A quick Sequential Organ Failure Assessment-negative result at triage is associated with low compliance with sepsis bundles: a retrospective analysis of a multicenter prospective registry

Diagnostic Accuracy of Plasma Renin Concentration and Renin Activity in Predicting Mortality and Kidney Outcomes in Patients With Septic Shock and Hypoperfusion or Hypotension: A Multicenter, Prospective, Observational Study

Background

Lactate is a commonly used biomarker for sepsis, although it has limitations in certain cases, suggesting the need for novel biomarkers. We evaluated the diagnostic accuracy of plasma renin concentration and renin activity for mortality and kidney outcomes in patients with sepsis with hypoperfusion or hypotension.

Methods

This was a multicenter, prospective, observational study of 117 patients with septic shock treated at three tertiary emergency departments between September 2021 and October 2022. The accuracy of renin activity, renin, and lactate concentrations in predicting 28-day mortality, acute kidney injury (AKI), and renal replacement requirement was assessed using the area under the ROC curve (AUC) analysis.

Results

The AUCs of initial renin activity, renin, and lactate concentrations for predicting 28-day mortality were 0.66 (95% confidence interval [CI], 0.55-0.77), 0.63 (95% CI, 0.52-0.75), and 0.65 (95% CI, 0.53-0.77), respectively, and those at 24 hrs were 0.74 (95% CI, 0.62-0.86), 0.70 (95% CI, 0.56-0.83), and 0.67 (95% CI, 0.54-0.79). Renin concentrations and renin activity outperformed initial lactate concentrations in predicting AKI within 14 days. The AUCs of renin and lactate concentrations were 0.71 (95% CI, 0.61-0.80) and 0.57 (95% CI, 0.46-0.67), respectively (P=0.030). The AUC of renin activity (0.70; 95% CI, 0.60-0.80) was also higher than that of lactate concentration (P=0.044).

Conclusions

Renin concentration and renin activity show comparable performance to lactate concentration in predicting 28-day mortality in patients with septic shock but superior performance in predicting AKI.

Early Mortality Stratification with Serum Albumin and the Sequential Organ Failure Assessment Score at Emergency Department Admission in Septic Shock Patients

Background: Early risk stratification is crucial due to septic patients' heterogeneity. Serum albumin level may reflect the severity of sepsis and host status. This study aimed to evaluate the prognostic ability of the initial sequential organ failure assessment (SOFA) score alone and combined with serum albumin levels for predicting 28-day mortality in patients with septic shock. Methods: We conducted an observational study using a prospective, multicenter registry of septic shock patients between October 2015 and May 2022 from 12 emergency departments in the Korean Shock Society and the results were validated by examining those from the septic shock cohort in Asan Medical Center. The primary outcome was 28-day mortality. The area under the receiver operating characteristic (ROC) curve was used to compare the predictive values of SOFA score alone and SOFA score combined with serum albumin level. Results: Among 5805 septic shock patients, 1529 (26.3%) died within 28 days. Mortality increased stepwise with decreasing serum albumin levels (13.6% in albumin ≥3.5, 20.7% in 3.5-3.0, 29.7% in 3.0-2.5, 44.0% in 2.5-2.0, 56.4% in <2.0). The albumin SOFA score was calculated by adding the initial SOFA score to the 4 points assigned for albumin levels. ROC analysis for predicting 28-day mortality showed that the area under the curve for the albumin SOFA score was 0.71 (95% CI 0.70-0.73), which was significantly higher than that of the initial SOFA score alone (0.68, 95% CI: 0.67-0.69). Conclusions: The combination of the initial SOFA score with albumin can improve prognostic accuracy for patients with septic shock, suggesting the albumin SOFA score may be used as an early mortality stratification tool.

