Comparison of severity of illness scoring systems in the prediction of hospital mortality in severe sepsis and septic shock

Unraveling the Predictive Potential of Rapid Scoring in Pleural Infection: A Critical Review

Pleural infection, or pleural empyema, is a severe medical condition associated with high morbidity and mortality rates. Timely and accurate prognostication is crucial for optimizing patient outcomes and resource allocation. Rapid scoring systems have emerged as promising tools in pleural infection prognostication, integrating various clinical and laboratory parameters to assess disease severity and quantitatively predict short-term and long-term outcomes. This review article critically evaluates existing rapid scoring systems, including CURB-65 (confusion, uremia, respiratory rate, blood pressure, age ≥ 65 years), A-DROP (age (male >70 years, female >75 years), dehydration, respiratory failure, orientation disturbance, and low blood pressure), and APACHE II (acute physiology and chronic health evaluation II), assessing their predictive accuracy and limitations. Our analysis highlights the potential clinical implications of rapid scoring, including risk stratification, treatment tailoring, and follow-up planning. We discuss practical considerations and challenges in implementing rapid scoring such as data accessibility and potential sources of bias. Furthermore, we emphasize the importance of validation, transparency, and multidisciplinary collaboration to refine and enhance the clinical applicability of these scoring systems. The prospects for rapid scoring in pleural infection management are promising, with ongoing research and data science advances offering improvement opportunities. Ultimately, the successful integration of rapid scoring into clinical practice can potentially improve patient care and outcomes in pleural infection management.

The Accuracy of Rapid Emergency Medicine Score in Predicting Mortality in Non-Surgical Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Emergency department (ED) physicians often need to quickly assess patients and determine vital signs to prioritize them by the severity of their condition and make optimal treatment decisions. Effective triage requires optimal scoring systems to accelerate and positively influence the treatment of trauma cases. To this end, a variety of scoring systems have been developed to enable rapid assessment of ED patients. The present systematic review and meta-analysis aimed to investigate the accuracy of the rapid emergency medicine score (REMS) system in predicting the mortality rate in non-surgical ED patients.

METHODS

A systematic search of articles published between 1990 and 2020 was conducted using various scientific databases (Medline, Embase, Scopus, Web of Science, ProQuest, Cochrane Library, IranDOC, Magiran, and Scientific Information Database). Both cross-sectional and cohort studies assessing the REMS system to predict mortality in ED settings were considered. Two reviewers appraised the selected articles independently using the National Institutes of Health (NIH) quality assessment tool. The random-effects model was used for meta-analysis. I² index and Q statistic were used to examine heterogeneity between the articles.

RESULTS

The search resulted in 1,310 hits from which, 29 articles were eventually selected. Out of these, for 25 articles, the area under the curve value of REMS ranged from 0.52 to 0.986. The predictive power of REMS for the in-hospital mortality rate was high in 19 articles (67.85%) and low in nine articles (32.15%).

CONCLUSION

The results showed that the REMS system is an effective tool to predict mortality in non-surgical patients presented to the ED. However, further evidence using high-quality design studies is required to substantiate our findings.

The Value of Physiological Scoring Criteria in Predicting the In-Hospital Mortality of Acute Patients; a Systematic Review and Meta-Analysis

Introduction:

There is no comprehensive meta-analysis on the value of physiological scoring systems in predicting the mortality of critically ill patients. Therefore, the present study intended to conduct a systematic review and meta-analysis to collect the available clinical evidence on the value of physiological scoring systems in predicting the in-hospital mortality of acute patients.

Method:

An extensive search was performed on Medline, Embase, Scopus, and Web of Science databases until the end of year 2020. Physiological models included Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), modified REMS (mREMS), and Worthing Physiological Score (WPS). Finally, the data were summarized and the findings were presented as summary receiver operating characteristics (SROC), sensitivity, specificity and diagnostic odds ratio (DOR).

Results:

Data from 25 articles were included. The overall analysis showed that the area under the SROC curve of REMS, RAPS, mREMS, and WPS criteria were 0.83 (95% CI: 0.79-0.86), 0.89 (95% CI: 0.86-0.92), 0.64 (95% CI: 0.60-0.68) and 0.86 (95% CI: 0.83-0.89), respectively. DOR for REMS, RAPS, mREMS and WPS models were 11 (95% CI: 8-16), 13 (95% CI: 4-41), 2 (95% CI: 2-4) and 17 (95% CI: 5-59) respectively. When analyses were limited to trauma patients, the DOR of the REMS and RAPS models were 112 and 431, respectively. Due to the lack of sufficient studies, it was not possible to limit the analyses for mREMS and WPS.

