The New York Sepsis Severity Score: Development of a Risk-Adjusted Severity Model for Sepsis

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model

BACKGROUND

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis).

RESEARCH QUESTION

Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ?

STUDY DESIGN AND METHODS

Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment.

RESULTS

Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals.

INTERPRETATION

The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model can aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points.

METHODS

PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction.

RESULTS

Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools.

CONCLUSION

Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units.

Development and validation of risk-adjusted quality indicators for the long-term outcome of acute sepsis care in German hospitals based on health claims data

Background

Methods for assessing long-term outcome quality of acute care for sepsis are lacking. We investigated a method for measuring long-term outcome quality based on health claims data in Germany.

Materials and methods

Analyses were based on data of the largest German health insurer, covering 32% of the population. Cases (aged 15 years and older) with ICD-10-codes for severe sepsis or septic shock according to sepsis-1-definitions hospitalized in 2014 were included. Short-term outcome was assessed by 90-day mortality; long-term outcome was assessed by a composite endpoint defined by 1-year mortality or increased dependency on chronic care. Risk factors were identified by logistic regressions with backward selection. Hierarchical generalized linear models were used to correct for clustering of cases in hospitals. Predictive validity of the models was assessed by internal validation using bootstrap-sampling. Risk-standardized mortality rates (RSMR) were calculated with and without reliability adjustment and their univariate and bivariate distributions were described.

Results

Among 35,552 included patients, 53.2% died within 90 days after admission; 39.8% of 90-day survivors died within the first year or had an increased dependency on chronic care. Both risk-models showed a sufficient predictive validity regarding discrimination [AUC = 0.748 (95% CI: 0.742; 0.752) for 90-day mortality; AUC = 0.675 (95% CI: 0.665; 0.685) for the 1-year composite outcome, respectively], calibration (Brier Score of 0.203 and 0.220; calibration slope of 1.094 and 0.978), and explained variance (R ² = 0.242 and R ² = 0.111). Because of a small case-volume per hospital, applying reliability adjustment to the RSMR led to a great decrease in variability across hospitals [from median (1st quartile, 3rd quartile) 54.2% (44.3%, 65.5%) to 53.2% (50.7%, 55.9%) for 90-day mortality; from 39.2% (27.8%, 51.1%) to 39.9% (39.5%, 40.4%) for the 1-year composite endpoint]. There was no substantial correlation between the two endpoints at hospital level (observed rates: ρ = 0, p = 0.99; RSMR: ρ = 0.017, p = 0.56; reliability-adjusted RSMR: ρ = 0.067; p = 0.026).

Conclusion

Quality assurance and epidemiological surveillance of sepsis care should include indicators of long-term mortality and morbidity. Claims-based risk-adjustment models for quality indicators of acute sepsis care showed satisfactory predictive validity. To increase reliability of measurement, data sources should cover the full population and hospitals need to improve ICD-10-coding of sepsis.

Melatonin use and the risk of 30-day mortality among US veterans with sepsis: A retrospective study

Prior research suggests melatonin has beneficial effects that could improve survival among sepsis patients. This exploratory analysis sought to compare 30-day survival among melatonin treated and untreated patients with sepsis. A retrospective cohort study was conducted among patients with a primary inpatient admission diagnosis for sepsis utilizing the International Classification of Diseases, versions 9 and 10, Clinical Modification (ICD-9-CM and ICD-10-CM) diagnosis codes between 2000 and 2021. Propensity score weighting was utilized, accounting for demographic, clinical, and laboratory factors. Weighted Cox models were estimated for 30-day in-hospital and 30-day overall survival. A total of 9386 patients were included in the study with 593 exposed to melatonin within the first day of hospitalization. Propensity score weighted Cox models reveal melatonin was associated with a 37.9% decreased risk of 30-day in-hospital mortality (HR = 0.621; 95% CI = [0.415-0.931]) and a 33.5% decreased risk of 30-day overall mortality (HR = 0.665; 95% CI = [0.493-0.897]). Factors associated with higher risk of both in-hospital and overall mortality include male sex, white race, age, higher Charlson comorbidity burden, sodium and potassium levels, intensive care unit stay, invasive ventilation, and vasopressor use. Higher serum albumin levels are associated with lower mortality risks. Among patients diagnosed with sepsis, exposure to melatonin was associated with a lower in-hospital and 30-day mortality. Additional research is warranted to fully understand the role of melatonin in sepsis.

Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study

BACKGROUND

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, has become one of the major causes of death in Intensive Care Units (ICUs). The heterogeneity and complexity of this syndrome lead to the absence of golden standards for its diagnosis, treatment, and prognosis. The early prediction of in-hospital mortality for sepsis patients is not only meaningful to medical decision making, but more importantly, relates to the well-being of patients.

METHODS

In this paper, a rule discovery and analysis (rule-based) method is used to predict the in-hospital death events of 2021 ICU patients diagnosed with sepsis using the MIMIC-III database. The method mainly includes two phases: rule discovery phase and rule analysis phase. In the rule discovery phase, the RuleFit method is employed to mine multiple hidden rules which are capable to predict individual in-hospital death events. In the rule analysis phase, survival analysis and decomposition analysis are carried out to test and justify the risk prediction ability of these rules. Then by leveraging a subset of these rules, we establish a prediction model that is both more accurate at the in-hospital death prediction task and more interpretable than most comparable methods.

RESULTS

In our experiment, RuleFit generates 77 risk prediction rules, and the average area under the curve (AUC) of the prediction model based on 62 of these rules reaches 0.781 ([Formula: see text]) which is comparable to or even better than the AUC of existing methods (i.e., commonly used medical scoring system and benchmark machine learning models). External validation of the prediction power of these 62 rules on another 1468 sepsis patients not included in MIMIC-III in ICU provides further supporting evidence for the superiority of the rule-based method. In addition, we discuss and explain in detail the rules with better risk prediction ability. Glasgow Coma Scale (GCS), serum potassium, and serum bilirubin are found to be the most important risk factors for predicting patient death.

CONCLUSION

Our study demonstrates that, with the rule-based method, we could not only make accurate prediction on in-hospital death events of sepsis patients, but also reveal the complex relationship between sepsis-related risk factors through the rules themselves, so as to improve our understanding of the complexity of sepsis as well as its population.

Relation of Procollagen Type III Amino Terminal Propeptide Level to Sepsis Severity in Pediatrics

BACKGROUND

Sepsis is still the main etiology of mortality in pediatric intensive care units (PICUs). Therefore, we performed this study to evaluate the value of procollagen Type III amino-terminal propeptide (PIIINP) as a biomarker for sepsis severity diagnosis and mortality.

METHOD

A prospective study was carried out on 170 critically ill children admitted into the PICU and 100 controls. The performed clinical examinations included calculation of the pediatric risk of mortality. Serum PIIINP was withdrawn from patients at admission and from the controls.

RESULTS

PIIINP level was significantly more increased in sepsis, severe sepsis, and septic shock than among the controls (p < 0.001). PIIINP was significantly higher in severe sepsis and septic shock (568.3 (32.5-1304.7) and 926.2 (460.6-1370), respectively) versus sepsis (149.5 (29.6-272.9)) (p < 0.001). PIIINP was significantly increased in non-survivors (935.4 (104.6-1370)) compared to survivors (586.5 (29.6-1169)) (p < 0.016). ROC curve analysis exhibited an area under the curve (AUC) of 0.833 for PIIINP, which is predictive for sepsis, while the cut-off point of 103.3 ng/mL had a sensitivity of 88% and specificity of 82%. The prognosis of the AUC curve for PIIINP to predict mortality was 0.651; the cut-off of 490.4 ng/mL had a sensitivity of 87.5% and specificity of 51.6%.

CONCLUSIONS

PIIINP levels are increased in sepsis, with significantly higher levels in severe sepsis, septic shock, and non-survivors, thus representing a promising biomarker for pediatric sepsis severity and mortality.

