Authors
Lin Liu, Yingchao Liu, Jinjing Tian, Jibin Gong, Xiufang Lu
Published in
Zhonghua wei zhong bing ji jiu yi xue. Volume 38. Issue 8. Pages 693-700.
Abstract
To establish a nomogram prediction model for septic shock in patients with sepsis based on serum sphingosine-1-phosphate (S1P), microRNA-155-5p (miR-155-5p) and soluble suppression of tumorigenicity 2 protein (sST2), and evaluate its predictive value.
A prospective observational study was conducted. A total of 400 patients with sepsis admitted to Liaocheng Second People's Hospital from January 2022 to January 2025 were enrolled. The patients were randomly divided into the training set and the validation set at a ratio of 7 : 3. General clinical data were collected. Arterial blood gas analysis, routine blood test, liver and renal function tests, and inflammatory indicators were measured upon admission. Enzyme linked immunosorbent assay was used to measure serum levels of S1P and sST2. Real-time fluorescence quantitative reverse transcription-polymerase chain reaction was applied to detect the expression of serum miR-155-5p. Disease severity and organ dysfunction were also assessed. Patients were divided into sepsis group and septic shock group according to the occurrence of septic shock. Multivariate Logistic regression analysis was performed to screen independent influencing factors for septic shock, and a nomogram prediction model was constructed. Bootstrap resampling, receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis were used to validate and evaluate the model.
A total of 400 patients with sepsis were enrolled. Septic shock developed in 94 (33.6%) of the 280 patients in the training set and 44 (36.7%) of the 120 patients in the validation set. There were no statistically significant differences in baseline clinical characteristics between the training set and the validation set (all P>0.05). In the training set, compared with the sepsis group, the septic shock group had a higher proportion of patients aged over 65 years (60.6% vs. 45.2%), and increased levels of lactic acid (mmol/L: 1.84±0.31 vs. 1.72±0.25), procalcitonin [PCT (μg/L): 11.23±3.15 vs. 8.54±2.57], interleukin-6 [IL-6 (ng/L): 138.37±36.12 vs. 112.54±31.73], Sequential Organ Failure Assessment (SOFA: 10.34±3.15 vs. 8.26±2.83), Acute Physiology and Chronic Health Evaluation II (APACHE II: 26.43±6.32 vs. 23.57±5.29), sST2 (μg/L: 615.73±117.85 vs. 473.68±84.37) and miR-155-5p (miR-155-5p/U6: 1.74±0.36 vs. 1.35±0.28), the level of S1P was significantly decreased (μg/L: 67.38±18.31 vs. 87.54±24.67), all differences were statistically significant (all P<0.05). Multivariate Logistic regression analysis indicated that age over 65 years old [odds ratio (OR)=1.425, 95% confidence interval (95%CI) was 1.050-1.935], elevated lactic acid (OR=1.538, 95%CI was 1.023-2.312), elevated PCT (OR=1.624, 95%CI was 1.049-2.514), higher SOFA score (OR=1.674, 95%CI was 1.107-2.531), higher APACHE II score (OR=2.547, 95%CI was 1.460-4.440), decreased S1P (OR=0.615, 95%CI was 0.405-0.934), elevated sST2 (OR=2.017, 95%CI was 1.270-3.203) and elevated miR-155-5p (OR=1.628, 95%CI was 1.072-2.472) were independent influencing factors for septic shock in sepsis patients (all P<0.05). A nomogram was established based on the above factors. ROC curve analysis showed that the area under the curve (AUC) was 0.865 (95%CI was 0.819-0.903) in the training set and 0.841 (95%CI was 0.793-0.882) in the validation set. The model presented good calibration, favourable net clinical benefit and acceptable threshold probability in both sets.
The nomogram prediction model established based on serum S1P, sST2 and miR-155-5p has good predictive performance for septic shock in patients with sepsis, which can provide a reference for clinical decision-making.
PMID:
42693964
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.
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