Patient characteristics and clinical parameters upon admission

Among the 226 patients admitted to the emergency department, 190 patients met the criteria for further analysis. The patient enrollment process is shown in Fig. 1. Among them, there were 83 cases (43.7%) classified as mild, 61 cases (32.1%) as moderate, and 46 cases (24.2%) as severe or critically ill. Ultimately, 23 cases (12.1%) died within 28 days of admission.

Table 1 describes the baseline characteristics and clinical parameters of the patients. Of the patients, 111 (58%) were male, and the median age was 69 years. The most common comorbidities among the patients were hypertension (80/190, 42%) and diabetes (42/190, 22%).

Compared to the patients who survived for 28 days, those who died within 28 days were older, had a higher proportion of diabetes and cerebrovascular disease, had higher body temperature upon admission, faster respiratory and heart rates, lower oxygen partial pressure and oxygenation index, and higher clinical scoring system scores (all p-values < 0.05). In terms of laboratory parameters, the 28-day mortality group had higher levels of Presepsin, PCT, CRP, lymphocyte count, blood glucose, aspartate aminotransferase (AST), and direct bilirubin (all p-values < 0.05). Regarding blood count-derived inflammatory markers, the levels of NLR, LCR, CAR, SIRI, and SII differed significantly between the two groups (all p-values < 0.05).

The predictive value of presepsin for the severity and prognosis of COVID-19 patients

The study found that Presepsin levels were associated with the severity of illness in COVID-19 patients, with higher levels observed in patients with more severe conditions (Fig. 2A). Patients requiring mechanical ventilation had significantly higher Presepsin levels compared to those not requiring mechanical ventilation (Fig. 2B). Additionally, patients who died within 28 days had higher Presepsin levels (Fig. 2C). Presepsin demonstrated good predictive value for the need for mechanical ventilation, with an area under the ROC curve of 0.866 (95% confidence interval [CI]: 0.800-0.932) (Fig. 2D), and for 28-day mortality in patients, with an area under the ROC curve of 0.828 (95% confidence interval [CI]: 0.737–0.920) (Fig. 2E). When patients were grouped based on the median value, those above the median value had a higher risk of mortality within 28 days compared to those below the median value (p-value < 0.05) (Fig. 2F).

Fig. 2

The predictive value of Presepsin for the severity of illness and 28-day mortality in COVID-19 patients. (A) Comparison of Presepsin levels among different severity groups of COVID-19 patients. (B) Comparison of Presepsin levels between the non-mechanical ventilation and mechanical ventilation groups. (C) Comparison of Presepsin levels between the 28-day survival and death groups. Data are displayed as a median with interquartile range (IQR) and were compared using the Mann-Whitney U test. Multiple samples were compared using the non-parametric Kruskal-Wallis test. (D) Receiver Operating Characteristic (ROC) curve of Presepsin for predicting the need for mechanical ventilation in COVID-19 patients, with an area under the ROC curve (AUC) of 0.866 (95% confidence interval [CI]: 0.800-0.932). (E) ROC curve of Presepsin for predicting 28-day mortality in COVID-19 patients, with an AUC of 0.828 (95% confidence interval [CI]: 0.737–0.920). (F) Kaplan-Meier curve for patients divided into two groups based on the median Presepsin level: above-median group and below-median group, for 28-day survival. A p-value < 0.05 was considered significant

The correlations between presepsin and age, clinical scoring systems, and laboratory inflammatory markers

The correlations and corresponding p-values among Presepsin, age, clinical scoring systems, and laboratory inflammatory markers, totaling 20 parameters, are displayed in a heatmap as shown in Fig. 3. Among these parameters, Presepsin exhibited a significant correlation with Age (r = 0.206, p = 0.0044). Additionally, significant correlations were observed between Presepsin and the following clinical scoring systems and laboratory inflammatory markers: sSOFA (r = 0.181, p = 0.0123), eSOFA (r = 0.358, p < 0.0001), qSOFA (r = 0.391, p < 0.0001), SOFA (r = 0.332, p < 0.0001), NEWS2 (r = 0.366, p < 0.0001), PSI risk class (r = 0.432, p < 0.0001), PSI (r = 0.455, p < 0.0001), COVID-GRAM (r = 0.366, p < 0.0001), CURB-65 (r = 0.489, p < 0.0001), CRP (r = 0.222, p = 0.0022), and CAR (r = 0.256, p = 0.0004), as shown in Table 2.

