
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39239874
10.1080/07853890.2024.2400312
2400312
Version of Record
Research Article
Infectious Diseases
Establishment and validation of a prognostic model based on common laboratory indicators for SARS-CoV-2 infection in Chinese population
A. Zhao et al.
Zhao Anjiang abc
Liu Yanyang de
Xia Junxiang af
Huang Lan ag
Lu Qing ah
Tang Qin ai
Gan Wei abc
a Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
b Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, China
c Clinical Laboratory Medicine Research Center of West China Hospital, Chengdu, China
d Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
e Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
f Department of Laboratory Medicine, Sichuan Province Orthopedic Hospital, Chengdu, China
g Department of Clinical Laboratory, Affiliated Hospital of Panzhihua University, Panzhihua, China
h Department of Clinical Laboratory, Guangnan County People’s Hospital, Wenshan, China
i Department of Clinical Laboratory, Yuechi County Hospital of Traditional Chinese Medicine, Guangan, Sichuan, China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2400312.

CONTACT Wei Gan 2004ganwei@scu.edu.cn Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
6 9 2024
2024
6 9 2024
56 1 24003128 8 2023
23 4 2024
23 4 2024
KnowledgeWorks Global Ltd.5 9 2024
published online in a building issue5 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Background

At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients.

Methods

The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit.

Results

888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram.

Conclusions

The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

Keywords

COVID-19
prognostic model
laboratory parameters
mortality
intensive care
Sichuan Province Science and Technology Program 2022NSFSC1283 This work was supported by the Sichuan Province Science and Technology Program (NO. 2022NSFSC1283)
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pmc1. Introduction

COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a pandemic in major cities in China during December 2022. Although the epidemic situation has been generally stable in China recently, there may still be a new wave of infections as the virus continues to mutate. Therefore, there is still a need for further research in this area. Studies have shown that a wide variety of factors may be associated with the severity and outcome of COVID-19 infection, including genetic background, immune system defenses, comorbidities such as diabetes, hypertension and malignancies, laboratory indicators such as CRP and neutrophil to lymphocyte ratio (NLR), age, and even gender differences [1–5]. However, how to combine these influencing factors and common laboratory indicators to conduct risk stratification and early intervention for COVID-19 patients was important to slow disease progression and reduce mortality.

During infection, the SARS-CoV-2 triggers various immune cascades, resulting in multiple organ dysfunction and thus changes in many laboratory indicators. It has been shown that laboratory indicators are of great value in early COVID-19 diagnosis, as well as determining curative effects, prognosis evaluation, and individualized medical treatment [6]. A growing number of studies have reported that multiple inflammatory indicators, such as C-reactive protein (CRP), interleukin-6 (IL-6), NLR have facilitated prognosis assessment and mortality prediction in COVID-19 patients [7,8]. Biochemical indicator analysis, the most routine one either in clinical, was also used to evaluate the prognosis of patients with COVID-19. A meta-analysis of 19 observational studies involving almost 3000 patients showed that COVID-19 patients had lower serum albumin (ALB) and higher lactate dehydrogenase (LDH) compared to healthy controls, and reduced serum albumin level was associated with increased mortality in COVID-19 patients [9,10]. Besides that, other laboratory abnormalities such as reduced serum bicarbonate (HCO3), elevated D-dimer, low hemoglobin (Hb) and elevated procalcitonin (PCT) have been reported in COVID-19 patients [11,12]. For prognosis evaluation purposes, in addition to the laboratory indicators mentioned above, elevated values of myocardial injury markers also have been found of great value [13]. Myocardial injury has been observed in substantial numbers of COVID-19 patients and has been described as a known consequence of COVID-19 with poor clinical outcome [14]. Therefore, combining these common laboratory indicators for prognostic prediction of COVID-19 is of immense value in terms of clinical application and economics.

Lately, there were several models that predict COVID-19 prognosis [15–18]. However, these studies had relatively small sample sizes or were from earlier data on the outbreak, which leads to possible limitations in the universality of these models. Data from a brief spike in infections in the beginning of December 2022, when the Chinese government made major adjustments to the epidemic prevention and control, were critical for constructing predictive models for COVID-19 patients.

