Predictive models for short-term mortality and length of hospital stay among adults with community-onset bacteraemia before and during the COVID-19 pandemic: application of early data dynamics

Utilizing the National Early Warning Score 2 (NEWS2) to confirm the impact of emergency department management in sepsis patients: a cohort study from taiwan 1998-2020

BACKGROUND

Most sepsis patients could potentially experience advantageous outcomes from targeted medical intervention, such as fluid resuscitation, antibiotic administration, respiratory support, and nursing care, promptly upon arrival at the emergency department (ED). Several scoring systems have been devised to predict hospital outcomes in sepsis patients, including the Sequential Organ Failure Assessment (SOFA) score. In contrast to prior research, our study introduces the novel approach of utilizing the National Early Warning Score 2 (NEWS2) as a means of assessing treatment efficacy and disease progression during an ED stay for sepsis.

OBJECTIVES

To evaluate the sepsis prognosis and effectiveness of treatment administered during ED admission in reducing overall hospital mortality rates resulting from sepsis, as measured by the NEWS2.

METHODS

The present investigation was conducted at a medical center from 1997 to 2020. The NEWS2 was calculated for patients with sepsis who were admitted to the ED in a consecutive manner. The computation was based on the initial and final parameters that were obtained during their stay in the ED. The alteration in the NEWS2 from the initial to the final measurements was utilized to evaluate the benefit of ED management to the hospital outcome of sepsis. Univariate and multivariate Cox regression analyses were performed, encompassing all clinically significant variables, to evaluate the adjusted hazard ratio (HR) for total hospital mortality in sepsis patients with reduced severity, measured by NEWS2 score difference, with a 95% confidence interval (adjusted HR with 95% CI). The study employed Kaplan-Meier analysis with a Log-rank test to assess variations in overall hospital mortality rates between two groups: the "improvement (reduced NEWS2)" and "non-improvement (no change or increased NEWS2)" groups.

RESULTS

The present investigation recruited a cohort of 11,011 individuals who experienced the first occurrence of sepsis as the primary diagnosis while hospitalized. The mean age of the improvement and non-improvement groups were 69.57 (± 16.19) and 68.82 (± 16.63) years, respectively. The mean SOFA score of the improvement and non-improvement groups were of no remarkable difference, 9.7 (± 3.39) and 9.8 (± 3.38) years, respectively. The total hospital mortality for sepsis was 42.92% (4,727/11,011). Following treatment by the prevailing guidelines at that time, a total of 5,598 out of 11,011 patients (50.88%) demonstrated improvement in the NEWS2, while the remaining 5,403 patients (49.12%) did not. The improvement group had a total hospital mortality rate of 38.51%, while the non-improvement group had a higher rate of 47.58%. The non-improvement group exhibited a lower prevalence of comorbidities such as congestive heart failure, cerebral vascular disease, and renal disease. The non-improvement group exhibited a lower Charlson comorbidity index score [4.73 (± 3.34)] compared to the improvement group [4.82 (± 3.38)] The group that underwent improvement exhibited a comparatively lower incidence of septic shock development in contrast to the non-improvement group (51.13% versus 54.34%, P < 0.001). The improvement group saw a total of 2,150 patients, which represents 38.41% of the overall sample size of 5,598, transition from the higher-risk to the medium-risk category. A total of 2,741 individuals, representing 48.96% of the sample size of 5,598 patients, exhibited a reduction in severity score only without risk category alteration. Out of the 5,403 patients (the non-improvement group) included in the study, 78.57% (4,245) demonstrated no alteration in the NEWS2. Conversely, 21.43% (1,158) of patients exhibited an escalation in severity score. The Cox regression analysis demonstrated that the implementation of interventions aimed at reducing the NEWS2 during a patient's stay in the ED had a significant positive impact on the outcome, as evidenced by the adjusted HRs of 0.889 (95% CI = 0.808, 0.978) and 0.891 (95% CI = 0.810, 0.981), respectively. The results obtained from the Kaplan-Meier analysis indicated that the survival rate of the improvement group was significantly higher than that of the non-improvement group (P < 0.001) in the hospitalization period.

CONCLUSION

The present study demonstrated that 50.88% of sepsis patients obtained improvement in ED, ascertained by means of the NEWS2 scoring system. The practical dynamics of NEWS2 could be utilized to depict such intricacies clearly. The findings also literally supported the importance of ED management in the comprehensive course of sepsis treatment in reducing the total hospital mortality rate.

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.