Impact of Point-of-Care Lactate Testing for Sepsis on Bundle Adherence and Clinical Outcomes in the Emergency Department: A Pre-Post Observational Study

Background: The early diagnosis and prompt treatment of sepsis can enhance clinical outcomes. This study aimed to assess the relationship between point-of-care testing (POCT) for lactate levels and both adherence to the Surviving Sepsis Campaign (SSC) guidelines and mortality rates among sepsis patients in the emergency department (ED). We hypothesized that bedside lactate POCT would lead to better clinical outcomes. Methods: We conducted a pre-post observational study utilizing data from a prospectively collected sepsis registry. Following the introduction of lactate POCT, lactate levels were determined using both the central laboratory pathway and a POCT device. We then compared the characteristics and clinical outcomes between the periods before and after the introduction of POCT lactate measurement. Results: The analysis included a total of 1191 patients. The introduction of bedside lactate POCT led to a significant reduction in the time taken to obtain lactate results (from 53 to 33 min) and an increase in the rate of subsequent lactate measurements (from 82.1% to 88.2%). Lactate POCT did not significantly affect adherence to the overall SSC guidelines bundle (47.5% vs. 45.0%) or reduce 30-day mortality rates (31.1% vs. 31.4%). However, bedside lactate POCT could decrease extremely delayed lactate measurements. Conclusions: Bedside lactate POCT successfully reduced the time to obtain lactate results. Although lactate POCT did not lead to improved adherence to the overall SSC guidelines bundle or affect short-term mortality rates in sepsis patients, it may have an advantage in a specific situation such as overcrowded ED where there are subsequent or multiple measurements required.

The usefulness of lactate/albumin ratio, C-reactive protein/albumin ratio, procalcitonin/albumin ratio, SOFA, and qSOFA in predicting the prognosis of patients with sepsis who presented to EDs

PURPOSE

Early identification of sepsis with a poor prognosis in the emergency department (ED) is crucial for prompt management and improved outcomes. This study aimed to examine the predictive value of sequential organ failure assessment (SOFA), quick SOFA (qSOFA), lactate to albumin ratio (LAR), C-reactive protein to albumin ratio (CAR), and procalcitonin to albumin ratio (PAR), obtained in the ED, as predictors for 28-day mortality in patients with sepsis and septic shock.

MATERIALS AND METHODS

We included 3499 patients (aged ≥19 years) from multicenter registry of the Korean Shock Society between October 2015 and December 2019. The SOFA score, qSOFA score, and lactate level at the time of registry enrollment were used. Albumin, C-reactive protein, and procalcitonin levels were obtained from the initial laboratory results measured upon ED arrival. We evaluated the predictive accuracy for 28-day mortality using the area under the receiver operating characteristic (AUROC) curve. A multivariable logistic regression analysis of the independent predictors of 28-day mortality was performed. The SOFA score, LAR, CAR, and PAR were converted to categorical variables using Youden's index and analyzed. Adjusting for confounding factors such as age, sex, comorbidities, and infection focus, adjusted odds ratios (aOR) were calculated.

RESULTS

Of the 3499 patients, 2707 (77.4%) were survivors, whereas 792 (22.6%) were non-survivors. The median age of the patients was 70 (25th-75th percentiles, 61-78), and 2042 (58.4%) were male. LAR for predicting 28-day mortality had the highest AUROC, followed by the SOFA score (0.715; 95% confidence interval (CI): 0.69-0.74 and 0.669; 95% CI: 0.65-0.69, respectively). The multivariable logistic regression analysis revealed that the aOR of LAR >1.52 was 3.75 (95% CI: 3.16-4.45), and the aOR, of SOFA score at enrollment >7.5 was 2.67 (95% CI: 2.25-3.17).

CONCLUSION

The results of this study showed that LAR is a relatively strong predictor of sepsis prognosis in the ED setting, indicating its potential as a straightforward and practical prognostic factor. This finding may assist healthcare providers in the ED by providing them with tools to risk-stratify patients and predict their mortality.

A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study

Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.

