny nose N 321 86.8 49 13.2 1 0.24 2.821 0.093
Y 38 97.4 1 2.6
Cough N 152 94.4 9 5.6 41 10.02 10.893 <0.001#
Y 207 83.5 41 16.5
Sputum N 225 93.4 16 6.6 34 8.31 17.061 <0.001#
Y 134 79.8 34 20.2
Sore throat N 330 87.5 47 12.5 3 0.73 0.054 0.817
Y 29 90.6 3 9.4
Shortness of breath N 345 92.5 28 7.5 22 5.38 82.992 <0.001#
Y 14 38.9 22 61.1
Chest tightness N 350 88.8 44 11.2 6 1.47 8.669 0.003**
Y 9 60.0 6 40.0
Nausea and vomiting N 346 88.0 47 12.0 3 0.73 0.179 0.692
Y 13 81.3 3 18.7
Fatigue N 333 90.0 37 10.0 13 3.18 15.793 <0.001#
Y 26 66.7 13 33.3
Loss of taste or smell N 350 87.5 50 12.5 0 0.00 0.381 0.537
Y 9 100.0 0 0.0
Diarrhea N 348 88.3 46 11.7 4 0.98 1.791 0.181
Y 11 73.3 4 26.7
Muscle pains N 342 88.1 46 11.9 4 0.98 0.407 0.523
Y 17 81.0 4 19.0
Poor appetite N 328 92.1 28 7.9 22 5.38 48.664 <0.001#
Y 31 58.5 22 41.5

N, No; Y, Yes. aFisher’s exact probability method, *P < 0.05, **P < 0.01, #P < 0.001.

In the univariate analysis of tongue features, indicators with a positive rate of <3% for severity (such as pale white, pale red, red, reddish-red, blue, bruise, withered, old, tender, fat, thin, punctured, cracked, white, gray, black, weak, moist, dry, rotten, peeling, or like peeling) were excluded. Finally, five indicators, namely purple (p < 0.001), fat (p < 0.001), teeth marks (p = 0.04), thick (p < 0.001), and greasy (p = 0.031), were included, as shown in Table 6.

Univariate analysis of tongue features.

Features n = 276 Non-severe group Severe group Positivity rate for severity χ2 P
n % n % n %
Tongue color
Pale white N 243 92.40 20 7.60 0 0.00 0.608a
Y 13 100.00 0 0.00
Light red N 162 90.50 17 9.50 3 1.09 3.839 0.050
Y 94 96.91 3 3.09
Red N 203 92.27 17 7.73 3 1.09 0.101 0.747
Y 53 94.64 3 5.36
Reddish-red N 225 92.59 18 7.41 2 0.72 0.000 1.000
Y 31 93.94 2 6.06
Purple N 193 96.02 8 3.98 12 4.35 11.741 <0.001#
Y 63 84.00 12 16.00
Blue N 256 92.75 20 7.25 0 0.00 NA
Y 0 0.00 0 0.00
Bruise N 251 92.62 20 7.38 0 0.00 1.000a
Y 5 100.00 0 0.00
Tongue shape
Normal N 147 89.10 18 10.90 2 0.72 8.189 0.004**
Y 109 98.20 2 1.80
Withered N 256 92.75 20 7.25 0 0.00 NA
Y 0 0.00 0 0.00
Old N 241 93.77 16 6.23 4 1.45 3.791 0.052
Y 15 78.95 4 21.05
Tender N 251 92.62 20 7.38 0 0.00 1.000a
Y 5 100.00 0 0.00
Fat N 223 95.30 11 4.70 9 3.26 12.440 <0.001#
Y 33 78.57 9 21.43
Thin N 253 92.67 20 7.33 0 0.00 1.000a
Y 3 100.00 0 0.00
Punctured N 256 92.75 20 7.25 0 0.00 NA
Y 0 0.00 0 0.00
Cracked N 236 92.91 18 7.09 2 0.72 0.000 1.000
Y 20 90.91 2 9.09
Teeth marks N 150 95.54 7 4.46 13 4.71 4.210 0.040*
Y 106 89.08 13 10.92
Color of tongue fur
White N 152 89.94 17 10.06 3 1.09 5.131 0.023*
Y 104 97.20 3 2.80
Yellow N 106 96.36 4 3.64 16 5.80 3.546 0.060
Y 150 90.36 16 9.64
Gray N 255 93.07 19 6.93 1 0.36 0.140a
Y 1 50.00 1 50.00
Black N 255 93.07 19 6.93 1 0.36 0.14a
Y 1 50.00 1 50.00
Quality of tongue fur
Scanty N 151 88.82 19 11.18 1 0.36 10.172 <0.001#
Y 105 99.06 1 0.94
Thick N 196 98.49 3 1.51 17 6.16 34.952 <0.001#
Y 60 77.92 17 22.08
Moist N 255 92.73 20 7.27 0 0.00 1.000a
Y 1 100.00 0 0.00
Dry N 216 93.51 15 6.49 5 1.81 0.607 0.436
Y 40 88.89 5 11.11
Slippery N 221 86.70 34 13.30 4 1.45 4.896 0.027*
Y 82 95.30 4 4.70
Greasy N 153 95.63 7 4.38 13 4.71 4.670 0.031*
Y 103 88.79 13 11.21
Rotten N 253 93.01 19 6.99 1 0.36 0.216a
Y 3 75.00 1 25.00
Peeling N 253 93.01 19 6.99 1 0.36 0.216a
Y 3 75.00 1 25.00
Like peeling N 250 93.28 18 6.72 2 0.72 0.107a
Y 6 75.00 2 25.00

