Using objective clinical data to track progress on preventing and treating sepsis: CDC's new 'Adult Sepsis Event' surveillance strategy

The Impact of Common Variations in Sequential Organ Failure Assessment Score Calculation on Sepsis Measurement Using Sepsis-3 Criteria: A Retrospective Analysis Using Electronic Health Record Data

OBJECTIVES

To assess the impact of different methods of calculating Sequential Organ Failure Assessment (SOFA) scores using electronic health record data on the incidence, outcomes, agreement, and predictive validity of Sepsis-3 criteria.

DESIGN

Retrospective observational study.

SETTING

Five Massachusetts hospitals.

PATIENTS

Hospitalized adults, 2015 to 2022.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We defined sepsis as a suspected infection (culture obtained and antibiotic administered) with a concurrent increase in SOFA score by greater than or equal to 2 points (Sepsis-3 criteria). Our reference SOFA implementation strategy imputed normal values for missing data, used Pa o2 /F io2 ratios for respiratory scores, and assumed normal baseline SOFA scores for community-onset sepsis. We then implemented SOFA scores using different missing data imputation strategies (averaging worst values from preceding and following days vs. carrying forward nonmissing values), imputing respiratory scores using Sp o2 /F io2 ratios, and incorporating comorbidities and prehospital laboratory data into baseline SOFA scores. Among 1,064,459 hospitalizations, 297,512 (27.9%) had suspected infection and 141,052 (13.3%) had sepsis with an in-hospital mortality rate of 10.3% using the reference SOFA method. The percentage of patients missing SOFA components for at least 1 day in the infection window was highest for Pa o2 /F io2 ratios (98.6%), followed by Sp o2 /F io2 ratios (73.5%), bilirubin (68.5%), and Glasgow Coma Scale scores (57.2%). Different missing data imputation strategies yielded near-perfect agreement in identifying sepsis (kappa 0.99). However, using Sp o2 /F io2 imputations yielded higher sepsis incidence (18.3%), lower mortality (8.1%), and slightly lower predictive validity for mortality (area under the receiver operating curves [AUROC] 0.76 vs. 0.78). For community-onset sepsis, incorporating comorbidities and historical laboratory data into baseline SOFA score estimates yielded lower sepsis incidence (6.9% vs. 11.6%), higher mortality (13.4% vs. 9.6%), and higher predictive validity (AUROC 0.79 vs. 0.75) relative to the reference SOFA implementation.

CONCLUSIONS

Common variations in calculating respiratory and baseline SOFA scores, but not in handling missing data, lead to substantial differences in observed incidence, mortality, agreement, and predictive validity of Sepsis-3 criteria.

[Urosepsis: pathophysiology, diagnosis, and management-an update]

Urinary tract infections vary widely in their clinical spectrum, ranging from uncomplicated cystitis to septic shock. Urosepsis accounts for 9-31% of all cases of septicemia and is often associated with nosocomial infections. A major risk factor for urosepsis is the presence of obstructive uropathy, caused by conditions such as urolithiasis, tumors, or strictures. The severity and course of urosepsis depend on both the virulence of the pathogen and the patient's specific immune response. Prompt therapy, including antimicrobial treatment and eradication of the infection source, along with supportive measures for circulatory and respiratory stabilization, and adjunctive therapies such as hemodialysis and glucocorticoid therapy, is crucial. Due to demographic changes, an increase in cases of urosepsis is expected-thus, it is of utmost importance for urologists to be familiar with targeted diagnostics and effective treatment.

The next frontier of healthcare-associated infection (HAI) surveillance metrics: Beyond device-associated infections

In recent years, it has become increasingly evident that surveillance metrics for invasive device-associated infections (ie, central-line-associated bloodstream infections, ventilator-associated pneumonias, and catheter-associated urinary tract infections) do not capture all harms; they capture only a subset of healthcare-associated infections (HAIs). Although prevention of device-associated infections remains critical, we need to address the full spectrum of potential harms from device use and non-device-associated infections. These include complications associated with additional devices, such as peripheral venous and arterial catheters, non-device-associated infections such as nonventilator hospital-acquired pneumonia, and noninfectious device complications such as trauma, thrombosis, and acute lung injury. As authors of the device-associated infection sections in the SHEA/IDSA/APIC Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals, we highlight catheter-associated urinary tract infection as an example of the strengths and limitations of the current emphasis on device-associated infection surveillance, suggest performance metrics that present a more comprehensive picture of patient harm, and provide a high-level overview of similar issues with other infection surveillance measures.

