Evaluation of qSOFA score, and conjugated bilirubin and creatinine levels for predicting 28‑day mortality in patients with sepsis

Identification of discriminatory factors and construction of nomograms for differentiating AOSD and sepsis

OBJECTIVE

This study aimed to develop nomogram prediction models to differentiate between adult-onset Still's disease (AOSD) and sepsis.

METHODS

We retrospectively collected laboratory test data from 107 hospitalized patients with AOSD and sepsis at the Affiliated Hospital of Xuzhou Medical University. Multivariate binary logistic regression was used to develop nomogram models using arthralgia, WBC, APTT, creatinine, PLT, and ferritin as independent factors. The performance of the model was evaluated by the bootstrap consistency index and calibration curve.

RESULTS

Model 1 had an AUC of 0.98 (95% CI, 0.96-1.00), specificity of 0.98, and sensitivity of 0.94. Model 2 had an AUC of 0.96 (95% CI, 0.93-1.00), specificity of 0.92, and sensitivity of 0.94. The fivefold cross-validation yielded an accuracy (ACC) of 0.92 and a kappa coefficient of 0.83 for Model 1, while for Model 2, the ACC was 0.87 and the kappa coefficient was 0.74.

CONCLUSION

The nomogram models developed in this study are useful tools for differentiating between AOSD and sepsis. Key Points • The differential diagnosis between AOSD and sepsis has always been a challenge • Delayed treatment of AOSD may lead to serious complications • We developed two nomogram models to distinguish AOSD from sepsis, which were not previously reported • Our models can be used to guide clinical practice with good discrimination.

Systemic immune-inflammation index combined with quick sequential organ failure assessment score for predicting mortality in sepsis patients

Objective

To evaluate the prognostic ability of systemic immune-inflammation index (SII) combine with quick Sequential Organ Failure Assessment (qSOFA) criteria in predicting the 28-day mortality of sepsis.

Methods

A retrospective cohort study was conducted, with the population comprised in whom sepsis was confirmed. Clinical and laboratory data recorded were analyzed. The score of Sequential Organ Failure Assessment (SOFA), SII, qSOFA were calculated. Multivariable regression, receiver operating characteristic (ROC) analysis and Kaplan-Meier method were used to identify and compared the predictors of prognosis among SOFA, qSOFA, and the combination of SII with qSOFA.

Results

A total of 349 patients admitted from December 2020 and December 2022 were included in the cohort. 95 (27.2%) of whom had died by day 28. The SII, SOFA, and qSOFA scores were significant higher in the non-survivors than that of survivors (P < 0.05), and identified as independent predictors of sepsis mortality. The addition of SII to qSOFA shown an area under receiver operator characteristic (AUROC) of 0.840 (95% CI: 0.787-0.884), manifested an effective ability in predicting poor outcome than other scoring systems. The optimum cutoff for SII (>1.7668) and qSOFA (>1) represented a high risk level in 28-day mortality of sepsis, were performed and identified in Kaplan-Meier survival curves (log-rank test, HR: 6.942, 95% CI: 3.976-12.121; P < 0.0001).

Conclusion

The SII in addition to qSOFA provided an effective prognostic tool for predicting mortality in sepsis.

Development of a Nomogram for Predicting Mortality Risk in Sepsis Patients During Hospitalization: A Retrospective Study

Purpose

We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization.

Patients and Methods

Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method.

Results

A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively.

Conclusion

The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.