Quick Sepsis-related Organ Failure Assessment predicts 72-h mortality in patients with suspected infection

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities

OBJECTIVE

In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score.

DESIGN

This retrospective cohort study was conducted between 2016 and 2021.

SETTING

Two university hospitals in Brazil.

PARTICIPANTS

Patients with sepsis.

INTERVENTIONS

Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities.

MAIN VARIABLE OF INTEREST

In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis.

RESULTS

A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction.

CONCLUSIONS

Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings

BACKGROUND

There is conflicting evidence on association between quick sequential organ failure assessment (qSOFA) and sepsis mortality in ICU patients. The primary aim of this study was to determine the association between qSOFA and 28-day mortality in ICU patients admitted for sepsis. Association of qSOFA with early (3-day), medium (28-day), late (90-day) mortality was assessed in low and lower middle income (LLMIC), upper middle income (UMIC) and high income (HIC) countries/regions.

METHODS

This was a secondary analysis of the MOSAICS II study, an international prospective observational study on sepsis epidemiology in Asian ICUs. Associations between qSOFA at ICU admission and mortality were separately assessed in LLMIC, UMIC and HIC countries/regions. Modified Poisson regression was used to determine the adjusted relative risk (RR) of qSOFA score on mortality at 28 days with adjustments for confounders identified in the MOSAICS II study.

RESULTS

Among the MOSAICS II study cohort of 4980 patients, 4826 patients from 343 ICUs and 22 countries were included in this secondary analysis. Higher qSOFA was associated with increasing 28-day mortality, but this was only observed in LLMIC (p < 0.001) and UMIC (p < 0.001) and not HIC (p = 0.220) countries/regions. Similarly, higher 90-day mortality was associated with increased qSOFA in LLMIC (p < 0.001) and UMIC (p < 0.001) only. In contrast, higher 3-day mortality with increasing qSOFA score was observed across all income countries/regions (p < 0.001). Multivariate analysis showed that qSOFA remained associated with 28-day mortality (adjusted RR 1.09 (1.00-1.18), p = 0.038) even after adjustments for covariates including APACHE II, SOFA, income country/region and administration of antibiotics within 3 h.

CONCLUSIONS

qSOFA was independently associated with 28-day mortality in ICU patients admitted for sepsis. In LLMIC and UMIC countries/regions, qSOFA was associated with early to late mortality but only early mortality in HIC countries/regions.

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.

The Value of Neutrophil/Lymphocyte Ratio Combined with Red Blood Cell Distribution Width in Evaluating the Prognosis of Emergency Patients with Sepsis

Sepsis is a dysfunction of various organs caused by a dysfunctional host response induced by infection. In recent years, the mortality rate of sepsis patients, especially the mortality rate of septic shock patients still remains high. Due to the complexity and heterogeneity of sepsis, there is currently a lack of clinical biomarkers that can be widely used for the early assessment of sepsis. In order to find more concise and accurate biomarkers for timely and adequate intervention in sepsis, we explored the value of neutrophil/lymphocyte ratio (NLR) combined with red blood cell distribution width (RDW) in assessing the prognosis of emergency sepsis patients. The results showed that NLR and RDW were closely related to the prognosis of emergency sepsis patients. The combination of the two can evaluate the prognosis of patients with emergency sepsis, which deserves close attention from clinicians.

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department

BACKGROUND AND IMPORTANCE

mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis.

OBJECTIVE

Validation of the host-mRNA infection severity classifier IMX-SEV-2.

DESIGN, SETTINGS AND PARTICIPANTS

Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections.

OUTCOME MEASURES AND ANALYSIS

Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure.

MAIN RESULTS

Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P  = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86).

CONCLUSION

The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.

Early warning model for death of sepsis via length insensitive temporal convolutional network

Sepsis is a life-threatening systemic syndrome characterized by various biological, biochemical, and physiological abnormalities. Due to its high mortality, identifying sepsis patients with high risk of in-hospital death early and accurately will help doctors make optimal clinical decisions and reduce the mortality of sepsis patients. In this paper, we propose a length insensitive TCN-based model to predict sepsis patient's death risk in the future k hours, which is the first work for sepsis death risk early warning model only based on vital signs time series to our best knowledge. Furthermore, we design residual connections between temporal residual blocks to improve the prediction performance and stability especially on short input sequences. We validate and evaluate our model on two freely-available datasets, i.e., MIMIC-IV and eICU, from which 16,520 and 29,620 patients are selected respectively. The experiment results show that our model outperforms LSTM and other machine learning methods, as it has the highest sensitivity and Youden index in almost all cases. Meanwhile, the Youden index of the TCN-based model only slightly decreases by 0.0233 and 0.0307 when the time range of the input sequence changes from 24 to 4 h for k equal to 6 and 12, respectively.

