The sensitivity of qSOFA calculated at triage and during emergency department treatment to rapidly identify sepsis patients

Hierarchical Capability in Distinguishing Severities of Sepsis via Serum Lactate: A Network Meta-Analysis

Background: Blood lactate is a potentially useful biomarker to predict the mortality and severity of sepsis. The purpose of this study is to systematically review the ability of lactate to predict hierarchical sepsis clinical outcomes and distinguish sepsis, severe sepsis and septic shock. Methods: We conducted an exhaustive search of the PubMed, Embase and Cochrane Library databases for studies published before 1 October 2022. Inclusion criteria mandated the presence of case-control, cohort studies and randomized controlled trials that established the association between before-treatment blood lactate levels and the mortality of individuals with sepsis, severe sepsis or septic shock. Data was analyzed using STATA Version 16.0. Results: A total of 127 studies, encompassing 107,445 patients, were ultimately incorporated into our analysis. Meta-analysis of blood lactate levels at varying thresholds revealed a statistically significant elevation in blood lactate levels predicting mortality (OR = 1.57, 95% CI 1.48-1.65, I2 = 92.8%, p < 0.00001). Blood lactate levels were significantly higher in non-survivors compared to survivors in sepsis patients (SMD = 0.77, 95% CI 0.74-0.79, I2 = 83.7%, p = 0.000). The prognostic utility of blood lactate in sepsis mortality was validated through hierarchical summary receiver operating characteristic curve (HSROC) analysis, yielding an area under the curve (AUC) of 0.72 (95% CI 0.68-0.76), accompanied by a summary sensitivity of 0.65 (95% CI 0.59-0.7) and a summary specificity of 0.7 (95% CI 0.64-0.75). Unfortunately, the network meta-analysis could not identify any significant differences in average blood lactate values' assessments among sepsis, severe sepsis and septic shock patients. Conclusions: This meta-analysis demonstrated that high-level blood lactate was associated with a higher risk of sepsis mortality. Lactate has a relatively accurate predictive ability for the mortality risk of sepsis. However, the network analysis found that the levels of blood lactate were not effective in distinguishing between patients with sepsis, severe sepsis and septic shock.

Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study

BACKGROUND

Despite its high lethality, sepsis can be difficult to detect on initial presentation to the emergency department (ED). Machine learning-based tools may provide avenues for earlier detection and lifesaving intervention.

OBJECTIVE

The study aimed to predict sepsis at the time of ED triage using natural language processing of nursing triage notes and available clinical data.

METHODS

We constructed a retrospective cohort of all 1,234,434 consecutive ED encounters in 2015-2021 from 4 separate clinically heterogeneous academically affiliated EDs. After exclusion criteria were applied, the final cohort included 1,059,386 adult ED encounters. The primary outcome criteria for sepsis were presumed severe infection and acute organ dysfunction. After vectorization and dimensional reduction of triage notes and clinical data available at triage, a decision tree-based ensemble (time-of-triage) model was trained to predict sepsis using the training subset (n=950,921). A separate (comprehensive) model was trained using these data and laboratory data, as it became available at 1-hour intervals, after triage. Model performances were evaluated using the test (n=108,465) subset.

RESULTS

Sepsis occurred in 35,318 encounters (incidence 3.45%). For sepsis prediction at the time of patient triage, using the primary definition, the area under the receiver operating characteristic curve (AUC) and macro F1-score for sepsis were 0.94 and 0.61, respectively. Sensitivity, specificity, and false positive rate were 0.87, 0.85, and 0.15, respectively. The time-of-triage model accurately predicted sepsis in 76% (1635/2150) of sepsis cases where sepsis screening was not initiated at triage and 97.5% (1630/1671) of cases where sepsis screening was initiated at triage. Positive and negative predictive values were 0.18 and 0.99, respectively. For sepsis prediction using laboratory data available each hour after ED arrival, the AUC peaked to 0.97 at 12 hours. Similar results were obtained when stratifying by hospital and when Centers for Disease Control and Prevention hospital toolkit for adult sepsis surveillance criteria were used to define sepsis. Among septic cases, sepsis was predicted in 36.1% (1375/3814), 49.9% (1902/3814), and 68.3% (2604/3814) of encounters, respectively, at 3, 2, and 1 hours prior to the first intravenous antibiotic order or where antibiotics where not ordered within the first 12 hours.