In this study, we used a large cohort to develop an easy-to-use and effective prognostic model based on common laboratory indicators. After collecting the clinical information and laboratory data, the models were constructed using predictors screened by different methods. These models were further assessed from the aspects of discrimination, calibration, and net benefit to ensure its performance, and an optimal model was finally determined. To our knowledge, this is the first prediction model based on COVID-19 epidemic data during December 2022 in China and currently one of the prediction models in China that includes the largest number of COVID-19 patients.

2. Methods

2.1. Study design and participants

The COVID-19 patients hospitalized between December 5, 2022 and January 4, 2023 were enrolled in this study. SARS-CoV-2 infection was proved by reverse transcriptase polymerase chain reaction assay using respiratory specimens from patients. The diagnosis of COVID-19 was made based on the Diagnosis and Treatment Scheme of Pneumonia Caused by Novel Coronavirus of China (the ninth version). The candidates will be excluded for the following reasons: (1) Unable to obtain outcomes; (2) Incomplete laboratory data; (3) Unclear outcome due to family members abandoning treatment. The final enrolled patients were randomly divided into a training cohort and a validation cohort in a 7:3 ratio. The study was approved by the Biomedical Ethics Review Committee of West China Hospital of Sichuan University and the written informed consent was waived due to its retrospective design.

2.2. Data collection

All clinical information, including gender, age, smoking history, COVID-19 vaccination status, Previous history of COVID-19 infection, the days since onset of illness (DSOI) for each patient, comorbidities, laboratory indicators, was collected immediately upon hospitalization, and the length of hospitalization (LOH) for each patient was retrospectively collected. Obesity was determined by a body mass index (BMI) ≥28 kg/m2. These laboratory indicators include routine biochemistry (Cobas c702 automatic biochemical analyzer, Roche Diagnostics, Germany), myocardial markers (Cobas e601 automatic chemiluminescence analyzer, Roche Diagnostics, Germany), arterial blood gas analysis (Cobas b123 automatic blood gas analyzer, Roche Diagnostics, Germany), blood coagulation (Automatic blood coagulation analyzer CS-5100, Sysmex, Japan), inflammatory factors (Cobas e601 automatic chemiluminescence analyzer, Roche Diagnostics, Germany) and blood cell counts (2100D routine hematology analyzer, Sysmex, Japan). The oxygen saturation in blood gas analysis was not analyzed, mainly due to the fact that most patients were admitted with ventilators and thus could not obtain the true value. The malignant tumor in the comorbidities include both solid tumors and hematologic malignancies. All patients were divided into intensive care unit (ICU) and non-ICU groups based on outcome. The ICU was defined as admission to the ICU and treatment for more than 24 h during hospitalization, and all-cause deaths that occurred during hospitalization were also included.

2.3. Sample size considerations

To fit a predictive model using logistic regression, it is recommended to have a minimum of 10 events per variable (EPV) (i.e. the outcome being ICU admission) [19]. We evaluated 11 variables in the logistic regression model; thus, the sample size was at least 110 events. Based on the previous study’s findings, the rate of COVID-19 patients being admitted to intensive care was assumed to be 26% [20]. Therefore, the validation cohort or training cohort should consist of at least 423 or 182 cases, respectively, with a total sample size of not less than 605 cases. Ultimately, a total of 888 cases in the study were included.