The timing of use of risk stratification tools affects their ability to predict mortality from sepsis. A meta-regression analysis

Background: Risk stratification tools (RSTs) are used in healthcare settings to identify patients at risk of sepsis and subsequent adverse outcomes. In practice RSTs are used on admission and thereafter as ‘trigger’ tools prompting sepsis management. However, studies investigating their performance report scores at a single timepoint which varies in relation to admission. The aim of this meta-analysis was to determine if the predictive performance of RSTs is altered by the timing of their use. Methods: We conducted a systematic review and meta-regression analysis of studies published from inception to 31 October 2018, using EMBASE and PubMed databases. Any cohort studies investigating the ability of an RST to predict mortality in adult sepsis patients admitted to hospital, from which a 2x2 table was available or could be constructed, were included. The diagnostic performance of RSTs in predicting mortality was the primary outcome. Sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver-operating curve (AUROC) were the primary measures, enabling further meta-regression analysis. Results: 47 studies were included, comprising 430,427 patients. Results of bivariate meta-regression analysis found tools using a first-recorded score were less sensitive than those using worst-recorded score (REML regression coefficient 0.57, 95% CI 0.07-1.08). Using worst-recorded score led to a large increase in sensitivity (summary sensitivity 0.76, 95% CI 0.67-0.83, for worst-recorded scores vs. 0.64 (0.57-0.71) for first-recorded scores). Scoring system type did not have a significant relationship with studies’ predictive ability. The most analysed RSTs were qSOFA (n=37) and EWS (n=14). Further analysis of these RSTs also found timing of their use to be associated with predictive performance. Conclusion: The timing of any RST is paramount to their predictive performance. This must be reflected in their use in practice, and lead to prospective studies in future.

Quick sequential organ failure assessment versus systemic inflammatory response syndrome criteria for emergency department patients with suspected infection

Previous studies have shown inconsistent prognostic accuracy for mortality with both quick sequential organ failure assessment (qSOFA) and the systemic inflammatory response syndrome (SIRS) criteria. We aimed to validate the accuracy of qSOFA and the SIRS criteria for predicting in-hospital mortality in patients with suspected infection in the emergency department. A prospective study was conducted including participants with suspected infection who were hospitalised or died in 34 emergency departments in Japan. Prognostic accuracy of qSOFA and SIRS criteria for in-hospital mortality was assessed by the area under the receiver operating characteristic (AUROC) curve. Of the 1060 participants, 402 (37.9%) and 915 (86.3%) had qSOFA ≥ 2 and SIRS criteria ≥ 2 (given thresholds), respectively, and there were 157 (14.8%) in-hospital deaths. Greater accuracy for in-hospital mortality was shown with qSOFA than with the SIRS criteria (AUROC: 0.64 versus 0.52, difference + 0.13, 95% CI [+ 0.07, + 0.18]). Sensitivity and specificity for predicting in-hospital mortality at the given thresholds were 0.55 and 0.65 based on qSOFA and 0.88 and 0.14 based on SIRS criteria, respectively. To predict in-hospital mortality in patients visiting to the emergency department with suspected infection, qSOFA was demonstrated to be modestly more accurate than the SIRS criteria albeit insufficiently sensitive.Clinical Trial Registration: The study was pre-registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000027258).

Does inappropriate initial antibiotic therapy affect in-hospital mortality of patients in the emergency department with Escherichia coli and Klebsiella pneumoniae bloodstream infections?

Extended-spectrum β-lactamase (ESBL)-positive bloodstream infection (BSI) is on the rise worldwide. The purpose of this study is to evaluate the impact of inappropriate initial antibiotic therapy (IIAT) on in-hospital mortality of patients in the emergency department (ED) with Escherichia coli and Klebsiella pneumoniae BSIs. This retrospective single-center cohort study included all adult patients with E. coli and K. pneumoniae BSIs between January 2007 and December 2013, who had undergone a blood culture test and initiation of antibiotics within 6 h of ED registration time. Multiple logistic regression was used to adjust for bacterial species, IIAT, time to antibiotics, age, sex, quick Sepsis Related Organ Failure Assessment (qSOFA) score ⩾ 2, and comorbidities. A total of 3533 patients were enrolled (2967 alive and 566 deceased, in-hospital mortality rate 16%). The patients with K. pneumoniae ESBL-positive BSI had the highest mortality rate. Non-survivors had qSOFA scores ⩾ 2 (33.6% vs 9.5%, P < 0.001), more IIAT (15.0% vs 10.7%, P = 0.004), but shorter mean time to antibiotics (1.70 vs 1.84 h, P < 0.001). A qSOFA score ⩾ 2 is the most significant predictor for in-hospital mortality; however, IIAT and time to antibiotics were not significant predictors in multiple logistic regression analysis. In subgroup analysis divided by qSOFA scores, IIAT was still not a significant predictor. Severity of the disease (qSOFA score ⩾ 2) is the key factor influencing in-hospital mortality of patients with E. coli and K. pneumoniae BSIs. The time to antibiotics and IIAT were not significant predictors because they in turn were affected by disease severity.

Emergency Severity Index as a predictor of in-hospital mortality in suspected sepsis patients in the emergency department

OBJECTIVES

To demonstrate the accuracy, sensitivity, and specificity of the Emergency Severity Index (ESI), quick Sepsis-related Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS) criteria, and National Early Warning Score (NEWS) for predicting in-hospital mortality and intensive care unit (ICU) admission in suspected sepsis patients.