CONCLUSIONS

Sepsis can accurately be predicted at ED presentation using nursing triage notes and clinical information available at the time of triage. This indicates that machine learning can facilitate timely and reliable alerting for intervention. Free-text data can improve the performance of predictive modeling at the time of triage and throughout the ED course.

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data

BACKGROUND

Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning.

METHODS

Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment.

RESULTS

DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346).

CONCLUSIONS

The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.

End-to-End Sepsis Solution Incorporating Expert Telemedicine Consultation

Introduction: Early detection and optimal resuscitation of critically ill sepsis patients may improve sepsis care delivery. The objective was to assess the feasibility of developing and implementing an end-to-end sepsis solution including early detection, monitoring, and teleconsultation. Methods: Prospective implementation of an end-to-end sepsis solution for potential sepsis patients presenting to a community hospital emergency department (ED) between 11 AM and 5 PM, Monday to Friday, during a 40-day period in 2019. Qualifying patients were compared with patients presenting at other times during the pilot screening period and to historic controls. Results: During the initial period, 203 patients met the screening criteria for potential sepsis; 77 patients (37.9%) had a primary diagnosis of sepsis, present on admission. Mean age was 60 ± 20 years; 50.7% were female; and 24 patients (11.8%) were primary sepsis, SEP-1 bundle eligible. Eighty of 203 (39.4%) had an initial lactate performed, mean, 2.7 ± 1.7 mmol/L. For the 24 primary sepsis, SEP-1 bundle eligible patients, 100% received antibiotics and intravenous fluid. Thirteen consults were performed on 12 patients; mean time from consult decision to beam in to the telemedicine robot was 7.3 ± 5.5 min; mean time from beam in to robot connection with the expert was 23.6 ± 13.2 s; mean consultation call time was 6.3 ± 4.3 min. Conclusions: In a convenience sample of patients with potential sepsis presenting to a community hospital ED, an end-to-end sepsis solution using early detection, tracking, and consultation was feasible and has the potential to improve sepsis detection and treatment.

SIRS, SOFA, qSOFA, and NEWS in the diagnosis of sepsis and prediction of adverse outcomes: a systematic review and meta-analysis

BACKGROUND

We compared Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) for sepsis diagnosis and adverse outcomes prediction.

METHODS

Clinical studies that used SIRS, SOFA, qSOFA, and NEWS for sepsis diagnosis and prognosis assessment were included. Data were extracted, and meta-analysis was performed for outcome measures, including sepsis diagnosis, in-hospital mortality, 7/10/14-day mortality, 28/30-day mortality, and ICU admission.

RESULTS

Fifty-seven included studies showed good overall quality. Regarding sepsis prediction, SIRS demonstrated high sensitivity (0.85) but low specificity (0.41), qSOFA showed low sensitivity (0.42) but high specificity (0.98), and NEWS exhibited high sensitivity (0.71) and specificity (0.85). For predicting in-hospital mortality, SOFA demonstrated the highest sensitivity (0.89) and specificity (0.69). In terms of predicting 7/10/14-day mortality, SIRS exhibited high sensitivity (0.87), while qSOFA had high specificity (0.75). For predicting 28/30-day mortality, SOFA showed high sensitivity (0.97) but low specificity (0.14), whereas qSOFA displayed low sensitivity (0.41) but high specificity (0.88).

CONCLUSIONS

NEWS independently demonstrates good diagnostic capability for sepsis, especially in high-income countries. SOFA emerges as the optimal choice for predicting in-hospital mortality and can be employed as a screening tool for 28/30-day mortality in low-income countries.

Combining C-reactive protein and quick sequential organ failure assessment (qSOFA) to improve prognostic accuracy for sepsis and mortality in adult inpatients: A systematic review

Background and Aims

Infections are common in hospitals, and if mismanaged can develop into sepsis, a leading cause of death and disability worldwide. This study aimed to examine whether combining C-reactive protein (CRP) with the quick sequential organ failure assessment (qSOFA) improves its accuracy for predicting mortality and sepsis in adult inpatients.

Methods

PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Science Direct, CINAHL, Open Grey, Grey Literature Report, and the Clinical Trials registry were searched using CRP and qSOFA search terms. Title, abstract, and full-text screening were performed by two independent reviewers using pre-determined eligibility criteria, followed by data extraction and a risk of bias assessment using the Quality Assessment tool for Diagnostic Accuracy Studies 2 (QUADAS-2). Disagreements were settled through discussion and consultation with a third reviewer.