Improving Sepsis Outcomes in the Era of Pay-for-Performance and Electronic Quality Measures: A Joint IDSA/ACEP/PIDS/SHEA/SHM/SIDP Position Paper

The Centers for Medicare & Medicaid Services (CMS) introduced the Severe Sepsis/Septic Shock Management Bundle (SEP-1) as a pay-for-reporting measure in 2015 and is now planning to make it a pay-for-performance measure by incorporating it into the Hospital Value-Based Purchasing Program. This joint IDSA/ACEP/PIDS/SHEA/SHM/SIPD position paper highlights concerns with this change. Multiple studies indicate that SEP-1 implementation was associated with increased broad-spectrum antibiotic use, lactate measurements, and aggressive fluid resuscitation for patients with suspected sepsis but not with decreased mortality rates. Increased focus on SEP-1 risks further diverting attention and resources from more effective measures and comprehensive sepsis care. We recommend retiring SEP-1 rather than using it in a payment model and shifting instead to new sepsis metrics that focus on patient outcomes. CMS is developing a community-onset sepsis 30-day mortality electronic clinical quality measure (eCQM) that is an important step in this direction. The eCQM preliminarily identifies sepsis using systemic inflammatory response syndrome (SIRS) criteria, antibiotic administrations or diagnosis codes for infection or sepsis, and clinical indicators of acute organ dysfunction. We support the eCQM but recommend removing SIRS criteria and diagnosis codes to streamline implementation, decrease variability between hospitals, maintain vigilance for patients with sepsis but without SIRS, and avoid promoting antibiotic use in uninfected patients with SIRS. We further advocate for CMS to harmonize the eCQM with the Centers for Disease Control and Prevention's (CDC) Adult Sepsis Event surveillance metric to promote unity in federal measures, decrease reporting burden for hospitals, and facilitate shared prevention initiatives. These steps will result in a more robust measure that will encourage hospitals to pay more attention to the full breadth of sepsis care, stimulate new innovations in diagnosis and treatment, and ultimately bring us closer to our shared goal of improving outcomes for patients.

Fucoxanthin Ameliorates Sepsis via Modulating Microbiota by Targeting IRF3 Activation

To improve patient survival in sepsis, it is necessary to curtail exaggerated inflammatory responses. Fucoxanthin (FX), a carotenoid derived from brown algae, efficiently suppresses pro-inflammatory cytokine expression via IRF3 activation, thereby reducing mortality in a mouse model of sepsis. However, the effects of FX-targeted IRF3 on the bacterial flora (which is disrupted in sepsis) and the mechanisms by which it impacts sepsis development remain unclear. This study aims to elucidate how FX-targeted IRF3 modulates intestinal microbiota compositions, influencing sepsis development. FX significantly reduced the bacterial load in the abdominal cavity of mice with cecal ligation and puncture (CLP)-induced sepsis via IRF3 activation and increased short-chain fatty acids, like acetic and propionic acids, with respect to their intestines. FX also altered the structure of the intestinal flora, notably elevating beneficial Verrucomicrobiota and Akkermansia spp. while reducing harmful Morganella spp. Investigating the inflammation-flora link, we found positive correlations between the abundances of Morganella spp., Proteus spp., Escherichia spp., and Klebsiella spp. and pro-inflammatory cytokines (IL-6, IL-1β, and TNF-α) induced by CLP. These bacteria were negatively correlated with acetic and propionic acid production. FX alters microbial diversity and promotes short-chain fatty acid production in mice with CLP-induced sepsis, reshaping gut homeostasis. These findings support the value of FX for the treatment of sepsis.

Comparison of Administrative versus Electronic Health Record-based Methods for Identifying Sepsis Hospitalizations