Persistently elevated early warning scores and lactate identifies patients at high risk of mortality in suspected sepsis

OBJECTIVE

In the UK, the National Early Warning Score (NEWS) is recommended as part of screening for suspicion of sepsis. Is a change in NEWS a better predictor of mortality than an isolated score when screening for suspicion of sepsis?.

METHODS

A prospectively gathered cohort of 1233 adults brought in by ambulance to two UK nonspecialist hospitals, with suspicion of sepsis at emergency department (ED) triage (2015-2017) was analysed. Associations with 30-day mortality and ICU admission rate were compared between groups with an isolated NEWS ≥5 points prehospital and those with persistently elevated NEWS prehospital, in ED and at ward admission. The effect of adding the ED (venous or arterial) lactate was also assessed.

RESULTS

Mortality increased if the NEWS persisted ≥5 at ED arrival 22.1% vs. 10.2% [odds ratio (OR) 2.5 (1.6-4.0); P < 0.001]. Adding an ED lactate ≥2 mmol/L was associated with an increase in mortality greater than for NEWS alone [32.2% vs. 13.3%, OR 3.1 (2.2-4.1); P < 0.001], and increased ICU admission [13.9% vs. 3.7%, OR 3.1 (2.2-4.3); P < 0.001]. If NEWS remained ≥5 at ward admission (predominantly within 4 h of ED arrival), mortality was 32.1% vs. 14.3%, [OR 2.8 (2.1-3.9); P < 0.001] and still higher if accompanied by an elevated ED lactate [42.1% vs. 16.4%, OR 3.7 (2.6-5.3); P < 0.001].

CONCLUSION

Persistently elevated NEWS, from prehospital through the ED to the time of ward admission, combined with an elevated ED lactate identifies patients with suspicion of sepsis at highest risk of in-hospital mortality.

Racial And Ethnic Disparities In Care Following The New York State Sepsis Initiative

After 2013, when New York State mandated that hospitals follow protocols to treat sepsis, completion of the protocols increased and mortality declined. Whether these encouraging trends have equitably benefited racial/ethnic minority populations is unknown. Although there were no significant racial/ethnic differences in rates of protocol completion at the onset of New York's Sepsis Initiative, over time white patients experienced a greater increase in protocol completion rates (14.0 percentage points) compared to black patients (5.3 percentage points). The emergence of this disparity was due to smaller performance improvements among hospitals with higher proportions of black patients, though white and black patients showed similar improvements when treated within the same hospital. Our study suggests an urgent need to understand why improvements in sepsis care lagged in hospitals in New York that care for higher proportions of minority patients. Policy makers should anticipate and monitor the effects of quality improvement initiatives on disparities to ensure that all racial/ethnic groups realize their benefits equitably.

Variation in Identifying Sepsis and Organ Dysfunction Using Administrative Versus Electronic Clinical Data and Impact on Hospital Outcome Comparisons

OBJECTIVES

Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons.

DESIGN, SETTING, AND PATIENTS

Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Sepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data.

CONCLUSIONS

Variation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.

Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

External validation of the sepsis severity score

INTRODUCTION

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Mortality rates are high, exceeding 50% in patients with septic shock. The sepsis severity score (SSS) was developed to determine the severity of sepsis and as a prognostic model. The aim of this study was to externally validate the SSS model.

METHODS

Calibration and discrimination of the SSS were retrospectively evaluated using data from a single-center sepsis registry.

RESULTS

Data from 156 septic patients were recorded; 56% of them had septic shock, 94% of patients required mechanical ventilation. The observed hospital mortality was 60.3%. The mean SSS value was 94.4 (95% CI 90.5-98.3). The SSS presented excellent discrimination with an area under the receiver operating characteristic curve (AUC) of 0.806 (95% CI 0.734-0.866). The pairwise comparison of APACHE II (AUC = 0.789; 95% CI 0.715-0.851) with SSS and 1st day SOFA (AUC = 0.75; 95% CI 0.673-0.817) with SSS revealed no significant differences in discrimination between the models. The calibration of the SSS was good with the Hosmer-Lemeshow goodness-of-fit H test 9.59, P > 0.05. Analyses of calibration curve show absence of accurate predictions in lower deciles of lower risk (2nd and 4th).