Results

Four retrospective studies with a total of 2070 patients were included in this review. Adding CRP to qSOFA improved the Area Under the Receiver Operating Characteristic Curve up to 9.7% for predicting mortality and by 14.9% for identifying sepsis. The sensitivity and specificity of the combined score for mortality prediction were available in two studies. CRP improved the sensitivity of qSOFA by 43% and 71% while only decreasing the specificity by 12% and 7%, respectively. A meta-analysis was not performed due to study heterogeneity.

Conclusion

This comprehensive review provided initial evidence that combining CRP with qSOFA may improve the accuracy of qSOFA alone in identifying sepsis or patients at risk of dying in hospital. The combined tool demonstrated the potential to improve patient outcomes, with implications for low-resource settings given its simplicity and low-cost.

Screening auf Sepsis in der Notfallmedizin – qSOFA ist uns nicht genug

Hintergrund

Die Sepsis ist eine häufige und lebensbedrohliche Komplikation einer akuten Infektion. In der Notfallmedizin hat sich zum Screening auf Sepsis der Quick Sequential-Organ-Failure-Assessment(qSOFA)-Score etabliert. Bereits mit der Einführung des Scores wurde dessen schwache Sensitivität kritisiert. Nun fordern aktuelle Leitlinien, den qSOFA-Score nicht mehr zum Screening auf Sepsis einzusetzen. Als eine Alternative wird der National Early Warning Score 2 (NEWS2) vorgeschlagen.

Ziel der Arbeit

In einer Subanalyse einer Kohorte von notfallmedizinischen Patient*innen soll die diagnostische Aussagekraft des qSOFA-Scores und des NEWS2 zur Erkennung einer Sepsis verglichen werden. Zusätzlich soll gezeigt werden, inwieweit mithilfe von abweichenden Vitalparametern bereits eine Risikoerhöhung für eine Sepsis ableitbar ist.

Methodik

Mittels AUROC (Area Under Receiver Operating Characteristics) und Odds Ratios wurden die Scores bzw. die Vitalparameter auf ihre Fähigkeit untersucht, septische Patient*innen zu erkennen.

Ergebnisse

Von 312 eingeschlossenen Patient*innen wurde bei 17,9 % eine Sepsis diagnostiziert. Der qSOFA-Score erkannte eine Sepsis mit einer AUROC von 0,77 (NEWS2 0,81). Für qSOFA fand sich eine Sensitivität von 57 % (Spezifität 83 %), für NEWS2 96 % (Spezifität 45 %). Die Analyse der einzelnen Vitalparameter zeigte, dass unter Patient*innen mit einer akuten Infektion eine Vigilanzminderung als deutliches Warnsignal für eine Sepsis zu werten ist.

Diskussion

In der Notfallmedizin sollte qSOFA nicht als alleiniges Tool für das Screening auf Sepsis verwendet werden. Bei Verdacht auf eine akute Infektion sollten grundsätzlich sämtliche Vitalparameter erfasst werden, um das Vorliegen einer akuten Organschädigung und somit einen septischen Krankheitsverlauf frühzeitig zu erkennen.

Graphic abstract

Comparison of the systematic Inflammatory response syndrome and the quick sequential organ failure assessment for prognostic accuracy in detecting sepsis in the emergency department: A systematic review

BACKGROUND

Awareness and prompt recognition of sepsis are essential for nurses working in the emergency department (ED), enabling them to make an initial assessment of patients and then to sort them according to their condition s severity. The aim of this systematic review was to investigate prognostic accuracy in detecting sepsis in the emergency department by comparing the previous sepsis-2 screening tool, the Systemic Inflammatory Response Syndrome (SIRS) and the current sepsis-3 screening tool, the Quick Sequential Organ Failure Assessment (qSOFA).

METHODS

This systematic review used the guideline by Bettany-Saltikov and McSherry and was reported according to the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) 2020 checklist. The protocol was registered in PROSPERO. A systematic search was conducted using the CINAHL, EMBASE and MEDLINE databases. Study selection and risk of bias was performed independently by pair of authors.

RESULTS

Five articles were included. Overall, SIRS showed higher sensitivity than qSOFA, while qSOFA showed higher specificity than SIRS. The positive predictive value for qSOFA was superior, while there was a minor deviation in negative predictive value between qSOFA and SIRS.