Fig. 3

Heatmap showing the correlation between Presepsin and age, clinical scores, and inflammation markers. (A) The values are presented as Spearman‘s correlation coefficient (r) for a sample of 190 runners regarding Presepsin. The colormap ranges from 1 to -1, with blue indicating the highest value and red indicating the lowest value. (B) The Heatmap of corresponding p-values.The colormap ranges from 0 to 1, with blue representing the largest value and white representing the smallest value. White cells without numerical values indicate that the p-value is smaller than 0.0001, indicating a highly significant correlation. Abbreviations s, e, q SOFA, simplified, early, quick sequential organ failure assessment; NEWS2, National Early Warning Score 2; PSI, Pneumonia Severity Index; PCT, Procalcitonin; CRP, C-reactive protein; NLR, Neutrophil-to-lymphocyte ratio; MLR, Monocyte-to-lymphocyte ratio; PLR, Platelet-to-lymphocyte ratio; LCR, Lymphocyte-to-C-reactive protein ratio; CAR, C-reactive protein-to-albumin ratio; SIRI, Systemic inflammation response index; SII: Systemic inflammation index. SIRI = (Neutrophil count × Monocyte count) / Lymphocyte count; SII = (Neutrophil count × Platelet count) / Lymphocyte count

Table 2 Correlation between Presepsin, age, clinical score systems, and laboratory markers of inflammationThe predictive value of clinical scoring systems and laboratory inflammatory markers for 28-day mortality in COVID-19 patients

Clinical scoring systems and laboratory inflammatory markers also have predictive value for 28-day mortality in COVID-19 patients. Among the clinical scoring systems, the CURB-65 demonstrated the best predictive performance, with an AUC of 0.897 (95% CI: 0.817–0.978) Among the inflammatory markers, LCR performed the best, with an AUC of 0.812 (95% CI: 0.716–0.907), as shown in Fig. 4.

Fig. 4

Predictive ability of clinical scores and inflammatory markers for 28-day mortality in COVID-19 patients. (A) Receiver Operating Characteristic (ROC) curves for different clinical prediction scores in predicting 28-day mortality in COVID-19 patients. The area under the curve (AUC) for sSOFA was 0.627 (95% confidence interval [CI]: 0.508–0.746), eSOFA, AUC was 0.831 (95% CI: 0.761–0.900); qSOFA, AUC was 0.889 (95% CI: 0.823–0.955); SOFA, AUC was 0.802 (95% CI: 0.705–0.900); NEWS2, AUC was 0.871 (95% CI: 0.808–0.934); PSI risk class, AUC was 0.846 (95% CI: 0.737–0.954); PSI, AUC was 0.878 (95% CI: 0.773–0.984); COVID-GRAM, AUC was 0.841 (95% CI: 0.730–0.953); CURB-65, AUC was 0.897 (95% CI: 0.817–0.978). (B) ROC curves for different laboratory inflammatory markers in predicting 28-day mortality in COVID-19 patients. PCT, AUC was 0.768 (95% CI: 0.670–0.866); CRP, AUC was 0.781 (95% CI: 0.685–0.878); NLR, AUC was 0.677 (95% CI: 0.546–0.808); MLR, AUC was 0.619 (95% CI: 0.488–0.750); PLR, AUC was 0.612 (95% CI: 0.480–0.743); LCR, AUC was 0.812 (95% CI: 0.716–0.907); CAR, AUC was 0.798 (95% CI: 0.706–0.890); SIRI, AUC was 0.628 (95% CI: 0.492–0.764); SII, AUC was 0.644 (95% CI: 0.513–0.775). Abbreviations TPR: true positive rate; FPR: false positive rate; s, e, q SOFA, simplified, early, quick sequential organ failure assessment; NEWS2, National Early Warning Score 2; PSI, Pneumonia Severity Index; PCT, Procalcitonin; CRP, C-reactive protein; NLR, Neutrophil-to- lymphocyte ratio; MLR, Monocyte-to-lymphocyte ratio; PLR, Platelet-to-lymphocyte ratio; LCR, Lymphocyte-to-C-reactive protein ratio; CAR, C-reactive protein-to-albumin ratio; SIRI, Systemic inflammation response index; SII: Systemic inflammation index. SIRI = (Neutrophil count × Monocyte count) / Lymphocyte count; SII = (Neutrophil count × Platelet count) / Lymphocyte count

We performed screening using the LASSO binary logistic regression model, and ultimately, three clinical scoring models (CURB-65, PSI, qSOFA) and four inflammation-related markers (PCT, CAR, LCR, NLR) were selected (Figure s1). Additionally, we were also interested in NEWS2, COVID-GRAM and CRP. Therefore, we ultimately selected a total of 10 parameters. The predictive value of these parameters is presented in Table 3.