2.4. Statistical analysis

For variables, all laboratory indicators were treated as continuous variables, except for HCO3, LDH, and PCT. We divided HCO3 into binary variables according to its reference interval (22–27 mmol/L), and LDH or PCT into binary variables according to the truncation value (PCT >0.5 ng/ml; LDH >300 IU/L) in literatures [21,22]. For normally distributed continuous variables, the values were expressed as mean ± SD and the unpaired t-test was used; for non-normally distributed continuous variables, the values were expressed as median (P25, P75) and the Wilcoxon signed-rank test was used; for categorical variables, the values were expressed as number (percentage) and Pearson’s chi-squared test was used. Multicollinearity was assessed using Pearson correlation coefficient statistic and Variance Inflation Factor in linear regression. To select variables by stepwise regression, unpaired t-test or Wilcoxon signed-rank test were first performed for each variable, and variables with p-value < 0.1 were used for forward stepwise regression. Least Absolute Shrinkage and Selection Operator (LASSO) regression studies the variations of mean squared error and the coefficients of all the variables with the change of λ value. In LASSO regression, λ.min is defined as the λ value corresponding to the lowest mean squared error, and λ.1se is defined as a 1-standard error to the right of λ.min. When the value is λ.1se, variables with non-zero coefficients included in LASSO regression were selected. In the logistic and LASSO regression, only the cohort of subjects with complete data in all variables was considered. The variables in the BASIC model included the initial information most readily available upon admission: age, comorbidities and smoking history. The model was appraised from three perspectives: the area under the receiver operating characteristic (ROC) curve, the calibration in the calibration curve, and the net benefit in the clinical decision curve. Finally, a nomogram was built using the variables of the optimal model and was used to calculate the total scores of each patient. The optimal cutoff was determined by maximizing the Youden index (sensitivity + specificity − 1). The unpaired t-test, Wilcoxon signed-rank test, Pearson’s chi-squared test and multicollinearity evaluation were performed using IBM SPSS Statistics 27 software. R (version 4.2.3) software was used for LASSO regression analysis and drawing all graphs. p < 0.05 was considered to be statistically significant.

3. Results

3.1. Cohort characteristics

We obtained laboratory and health information from 2244 patients with confirmed COVID-19, all of whom were adults. Finally, 888 patients with complete information according to the exclusion criteria were enrolled in the follow-up study (Figure 1). The clinical characteristics of all enrolled patients are shown in Table 1. Overall, 335 (37.7%) of the 888 patients were admitted to the ICU or died. The ICU cohort was older, more male, had shorter length of hospitalization (LOH) and days since onset of illness (DSOI), higher smoking rates and lower rates of COVID-19 vaccination compared with the non-ICU cohort. In terms of comorbidities, the ICU cohort also had more patients with hypertension, diabetes, and chronic kidney disease (CKD) of stage 4–5 than the non-ICU cohort. After we eliminated the indicators with multicollinearity, the remaining laboratory indicators were different between the ICU cohort and the non-ICU cohort, except for thrombin time (TT).

Figure 1. Flow chart of all excluded and included patients.

Table 1. Clinical characteristics of patients with COVID 19.