CONCLUSION

The overall recommendation based on the included studies indicates that qSOFA is the better-suited screening tool for prognostic accuracy in detecting sepsis in the emergency department.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Could lactate clearance be a marker of mortality in pediatric intensive care unit?

BACKGROUND

Hyperlactatemia is a common finding in critically ill patients and has significant prognostic implications. However, a single lactate measurement has not been correlated to mortality consistently. In this study, we aimed to correlate the clinical efficacy of lactate clearance for the prediction of mortality in pediatric intensive care unit patients.

METHODS

This retrospective observational study was performed in the pediatric intensive care unit in patients with lactate level >3 mmol/lt. Initial, 6th h, and 24th h lactate levels were recorded and lactate clearance was calculated using these values (lactate level at admission - level 6 h later × 100/lactate level at admission).

RESULTS

A total of 172 patients were included in the study. Forty-four out of 172 patients died. Median (IQR) lactate (mmol/L) at admission was low in those who survived in comparison to nonsurvivors 4.4 (3.1) vs. 5.75 (7.7) (p = 0.002). Clearance at 6th h was significantly lower in those who died (11.7%) than those who survived (36.7) (p = 0.001). 6th h lactate clearance level <20.7% predicted mortality with a sensitivity of 63.6% and specificity of 69.5% along with a positive predictive value of 41.8 and a negative predictive value of 84.8 (p = 0.004). Both lactate levels and lactate clearance values were significantly predictive factors for mortality (p < 0.05). Only a positive moderate correlation was found between the percentage of PRISM-IV % and 6th h lactate level.

DISCUSSION

The present study revealed that lactate clearance is a simple and rapid risk-stratification tool holding to be a potential biomarker of managing the treatment efficacy of children in the pediatric intensive care unit.

Leveraging electronic health records for data science: common pitfalls and how to avoid them

Analysis of electronic health records (EHRs) is an increasingly common approach for studying real-world patient data. Use of routinely collected data offers several advantages compared with other study designs, including reduced administrative costs, the ability to update analysis as practice patterns evolve, and larger sample sizes. Methodologically, EHR analysis is subject to distinct challenges because data are not collected for research purposes. In this Viewpoint, we elaborate on the importance of in-depth knowledge of clinical workflows and describe six potential pitfalls to be avoided when working with EHR data, drawing on examples from the literature and our experience. We propose solutions for prevention or mitigation of factors associated with each of these six pitfalls-sample selection bias, imprecise variable definitions, limitations to deployment, variable measurement frequency, subjective treatment allocation, and model overfitting. Ultimately, we hope that this Viewpoint will guide researchers to further improve the methodological robustness of EHR analysis.

The prognostic value of sepsis scores and dichotomized triage score in patients presenting to the emergency department with fever: A prospective, observational study

BACKGROUND

The performance of the Quick Sequential Organ Failure Assessment (qSOFA) score needs to be explored further in the emergency triage room. This study aims to explore the performance of triage (tqSOFA) versus the dichotomized triage score (DTS) in patients admitted to the emergency room triage with fever.

METHODS

This research was designed as a prospective, observational study within a six-month period, including patients who presented to the emergency room triage with infrared fever ≥ 37.5 °C.

RESULTS

771 patients were analyzed.The highest sensitivity for predicting overall hospitalization and intensive care admission was seen for DTS (95.4 %, 100 %; p < 0.0001, p < 0.0001, respectively) (AUC:0.697, 95 % CI 0.663 to 0.730; AUC:0.684, 95 % CI 0.650 to 0.717, respectively). The highest sensitivity for predicting 1st week and 1st month mortality was found for DTS (100 %, 96.3 %; p < 0.0001, p < 0.0001, respectively). However, the highest specificity for predicting 1st week and 1st month mortality was observed in tqSOFA (94.1 %, 95.16; p = 0.0845, p < 0.0001, respectively) (AUC:0.658, 95 % CI 0.623 to 0.691; AUC:0.698, 95 % CI 0.664 to 0.730, respectively).

CONCLUSION

We found DTS to be as effective as tqSOFA and SIRS in determining all hospitalization times and mortality.

Advanced Triage Protocol: The Role of an Automated Lactate Order in Expediting Rapid Identification of Patients at Risk of Sepsis in the Emergency Department

We undertook a process improvement initiative to expedite rapid identification of potential sepsis patients based on triage chief complaint, vital signs, and initial lactate level.