Table 3 Predicted value information of different variable parameters for 28-day mortality in COVID-19 patients

Finally, to account for potential confounding factors such as age, diabetes mellitus, malignant tumor, body temperature, respiratory rate, heart rate, PaO2/FiO2, AST, and DBIL, we included these variables as covariates in a multivariable Cox regression analysis. Ultimately, we found that Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR and LCR were the nine independent predictors of 28-day mortality in COVID-19 patients, as shown in Table 4; Fig. 5.

Table 4 Risk factors for 28-day Mortality in COVID-19 patientsFig. 5

Kaplan-Meier curves for 28-day survival categorized by different parameters. Presepsin (A), qSOFA (B), NEWS2 (C), PSI (D), CURB-65 (E), CRP (F), NLR (G), CAR (H), and LCR (I). Abbreviations qSOFA, quick sequential organ failure assessment; NEWS2, National Early Warning Score 2; PSI, Pneumonia Severity Index; CRP, C-reactive protein; NLR, Neutrophil-to-lymphocyte ratio; CAR, C-reactive protein-to-albumin ratio; LCR, Lymphocyte-to-C-reactive protein ratio

The combined predictive value of presepsin with clinical scoring systems and laboratory inflammatory markers for 28-day mortality in COVID-19 patients

We combined Presepsin with the selected 10 parameters to compare the predictive efficacy for the 28-day prognosis of COVID-19 patients. It was found that Presepsin + qSOFA had the best predictive performance, with an area under the curve (AUC) of 0.933 (95% confidence interval [CI]: 0.893–0.972). Presepsin + CURB-65 ranked second with an AUC of 0.914 (95% CI: 0.840–0.988), followed by Presepsin + NEWS2 with an AUC of 0.906 (95% CI: 0.856–0.955) as the third best predictor. Among the inflammation-related markers, Presepsin + CAR exhibited the best predictive performance with an AUC of 0.888 (95% CI: 0.833–0.944). As shown in Table 5; Fig. 6.

Table 5 AUC for predicting COVID-19 mortality using various parameters and modelsFig. 6

Presepsin’s predictive ability for 28-day mortality in COVID-19 patients with clinical scores or inflammatory markers. The area under the curve (AUC) for Presesin + qSOFA was 0.933 (95% confidence interval [CI]: 0.893–0.972), Presesin + NEWS2, AUC was 0.906 (95% CI: 0.856–0.955); Presesin + PSI, AUC was 0.888 (95% CI: 0.789–0.987); Presesin + GRAM, AUC was 0.866 (95% CI: 0.767–0.966); Presesin + CURB-65, AUC was 0.914 (95% CI: 0.840–0.988); Presesin + PCT, AUC was 0.847 (95% CI: 0.770–0.925); Presesin + CRP, AUC was 0.887 (95% CI: 0.831–0.943); Presesin + NLR, AUC was 0.847 (95% CI: 0.765–0.929); Presesin + CAR, AUC was 0.888 (95% CI: 0.833–0.944); Presesin + LCR, AUC was 0.885 (95% CI: 0.825–0.945). Abbreviations qSOFA, quick sequential organ failure assessment; NEWS2, National Early Warning Score 2; PSI, Pneumonia Severity Index; PCT, Procalcitonin; CRP, C-reactive protein; NLR, Neutrophil-to-lymphocyte ratio; CAR, C-reactive protein-to-albumin ratio; LCR, Lymphocyte-to-C-reactive protein ratio

Finally, for the convenience of clinical decision-making, we constructed a nomogram for the combined model of Presepsin and these four indicators (Fig. 7). The calibration curve of the nomogram for predicting the risk of 28-day mortality in COVID-19 patients is presented in Figure s2, showing good agreement between the predicted probabilities of 28-day mortality by the nomogram and the observed probabilities (all p-values > 0.05). The decision curve analysis (DCA) of the nomogram model is displayed in Figure s3, covering a threshold probability range from 1 to 90%. These results indicate that the nomogram’s calibration was acceptable and the model was reliable for clinical utility.