N, No; Y, Yes. aFisher’s exact probability method, *P < 0.05, **P < 0.01, #P < 0.001. NA, a frequency of 0 and no chi-square test condition.

In the univariate analysis of testing information, indicators with a positive rate of <3% for severity (such as PT, INR, TT, APTT, and CKMB) were excluded. Finally, nine indicators, namely NE (p = 0.007), LY (p < 0.001), PLT (p < 0.001), IL-6 (p < 0.001), PCT (p < 0.001), D-D (p < 0.001), CK (p < 0.001), AST (p < 0.001), and LDH (p < 0.001), were included, as shown in Table 7. Among them, CRP, IL-6, FDP, and D-D are highly correlated. Previous studies (20, 21) support the greater significance of IL-6, while D-D has a higher positivity rate. A lower p-value was selected for inclusion.

Univariate analysis of testing information.

Test n Classification Non-severe group Severe group Positivity rate for severity χ2 P
n % n % n %
WBC 379 Normal 279 88.29 37 11.71 13 3.43 3.654 0.056
Abnormal 50 79.37 13 20.63
NE 379 Normal 266 89.26 32 10.74 18 4.75 7.334 0.007**
Abnormal 63 77.78 18 22.22
LY 379 Normal 255 94.44 15 5.56 35 9.23 47.812 <0.001#
Abnormal 74 67.89 35 32.11
HGB 379 Normal 235 88.35 31 11.65 19 5.01 1.844 0.174
Abnormal 94 83.19 19 16.81
PLT 364 Normal 282 90.68 29 9.32 14 3.85 12.696 <0.001#
Abnormal 39 73.58 14 26.42
CRP 371 Normal 199 99.00 2 1.00 48 12.94 58.609 <0.001#
Abnormal 122 71.76 48 28.24
IL-6 298 Normal 166 98.22 3 1.78 42 14.09 54.073 <0.001#
Abnormal 87 67.44 42 32.56
PCT 323 Normal 258 95.20 13 4.80 33 10.22 122.939 <0.001#
Abnormal 19 36.54 33 63.46
PT 316 Normal 269 86.77 41 13.23 3 0.95 0.037*a
Abnormal 3 50.00 3 50.00
INR 316 Normal 270 86.54 42 13.46 2 0.63 0.095a
Abnormal 2 50.00 2 50.00
APTT 315 Normal 253 88.15 34 11.85 10 3.17 10.189 0.001**
Abnormal 18 64.29 10 35.71
TT 315 Normal 265 86.32 42 13.68 2 0.63 0.156 0.693
Abnormal 6 75.00 2 25.00
Fib 315 Normal 153 91.07 15 8.93 29 9.21 7.609 0.006**
Abnormal 118 80.27 29 19.73
FDP 281 Normal 170 90.91 17 9.09 19 6.76 6.927 0.008**
Abnormal 75 79.79 19 20.21
D-D 313 Normal 201 92.63 16 7.37 29 9.27 28.192 <0.001#
Abnormal 67 69.79 29 30.21
PTA 316 Normal 264 86.84 40 13.16 4 1.27 2.418 0.12
Abnormal 8 66.67 4 33.33
CK 320 Normal 241 89.59 28 10.41 18 5.63 21.570 <0.001#
Abnormal 33 64.71 18 35.29
CKMB 295 Normal 245 86.57 38 13.43 6 2.03 9.422 0.002**
Abnormal 6 50.00 6 50.00
ALT 342 Normal 243 87.73 34 12.27 11 3.22 0.996 0.318
Abnormal 54 83.08 11 16.92
AST 349 Normal 238 91.89 21 8.11 28 8.02 29.283 <0.001#
Abnormal 62 68.89 28 31.11
Cr 345 Normal 236 88.06 32 11.94 15 4.35 2.890 0.089
Abnormal 62 80.52 15 19.48
LDH 320 Normal 169 93.89 11 6.11 36 11.25 24.151 <0.001#
Abnormal 104 74.29 36 25.71