Rationale: Despite the importance of sepsis surveillance, no optimal approach for identifying sepsis hospitalizations exists. The Centers for Disease Control and Prevention Adult Sepsis Event Definition (CDC-ASE) is an electronic medical record-based algorithm that yields more stable estimates over time than diagnostic coding-based approaches but may still result in misclassification. Objectives: We sought to assess three approaches to identifying sepsis hospitalizations, including a modified CDC-ASE. Methods: This cross-sectional study included patients in the Veterans Affairs Ann Arbor Healthcare System admitted via the emergency department (February 2021 to February 2022) with at least one episode of acute organ dysfunction within 48 hours of emergency department presentation. Patients were assessed for community-onset sepsis using three methods: 1) explicit diagnosis codes, 2) the CDC-ASE, and 3) a modified CDC-ASE. The modified CDC-ASE required at least two systemic inflammatory response syndrome criteria instead of blood culture collection and had a more sensitive definition of respiratory dysfunction. Each method was compared with a reference standard of physician adjudication via medical record review. Patients were considered to have sepsis if they had at least one episode of acute organ dysfunction graded as "definitely" or "probably" infection related on physician review. Results: Of 821 eligible hospitalizations, 449 were selected for physician review. Of these, 98 (21.8%) were classified as sepsis by medical record review, 103 (22.9%) by the CDC-ASE, 132 (29.4%) by the modified CDC-ASE, and 37 (8.2%) by diagnostic codes. Accuracy was similar across the three methods of interest (80.6% for the CDC-ASE, 79.6% for the modified CDC-ADE, and 84.2% for diagnostic codes), but sensitivity and specificity varied. The CDC-ASE algorithm had sensitivity of 58.2% (95% confidence interval [CI], 47.2-68.1%) and specificity of 86.9% (95% CI, 82.9-90.2%). The modified CDC-ASE algorithm had greater sensitivity (69.4% [95% CI, 59.3-78.3%]) but lower specificity (81.8% [95% CI, 77.3-85.7%]). Diagnostic codes had lower sensitivity (32.7% [95% CI, 23.5-42.9%]) but greater specificity (98.6% [95% CI, 96.7-99.55%]). Conclusions: There are several approaches to identifying sepsis hospitalizations for surveillance that have acceptable accuracy. These approaches yield varying sensitivity and specificity, so investigators should carefully consider the test characteristics of each method before determining an appropriate method for their intended use.

Performance of bedside tools for predicting infection-related mortality and administrative data for sepsis surveillance: An observational cohort study

BACKGROUND

Measuring sepsis incidence and associated mortality at scale using administrative data is hampered by variation in diagnostic coding. This study aimed first to compare how well bedside severity scores predict 30-day mortality in hospitalised patients with infection, then to assess the ability of combinations of administrative data items to identify patients with sepsis.

METHODS

This retrospective case note review examined 958 adult hospital admissions between October 2015 and March 2016. Admissions with blood culture sampling were matched 1:1 to admissions without a blood culture. Case note review data were linked to discharge coding and mortality. For patients with infection the performance characteristics of Sequential Organ Failure Assessment (SOFA), National Early Warning System (NEWS), quick SOFA (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS) were calculated for predicting 30-day mortality. Next, the performance characteristics of administrative data (blood cultures and discharge codes) for identifying patients with sepsis, defined as SOFA ≥2 because of infection, were calculated.

RESULTS

Infection was documented in 630 (65.8%) admissions and 347 (55.1%) patients with infection had sepsis. NEWS (Area Under the Receiver Operating Characteristic, AUROC 0.78 95%CI 0.72-0.83) and SOFA (AUROC 0.77, 95%CI 0.72-0.83), performed similarly well for prediction of 30-day mortality. Having an infection and/or sepsis International Classification of Diseases, Tenth Revision (ICD-10) code (AUROC 0.68, 95%CI 0.64-0.71) performed as well in identifying patients with sepsis as having at least one of: an infection code; sepsis code, or; blood culture (AUROC 0.68, 95%CI 0.65-0.71), Sepsis codes (AUROC 0.53, 95%CI 0.49-0.57) and positive blood cultures (AUROC 0.52, 95%CI 0.49-0.56) performed least well.

CONCLUSIONS

SOFA and NEWS best predicted 30-day mortality in patients with infection. Sepsis ICD-10 codes lack sensitivity. For health systems without suitable electronic health records, blood culture sampling has potential utility as a clinical component of a proxy marker for sepsis surveillance.

Integrating structured and unstructured data for timely prediction of bloodstream infection among children

BACKGROUND

Hospitalized children with central venous lines (CVLs) are at higher risk of hospital-acquired infections. Information in electronic health records (EHRs) can be employed in training deep learning models to predict the onset of these infections. We incorporated clinical notes in addition to structured EHR data to predict serious bloodstream infections, defined as positive blood culture followed by at least 4 days of new antimicrobial agent administration, among hospitalized children with CVLs.

METHODS

Structured EHR information and clinical notes were extracted for a retrospective cohort including all hospitalized patients with CVLs at a single tertiary care pediatric health system from 2013 to 2018. Deep learning models were trained to determine the added benefit of incorporating the information embedded in clinical notes in predicting serious bloodstream infection.

RESULTS

A total of 24,351 patient encounters met inclusion criteria. The best-performing model restricted to structured EHR data had a specificity of 0.951 and positive predictive value (PPV) of 0.056 when the sensitivity was set to 0.85. The addition of contextualized word embeddings improved the specificity to 0.981 and PPV to 0.113.

CONCLUSIONS

Integrating clinical notes with structured EHR data improved the prediction of serious bloodstream infections among pediatric patients with CVLs.

IMPACT

Developed an advanced infection prediction model in pediatrics that integrates the structured and unstructured EHRs. Extracted information from clinical notes to do timely prediction in a clinical setting. Developed a deep learning model framework that can be employed in predicting rare events in a complex and dynamic environment.