Variables	Total (n = 888)	Non-ICU (553) (n = 553)	ICU (n = 335)	p	
Patient Characteristics	
Age, years	73.1(59.4, 82.8)	70.8(56.7, 81.4)	76.7(66.2, 84.3)	<.001	
Male	615(69.3)	359(64.9)	256(76.4)	<.001	
LOH, days	12.0(8.0, 19.0)	14.0(9.0, 20.0)	10.0(5.0, 17.0)	<.001	
Smoke	111(12.5)	54(9.8)	57(17.0)	.002	
DSOI, days	7.0(3.0, 10.0)	7.0(4.0, 10.0)	6.0(3.0, 9.0)	<.001	
Vaccination status	754(84.9)	485(87.7)	269(80.3)	.003	
Comorbidities	
Hypertension	397(44.7)	229(41.4)	168(50.1)	.011	
Diabetes	282(31.8)	160(29.0)	122(36.4)	.020	
CKD4-5	67(7.5)	22(4.0)	45(13.4)	<.001	
Malignant tumor	140(15.8)	83(15.0)	57(17.0)	.427	
COPD	123(13.9)	70(12.7)	53(15.8)	.186	
AS	161(18.1)	94(17.0)	67(20.0)	.261	
Obesity	86(9.7)	56(10.1)	30(8.9)	.567	
Admission Laboratory Data	
LDH, IU/L	277.0(214.3, 382.8)	246.0(202.0, 312.5)	359.0(263.0, 503.0)	<.001	
LDH > 300 IU/L	381(42.9)	159(28.8)	222(66.3)	<.001	
HCO3, mmol/L	22.8(20.1, 25.5)	23.5(21.1, 26.0)	21.1(18.1, 24.1)	<.001	
HCO3 < 22 mmol/L	371(41.8)	176(31.8)	195(58.2)	<.001	
HCO3 > 27 mmol/L	129(14.5)	95(17.2)	34(10.1)	.004	
ALB, g/L	35.33 ± 6.06	37.26 ± 5.48	32.16 ± 5.61	<.001	
GLU, mmol/L	7.5(6.0, 10.5)	6.9(5.8, 9.7)	8.2(6.5, 12.8)	<.001	
CK, IU/L	92.0(53.0, 194.8)	77.0(49.5, 140.5)	142.0(67.0, 332.0)	<.001	
Urea, mmol/L	7.3(4.9, 11.7)	6.3(4.5, 9.3)	10.2(6.0, 17.2)	<.001	
HDL-C, mmol/L	1.05(0.83, 1.33)	1.07(0.87, 1.33)	1.01(0.75, 1.32)	.007	
LDL-C, mmol/L	1.79(1.26, 2.36)	1.92(1.40, 2.48)	1.53(1.05, 2.09)	<.001	
CA, mmol/L	2.11(2.01, 2.20)	2.13(2.04, 2.22)	2.06(1.96, 2.17)	<.001	
K, mmol/L	3.81(3.40, 4.24)	3.77(3.39, 4.15)	3.90(3.44, 4.41)	.002	
β-HBA, mmol/L	0.25(0.11, 0.55)	0.22(0.11, 0.53)	0.30(0.13, 0.62)	.004	
DB, µmol/L	3.6(2.5, 5.4)	3.5(2.4, 5.0)	3.9(2.7, 6.5)	<.001	
AST, IU/L	34.4(22.9, 52.9)	31.6(22.0, 46.0)	39.5(24.6, 64.3)	<.001	
TP, g/L	65.9(59.9, 70.9)	66.9(61.5, 71.6)	63.9(58.5, 69.2)	<.001	
UA, µmol/L	289.5(209.3, 395.0)	276.0(207.5, 363.0)	325.0(215.0, 442.0)	<.001	
CK-MB, ng/ml	1.56(0.86, 3.02)	1.30(0.72, 2.22)	2.20(1.23, 5.04)	<.001	
NT-proBNP, ng/L	593.5(187.2, 1914.8)	360.2(119.3, 974.7)	1312.0(480.0, 4083.0)	<.001	
TNT-T, ng/L	19.15(11.12, 39.99)	14.80(9.42, 26.01)	31.72(16.51, 72.50)	<.001	
MYO, ng/ml	66.09(33.55, 176.38)	50.09(27.87, 97.06)	140.30(61.35, 337.20)	<.001	
D-dimer, mg/L	1.13(0.58, 2.98)	0.86(0.46, 1.81)	2.23(1.02, 7.11)	<.001	
PT, s	11.4(10.7, 12.4)	11.2(10.6, 12.0)	11.8(11.0, 13.2)	<.001	
APTT, s	29.6(27.0, 32.9)	29.2(26.6, 32.1)	30.7(28.0, 34.4)	<.001	
TT, s	16.7(15.9, 17.5)	16.6(15.9, 17.4)	16.7(16.0, 17.6)	.068	
Hb, g/L	122.0(104.0, 135.0)	124.0(109.0, 136.5)	118.0(97.0, 131.0)	<.001	
PLT,109/L	162.0(117.0, 219.0)	170.0(125.5, 231.0)	151.0(109.0, 202.0)	<.001	
NLR	8.17(4.50, 15.83)	6.09(3.68, 11.88)	12.94(7.71, 23.62)	<.001	
MON, 109/L	0.51(0.33, 0.73)	0.54(0.36, 0.74)	0.45(0.27, 0.70)	<.001	
IL-6, pg/ml	34.5(10.6, 96.2)	22.7(6.8, 62.4)	73.5(23.5, 183.0)	<.001	
CRP, mg/L	74.4(20.7, 124.0)	43.9(14.2, 97.2)	107.0(66.3, 170.0)	<.001	
PCT, ng/ml	0.18(0.07, 0.79)	0.10(0.06, 0.28)	0.61(0.17, 3.53)	<.001	
PCT > 0.5 ng/ml	281(31.6)	96(17.4)	185(55.2)	<.001	
LAC, mmol/L	1.7(1.3, 2.3)	1.6(1.3, 2.1)	1.9(1.4, 2.7)	<.001	
PH	7.44(7.39, 7.48)	7.44(7.40, 7.48)	7.43(7.37, 7.47)	<.001	
LOH: length of hospitalization; DSOI: the days since onset of illness; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; AS: atherosclerosis; LDH: lactate dehydrogenase; HCO3: bicarbonate; ALB: albumin; GLU: glucose; CK: creatine kinase; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; Ca: calcium; K: kalium; β-HBA: β-hydroxybutyrate; DB: direct bilirubin; AST: aspartate aminotransferase; TP: total protein; UA: uric acid; CK-MB: Creatine kinase isoenzyme MB mass; NT-proBNP: N-terminal pro-brain natriuretic peptide; TNT-T: troponin T; MYO: myohemoglobin; PT: prothrombin time; APTT: activated partial thromboplastin time; TT: thrombin time; Hb: hemoglobin; PLT: platelet; NLR: neutrophil to lymphocyte ratio; MON: monocyte; IL-6: interleukin-6; CRP: C-reactive protein; PCT: procalcitonin; LAC: lactate.

