Prevalence, Characteristics, and Outcomes of Emergency Department Discharge Among Patients With Sepsis

Study of Phospholipase A2 Levels and Its Comparison With Procalcitonin Levels in Patients With Sepsis Admitted in a Tertiary Care Hospital, Karnataka, India

INTRODUCTION

Sepsis is a complicated host response to infection involving organ failure which ultimately causes death of the host. Procalcitonin (PCT) is an effective marker used to diagnose sepsis but until now, there has been no ideal marker for sepsis. Phospholipase A2 (PLA2) also increases infections; however, only a few studies have assessed its capacity as a biomarker to diagnose sepsis. Thus, we aimed to examine PLA2 and compare its diagnostic capacity and accuracy with PCT as a biomarker of sepsis.

MATERIAL AND METHODS

Our study was a hospital-oriented cross-sectional study. Our study group included 80 patients of both sexes older than 18 years, meeting the quick sequential organ failure assessment (qSOFA) or systemic inflammatory response syndrome (SIRS) criteria of ≥2, hospitalized in a tertiary care hospital in Karnataka, India from January 2021 to December 2021. Out of them, 59 were found to have sepsis. Samples of all the patients were evaluated for relevant parameters, and data were statistically analyzed using SPSS v21 running on Windows 10. The statistical significance was set at p-value <0.05.

RESULTS

The mean PCT and PLA2 were significantly raised in sepsis patients compared to non-sepsis patients. Out of 59 septic patients, 45.76% had positive blood cultures, and 16.95% had positive urine culture reports. In blood cultures, the most common Gram-positive organism found was Staphylococcus, and the most common Gram-negative organism was Enterobacter. In urine cultures, Escherichia coli was the most common species. PLA2 was significantly higher in patients with bacterial etiology and Gram-positive cultures. The diagnostic capability, sensitivity, specificity, and accuracy of PLA2 were demonstrably higher than those of PCT.

CONCLUSION

Our study proves that PLA2 is a much better and more efficient biomarker in sepsis than PCT. The diagnostic capacity and accuracy of PLA2 clearly surpass PCT, so using PLA2 in sepsis as a biomarker can help clinicians in deciding on timely and appropriate management to speed the recovery of patients.

Costs and Consequences of a Novel Emergency Department Sepsis Diagnostic Test: The IntelliSep Index

Sepsis causes 270,000 deaths and costs $38 billion annually in the United States. Most cases of sepsis present in the emergency department (ED), where rapid diagnosis remains challenging. The IntelliSep Index (ISI) is a novel diagnostic test that analyzes characteristics of WBC structure and provides a reliable early signal for sepsis. This study performs a cost-consequence analysis of the ISI relative to procalcitonin for early sepsis diagnosis in the ED.

PERSPECTIVE

U.S. healthcare system.

SETTING

Community hospital ED.

METHODS

A decision tree analysis was performed comparing ISI with procalcitonin. Model parameters included prevalence of sepsis, sensitivity and specificity of diagnostic tests (both ISI and procalcitonin), costs of hospitalization, and mortality rate stratified by diagnostic test result. Mortality and prevalence of sepsis were estimated from best available literature. Costs were estimated based on an analysis of a large, national discharge dataset, and adjusted to 2018 U.S. dollars. Outcomes included expected costs and survival.

RESULTS

Assuming a confirmed sepsis prevalence of 16.9% (adjudicated to Sepsis-3), the ISI strategy had an expected cost per patient of $3,849 and expected survival rate of 95.08%, whereas the procalcitonin strategy had an expected cost of $4,656 per patient and an expected survival of 94.98%. ISI was both less costly and more effective than procalcitonin, primarily because of fewer false-negative results. These results were robust in sensitivity analyses.

CONCLUSIONS

ISI was both less costly and more effective in preventing mortality than procalcitonin, primarily because of fewer false-negative results. The ISI may provide health systems with a higher-value diagnostic test in ED sepsis evaluation. Additional work is needed to validate these results in clinical practice.

Concordance Between Initial Presumptive and Final Adjudicated Diagnoses of Infection Among Patients Meeting Sepsis-3 Criteria in the Emergency Department

BACKGROUND

Guidelines emphasize rapid antibiotic treatment for sepsis, but infection presence is often uncertain at initial presentation. We investigated the incidence and drivers of false-positive presumptive infection diagnosis among emergency department (ED) patients meeting Sepsis-3 criteria.