CONCLUSION

The SSS demonstrated excellent discrimination. The calibration evaluation gave conflicting results; the H-L test result indicated a good calibration, while the visual analysis of the calibration curve suggested the opposite. The SSS requires further evaluation before it can be safely recommended as an outcome prediction model.

A National Approach to Pediatric Sepsis Surveillance

Pediatric sepsis is a major public health concern, and robust surveillance tools are needed to characterize its incidence, outcomes, and trends. The increasing use of electronic health records (EHRs) in the United States creates an opportunity to conduct reliable, pragmatic, and generalizable population-level surveillance using routinely collected clinical data rather than administrative claims or resource-intensive chart review. In 2015, the US Centers for Disease Control and Prevention recruited sepsis investigators and representatives of key professional societies to develop an approach to adult sepsis surveillance using clinical data recorded in EHRs. This led to the creation of the adult sepsis event definition, which was used to estimate the national burden of sepsis in adults and has been adapted into a tool kit to facilitate widespread implementation by hospitals. In July 2018, the Centers for Disease Control and Prevention convened a new multidisciplinary pediatric working group to tailor an EHR-based national sepsis surveillance approach to infants and children. Here, we describe the challenges specific to pediatric sepsis surveillance, including evolving clinical definitions of sepsis, accommodation of age-dependent physiologic differences, identifying appropriate EHR markers of infection and organ dysfunction among infants and children, and the need to account for children with medical complexity and the growing regionalization of pediatric care. We propose a preliminary pediatric sepsis event surveillance definition and outline next steps for refining and validating these criteria so that they may be used to estimate the national burden of pediatric sepsis and support site-specific surveillance to complement ongoing initiatives to improve sepsis prevention, recognition, and treatment.

Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data

Variability in hospital-level sepsis mortality rates may be due to differences in case mix, quality of care, or diagnosis and coding practices. Centers for Disease Control and Prevention's Adult Sepsis Event definition could facilitate objective comparisons of sepsis mortality rates between hospitals but requires rigorous risk-adjustment tools. We developed risk-adjustment models for Adult Sepsis Events using administrative and electronic health record data.

DESIGN

Retrospective cohort study.

SETTING

One hundred thirty-six U.S. hospitals in Cerner HealthFacts (derivation dataset) and 137 HCA Healthcare hospitals (validation dataset).

PATIENTS

A total of 95,154 hospitalized adult patients (derivation) and 201,997 patients (validation) meeting Centers for Disease Control and Prevention Adult Sepsis Event criteria.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We created logistic regression models of increasing complexity using administrative and electronic health record data to predict in-hospital mortality. An administrative model using demographics, comorbidities, and coded markers of severity of illness at admission achieved an area under the receiver operating curve of 0.776 (95% CI, 0.770-0.783) in the Cerner cohort, with diminishing calibration at higher baseline risk deciles. An electronic health record-based model that integrated administrative data with laboratory results, vasopressors, and mechanical ventilation achieved an area under the receiver operating curve of 0.826 (95% CI, 0.820-0.831) in the derivation cohort and 0.827 (95% CI, 0.824-0.829) in the validation cohort, with better calibration than the administrative model. Adding vital signs and Glasgow Coma Score minimally improved performance.

CONCLUSIONS

Models incorporating electronic health record data accurately predict hospital mortality for patients with Adult Sepsis Events and outperform models using administrative data alone. Utilizing laboratory test results, vasopressors, and mechanical ventilation without vital signs may achieve a good balance between data collection needs and model performance, but electronic health record-based models must be attentive to potential variability in data quality and availability. With ongoing testing and refinement of these risk-adjustment models, Adult Sepsis Event surveillance may enable more meaningful comparisons of hospital sepsis outcomes and provide an important window into quality of care.