3.2. Risk factors for death in COVID-19 patients

The percentage of all-cause death during hospitalization was 19.48% (173/888). To identify the risk factors for death in COVID-19 patients, multivariate logistic regression was used for all study variables, showing that LDH > 300IU/L (OR = 3.068, 95%CI: 1.799–5.233, p < .01), PCT > 0.5 ng/ml (OR = 1.963, 95%CI: 1.090–3.536, p = .025), age (OR = 1.059, 95%CI: 1.037–1.081, p < .001), smoke (OR = 2.853, 95%CI: 1.481–5.497, p = .002), malignant tumor (OR = 2.338, 95%CI: 1.273–4.296, p = .006), lactate (LAC) (OR = 1.283, 95%CI: 1.095–1.503, p = .002), urea (OR = 1.049, 95%CI: 1.007–1.093, p = .021) were independent risk factors and albumin (ALB) (OR = 0.800, 95%CI: 0.755–0.847, p < .001) and vaccination (OR = 0.472, 95%CI: 0.267–0.833, p = .010) were independent protective factors. It is an unexpected finding that high-density lipoprotein cholesterol (HDL-C) was an independent risk factor for COVID-19 patients with an OR of 3.816 (95%CI: 1.929–7.547, p < .001) after controlling for the above confounders (Table 2).

Table 2. Multivariable logistic regression analysis of 715 survivors and 173 deaths.

Variables	OR (95%CI)	p value	
LDH > 300 IU/L	3.068(1.799, 5.233)	<.001	
ALB	0.800(0.755, 0.847)	<.001	
PCT > 0.5 ng/ml	1.963(1.090, 3.536)	.025	
Age(years)	1.059(1.037, 1.081)	<.001	
Smoke	2.853(1.481, 5.497)	.002	
Malignant tumor	2.338(1.273, 4.296)	.006	
HDL-C	3.816(1.929, 7.547)	<.001	
LAC	1.283(1.095, 1.503)	.002	
Vaccination status	0.472(0.267, 0.833)	.010	
Urea	1.049(1.007, 1.093)	.021	
LDH: lactate dehydrogenase; ALB: albumin; PCT: procalcitonin; HDL-C: high-density lipoprotein cholesterol; LAC: lactate.

3.3. Construction of the prediction model

LASSO penalized regression is a variable selection tool that performed well in picking the most influential variable and reducing the overfitting of predictive models [23]. The significant variables related to the prognosis of COVID-19 patients were screened by LASSO regression and cross-validation, two penalty values (λ) were ultimately obtained (Figure 2b). One is the λ.min, corresponding to the best precision model, while the other is the λ.1se, corresponding to the optimal model with the least number of independent variables. When the penalty value is λ.1se, seven variables including NLR, D-dimer, PCT, CRP, ALB, HCO3 and LDH were retained, and the logistic regression model constructed with them was called the LASSO model (Figure 2c). By forward stepwise logistic regression, eleven variables were retained, including smoke, Troponin T (TNT-T), HCO3, age, HDL-C, NLR, D-dimer, PCT, CRP, ALB and LDH, and the regression model constructed using these variables was named STEPWISE model (Figure 2d). Moreover, to evaluate the predictive performance of selected laboratory indicators, a model named BASIC model, only including age, smoke, comorbidities (diabetes, hypertension, chronic obstructive pulmonary disease (COPD), atherosclerosis (AS), malignant tumor) was established and compared with the STEPWISE and LASSO models (Figure 2e).