Conclusion:

The findings of the present study showed that three models of RAPS, REMS and WPS have a high predictive value for in-hospital mortality. In addition, the value of these models in trauma patients is much higher than other patient settings.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Introduction

Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.

Methods

A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.

Results

A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80–0.84) and 0.84 (95% CI: 0.81–0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78–0.92) compared to abbMEDS (0.63,0.54–0.73), mREMS (0.63,0.54–0.72) and internal medicine physicians (0.74,0.65–0.82).

Conclusion

Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

Performance of the MEDS score in predicting mortality among emergency department patients with a suspected infection: a meta-analysis

OBJECTIVES

To carry out a meta-analysis to examine the prognostic performance of the Mortality in Emergency Department Sepsis (MEDS) score in predicting mortality among emergency department patients with a suspected infection.

METHODS

Electronic databases-PubMed, Embase, Scopus, EBSCO and the Cochrane Library-were searched for eligible articles from their respective inception through February 2019. Sensitivity, specificity, likelihood ratios and receiver operator characteristic area under the curve were calculated. Subgroup analyses were performed to explore the prognostic performance of MEDS in selected populations.

RESULTS

We identified 24 studies involving 21 246 participants. The pooled sensitivity of MEDS to predict mortality was 79% (95% CI 72% to 84%); specificity was 74% (95% CI 68% to 80%); positive likelihood ratio 3.07 (95% CI 2.47 to 3.82); negative likelihood ratio 0.29 (95% CI 0.22 to 0.37) and area under the curve 0.83 (95% CI 0.80 to 0.86). Significant heterogeneity was seen among included studies. Meta-regression analyses showed that the time at which the MEDS score was measured and the cut-off value used were important sources of heterogeneity.

CONCLUSION

The MEDS score has moderate accuracy in predicting mortality among emergency department patients with a suspected infection. A study comparison MEDS and qSOFA in the same population is needed.

The Rapid Emergency Medicine Score: A Critical Appraisal of Its Measurement Properties and Applicability to the Air Retrieval Environment

OBJECTIVE

The Rapid Emergency Medicine Score (REMS) was designed to predict in-hospital mortality using variables that are available in the prehospital setting. The objective of this article is to critically appraise the development and summarize the evidence regarding the measurement properties (sensitivity, reliability and validity) of the REMS.

METHODS

A literature search was performed identifying all studies describing the REMS. The original validation study was critically appraised for its development. All other studies that reported any measurement properties of the REMS were also appraised for evidence of calibration, reliability, and validity.

RESULTS

In total, 26 studies reported on the measurement properties of the REMS. Overall, the REMS was developed with robust methodology and has good sensibility with adequate content and face validity. It is easy to understand and feasible to be calculated within minutes of patient assessment. The REMS has the necessary measurement properties to be both a predictive and evaluative clinical index to measure prehospital severity of illness; however, no studies have adequately addressed the intra or inter-rater reliability of the score.

CONCLUSIONS

There is evidence to support the use of the REMS as a predictive or evaluative instrument. In most studies, it performed as well or better than other illness severity scores in predicting mortality.

Comparison of SIRS, qSOFA, and NEWS for the early identification of sepsis in the Emergency Department

OBJECTIVES

The increasing use of sepsis screening in the Emergency Department (ED) and the Sepsis-3 recommendation to use the quick Sepsis-related Organ Failure Assessment (qSOFA) necessitates validation. We compared Systemic Inflammatory Response Syndrome (SIRS), qSOFA, and the National Early Warning Score (NEWS) for the identification of severe sepsis and septic shock (SS/SS) during ED triage.

METHODS

This was a retrospective analysis from an urban, tertiary-care academic center that included 130,595 adult visits to the ED, excluding dispositions lacking adequate clinical evaluation (n = 14,861, 11.4%). The SS/SS group (n = 930) was selected using discharge diagnoses and chart review. We measured sensitivity, specificity, and area under the receiver-operating characteristic (AUROC) for the detection of sepsis endpoints.