Clinical Trajectories and Causes of Death in Septic Patients with a Low APACHE II Score

Clinical course and mortality in septic patients with low disease severity remain poorly understood and is worth further investigation. We enrolled septic patients admitted to intensive care units (ICUs) between 2010 and 2014 with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores of ≤15. We sought to determine their clinical trajectories and causes of death, and to analyze risk factors associated with in-hospital mortality. A total of 352 patients were included, of whom 89 (25%) did not survive to hospital discharge, at a rate higher than predicted (<21%) by the APACHE II score. Approximately one third (31/89) of non-survivors succumbed to index sepsis; however, more patients (34/89) died of subsequent sepsis. New-onset ICU sepsis developed in 99 (28%) patients and was an independent risk factor for mortality. In addition, septic patients with comorbid malignancy or index infection acquired in the hospital settings were more likely to have in-hospital mortality than those without. In conclusion, septic patients with low APACHE II scores were at a higher mortality risk than expected, and subsequent sepsis rather than index sepsis was the primary cause of death. This study provides insight into unexpected clinical trajectories and outcomes of septic patients with low disease severity at ICU admission and highlights the need for more research and clinical attention in this patient population.

Validation the performance of New York Sepsis Severity Score compared with Sepsis Severity Score in predicting hospital mortality among sepsis patients

PURPOSE

The aim of this study was to compare the performance of the New York Sepsis Severity Score (NYSSS) with the Sepsis Severity Score (SSS) and Acute Physiology and Chronic Health Evaluation and Simplified Acute Physiology Scores for predicting mortality in sepsis patients.

METHOD

A retrospective analysis was conducted in the intensive care unit. The primary outcome was in-hospital mortality.

RESULTS

Overall 1680 sepsis patients were enrolled. The hospital mortality rate was 44.4%. The NYSSS underestimated actual mortality with standard mortality ratio (SMR) of 1.28 (95%CI 1.19-1.38). However, the SSS slightly overestimated the actual mortality with an SMR of 0.94 (0.88-1.01). The NYSSS had moderate discrimination with an AUC of 0.772 (0.750-0.794), in contrast to the SSS which had good discrimination with an AUC of 0.889 (0.873-0.904). The AUC of the SSS was statistically higher than that of the NYSSS. The AUCs of both the NYSSS and SSS were significantly lower than other standard severity scores. The calibrations for all severity scores were poor. The SSS had better overall performance than the NYSSS (Brier score 0.149 and 0.201, respectively).

CONCLUSION

The SSS had better discrimination and overall performance than the NYSSS. However, both sepsis severity scores were poorly calibrated.

Non-operative management of perforated diverticulitis with extraluminal or free air - a retrospective single center cohort study

OBJECTIVES

The aim of this study was to describe patient characteristics and results of non-operative management for patients presenting with computed tomography (CT) verified perforated diverticulitis with extraluminal or free air.

METHODS

All patients treated for diverticulitis (ICD-10: K-57) during 2010-2014 were identified and medical records were reviewed. Re-evaluations of CT examinations for all patients with complicated disease according to medical records were performed. All patients diagnosed with perforated diverticulitis and extraluminal or free air on re-evaluation were included and characteristics of patients having immediate surgery and those whom non-operative management was attempted are described.

RESULTS

Of 141 patients with perforated diverticulitis according to medical records, 136 were confirmed on CT re-evaluation. Emergency surgical intervention within 24 h of admission was performed in 29 (21%) patients. Non-operative management with iv antibiotics was attempted for 107 patients and was successful in 101 (94%). The 30-day mortality rate was 2%. The presence of a simultaneous abscess was higher for patients with failure of non-operative management compared with those that were successfully managed non-operatively (67% compared to 17%, p = .013). Eleven out of thirty-two patients (34%) with free air were successfully managed conservatively. Patients that were operated within 24 h from admission were more commonly on immunosuppressive therapy, had more commonly free intraperitoneal air and free fluid in the peritoneal cavity.