Figure 2. Variable selection using the LASSO and the Forest plots of the STEPWISE and BASIC models. (a) LASSO coefficient of the 45 variables with the change of log lambda. (b) Optimal variable selection in the LASSO model used a tenfold cross-validation. Variation of mean-squared error with the change of log(λ) in LASSO regression. First vertical dotted line: λ.min, the λ value corresponding to the lowest mean squared error. Second vertical dotted line: λ.1se, λ.min + standard error. (c) Logistic regression results of the LASSO screened variables were displayed in the Forest plots as or values with 95% CI and P-values. Logistic regression results of the STEPWISE model (d) and BASIC model (e) were displayed in the Forest plots as or values with 95% CI and p-values. LASSO: Least Absolute Shrinkage and Selection Operator; TNT-T: troponin T; HCO3: bicarbonate; HDL-C: high-density lipoprotein cholesterol; NLR: neutrophil to lymphocyte ratio; LDH: lactate dehydrogenase; ALB: albumin; CRP: C-reactive protein; PCT: procalcitonin; COPD: chronic obstructive pulmonary disease; as: atherosclerosis.

3.4. Comparison and verification of prediction models

The 888 patients were randomly separated into the training and validation cohorts, no statistical difference in the indicators were found between the two cohorts (Table S1). To verify the prognostic predictive ability of these models, the receiver operator characteristic (ROC) curve was drawn. As shown in Figure 3a, the area under the curve (AUC) of the LASSO and STEPWISE models were over 0.8 in both the training and validation cohorts, and all the values were higher than those of the BASIC model (Figure 3a,d). In addition, the models’ calibration plots graphically showed good agreement between the prediction probability and actual ICU admission proportions prediction after 1000 bootstrap sampling (Figure 3b,e).

Figure 3. Discrimination, calibration and DCA analysis of the LASSO, STEPWISE and BASIC models. Receiver operating characteristic (ROC) curves (a), calibration plots (b) and decision curve analysis (c) of the LASSO, STEPWISE and BASIC models based on the training cohort. ROC curves (d), calibration plots (e) and decision curve analysis (f) of the LASSO, STEPWISE and BASIC models based on the validation cohort. (g) The final nomogram consisting of LDH > 300 IU/L, ALB, CRP, PCT > 0.5 ng/ml, D-dimer, NLR and HCO3 < 22mmol/L. LDH: lactate dehydrogenase; ALB: albumin; CRP; C-reactive protein; PCT: procalcitonin; NLR: neutrophil to lymphocyte ratio; HCO3: bicarbonate.

Subsequently, DCA curves were conducted to determine the clinical usefulness of the models by quantifying the net benefits at different threshold probabilities in the training cohort and validation cohort [24]. In the training cohort and validation cohort, the DCA curves showed that both LASSO and STEPWISE models had a higher clinical net benefit for COVID-19 patient outcomes than either the treat-all-patient scheme or the treat-none scheme in the range of risk threshold of 0.10-0.85, while the LASSO and STEPWISE models all had higher clinical net benefit than the BASIC model in all risk thresholds (Figure 3c,f). These results indicated that the LASSO and STEPWISE models have satisfactory performance compared with the BASIC model in all aspects.

3.5. Model specification

By comparing the LASSO model with the STEPWISE model, it can be found that the prediction performance of the STEPWISE model in the training cohort was slightly better than that of the LASSO model. However, the performance of the two models was difficult to distinguish in the validation cohort. Therefore, considering that the LASSO model had the fewest number of variables and overlapped with the variables in the STEPWISE model, we ultimately selected the LASSO model as the candidate optimal model. Due to the potential significant impact of DSOI and vaccine status on the outcomes of COVID-19 patients, these two variables were additionally incorporated into the seven variables selected by LASSO. The logistic regression model constructed using these nine variables was named as the potential optimal model. We compared the potential optimal model with the candidate optimal model from three aspects: ROC curve, calibration curve, and DCA curve. The results showed no significant difference between the two models, indicating that the contribution of DSOI and vaccine status to model construction was not significant (Supplementary Figure 1).