RESULTS

NEWS was most accurate for triage detection of SS/SS (AUROC = 0.91, 0.88, 0.81), septic shock (AUROC = 0.93, 0.88, 0.84), and sepsis-related mortality (AUROC = 0.95, 0.89, 0.87) for NEWS, SIRS, and qSOFA, respectively (p < 0.01 for NEWS versus SIRS and qSOFA). For the detection of SS/SS (95% CI), sensitivities were 84.2% (81.5-86.5%), 86.1% (83.6-88.2%), and 28.5% (25.6-31.7%) and specificities were 85.0% (84.8-85.3%), 79.1% (78.9-79.3%), and 98.9% (98.8-99.0%) for NEWS ≥ 4, SIRS ≥ 2, and qSOFA ≥ 2, respectively.

CONCLUSIONS

NEWS was the most accurate scoring system for the detection of all sepsis endpoints. Furthermore, NEWS was more specific with similar sensitivity relative to SIRS, improves with disease severity, and is immediately available as it does not require laboratories. However, scoring NEWS is more involved and may be better suited for automated computation. QSOFA had the lowest sensitivity and is a poor tool for ED sepsis screening.

A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department

A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.

Impact of a Combination Antibiotic Bag on Compliance With Surviving Sepsis Campaign Goals in Emergency Department Patients With Severe Sepsis and Septic Shock

BACKGROUND

Severe sepsis and septic shock represent common presentations in the emergency department (ED) and have high rates of mortality. Guideline-recommended goals of care have been shown to benefit these patients, but can be difficult to provide.

OBJECTIVE

To determine whether the use of a premixed bag consisting of 2 g cefepime and 1 g vancomycin in 1000 mL of normal saline increases the probability of patients receiving Surviving Sepsis Campaign (SSC) recommendations for the initiation of antimicrobials and fluid challenge.

METHODS

This was a 6-month retrospective analysis conducted to determine the impact of an intervention on time to antimicrobials and fluid administration in patients with severe sepsis and septic shock. Patients presenting to the ED who received a diagnosis of severe sepsis or septic shock and were administered 2 antibiotics were eligible for inclusion. The primary outcome assessed was compliance with SSC recommendations for antibiotic and fluid goals within 3 hours of ED arrival.

RESULTS

A total of 160 patients were included. In the intervention group, 63.8% of patients met the primary outcome compared with 22.5% in the historical group (odds ratio = 2.32; 95% CI = 1.67-3.23). Time to administration of antibiotics was less with the combination antibiotic bag (CAB: median (IQR) = 72 (48-115) minutes; non-CAB: median (IQR) = 135 (102-244) minutes; P ≤ 0.001).

CONCLUSION

This intervention significantly increased the proportion of patients provided with SSC goals of care. Such interventions have not been reported previously and could be meaningful in the management of severe sepsis and septic shock.

Early goal-directed therapy in severe sepsis and septic shock: insights and comparisons to ProCESS, ProMISe, and ARISE

Prior to 2001 there was no standard for early management of severe sepsis and septic shock in the emergency department. In the presence of standard or usual care, the prevailing mortality was over 40-50 %. In response, a systems-based approach, similar to that in acute myocardial infarction, stroke and trauma, called early goal-directed therapy was compared to standard care and this clinical trial resulted in a significant mortality reduction. Since the publication of that trial, similar outcome benefits have been reported in over 70 observational and randomized controlled studies comprising over 70,000 patients. As a result, early goal-directed therapy was largely incorporated into the first 6 hours of sepsis management (resuscitation bundle) adopted by the Surviving Sepsis Campaign and disseminated internationally as the standard of care for early sepsis management. Recently a trio of trials (ProCESS, ARISE, and ProMISe), while reporting an all-time low sepsis mortality, question the continued need for all of the elements of early goal-directed therapy or the need for protocolized care for patients with severe and septic shock. A review of the early hemodynamic pathogenesis, historical development, and definition of early goal-directed therapy, comparing trial conduction methodology and the changing landscape of sepsis mortality, are essential for an appropriate interpretation of these trials and their conclusions.