CONCLUSIONS

Non-operative management is successful in the majority of patients with CT-verified perforated diverticulitis with extraluminal air, and also in one-third of those with free air in the peritoneal cavity.

A Nationwide Comparison Between Sepsis-2 and Sepsis-3 Definition in Japan

BACKGROUND

Currently, it remains controversial whether the Sepsis-3 definition provides the most appropriate criteria for clinical use. The purpose of this study was to compare between the Sepsis-2 and Sepsis-3 definitions using Japan's nationwide registry.

METHODS

Data were obtained from a multicenter registry conducted at 42 intensive care units (ICUs) throughout Japan, in which patients received treatment for severe sepsis or septic shock between January 2011 and December 2013.

RESULTS

A total of 2797 patients diagnosed using the Sepsis-2 criteria were included in the present study. These patients were categorized into "Severe sepsis" (n = 1154) and "Sepsis-2 shock" (n = 1643) groups. Among the "Sepsis-2 shock" group, patients who did not meet the Sepsis-3 criteria for septic shock were categorized into the "Sepsis-2 shock-only" (n = 448, 27.3%) group, while patients who met the Sepsis-3 criteria for septic shock were categorized into "Sepsis-3 shock (n = 1195, 72.7%)" group. The ICU mortality in the "Sepsis-3 shock" group, "Sepsis-2 shock-only" group, and "Severe sepsis" group was 28.5%, 10.9%, and 14.1%, respectively. We observed no significant difference between the "Severe sepsis" and "Sepsis-2 shock-only" groups in terms of in-hospital survival (P = .098), while the "Sepsis-3 shock" group had the highest in-hospital mortality rate (P < .001). In a multivariate logistic regression analysis, liver insufficiency and immunocompromised status were independent prognostic factors in the "Sepsis-2 shock-only" group. In contrast, chronic heart disease and chronic hemodialysis were independent prognostic factors in the "Sepsis-3 shock" group.

CONCLUSIONS

The ICU mortality of the "Sepsis-2 shock-only" group was significantly low. Besides septic shock diagnosed by the Sepsis-3 definition selects patients with more severe cases of sepsis among the "Sepsis-2 shock" group.

Mortality Changes Associated with Mandated Public Reporting for Sepsis. The Results of the New York State Initiative

RATIONALE

In 2013, the New York State Department of Health (NYSDOH) began a mandatory state-wide initiative to improve early recognition and treatment of severe sepsis and septic shock.

OBJECTIVES

This study examines protocol initiation, 3-hour and 6-hour sepsis bundle completion, and risk-adjusted hospital mortality among adult patients with severe sepsis and septic shock.

METHODS

Cohort analysis included all patients from all 185 hospitals in New York State reported to the NYSDOH from April 1, 2014, to June 30, 2016. A total of 113,380 cases were submitted to NYSDOH, of which 91,357 hospitalizations from 183 hospitals met study inclusion criteria. NYSDOH required all hospitals to submit and follow evidence-informed protocols (including elements of 3-h and 6-h sepsis bundles: lactate measurement, early blood cultures and antibiotic administration, fluids, and vasopressors) for early identification and treatment of severe sepsis or septic shock.

MEASUREMENTS AND MAIN RESULTS

Compliance with elements of the sepsis bundles and risk-adjusted mortality were studied. Of 91,357 patients, 74,293 (81.3%) had the sepsis protocol initiated. Among these individuals, 3-hour bundle compliance increased from 53.4% to 64.7% during the study period (P < 0.001), whereas among those eligible for the 6-hour bundle (n = 35,307) compliance increased from 23.9% to 30.8% (P < 0.001). Risk-adjusted mortality decreased from 28.8% to 24.4% (P < 0.001) in patients among whom a sepsis protocol was initiated. Greater hospital compliance with 3-hour and 6-hour bundles was associated with shorter length of stay and lower risk and reliability-adjusted mortality.

CONCLUSIONS

New York's statewide initiative increased compliance with sepsis-performance measures. Risk-adjusted sepsis mortality decreased during the initiative and was associated with increased hospital-level compliance.