Finally, the LASSO model was used as the optimal model, these independently associated predictive factors in the LASSO model was ultimately used to form a prognostic estimation nomogram (Figure 3g). The optimal cutoff value for the total score of COVID-19 patients was determined to be 81.220 based on the nomogram. At the optimal cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 68.6%, 84.4%, 72.0%, 82.1%, 4.406 and 0.372 respectively (Table 3).

Table 3. Accuracy of the prediction score of the nomogram for estimating the probability of ICU in patients with COVID-19.

Variable	Value (95% CI)	
Area under the ROC curve, C index	0.842 (0.811, 0.874)	
Cutoff score	81.220	
Sensitivity, %	68.6 (62.5, 74.6)	
Specificity, %	84.4 (80.9, 88.0)	
Positive predictive value, %	72.0 (66.1, 78.0)	
Negative predictive value, %	82.1 (78.4, 85.9)	
Positive likelihood ratio	4.406 (3.442, 5.639)	
Negative likelihood ratio	0.372 (0.306, 0.453)	
C index: concordance index; ROC: receiver operating characteristic.

4. Discussion

It was heartening to note that just recently, the World Health Organization (WHO) announced hopefully that the COVID-19 epidemic no longer constitutes a public health emergency of international concern. However, this does not mean that the COVID-19 epidemic is no longer a threat to global health. Currently, the COVID-19 epidemic is still progressing around the world, and many patients in ICU are still fighting against the virus. Our understanding of COVID-19 has improved dramatically over the past three years. At present, the key point of handling COVID-19 has become to recognize potentially severe patients early so that early intervention can be implemented to improve prognosis and reduce mortality [18]. In view of this, we collected data on the brief spike in infections in China at the beginning of December 2022 in order to establish an effective COVID-19 prognostic model.

In this study, 888 hospitalized patients with clear prognosis and complete data were collected, with ICU accounting for 37.7% and all-cause mortality accounting for 19.5%, significantly higher than previous studies from China [16–18]. This is likely due to the fact that the West China Hospital, as the largest medical center of West China, is a major referral hospital for critical and severe patients. Increasing evidence supports the value of laboratory indicators in predicting the severity and prognosis of patients with COVID-19. In our study, we compared common clinical data between ICU and non-ICU cohorts, showing differences in age, gender, smoking history, length of hospital, days since onset of illness, vaccination status, comorbidities, and laboratory indicators between the two cohorts, which have been reported in many previous studies [25–27]. The decrease in HDL-C and LDL-C concentrations in adverse outcome of patients with COVID-19 reported here is consistent with previous studies (see Table 1 for details) [28,29]. Subsequently, multivariate logistic regression was used to analyze factors affecting the prognosis and survival of patients with COVID-19. It was expected that LDH > 300IU/L and PCT > 0.5 ng/ml were independent risk factors affecting the prognosis and survival of patients with COVID-19, ALB and vaccine status were the independent protective factors. However, to our own surprise, HDL-C was dramatically an independent risk factor affecting the outcome of patients with COVID-19, which was completely different from the previously reported conclusion that decreased HDL-C levels were associated with poor outcome in patients with COVID-19 (see Table 2 for details) [28–30].

The previous report indicated that declined serum HDL-C is not only associated with severe disease and mortality in COVID-19, but also with a higher SARS-Cov-2 infection risk [29–32]. However, all of these associations were based on univariate analysis targeting HDL-C, confounding factors were not corrected, leading to the one-sidedness of the results. In fact, several past studies have found a U-shaped relationship between HDL-C levels and the risk and outcome of infectious diseases [33]. During the acute-phase response, the antioxidant function of HDL was depleted due to changes in HDL-associated proteins, converting HDL into a prooxidant, proinflammatory lipoprotein [34]. Due to the severe inflammatory response in the ICU cohort, therefore, it was not difficult to understand why HDL-C is an independent risk factor affecting the outcome of patients with COVID-19 in our study. In addition, HDL also plays an important role in promoting the entry of SARS-CoV-2 into host cells. HDL could bind to SARS-2-S protein, and then HDL carrying SARS-2-S protein bound to the high-density lipoprotein scavenger receptor B type 1 (SR-B1) on the host cell surface, facilitating the adhesion and invasion of SARS-CoV-2 to cells, which indirectly confirmed the correlation between HDL-C and poor prognosis of patients with COVID-19 [35].