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

OBJECTIVE

We investigated the effects of a quick Sequential Organ Failure Assessment (qSOFA)-negative result (qSOFA score <2 points) at triage on the compliance with sepsis bundles among patients with sepsis who presented to the emergency department (ED).

METHODS

Prospective sepsis registry data from 11 urban tertiary hospital EDs between October 2015 and April 2018 were retrospectively reviewed. Patients who met the Third International Consensus Definitions for Sepsis and Septic Shock criteria were included. Primary exposure was defined as a qSOFA score ≥2 points at ED triage. The primary outcome was defined as 3-hour bundle compliance, including lactate measurement, blood culture, broad-spectrum antibiotics administration, and 30 mL/kg crystalloid administration. Multivariate logistic regression analysis to predict 3-hour bundle compliance was performed.

RESULTS

Among the 2,250 patients enrolled in the registry, 2,087 fulfilled the sepsis criteria. Only 31.4% (656/2,087) of the sepsis patients had qSOFA scores ≥2 points at triage. Patients with qSOFA scores <2 points had lower lactate levels, lower SOFA scores, and a lower 28-day mortality rate. Rates of compliance with lactate measurement (adjusted odds ratio [aOR], 0.47; 95% confidence interval [CI], 0.29-0.75), antibiotics administration (aOR, 0.64; 95% CI, 0.52-0.78), and 30 mL/kg crystalloid administration (aOR, 0.62; 95% CI, 0.49-0.77) within 3 hours from triage were significantly lower in patients with qSOFA scores <2 points. However, the rate of compliance with blood culture within 3 hours from triage (aOR, 1.66; 95% CI, 1.33-2.08) was higher in patients with qSOFA scores <2 points.

CONCLUSION

A qSOFA-negative result at ED triage is associated with low compliance with lactate measurement, broad-spectrum antibiotics administration, and 30 mL/kg crystalloid administration within 3 hours in sepsis patients.

Development and validation of a comprehensive early risk prediction model for patients with undifferentiated acute chest pain

Aims

Existing risk scores for undifferentiated chest pain focus on excluding coronary events and do not represent a comprehensive risk assessment if an alternate serious diagnosis is present. This study aimed to develop and validate an all-inclusive risk prediction model among patients with undifferentiated chest pain.

Methods

We developed and validated a multivariable logistic regression model for a composite measure of early all-inclusive risk (defined as hospital admission excluding a discharge diagnosis of non-specific pain, 30-day all-cause mortality, or 30-day myocardial infarction [MI]) among adults assessed by emergency medical services (EMS) for non-traumatic chest pain using a large population-based cohort (January 2015 to June 2019). The cohort was randomly divided into development (146,507 patients [70%]) and validation (62,788 patients [30%]) cohorts.

Results

The composite outcome occurred in 28.4%, comprising hospital admission in 27.7%, mortality within 30-days in 1.8%, and MI within 30-days in 0.4%. The Early Chest pain Admission, MI, and Mortality (ECAMM) risk model was developed, demonstrating good discrimination in the development (C-statistic 0.775, 95% CI 0.772-0.777) and validation cohorts (C-statistic 0.765, 95% CI 0.761-0.769) with excellent calibration. Discriminatory performance for the composite outcome and individual components was higher than existing scores commonly used in undifferentiated chest pain risk stratification.

Conclusions

The ECAMM risk score model can be used as an all-inclusive risk stratification assessment of patients with non-traumatic chest pain without the limitation of a single diagnostic outcome. This model could be clinically useful to help guide decisions surrounding the need for non-coronary investigations and safety of early discharge.

Procalcitonin prognostic value in predicting mortality among adult patients with sepsis due to Gram-negative bacteria

BACKGROUND Sepsis is a leading cause of mortality and morbidity globally. Gram-negative bacteremia was reported to have a high risk of septic shock and poor prognosis. This study aimed to evaluate the role of procalcitonin in predicting mortality in patients with sepsis due to Gram-negative bacteria. METHODS This was a retrospective cohort study performed based on medical records and sepsis registry of Tropical and Infectious Disease Division, Department of Internal Medicine, Cipto Mangunkusumo Hospital. The inclusion criteria were patients aged ≥18 years diagnosed with sepsis due to Gram-negative bacteria based on blood culture on admission and hospitalized between March 2017 and October 2020. Data taken from medical records included subjects’ characteristics, laboratory parameters, and 28-day mortality outcomes during hospitalization. Receiver operating characteristic was used to determine the area under the curve (AUC) of procalcitonin and its accuracy. RESULTS A total of 128 patients were eligible. The cumulative survival of patients with Gram-negative bacteremia was 48.4% (standard error 0.96%). The AUC of procalcitonin to predict mortality was 0.45 (95% confidence interval 0.36–0.54). Escherichia coli was the predominant microorganism in blood culture (n = 38, 29.7%). CONCLUSIONS Procalcitonin has a poor performance in predicting mortality of patients with sepsis due to Gram-negative bacteria.