METHODS

For a retrospective cohort of patients hospitalized after meeting Sepsis-3 criteria (acute organ failure and suspected infection including blood cultures drawn and intravenous antimicrobials administered) in 1 of 4 EDs from 2013 to 2017, trained reviewers first identified the ED-diagnosed source of infection and adjudicated the presence and source of infection on final assessment. Reviewers subsequently adjudicated final infection probability for a randomly selected 10% subset of subjects. Risk factors for false-positive infection diagnosis and its association with 30-day mortality were evaluated using multivariable regression.

RESULTS

Of 8267 patients meeting Sepsis-3 criteria in the ED, 699 (8.5%) did not have an infection on final adjudication and 1488 (18.0%) patients with confirmed infections had a different source of infection diagnosed in the ED versus final adjudication (ie, initial/final source diagnosis discordance). Among the subset of patients whose final infection probability was adjudicated (n = 812), 79 (9.7%) had only "possible" infection and 77 (9.5%) were not infected. Factors associated with false-positive infection diagnosis included hypothermia, altered mental status, comorbidity burden, and an "unknown infection source" diagnosis in the ED (odds ratio: 6.39; 95% confidence interval: 5.14-7.94). False-positive infection diagnosis was not associated with 30-day mortality.

CONCLUSIONS

In this large multihospital study, <20% of ED patients meeting Sepsis-3 criteria had no infection or only possible infection on retrospective adjudication.

Host Response Biomarkers for Sepsis in the Emergency Room

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Antibiotics: it is all about timing, isn't it?

PURPOSE OF REVIEW

Sepsis guidelines and quality measures set aggressive deadlines for administering antibiotics to patients with possible sepsis or septic shock. However, the diagnosis of sepsis is often uncertain, particularly upon initial presentation, and pressure to treat more rapidly may harm some patients by exposing them to unnecessary or inappropriate broad-spectrum antibiotics.

RECENT FINDINGS

Observational studies that report that each hour until antibiotics increases mortality often fail to adequately adjust for comorbidities and severity of illness, fail to account for antibiotics given to uninfected patients, and inappropriately blend the effects of long delays with short delays. Accounting for these factors weakens or eliminates the association between time-to-antibiotics and mortality, especially for patients without shock. These findings are underscored by analyses of the Centers for Medicaid and Medicare Services SEP-1 measure: it has increased sepsis diagnoses and broad-spectrum antibiotic use but has not improved outcomes.

SUMMARY

Clinicians are advised to tailor the urgency of antibiotics to their certainty of infection and patients' severity of illness. Immediate antibiotics are warranted for patients with possible septic shock or high likelihood of infection. Antibiotics can safely be withheld to allow for more investigation, however, in most patients with less severe illnesses if the diagnosis of infection is uncertain.

Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission

INTRODUCTION

Septic patients requiring intensive care unit (ICU) readmission are at high risk of mortality, but research focusing on the association of ICU readmission due to sepsis and mortality is limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting in-hospital mortality in septic patients readmitted to the ICU using routinely available clinical data.

METHODS

The data used in this study were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV, v1.0) database, between 2008 and 2019. The study cohort included patients with sepsis requiring ICU readmission. The data were randomly split into a training (75%) data set and a validation (25%) data set. Nine popular ML models were developed to predict mortality in septic patients readmitted to the ICU. The model with the best accuracy and area under the curve (A.C.) in the validation cohort was defined as the optimal model and was selected for further prediction studies. The SHAPELY Additive explanations (SHAP) values and Local Interpretable Model-Agnostic Explanation (LIME) methods were used to improve the interpretability of the optimal model.

RESULTS

A total of 1117 septic patients who had required ICU readmission during the study period were enrolled in the study. Of these participants, 434 (38.9%) were female, and the median (interquartile range [IQR]) age was 68.6 (58.4-79.2) years. The median (IQR) ICU interval duration was 2.60 (0.64-5.78) days. After feature selection, 31 of 47 clinical factors were ultimately chosen for use in model construction. Of the nine ML models tested, the best performance was achieved with the random forest (RF) model, with an A.C. of 0.81, an accuracy of 85% and a precision of 62% in the validation cohort. The SHAP summary analysis revealed that Glasgow Coma Scale score, urine output, blood urea nitrogen, lactate, platelet count and systolic blood pressure were the top six most important factors contributing to the RF model. Additionally, the LIME method demonstrated how the RF model works in terms of explaining risk of death prediction in septic patients requiring ICU readmission.

CONCLUSION

The ML models reported here showed a good prognostic prediction ability in septic patients requiring ICU readmission. Of the features selected, the parameters related to organ perfusion made the largest contribution to outcome prediction during ICU readmission in septic patients.