The variables we selected through LASSO regression and stepwise logistic regression was clinically accessible and comprehensive, encompassing demography, biochemistry, coagulation, blood gas, myocardial markers, blood cell counts, and inflammatory indicators. Many of the previous studies have focused on a certain type of predictors like inflammatory cytokine, myocardical injury or blood cell count, while studies combining multiple indicators included relatively few patients [4,16,18,36–38]. The differences between the variables screened by stepwise logistic regression and those screened by LASSO regression were smoke history, TNT-T, age, and HDL-C. However, it was found that the difference was not significant after evaluating the two models using the DCA curve, calibration curve and ROC, indicating these three variables contributed little to the model. In the LASSO model, elevated NLR, D-dimer, and CRP, decreased ALB, along with PCT > 0.5 ng/ml, LDH > 300IU/L and HCO3 < 22mmol/L were considered factors facilitating ICU admission.

It was well known that obesity, vaccine status and DSOI were important factors influencing the prognosis of COVID-19 [39–41]. We explored the possibility of incorporating DSOI and vaccine status into the LASSO model and compared its performance with the original LASSO model. However, the significant contribution of these two variables to model performance was not observed. Finally, the LASSO model was defined as the optimal prognosis model, and a prognostic estimation nomogram was developed with an optimal cutoff value of 81.220. In our study, no significant difference in obesity prevalence between the ICU and non-ICU cohorts was observed, and the DSOI in the ICU cohort was significantly shorter than that in the non-ICU cohort, which may be related to the fact that the majority of the included population were elderly and the ICU cohort had a higher prevalence of comorbidities. Vaccination status not only between ICU and non-ICU cohorts, but also closely correlates with mortality in COVID-19 patients. However, this variable was ultimately not included in the model, possibly due to its strong correlation with other variables.

There have been many studies on their association with poor outcomes in patients with COVID-19. However, it is worth mentioning that there are different opinions on the association between HCO3 concentration and prognosis of COVID-19 patients. High HCO3 concentration, metabolic alkalosis, and hypokalemia was reported to be common in patients with COVID-19 due to the activation of the renin-angiotensin-aldosterone (RAA) system via the downregulation of angiotensin-converting enzyme 2 (ACE2) by SARS-CoV-2 [42]. Low HCO3 concentration have also been found to be associated with severe COVID-19 patients, which was in accordance with our study [43,44]. In general, both high and low HCO3 concentration were associated with a higher clinical worsening rate in COVID-19 patients [27]. Nevertheless, we did not find a correlation between high HCO3 concentration and poor outcome of COVID-19 patients, possibly due to the low incidence of high HCO3 concentration in our study.

5. Limitations

Our study had several limitations. First, many patients were excluded due to insufficient data, including outpatients and some severe patients with unclear outcome, thus, a selection bias might exist. Second, the subvariant of COVID-19 patients was not clear, leading to the possibility that the model established in this study may not be fully applicable. Third, this was a retrospective, single-center investigation, and external validation was not performed, which may also result in reduced model performance. Fourthly, the impact of previous COVID-19 infection history on prognosis could not be explored due to the presence of only one patient with such a history among all patients included in the final analysis.

6. Conclusion

Overall, we analyzed the prognostic factors of COVID-19 patients and derived and internally validated a model to predict the severity of COVID-19 patients based on seven predictors: NLR, D-dimer, PCT, CRP, ALB, bicarbonate and LDH. This prognostic model has high discrimination, calibration, and net benefit with good potential for a wide clinical application.

Supplementary Material

Supplemental Material

Acknowledgments

The authors thank the teams from the Information Center and Clinical Research Management Department of West China Hospital, Sichuan University. And the authors also extend the profound acknowledgment to Professor Chen Lei for her leadership in establishing the novel coronavirus infection database at West China Hospital of Sichuan University, which has provided a valuable source of data for our research.

Authors contributions

A.Z. conducted literature search, data analysis, statistical analysis and manuscript preparation; J.X. conducted data analysis and statistical analysis; L.H., Q.L. and Q.T. conducted data acquisition; Y.L. contributed to the statistical analysis and scientific discussion of the manuscript; W.G. provided financial support, statistical analysis, administrative support, and manuscript editing and review. W.G. takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The deidentified data presented in this study is available upon request from the corresponding author.
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