Comparison of different versions of the quick sequential organ failure assessment for predicting in-hospital mortality of sepsis patients: A retrospective observational study

BACKGROUND

The quick sequential organ failure assessment (qSOFA) is recommended to identify sepsis and predict sepsis mortality. However, some studies have recently shown its poor performance in sepsis mortality prediction. To enhance its effectiveness, researchers have developed various revised versions of the qSOFA by adding other parameters, such as the lactate-enhanced qSOFA (LqSOFA), the procalcitonin-enhanced qSOFA (PqSOFA), and the modified qSOFA (MqSOFA). This study aimed to compare the performance of these versions of the qSOFA in predicting sepsis mortality in the emergency department (ED).

METHODS

This retrospective study analyzed data obtained from an electronic register system of adult patients with sepsis between January 1 and December 31, 2019. Receiver operating characteristic (ROC) curve analyses were performed to determine the area under the curve (AUC), with sensitivity, specificity, and positive and negative predictive values calculated for the various scores.

RESULTS

Among the 936 enrolled cases, there were 835 survivors and 101 deaths. The AUCs of the LqSOFA, MqSOFA, PqSOFA, and qSOFA were 0.740, 0.731, 0.712, and 0.705, respectively. The sensitivity of the LqSOFA, MqSOFA, PqSOFA, and qSOFA were 64.36%, 51.40%, 71.29%, and 39.60%, respectively. The specificity of the four scores were 70.78%, 80.96%, 61.68%, and 91.62%, respectively. The LqSOFA and MqSOFA were superior to the qSOFA in predicting in-hospital mortality.

CONCLUSIONS

Among patients with sepsis in the ED, the performance of the PqSOFA was similar to that of the qSOFA and the values of the LqSOFA and MqSOFA in predicting in-hospital mortality were greater compared to qSOFA. As the added parameter of the MqSOFA was more convenient compared to the LqSOFA, the MqSOFA could be used as a candidate for the revised qSOFA to increase the performance of the early prediction of sepsis mortality.

Sepsis of Patients Infected by SARS-CoV-2: Real-World Experience From the International HOPE-COVID-19-Registry and Validation of HOPE Sepsis Score

Background: Patients with sepsis with a concomitant coronavirus (COVID-19) infection are related to a high morbidity and mortality rate. We investigated a large cohort of patients with sepsis with a concomitant COVID-19, and we developed a risk score for the estimation of sepsis risk in COVID-19. Methods: We conducted a sub-analysis from the international Health Outcome Predictive Evaluation Registry for COVID-19 (HOPE-COVID-19-Registry, NCT04334291). Out of 5,837 patients with COVID-19, 624 patients were diagnosed with sepsis according to the Sepsis-3 International Consensus. Results: In multivariable analysis, the following risk factors were identified as independent predictors for developing sepsis: current smoking, tachypnoea (>22 breath per minute), hemoptysis, peripheral oxygen saturation (SpO₂) <92%, blood pressure (BP) (systolic BP <90 mmHg and diastolic BP <60 mmHg), Glasgow Coma Scale (GCS) <15, elevated procalcitonin (PCT), elevated troponin I (TnI), and elevated creatinine >1.5 mg/dl. By assigning odds ratio (OR) weighted points to these variables, the following three risk categories were defined to develop sepsis during admission: low-risk group (probability of sepsis 3.1-11.8%); intermediate-risk group (24.8-53.8%); and high-risk-group (58.3-100%). A score of 1 was assigned to current smoking, tachypnoea, decreased SpO₂, decreased BP, decreased GCS, elevated PCT, TnI, and creatinine, whereas a score of 2 was assigned to hemoptysis. Conclusions: The HOPE Sepsis Score including nine parameters is useful in identifying high-risk COVID-19 patients to develop sepsis. Sepsis in COVID-19 is associated with a high mortality rate.