Early prediction of survival at different time intervals in sepsis patients: A visualized prediction model with nomogram and observation study

Prognostic accuracy of SOFA, MEWS, and SIRS criteria in predicting the mortality rate of patients with sepsis: A meta-analysis

BACKGROUND

In recent years, some studies classified patients with sepsis and predicted their mortality by using some evaluation scales. Several studies reported significant differences in the predictive values of several tools, and the non-uniformity of the cut-off value.

AIM

To determine and compare the prognostic accuracy of Sequential Organ Failure Assessment (SOFA) score, Modified Early Warning Score (MEWS), and Systemic Inflammatory Response Syndrome (SIRS) criteria in predicting the mortality of patients with sepsis.

METHODS

This study comprised of systematic literature review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We searched PubMed, Embase, Web of Science and Cochrane Library databases from their establishment to July 31, 2022. The research articles published in the index journals provide sufficient data (true positive, false positive, true negative, and false negative results) for patients with sepsis. The combined sensitivity and specificity of the 95% confidence interval (CI) were calculated using the bivariate random effect model (BRM). The hierarchical overall subject working characteristics (HSROC) curve was drawn to evaluate the accuracy of the overall prognosis.

RESULTS

Data of 55 088 patients from 32 studies were included in this meta-analysis. SOFA had an intermediate sensitivity of 0.73 (95% CI: 0.67-0.78) and a specificity of 0.70 (0.63-0.76). SIRS criteria had the highest sensitivity of 0.75 (0.66-0.82) and the lowest specificity of 0.40 (0.29-0.52). MEWS had the lowest sensitivity of 0.49 (0.40-0.59) and the highest specificity of 0.82 (0.78-0.86).

CONCLUSIONS

Among SOFA, MEWS, and SIRS criteria, SOFA showed moderate sensitivity and specificity for predicting mortality in patients with sepsis, the highest sensitivity of SIRS and the strongest specificity of MEWS for predicting mortality in patients with sepsis. The future research direction is to combine the relevant indicators of MEWS and SIRS to develop a measurement tool with high reliability and validity.

RELEVANCE TO CLINICAL PRACTICE

The review provides useful insights into the prognostic accuracy of different assessment tools in predicting mortality in sepsis patients, which will help clinicians choose the most appropriate tool for early identification and treatment of sepsis. The findings may also contribute to the development of more accurate and reliable prognostic models for sepsis.

Development and validation of a nomogram model for predicting 28-day mortality in patients with sepsis

Background

This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit (ICU).

Methods

We retrospectively analyzed data from 331 patients with sepsis admitted to the ICU as a training set and collected a validation set of 120 patients. Both groups were followed for 28 days. Logistic regression analyses were performed to identify the potential prognostic factors for sepsis-related 28-day mortality. A nomogram model was generated to predict 28-day mortality in patients with sepsis in the ICU. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to evaluate the model's prediction performance and clinical application. In addition, we used ROC curve analysis and DCA to compare this model with the sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) scores and further assessed the clinical value of our model.

Results

Logistic multivariate regression analysis revealed that mechanical ventilation, oxygenation index, and lactate and blood urea nitrogen (BUN) levels were independent predictors of 28-day mortality in patients with sepsis in the ICU. We developed a nomogram model based on these results to further predict 28-day mortality. The model demonstrated satisfactory calibration curves for both training and validation sets. Additionally, in the training set, the area under the ROC curve (AUC) for this model was 0.80. In the validation set, the AUC was 0.82. DCA showed that the high-risk thresholds ranged between 0 and 0.86 in the training set and between 0 and 0.75 in the validation set. We compared the ROC curve and DCA of this model with those of SOFA and APACHE II scores in both the training and validation sets. In the training set, the AUC of this model was significantly higher than those of the SOFA (P = 0.032) and APACHE II (P = 0.004) scores. Although the validation set showed a similar trend, the differences were not statistically significant for the SOFA (P = 0.273) and APACHE II (P = 0.320) scores. Additionally, the DCA showed comparable clinical utility in all three assessments.

Conclusion

The present study used four common clinical variables, including mechanical ventilation, oxygenation index and lactate and BUN levels, to develop a nomogram model to predict 28-day mortality in patients with sepsis in the ICU. Our model demonstrated robust prediction performance and clinical application after validation and comparison.

Pre-hospital Prognostic Factors of 30-Day Survival in Sepsis Patients; a Retrospective Cohort Study

Introduction

According to excising findings, if the emergency management system (EMS) operation will be developed, the survival outcome of sepsis patients might improve. This study aimed to evaluate the pre-hospital associated factors of survival in sepsis patients.

Methods

This retrospective cohort study was conducted on patients diagnosed with sepsis, coded with the Thailand emergency medical triage protocol and criteria-based dispatch symptom group 17. Information on the 30-day survival rate of patients was obtained from the electronic medical records. Pre-hospital factors associated with 30-day survival were analyzed using univariate and multivariate logistic regression analyses and were reported using odds ratio (OR) with 95% confidence interval (CI).

Results

300 patients diagnosed with sepsis were enrolled. Among them, 232 (77.3%) survived within 30 days. Non-survived cases had significantly older age (p = 0.019), lower oxygen saturations (92.5% vs. 95.0%; p = 0.003), higher heart rate (p = 0.001), higher respiratory rate (p < 0.001), lower level of consciousness (p < 0.001), higher disease severity based on qSOFA score (p = 0.001), and higher need for invasive airway management (p = 0.001) and supplementary oxygen (p = 0.001). The survival rate improved by 3.5% with every 1% increase in pre-hospital oxygen saturation (adjusted OR = 1.035, 95% CI: 1.005-1.066, p = 0.020) and the survival probabilities of patients who responded to voice (adjusted OR = 0.170, 95% CI: 0.050-0.579, p = 0.005), those who responded to pain (adjusted OR = 0.130, 95% CI: 0.036-0.475, p = 0.002), and those who were unresponsive (adjusted OR = 0.086, 95% CI: 0.026-0.278, p-value < 0.001) were lower than patients who were alert.

Conclusion

The 30-day survival rate of patients with sepsis managed by the EMS team was 77.3%. Pre-hospital oxygen saturation and level of consciousness were associated with the survival of patients with sepsis who were managed in the pre-hospital setting.