Prehospital delay is an important risk factor for mortality in community-acquired bloodstream infection (CA-BSI): a matched case-control study

Risk Factors for 30-Day Mortality of Community-Acquired Bloodstream Infection Patients in Changsha City, Hunan Province, China

Purpose

To analyze the factors affecting patients' prognoses based on the community acquired-bloodstream infection patient data from 2017 to 2021.

Patients and Methods

The data of 940 patients were retrieved, having at least one positive bilateral blood culture within 48 hours of hospitalization, and grouped into survivor and non-survivor groups. The clinical characteristics, laboratory results, causative pathogen and other indicators were collected and compared, and risk factors were identified by applying Cox proportional hazard regression model to the data.

Results

Community acquired-bloodstream infection is most commonly caused by Escherichia coli, Klebsiella species and Staphylococcus hominis. Among the total of 940 selected patients, 52 (5.5%) died during hospitalization. The demographic parameters like age and gender, clinical protocols like maintenance hemodialysis, glucocorticoid use during hospitalization, catheter placement, procaicitonin, total protein, albumin, creatinine, uric acid contents and Sequential Organ Failure Assessment scores were significantly different between the survivor and non-survivor groups. The survival analysis results revealed that age (HR=1.02, 95% CI: 1.00-1.05, P=0.002), glucocorticoid use during hospitalization (HR=3.69, 95% CI: 1.62-8.37, P=0.021) and Sequential Organ Failure Assessment score (HR=1.10, 95% CI: 1.03-1.18, P=0.004) might be the risk factors affecting 30-day mortality in patients with community acquired-bloodstream infection.

Conclusion

The identified risk factors may help guide clinical treatment protocol for patients with community acquired-bloodstream infection, providing more effective treatment strategy selection with improved clinical outcomes.

Use of healthcare before and after sepsis in Sweden: a case-control study

OBJECTIVES

The aim of this study was to compare readmissions and death between sepsis and non-sepsis hospitalisations the first year after discharge, and to investigate what diagnoses patients with sepsis present with at readmission. The aim was also to evaluate to what degree patients hospitalised for sepsis seek medical attention prior to hospitalisation.

DESIGN

Retrospective case-control study with data validated through clinical chart review. A disproportionate stratified sampling model was used to include a relatively larger number of sepsis hospitalisations.

SETTING

All eight public hospitals in region Scania, Sweden (1 January to 3 December 2019).

PARTICIPANTS

There were 447 patients hospitalised for sepsis (cases), and 541 hospitalised for other causes (control) identified through clinical chart review.

OUTCOME MEASURES

Cox regression was used to analyse readmission and death the year after discharge, and logistic regression was used to analyse healthcare the week prior to hospitalisation. Both analyses were made unadjusted, and adjusted for age, sex and comorbidities.

RESULTS

Out of patients who survived a sepsis hospitalisation, 48% were readmitted the year after discharge, compared with 39% for patients without sepsis (HR 1.50, 95% CI 1.03 to 2.19), p=0.04. The majority (52%) of readmissions occurred within 90 days and 75% within 180 days. The readmissions were most often caused by infection (32%), and 18% by cardiovascular disease. Finally, 34% of patients with sepsis had sought prehospital contact with a physician the week before hospitalisation, compared with 22% for patients without sepsis (OR 1.80, 95% CI 1.06 to 3.04), p=0.03.

CONCLUSION

Patients hospitalised for sepsis had a higher risk of readmission the year after discharge compared with patients without sepsis. The most common diagnoses at readmission were infection followed by cardiovascular disease. With better follow-up, some of these readmissions could potentially be prevented. Patients hospitalised for sepsis had sought prehospital contact the week prior to hospitalisation to a greater extent than patients without sepsis.

Utility of metagenomics next-generation sequencing in the diagnosis and treatment of severe infectious diseases in the intensive care unit

BACKGROUND

Metagenomic next-generation sequencing (mNGS) is a new method that combines high-throughput sequencing and bioinformatics analysis. However, it has not become as popular due to the limited testing equipment and high costs and lack of family awareness with not much relevant intensive care unit (ICU) research data.

OBJECTIVE

To explore the clinical use and value of metagenomics next-generation sequencing (mNGS) in patients with sepsis in the ICU.

METHODS

We conducted a retrospective analysis of 102 patients with sepsis admitted to the ICU of Peking University International Hospital from January 2018 to January 2022. Based on whether mNGS was performed, the identified patients were divided into the observation group (n= 51) and the control group (n= 51), respectively. Routine laboratory tests, including routine blood test, C-reactive protein, procalcitonin, and culture of suspicious lesion specimens were performed in both groups within 2 hours after admission to the ICU, while mNGS tests were performed in the observation group. Patients in both groups were routinely given initial anti-infective, anti-shock, and organ support treatment. Antibiotic regimens were optimized in a timely manner according to the etiological results. Relevant clinical data were collected.

RESULTS

The testing cycle of mNGS was shorter than that of the conventional culture (30.79 ± 4.01 h vs. 85.38 ± 9.94 h, P< 0.001), while the positive rate of mNGS was higher than that of the conventional culture (82.35% vs. 45.1%, P< 0.05), with obvious superiority in the detection of viruses and fungus. There were significant differences in the optimal time of antibiotics (48 h vs.100 h) and length of ICU stay (11 d vs. 16 d) between the observation group and control group (P< 0.01) respectively, with no difference in 28-day mortality (33.3% vs. 41.2%, P> 0.05).

CONCLUSION

mNGS is useful in the detection of sepsis-causing pathogens in the ICU with the advantages of short testing time and high positive rate. There was no difference in the 28-day outcome between the two groups, which may be related to other confounding factors such as small sample size. Additional studies with extended sample size are needed.

Development and validation of an emergency bloodstream infection score for predicting in-hospital mortality in patients with community-acquired bloodstream infections

BACKGROUND

Community-acquired bloodstream infections (CABSIs) are common in the emergency departments, and some progress to sepsis and even lead to death. However, limited information is available regarding the prediction of patients with high risk of death.

METHODS

The Emergency Bloodstream Infection Score (EBS) for CABSIs was developed to visualize the output of a logistic regression model and was validated by the area under the curve (AUC). The Mortality in Emergency Department Sepsis (MEDS), Pitt Bacteremia Score (PBS), Sequential Organ Failure Assessment (SOFA), quick Sequential Organ Failure Assessment (qSOFA), Charlson Comorbidity Index (CCI), and McCabe-Jackson Comorbid Classification (MJCC) for patients with CABSIs were computed to compare them with EBS in terms of the AUC and decision curve analysis (DCA). The net reclassification improvement (NRI) index and integrated discrimination improvement (IDI) index were compared between the SOFA and EBS.

RESULTS

A total of 547 patients with CABSIs were included. The AUC (0.853) of the EBS was larger than those of the MEDS, PBS, SOFA, and qSOFA (all P<0.001). The NRI index of EBS in predicting the in-hospital mortality of CABSIs patients was 0.368 (P=0.04), and the IDI index was 0.079 (P=0.03). DCA showed that when the threshold probability was < 0.1, the net benefit of the EBS model was higher than those of the other models.

CONCLUSION

The EBS prognostic models were better than the SOFA, qSOFA, MEDS, and PBS models in predicting the in-hospital mortality of patients with CABSIs.