Development and derivation of bacteremia prediction model in patients with hepatobiliary infection

INTRODUCTION

Hepatobiliary infections are common in the emergency department (ED), and the mortality rate for this condition is high. A suitable bacteremia prediction model would support prompt identification of bacteremia and appropriate management of hepatobiliary infections in the ED. Therefore, we attempted to produce a bacteremia prediction model with both internal and external validation for hepatobiliary infections in the ED.

METHODS

Patients with hepatobiliary infection were extracted from retrospective cohort databases of two tertiary hospitals from January 2018 to December 2019 and from January 2016 to December 2019, respectively. Independent risk factors were determined using multivariable logistic regression in a developmental cohort. We assigned a weighted value to predictive factors and developed a prediction model, which was validated both internally and externally. We assessed discrimination using the area under the receiver operating characteristics curve (AUC).

RESULTS

One hospital cohort of 1568 patients was randomly divided into a developmental group of 927 patients (60%) and an internal validation group of 641 patients (40%), and 736 people from the other hospital cohort were used for external validation. Bacteremia rates were 20.5%, 18.1%, and 23.1% in the developmental, internal, and external validation cohorts, respectively. Nine significant factors were used for predicting bacteremia, including age, three vital signs, and five laboratory tests. After applying our bacteremia prediction rule to the validation cohort, 56.5% and 53.8% of the internal and external validation groups were classified as low-risk bacteremia groups (bacteremia rates: 8.6% and 13.9%, respectively). The AUCs were 0.727 (95% confidence interval [CI]: 0.686-0.767), 0.730 (95% CI: 0.679-0.781), and 0.715 (95% CI: 0.672-0.758) for the developmental, internal, and external validation cohorts, respectively. The sensitivity and specificity for internal validation/external validation was 73.2%/67.6% and 63.0%/60.2%, respectively.

CONCLUSION

A bacteremia prediction model for hepatobiliary infection might be useful to predict the risk of bacteremia. It might also reduce the need for blood culture in low-risk patients.

EARLY PREDICTION OF UNEXPECTED LATENT SHOCK IN THE EMERGENCY DEPARTMENT USING VITAL SIGNS

ABSTRACT

Objective/Introduction : Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods : The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multilayer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results : Data of 89,250 visits of patients who met prespecified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion : We developed a latent shock prediction model based on 24 h of vital-sign sequence that changed with time and predicted the results by individual.

Prognostic accuracy of initial and 24-h maximum SOFA scores of septic shock patients in the emergency department

Background

We compared the prognostic accuracy of in-hospital mortality of the initial Sequential Organ Failure Assessment (SOFAini) score at the time of sepsis recognition and resuscitation and the maximum SOFA score (SOFAmax) using the worst variables in the 24 h after the initial score measurement in emergency department (ED) patients with septic shock.

Methods

This was a retrospective observational study using a multicenter prospective registry of septic shock patients in the ED between October 2015 and December 2019. The primary outcome was in-hospital mortality. The prognostic accuracies of SOFAini and SOFAmax were evaluated using the area under the receiver operating characteristic (AUC) curve.

Results

A total of 4860 patients was included, and the in-hospital mortality was 22.1%. In 59.7% of patients, SOFAmax increased compared with SOFAini, and the mean change of total SOFA score was 2.0 (standard deviation, 2.3). There was a significant difference in in-hospital mortality according to total SOFA score and the SOFA component scores, except cardiovascular SOFA score. The AUC of SOFAmax (0.71; 95% confidence interval [CI], 0.69-0.72) was significantly higher than that of SOFAini (AUC, 0.67; 95% CI, 0.66-0.69) in predicting in-hospital mortality. The AUCs of all scores of the six components were higher for the maximum values.

Conclusion

The prognostic accuracy of the initial SOFA score at the time of sepsis recognition was lower than the 24-h maximal SOFA score in ED patients with septic shock. Follow-up assessments of organ failure may improve discrimination of the SOFA score for predicting mortality.