METHODS

A retrospective cohort study conducted at a tertiary care hospital, Thailand. Suspected sepsis was defined by a combination of (1) hemoculture collection and (2) the initiation of intravenous antibiotics therapy during the emergency department (ED) visit. The accuracy of each scoring system for predicting in-hospital mortality and ICU admission was analyzed.

RESULTS

A total of 8177 patients (median age: 62 years, 52.3% men) were enrolled in the study, 509 (6.2%) of whom died and 1810 (22.1%) of whom were admitted to the ICU. The ESI and NEWS had comparable accuracy for predicting in-hospital mortality (AUC of 0.70, 95% confidence interval [CI] 0.68 to 0.73 and AUC of 0.73, 95% CI 0.70 to 0.75) and ICU admission (AUC of 0.75, 95% CI 0.74 to 0.76 and AUC of 0.74, 95% CI 0.72 to 0.75). The ESI level 1-2 had the highest sensitivity for predicting in-hospital mortality (96.7%), and qSOFA ≥2 had the highest specificity (86.6%).

CONCLUSION

The ESI was accurate and had the highest sensitivity for predicting in-hospital mortality and ICU admission in suspected sepsis patients in the ED. This confirms that the ESI is useful in both ED triage and predicting adverse outcomes in these patients.

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

Quantitative capillary refill time predicts sepsis in patients with suspected infection in the emergency department: an observational study

Background

Outcomes in emergent patients with suspected infection depend on how quickly clinicians evaluate the patients and start treatment. This study was performed to compare the predictive ability of the quantitative capillary refill time (Q-CRT) as a new rapid index versus the quick sequential organ failure assessment (qSOFA) score and the systemic inflammatory response syndrome (SIRS) score for sepsis screening in the emergency department.

Methods

This was a multicenter, observational, retrospective study of adult patients with suspected infection. The area under the curve (AUC) of receiver operating characteristic curve analyses and multivariate analyses were used to explore associations of the Q-CRT with the qSOFA score, SIRS score, and lactate concentration.

Results

Of the 75 enrolled patients, 48 had sepsis. The AUC, sensitivity, and specificity of Q-CRT were 0.74, 58%, and 81%, respectively; those for the qSOFA score were 0.83, 66%, and 100%, respectively; those for the SIRS score were 0.61, 81%, and 40%, respectively, for SIRS score; and those for the lactate concentration were 0.76, 72%, and 81%, respectively. We found no statistically significant differences in the AUC between the scores. We then combined the Q-CRT and qSOFA score (Q-CRT/qSOFA combination) for sepsis screening. The AUC, sensitivity, and specificity of Q-CRT/qSOFA combination were 0.82, 83%, and 81%, respectively.

Conclusions

In this study, Q-CRT/qSOFA combination had better sensitivity than the qSOFA score alone and better specificity than the SIRS score alone. There was no significant difference in accuracy between Q-CRT/qSOFA combination and the qSOFA score or lactate concentration. The ability of the Q-CRT to predict sepsis may be similar to that of the qSOFA score or serum lactate concentration; therefore, measurement of the Q-CRT may be an alternative for invasive measurement of the blood lactate concentration in evaluating patients with suspected sepsis.

Electronic supplementary material

The online version of this article (10.1186/s40560-019-0382-4) contains supplementary material, which is available to authorized users.

Metformin Affects Serum Lactate Levels in Predicting Mortality of Patients with Sepsis and Bacteremia

This study determined if the use of metformin affected the prognostic value of hyperlactatemia in predicting 28-day mortality among patients with sepsis and bacteremia. We enrolled adult diabetic patients with sepsis and bacteremia. Of 590 patients, 162 and 162 metformin users and nonusers, respectively, were selected in propensity matching. The mean serum lactate levels in metformin users were higher than those in nonusers (4.7 vs. 3.9 mmol/L, p = 0.044). We divided the patients into four groups based on quick Sepsis-related Organ Failure Assessment (qSOFA) scores. No significant difference was found among nonusers with qSOFA score <2, nonusers with qSOFA score ≥2, and metformin users with qSOFA score <2. The lactate levels in metformin users with qSOFA score ≥2 were higher than those in other groups, and significant differences were found in both nonsurvivors (8.9 vs. 4.6 mmol/L, p = 0.027) and survivors (6.4 vs. 3.8 mmol/L, p = 0.049) compared with metformin users with qSOFA score <2. The best cut-off point to predict 28-day mortality in metformin users (5.9 mmol/L; area under the receiver operating characteristic curve (AUROC), 0.66; 95% confidence interval (CI), 0.55⁻0.77) was higher than that in nonusers (3.6 mmol/L; AUROC 0.63; 95% CI, 0.56⁻0.70). Metformin users had higher lactate levels than nonusers in increasing sepsis severity. Serum lactate levels could be useful in predicting mortality in patients using metformin, but higher levels are required to obtain more precise results.