aFisher’s exact probability method, *P < 0.05, **P < 0.01, #P < 0.001.

 Binary unconditional stepwise logistic analysis

1 Warning indicators of severity without testing information.

Including positive indicators of general information (four items), clinical symptom (six items), and tongue feature (five items), with severity or not as an outcome, through binary unconditional stepwise logistic analysis, we obtained four correlations, namely age (p < 0.001), fever (p < 0.001), fat tongue shape (p < 0.001), and thick tongue fur (p < 0.001), as shown in Table 8.

2 Warning indicators of severity without tongue feature.

Warning indicators of severity without testing information by logistic analysis.

Indicator β S1 Wald P OR 95% CI
Lower Upper
Age (year) 0.098 0.021 22.282 <0.001# 1.104 1.059 1.150
Fever (yes) 3.340 0.594 31.616 <0.001# 28.209 8.807 90.356
Fat tongue shape (yes) 2.526 0.719 12.325 <0.001# 12.501 3.052 51.213
Thick tongue fur (yes) 2.569 0.774 11.005 <0.001# 13.050 2.861 59.529

*P < 0.05, **P < 0.01, #P < 0.001. β, partial regression coefficient; S1, standard error of β1; OR, odds ratio; 95% CI, 95% confidence interval.

Including positive indicators of general information (four items), clinical symptom (six items), and testing information (five items), with severity or not as an outcome, through binary unconditional stepwise logistic analysis, we obtained three correlations, namely age (p < 0.001), fever (p < 0.001), and PCT (p < 0.001), as shown in Table 9.

3 Aggregate indicators.

Warning indicators of severity without tongue feature by logistic analysis.

Indicator β S1 Wald P OR 95% CI
Lower Upper
Age (year) 0.105 0.020 27.345 <0.001# 1.111 1.068 1.156
Fever (yes) 2.701 0.638 17.905 <0.001# 14.890 4.262 52.020
PCT (>0.1 ng/mL) 2.812 0.568 24.474 <0.001# 16.640 5.462 50.694

*P < 0.05, **P < 0.01, #P < 0.001. β, partial regression coefficient; S1, standard error of β1; OR, odds ratio; 95% CI, 95% Confidence interval.

Including positive indicators of the above two parts, with severity or not as an outcome, through binary unconditional stepwise logistic analysis, we obtained five correlations, namely age (p < 0.001), fever (p < 0.001), PCT (p < 0.001), thick tongue fur (p = 0.004), and fat tongue shape (p = 0.002), as shown in Table 10.

4 Model comparison.

Aggregate warning indicators of severity by logistic analysis.

Indicator β S1 Wald P OR 95% CI
Lower Upper
Age (year) 0.103 0.022 21.456 <0.001# 1.109 1.061 1.158
Fever (yes) 3.146 0.648 23.565 <0.001# 23.232 6.524 82.728
PCT (>0.1 ng/mL) 2.491 0.564 19.498 <0.001# 12.077 3.997 36.491
Thick tongue fur (yes) 2.495 0.852 8.588 0.003** 12.127 2.285 64.356
Fat tongue shape (yes) 2.385 0.826 8.327 0.004** 10.858 2.149 54.854

*P < 0.05, **P < 0.01, #P < 0.001. β, partial regression coefficient; S1, standard error of β1; OR, odds ratio; 95% CI, 95% Confidence interval.

