Comparison of qSOFA and SOFA score for predicting mortality in severe sepsis and septic shock patients in the emergency department of a low middle income country

Comparison of Sepsis-1, 2 and 3 for Predicting Mortality in Septic Patients of a Middle-Income Country: A Retrospective Observational Cohort Study

INTRODUCTION

The diagnosis of sepsis is based on expert consensus and does not yet have a "gold standard." With the aim of improving and standardizing diagnostic methods, there have already been three major consensuses on the subject. However, there are still few studies in middle-income countries comparing the methods. This study describes the characteristics of patients diagnosed with sepsis and evaluates and compares the performance of Sepsis-1, 2, and 3 criteria in predicting 28 days, and in-hospital mortality.

PATIENTS AND METHODS

A retrospective observational cohort study was conducted in the intensive care unit of a tertiary hospital. All admissions between January 1, 2018, and December 31, 2019, were reviewed. Patients diagnosed with sepsis were included.

RESULTS

During the study period, 653 patients diagnosed with sepsis (by any of the studied criteria) were included in the study. The 28 days mortality rate was 45.8%, and the in-hospital mortality rate was 59.7%. We observed that 72.1% of patients met the minimum criteria for diagnosing sepsis according to the three protocols, and this group also had the highest mortality rate. Age and comorbidities such as cancer and liver cirrhosis were significantly associated with in-hospital mortality. The most common microorganisms were Escherichia coli, Klebsiella spp., and Staphylococcus spp.

CONCLUSIONS

The study found that most patients met the diagnostic criteria for sepsis using the three methods. Sepsis-2 showed greater sensitivity to predict mortality, while Sequential Organ Failure Assessment showed low accuracy, but was the only significant one. Furthermore, quick Sequential Organ Failure Assessment (qSOFA) had the highest specificity for mortality. Overall, these findings suggest that, although all three methods contribute to the diagnosis and prognosis of sepsis, Sepsis-2 is particularly sensitive in predicting mortality. Sepsis-3 shows some accuracy but requires improvement, and qSOFA exhibits the highest specificity. More research is needed to improve predictive capabilities and patient outcomes.

Sepsis Management in Southeast Asia: A Review and Clinical Experience

Sepsis is a life-threatening condition that causes a global health burden associated with high mortality and morbidity. Often life-threatening, sepsis can be caused by bacteria, viruses, parasites or fungi. Sepsis management primarily focuses on source control and early broad-spectrum antibiotics, plus organ function support. Comprehensive changes in the way we manage sepsis patients include early identification, infective focus identification and immediate treatment with antimicrobial therapy, appropriate supportive care and hemodynamic optimization. Despite all efforts of clinical and experimental research over thirty years, the capacity to positively influence the outcome of the disease remains limited. This can be due to limited studies available on sepsis in developing countries, especially in Southeast Asia. This review summarizes the progress made in the diagnosis and time associated with sepsis, colistin resistance and chloramphenicol boon, antibiotic abuse, resource constraints and association of sepsis with COVID-19 in Southeast Asia. A personalized approach and innovative therapeutic alternatives such as CytoSorb® are highlighted as potential options for the treatment of patients with sepsis in Southeast Asia.

Urine Output Is Associated With In-hospital Mortality in Intensive Care Patients With Septic Shock: A Propensity Score Matching Analysis

Background: The relationship between urine output (UO) and in-hospital mortality in intensive care patients with septic shock is currently inconclusive. Methods: The baseline data, UO, and in-hospital prognosis of intensive care patients with septic shock were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. By drawing receiver operating characteristic (ROC) curves and comparing the areas under the ROC curves (AUC) to determine the predictive value of UO for in-hospital mortality, and by drawing the Kaplan-Meier curves to compare the difference in in-hospital mortality between different groups of UO. Results: Before and after the propensity score matching (PSM) analysis, UO was always a risk factor for in-hospital mortality in patients with septic shock. The AUC of UO was comparable to the Sequential Organ Failure Assessment (SOFA) scoring system, while the AUC of combining UO and SOFA was greater than that of SOFA. The median survival time of the high-UO group (UO > 0.39 ml/kg/h, before PSM; UO > 0.38 ml/kg/h, after PSM) was longer than that of the low-UO group. Compared with the high-UO group, the hazard ratios (HR) of the low-UO group were 2.6857 (before PSM) and 1.7879 (after PSM). Conclusions: UO is an independent risk factor for septic shock. Low levels of UO significantly increase the in-hospital mortality of intensive care patients with septic shock. The predictive value of UO is comparable to the SOFA scoring system, and the combined predictive value of the two surpasses SOFA alone.