Predictive performance of quick Sepsis-related Organ Failure Assessment for mortality and ICU admission in patients with infection at the ED

OBJECTIVE

The objectives of this study are to investigate the performance of the quick Sepsis-related Organ Failure Assessment (qSOFA) in predicting mortality and intensive care unit (ICU) admission in patients with clinically diagnosed infection and to compare its performance with that of Mortality in Emergency Department Sepsis (MEDS), Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sepsis-related Organ Failure Assessment (SOFA).

METHODS

From July to December 2015, we retrospectively analyzed 477 patients clinically diagnosed with infection in the emergency department. We compared the performance of SOFA, MEDS, APACHE II, and qSOFA in predicting ICU admission and 28-day mortality.

RESULTS

All scores were higher in nonsurvivors and ICU patients than in survivors and non-ICU patients (P< .001). The area under the receiver operating characteristic curve of qSOFA was lower than that of MEDS (0.666 vs 0.751; P< .05) and similar to that of SOFA (0.729) and APACHE II (0.732) in predicting 28-day mortality. The areas under the receiver operating characteristic curve of qSOFA, SOFA, MEDS, and APACHE II in predicting ICU admission were 0.636, 0.682, 0.661, and 0.640, respectively. There were no significant differences among the score systems. In patients with qSOFA scores less than 2 and greater than or equal to 2, 28-day mortality rates were 17.4% and 42.9% (P< .001), and ICU admission rates were 16.0% and 33.3% (P< .001).

CONCLUSIONS

Quick SOFA predicted ICU admission with similar performance to that of SOFA, MEDS, and APACHE II. Its prognostic ability was similar to that of SOFA and APACHE II but slightly inferior to that of MEDS.

Cardiac Troponin Is a Predictor of Septic Shock Mortality in Cancer Patients in an Emergency Department: A Retrospective Cohort Study

Background

Septic shock may be associated with myocardial damage; however, the prognostic value of cardiac enzymes in cancer patients with septic shock is unknown. In this study, we evaluated the prognostic significance of cardiac enzymes in combination with established prognostic factors in predicting the 7-day mortality rate of patients with septic shock, and we constructed a new scoring system, Septic Oncologic Patients in Emergency Department (SOPED), which includes cardiac enzymes, to predict 7-day mortality rates.

Methods and Findings

We performed a retrospective cohort study of 375 adult cancer patients with septic shock who visited the emergency department of a comprehensive cancer center between 01/01/2004 and 12/31/2013. The 7-day and 28-day mortality rates were 19.7% and 37.6%, respectively. The creatine kinase myocardial band fraction and troponin-I were significantly higher in patients who died in ≤7 days and ≤28 days than in those who did not. In Cox regression models, troponin-I >0.05 ng/mL plus Predisposition, Infection, Response, and Organ Failure (PIRO2011) or Mortality in Emergency Department Sepsis (MEDS) score was a significant predictor of survival for ≤7 days. With our new SOPED scoring system, the receiver operating characteristic area under the curve was 0.836, higher than those for PIRO2011 and MEDS.

Conclusions

Troponin-I >0.05 ng/mL was an important predictor of short-term mortality (≤7 days). The SOPED scoring system, which incorporated troponin-I, was more prognostically accurate than were other scores for 7-day mortality. Large multicenter studies are needed to verify our results and prospectively validate the prognostic performance of the SOPED score.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

OBJECTIVES

Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case.

METHODS

This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics.

RESULTS

There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons).

CONCLUSIONS

In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions.

Clinical deterioration in older adults with delirium during early hospitalisation: a prospective cohort study

OBJECTIVES

To measure the prevalence and incidence of delirium in older adults as they transition from the emergency department (ED) to the inpatient ward, and to determine the association between delirium during early hospitalisation and subsequent clinical deterioration.

DESIGN

Prospective cohort study.

SETTING

Urban tertiary care hospital in Bronx, New York.

PARTICIPANTS

Adults aged 65 years or older admitted to the inpatient ward from the ED (n=260).

MEASUREMENTS

Beginning in the ED, delirium was assessed daily for 3 days, using the Confusion Assessment Method for the Intensive Care Unit.

OUTCOMES

(1) Clinical deterioration, defined as unanticipated intensive care unit (ICU) admission or in-hospital death (primary outcome); (2) decline in discharge status, defined as discharge to higher level of care, hospice or in-hospital death.