We evaluated three logistic models using the Cox and Snell R and Negelkerke R methods. The closer the score is to 1, the better the fit. Aggregate indicators performed the best, as shown in Table 11.

Comparison of three logistic models.

Model Cox and Snell R Negelkerke R
Model without testing information 0.341 0.650
Model without tongue feature 0.372 0.709
Aggregate model 0.388 0.741
Discussion

This study highlights the prognostic value of integrating traditional Chinese medicine (TCM) tongue features with clinical biomarkers. It has always been challenging to associate tongue features with systemic inflammation. Studies have found that increased reproductive activity of glossal epithelial cells is one of the main characteristics in the formation of thick, greasy tongue fur, and that increased vascular permeability is closely associated with its formation (22). COVID-19 is a virus that causes systemic inflammatory storms and a concomitant increase in vascular permeability. The oral cavity and intestine are the main distribution sites for human digestive bacteria. Some data suggest that the abundance of the same flora in both sites may follow a common change trend (23). Systemic immune dysfunction and bacteriological disorders, which are usually present in severe patients of COVID-19, may be related. Changes in tongue features are associated with systemic metabolism. A fat tongue is associated with abnormal upper airway patency and whole-body adiposity (24). This means that these patients have an underlying condition of airway problems and increased airway resistance and are also more likely to develop serious conditions, such as respiratory failure. These factors appear to be connected. Thick fur and fat shape reflect systemic inflammation and fluid retention in TCM theory, aligning with severe COVID-19 pathophysiology (e.g., cytokine storm, endothelial dysfunction, and airway problems).

According to Traditional Chinese Medicine theory, tongue diagnosis serves as a significant indicator for evaluating disease progression and severity. They help summarize the main etiologies of severe acute respiratory diseases from a TCM perspective (25). Studies have shown that the characteristics of the tongue and tongue coating in patients with severe acute respiratory syndrome are distinct, indicating a close relationship between the tongue appearance and the state of illness (26, 27). Other studies suggested that patients diagnosed with severe COVID-19 had a purple tongue and yellow tongue coating, while non-severe ones commonly had a light red tongue and white tongue coating. Tongue features can serve as potential indicators for evaluating disease prognosis (28). These studies suggest that while tongue features may predict disease progression, their relevance can vary over time, across locations, and with different strains.

This retrospective observational study may be subject to bias due to factors such as inaccurate data, patient memory errors, and different photo exposures. While we made every effort to verify the data, some bias may still exist. Moreover, different seasons and strains of the virus may produce variations in tongue features. This study was conducted during the summer amid the Delta variant pandemic.

 Conclusion

Age, fever, PCT, thick tongue fur, and fat tongue shape together form a robust predictive model for COVID-19 severity. TCM-derived indicators contribute to early risk stratification, particularly in settings with limited laboratory access. Further validation across diverse populations and variants is warranted.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

 Ethics statement

The studies involving humans were approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine: BF2021-113-01; Ethics Committee of Xiamen Hospital of Chinese Medicine: 2022-K017-01. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin. Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.

 Author contributions

ZJ: Writing – original draft. LY: Methodology, Supervision, Writing – review & editing. ZD: Data curation, Investigation, Writing – review & editing. YG: Methodology, Supervision, Writing – review & editing. CQ: Data curation, Resources, Supervision, Writing – review & editing. HJ: Data curation, Formal analysis, Writing – review & editing. WJ: Data curation, Investigation, Writing – review & editing. MZ: Data curation, Investigation, Writing – review & editing. ZZ: Supervision, Writing – review & editing.

 Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Xiamen Municipal Program for Supporting Chinese Medicine (no.: XWZY-2023-0612), 2022 Xiamen Health Guidance Program (no: 3502Z20224ZD1179), Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (no.: ZYYCXTD-D-202203), Xiamen Young and Middle-aged TCM Reserve Talents Training Project, and The Fifth Batch of National Traditional Chinese Medicine Excellent Clinical Talents Training Project (Announcement from the Personnel and Education Department of the National Administration of Traditional Chinese Medicine. no. 2022–1).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2025.1500605/full#supplementary-material

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