A comparison of different scores for diagnosis and mortality prediction of adults with sepsis in Low-and-Middlencome Countries: a systematic review and meta-analysis

BACKGROUND

Clinical scores for sepsis have been primarily developed for, and applied in High-Income Countries. This systematic review and meta-analysis examined the performance of the quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), Modified Early Warning Score (MEWS), and Universal Vital Assessment (UVA) scores for diagnosis and prediction of mortality in patients with suspected infection in Low-and-Middle-Income Countries.

METHODS

PubMed, Science Direct, Web of Science, and the Cochrane Central Register of Controlled Trials databases were searched until May 18, 2021. Studies reporting the performance of at least one of the above-mentioned scores for predicting mortality in patients of 15 years of age and older with suspected infection or sepsis were eligible. The Quality Assessment of Diagnostic Accuracy Studies tool was used for risk-of-bias assessment. PRISMA guidelines were followed (PROSPERO registration: CRD42020153906). The bivariate random-effects regression model was used to pool the individual sensitivities, specificities and areas-under-the-curve (AUC).

FINDINGS

Twenty-four articles (of 5669 identified) with 27,237 patients were eligible for inclusion. qSOFA pooled sensitivity was 0·70 (95% confidence interval [CI] 0·60-0·78), specificity 0·73 (95% CI 0·67-0·79), and AUC 0·77 (95% CI 0·72-0·82). SIRS pooled sensitivity, specificity and AUC were 0·88 (95% CI 0·79 -0·93), 0·34 (95% CI 0·25-0·44), and 0·69 (95% CI 0·50-0·83), respectively. MEWS pooled sensitivity, specificity and AUC were 0·70 (95% CI 0·57 -0·81), 0·61 (95% CI 0·42-0·77), and 0·72 (95% CI 0·64-0·77), respectively. UVA pooled sensitivity, specificity and AUC were 0·49 (95% CI 0·33 -0·65), 0·91(95% CI 0·84-0·96), and 0·76 (95% CI 0·44-0·93), respectively. Significant heterogeneity was observed in the pooled analysis.

INTERPRETATION

Individual score performances ranged from poor to acceptable. Future studies should combine selected or modified elements of different scores.

FUNDING

Partially funded by the UK National Institute for Health Research (NIHR) (17/63/42).

QSIRS Can Improve Accuracy of QSOFA and SIRS in Prediction of Mortality in Surgical Emergencies

Background  The quick sequential organ failure assessment (QSOFA) score and the systemic inflammatory response syndrome (SIRS) criteria were developed to predict the risk of sepsis and death in patients received in emergency. To improve sensitivity in predicting death, the association of the two scores was proposed under the term QSIRS (QSOFA + SIRS). Our aim was to determine the accuracy of QSOFA, SIRS, and QSIRS in prediction of mortality in surgical emergencies, and to compare these scores.

Patients and Methods  This is a prospective study over a period of 1 year. Patients older than 15 years who presented a digestive surgical emergency (bowel obstruction, peritonitis, appendicitis, strangulated hernia) were included. For each score, the specificity, the sensitivity, the positive predictive value, the negative predictive value, and areas under the receiver operating characteristic (ROC) curve (AUC) were compared.

Results  One hundred and eighteen patients were included and 11 deaths were recorded (9.3%). There was a statistically significant relationship between each score and death (QSOFA p  = 0.01, SIRS p  = 0.003, and QSIRS p  = 0.004). The realization of the ROC curve found a higher AUC for QSIRS (0.845 [0.767–0.905]) compared with QSOFA (0.783 [0.698–0.854]) and SIRS (0.737 [0.648–0.813]). QSIRS (90.9%) had a higher sensitivity compared with the two other scores alone (SIRS = 81.9% and QSOFA = 36.3%).

Conclusion  Our study found that QSIRS improves the ability to predict death in digestive surgical emergencies.

The Surviving Sepsis Campaign: research priorities for the administration, epidemiology, scoring and identification of sepsis

Objective

To identify priorities for administrative, epidemiologic and diagnostic research in sepsis.

Design

As a follow-up to a previous consensus statement about sepsis research, members of the Surviving Sepsis Campaign Research Committee, representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine addressed six questions regarding care delivery, epidemiology, organ dysfunction, screening, identification of septic shock, and information that can predict outcomes in sepsis.