RESULTS

38 of 260 participants (15%) were delirious at least once during the first 3 days of hospitalisation. Of the 29 (11%) patients with delirium in the ED (ie, hospital day 1), delirium persisted into hospital day 2 in 72% (n=21), and persisted for all 3 days in 52% (n=15). In multivariate analyses, as little as 1 episode of delirium during the first 3 days was associated with increased odds of unanticipated ICU admission or in-hospital death (adjusted OR 8.07 (95% CI 1.91 to 34.14); p=0.005). Delirium that persisted for all 3 days was associated with a decline in discharge status, even after adjusting for factors such as severity of illness and baseline cognitive impairment (adjusted OR 4.70 (95% CI 1.41 to 15.63); p=0.012).

CONCLUSIONS

Delirium during the first few days of hospitalisation was associated with poor outcomes in older adults admitted from the ED to the inpatient ward. These findings suggest the need for serial delirium monitoring that begins in the ED to identify a high-risk population that may benefit from closer follow-up and intervention.

Evaluation of the modified MEDS, MEWS score and Charlson comorbidity index in patients with community acquired sepsis in the emergency department

Sepsis is one of the most important causes of morbidity and mortality in patients presenting to the emergency department. SIRS criteria that define sepsis are not specific and do not reflect the severity of infection. We aimed to evaluate the ability of the modified mortality in emergency department sepsis (MEDS) score, the modified early warning score (MEWS) and the Charlson comorbidity index (CCI) to predict prognosis in patients who are diagnosed in sepsis. We prospectively investigated the value of the CCI, MEWS and modified MEDS Score in the prediction of 28-day mortality in patients presenting to the emergency department who were diagnosed with sepsis. 230 patients were enrolled in the study. In these patients, the 5-day mortality was 17 % (n = 40) and the 28-day mortality was 32.2 % (n = 74). A significant difference was found between surviving patients and those who died in terms of their modified MEDS, MEWS and Charlson scores for both 5-day mortality (p < 0.001, p = 0.013 and p = 0.006, respectively) and 28-day mortality (p < 0.001, p = 0.008 and p < 0.001, respectively). The area under the curve (AUC) for the modified MEDS score in terms of 28-day mortality was 0.77. The MEDS score had a greater prognostic value compared to the MEWS and CCI scores. The performance of modified MEDS score was better than that of other scoring systems, in our study. Therefore, we believe that the modified MEDS score can be reliably used for the prediction of mortality in sepsis.

Sepsis chronically in MARS: systemic cytokine responses are always mixed regardless of the outcome, magnitude, or phase of sepsis

The paradigm of systemic inflammatory response syndrome-to-compensatory anti-inflammatory response syndrome transition implies that hyperinflammation triggers acute sepsis mortality, whereas hypoinflammation (release of anti-inflammatory cytokines) in late sepsis induces chronic deaths. However, the exact humoral inflammatory mechanisms attributable to sepsis outcomes remain elusive. In the first part of this study, we characterized the systemic dynamics of the chronic inflammation in dying (DIE) and surviving (SUR) mice suffering from cecal ligation and puncture sepsis (days 6-28). In the second part, we combined the current chronic and previous acute/chronic sepsis data to compare the outcome-dependent inflammatory signatures between these two phases. A composite cytokine score (CCS) was calculated to compare global inflammatory responses. Mice were never sacrificed but were sampled daily (20 μl) for blood. In the first part of the study, parameters from chronic DIE mice were clustered into the 72, 48, and 24 h before death time points and compared with SUR of the same post-cecal ligation and puncture day. Cytokine increases were mixed and never preceded chronic deaths earlier than 48 h (3- to 180-fold increase). CCS demonstrated simultaneous and similar upregulation of proinflammatory and anti-inflammatory compartments at 24 h before chronic death (DIE 80- and 50-fold higher versus SUR). In the second part of the study, cytokine ratios across sepsis phases/outcomes indicated steady proinflammatory versus anti-inflammatory balance. CCS showed the inflammatory response in chronic DIE was 5-fold lower than acute DIE mice, but identical to acute SUR. The systemic mixed anti-inflammatory response syndrome-like pattern (concurrent release of proinflammatory and anti-inflammatory cytokines) occurs irrespective of the sepsis phase, response magnitude, and/or outcome. Although different in magnitude, neither acute nor chronic septic mortality is associated with a predominating proinflammatory and/or anti-inflammatory signature in the blood.