Methods

Six questions from the Scoring/Identification and Administration sections of the original Research Priorities publication were explored in greater detail to better examine the knowledge gaps and rationales for questions that were previously identified through a consensus process.

Results

The document provides a framework for priorities in research to address the following questions: (1) What is the optimal model of delivering sepsis care?; (2) What is the epidemiology of sepsis susceptibility and response to treatment?; (3) What information identifies organ dysfunction?; (4) How can we screen for sepsis in various settings?; (5) How do we identify septic shock?; and (6) What in-hospital clinical information is associated with important outcomes in patients with sepsis?

Conclusions

There is substantial knowledge of sepsis epidemiology and ways to identify and treat sepsis patients, but many gaps remain. Areas of uncertainty identified in this manuscript can help prioritize initiatives to improve an understanding of individual patient and demographic heterogeneity with sepsis and septic shock, biomarkers and accurate patient identification, organ dysfunction, and ways to improve sepsis care.

Comparison of SOFA Score, SIRS, qSOFA, and qSOFA + L Criteria in the Diagnosis and Prognosis of Sepsis

Objective

Sepsis has been defined as a life-threatening organ dysfunction that develops as a result of impaired host response to infection. This study aimed to investigate sequential organ failure assessment (SOFA) score, systemic inflammatory response syndrome (SIRS), quick SOFA (qSOFA), and qSOFA + lactate criteria (qSOFA+L) in the diagnosis and prognosis of sepsis.

Materials and Methods

A retrospective study was performed that included all patients diagnosed with sepsis between January 1, 2013 and December 31, 2017 in Izmir Tepecik Training and Research Hospital Infectious Diseases and Clinical Microbiology Clinic.

Results

A total of 976 patients diagnosed with sepsis (mean age 72.5±13.7 years, 52.7% women) over five years were included in this study. Of all patients admitted to the emergency department and diagnosed with sepsis, 37.4% (n=365) were hospitalized and 52.3% (n=191) of these patients died. Emergency department mortality was 12.5% (n=122). The mortality rate was higher in patients with qSOFA and qSOFA+L criteria ≥2 in the emergency department. There was no statistically significant difference in terms of SIRS, qSOFA, or qSOFA+L criteria among patients who died in the hospital. The SOFA score (area under receiver operator characteristic curve, AUC=0.89) was highly discriminative in predicting sepsis. When the SOFA score was>11, its sensitivity and negative predictive values were both 100%. The SOFA score (AUC=0.75 and 0.72, respectively) was also highly discriminative in predicting emergency and in-hospital mortality. When the SOFA score was>11, the sensitivity and specificity of predicting emergency department mortality were 63.5% and 78.8%, respectively. The sensitivity was 65.8% and the specificity was 75.5% when describing in-hospital mortality for SOFA scores>9.

Conclusion

The SOFA score was highly sensitive and predictive in the diagnosis of sepsis. The SOFA score had a high discriminative ability to predict emergency and in-hospital mortality.

Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

RATIONALE

Mortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.

OBJECTIVE

To determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.

DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES

We prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.

RESULTS

We screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.

CONCLUSION

Three scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

Predicting in-hospital mortality for sepsis: a comparison between qSOFA and modified qSOFA in a 2-year single-centre retrospective analysis

Sepsis is a life-threating organ dysfunction caused by a dysregulated host response to infection. This study proposed a new tool, i.e. modified qSOFA, for the early prognostic assessment of septic patients. All cases of sepsis/septic shock consecutively observed in 2 years (January 2017–December 2018), at St. Anna University Hospital of Ferrara, Italy, were included. Each patient was evaluated with qSOFA and a modified qSOFA (MqSOFA), i.e. adding a SpO2/FiO2 ratio to qSOFA. Logistic regression and survival analyses were applied to compare the two scores. A total number of 1137 consecutive cases of sepsis and septic shock were considered. Among them 136 were excluded for incomplete report of vital parameters. A total number of 668 patients (66.7%) were discharged, whereas 333 (33.3%) died because of sepsis-related complications. Data analysis showed that MqSOFA (AUC 0.805, 95% C.I. 0.776–0.833) had a greater ability to detect in-hospital mortality than qSOFA (AUC 0.712, 95% C.I. 0.678–0.746) (p < 0.001). Eighty-five patients (8.5%) were reclassified as high-risk (qSOFA< 2 and MqSOFA≥ 2) resulting in an improvement of sensitivity with a minor reduction in specificity. A significant difference of in-hospital mortality was observed between low-risk and reclassified high-risk (p < 0.001) and low-risk vs. high-risk groups (p < 0.001). We demonstrated that MqSOFA provided a better predictive score than qSOFA regarding patient’s outcome. Since sepsis is an underhanded and time-dependent disease, physicians may rely upon the herein proposed simple score, i.e. MqSOFA, to establish patients’ severity and outcome.

Validity of Sequential Organ Failure Assessment and Quick Sequential Organ Failure Assessment in Assessing Mortality Rate in the Intensive Care Unit With or Without Sepsis

Introduction

Sepsis and septic shock (sepsis-induced hypotension not improved by adequate fluid resuscitation) are among the most common reasons for admission to an intensive care unit (ICU) and display high mortality rates. Different scoring systems are used to diagnose and predict the mortality of patients having sepsis. This study aims to validate the prognostic accuracy of Sequential Organ Failure Assessment (SOFA) and Quick Sequential Organ Failure Assessment (qSOFA) in determining the mortality of both septic and non-septic patients.

Materials and methods

This retrospective cohort study was conducted in May 2018 in the Surgical Intensive Care Unit (SICU) of a tertiary care hospital in Karachi, Pakistan. Past 200 patient records, from January 2018 to April 2018, were examined, and 20 records were discarded due to insufficient data. Sufficient observational data were collected, which was used to assess the validity of the SOFA and qSOFA in determining the mortality rate of sepsis. A comparison of the two modalities was made.

Results

Out of the 200 patients, 180 were enrolled. Data from their entire ICU stay were used to calculate their initial, highest, and mean SOFA and qSOFA. Mean SOFA score up to nine correlated with a mortality rate of up to <79%, while scores 10 and above predicted a 100% mortality rate. A mean qSOFA score of three predicted a 67% mortality rate. Univariate logistic analysis performed with odds ratio showed that the mean qSOFA score was in comparison more closely able to predict mortality, followed by mean SOFA score (p values < 0.01).

Conclusions

This study concluded that both SOFA and qSOFA scores are good predictors of mortality. However, qSOFA is more closely accurate in predicting mortality than SOFA. But further analysis with larger sample size for a longer duration as well as the application of these scores in the emergency departments and general wards can prove the precision of this study.

qSOFA score for prediction of sepsis outcome in emergency department

Objective:

The third international consensus definition for sepsis and septic shock (sepsis 3) task force recently introduced qSOFA (quick sequential organ failure assessment) as a score for detection of patients at risk of sepsis outside of intensive care units. We performed this study to evaluate the validity of qSOFA for early detection and risk stratification of septic patients in emergency department.

Methods:

We conducted this study in an emergency department of the largest university affiliated hospital in northwest of Iran from Sept 2015 to Sept 2016. One hundred and forty patients who were SIRS positive with a suspected infection without alternative diagnosis and a microbiological proven infection were enrolled in this study. qSOFA was calculated for each patient and correlated with sepsis grades and mortality.

Results:

From 140 patients 84 (60%) had positive qSOFA score and 56 (40%) patients had negative qSOFA score. Our results showed that near half of patients with positive qSOFA expired during their stay in hospital while this was about 5% for patients with negative qSOFA. ROC curve of study regarding prediction of outcome with qSOFA showed an area under curve of 0.59. (P value: 0.04). Time spent to sepsis detection was 16 minutes shorter with qSOFA score compared to SIRS criteria in this study.

Conclusion:

In patients with suspected sepsis, qSOFA has acceptable value for risk stratification of severity, multi organ failure and mortality. It seems that education of medical staff and frequent screening of patients for warning signs can help to increase the value of qSOFA in prediction of mortality in critically ill septic patients.

Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department

Objectives

The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome.

Methods

The retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were used for LD modeling for efficiency.

Results

A total of 86,304 patient visits were included, with an overall outcome rate of 3.5%. The area under the curve (AUC) values for the KTAS model were 76.8 (74.9–78.6) with logistic regression and 74.0 (72.1–75.9) for the SOFA model, while the AUC values of the INA model were 87.2 (85.9–88.6) and 87.6 (86.3–88.9) with logistic regression and deep learning, suggesting that the ML and INA-based triage system result more accurately predicted the outcomes. The AUC values for the LD model were 81.2 (79.4–82.9) and 80.7 (78.9–82.5) for logistic regression and deep learning, respectively.

Conclusions

We developed an ML and INA-based triage system for EDs. The novel system was able to predict clinical outcomes more accurately than existing triage systems, KTAS and SOFA.