Defining Posttraumatic Sepsis for Population-Level Research

IMPORTANCE

Multiple classification methods are used to identify sepsis from existing data. In the trauma population, it is unknown how administrative methods compare with clinical criteria for sepsis classification.

OBJECTIVES

To characterize the agreement between 3 approaches to sepsis classification among critically ill patients with trauma and compare the sepsis-associated risk of adverse outcomes when each method was used to define sepsis.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used data collected between January 1, 2012, and December 31, 2020, from patients aged 16 years or older with traumatic injury, admitted to the intensive care unit of a single-institution level 1 trauma center and requiring invasive mechanical ventilation for at least 3 days. Statistical analysis was conducted from August 1, 2021, to March 31, 2022.

EXPOSURE

Hospital-acquired sepsis, as classified by 3 methods: a novel automated clinical method based on data from the electronic health record, the National Trauma Data Bank (NTDB), and explicit and implicit medical billing codes.

MAIN OUTCOMES AND MEASURES

The primary outcomes were chronic critical illness and in-hospital mortality. Secondary outcomes included number of days in an intensive care unit, number of days receiving mechanical ventilation, discharge to a skilled nursing or long-term care facility, and discharge to home without assistance.

RESULTS

Of 3194 patients meeting inclusion criteria, the median age was 49 years (IQR, 31-64 years), 2380 (74%) were male, and 2826 (88%) sustained severe blunt injury (median Injury Severity Score, 29 [IQR, 21-38]). Sepsis was identified in 747 patients (23%) meeting automated clinical criteria, 118 (4%) meeting NTDB criteria, and 529 (17%) using medical billing codes. The Light κ value for 3-way agreement was 0.16 (95% CI, 0.14-0.19). The adjusted relative risk of chronic critical illness was 9.9 (95% CI, 8.0-12.3) for sepsis identified by automated clinical criteria, 5.0 (95% CI, 3.4-7.3) for sepsis identified by the NTDB, and 4.5 (95% CI, 3.6-5.6) for sepsis identified using medical billing codes. The adjusted relative risk for in-hospital mortality was 1.3 (95% CI, 1.0-1.6) for sepsis identified by automated clinical criteria, 2.7 (95% CI, 1.7-4.3) for sepsis identified by the NTDB, and 1.0 (95% CI, 0.7-1.2) for sepsis identified using medical billing codes.

CONCLUSIONS AND RELEVANCE

In this cohort study of critically ill patients with trauma, administrative methods misclassified sepsis and underestimated the incidence and severity of sepsis compared with an automated clinical method using data from the electronic health record. This study suggests that an automated approach to sepsis classification consistent with Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) clinical criteria is feasible and may improve existing approaches to health services and population-based research in this population.

Mortality of patients with hospital-onset sepsis in hospitals with all-day and non-all-day rapid response teams: a prospective nationwide multicenter cohort study

Background

Hospital-onset sepsis is associated with a higher in-hospital mortality rate than community-onset sepsis. Many hospitals have implemented rapid response teams (RRTs) for early detection and timely management of at-risk hospitalized patients. However, the effectiveness of an all-day RRT over a non-all-day RRT in reducing the risk of in-hospital mortality in patient with hospital-onset sepsis is unclear. We aimed to determine the effect of the RRT’s operating hours on in-hospital mortality in inpatient patients with sepsis.

Methods

We conducted a nationwide cohort study of adult patients with hospital-onset sepsis prospectively collected from the Korean Sepsis Alliance (KSA) Database from 16 tertiary referral or university-affiliated hospitals in South Korea between September of 2019 and February of 2020. RRT was implemented in 11 hospitals, of which 5 (45.5%) operated 24-h RRT (all-day RRT) and the remaining 6 (54.5%) had part-day RRT (non-all-day RRT). The primary outcome was in-hospital mortality between the two groups.

Results

Of the 405 patients with hospital-onset sepsis, 206 (50.9%) were admitted to hospitals operating all-day RRT, whereas 199 (49.1%) were hospitalized in hospitals with non-all-day RRT. A total of 73 of the 206 patients in the all-day group (35.4%) and 85 of the 199 patients in the non-all-day group (42.7%) died in the hospital (P = 0.133). After adjustments for co-variables, the implementation of all-day RRT was associated with a significant reduction in in-hospital mortality (adjusted odds ratio 0.57; 95% confidence interval 0.35–0.93; P = 0.024).

Conclusions

In comparison with non-all-day RRTs, the availability of all-day RRTs was associated with reduced in-hospital mortality among patients with hospital-onset sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04149-z.

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). During the study, 590,736 patients were monitored by TREWS across five hospitals. We focused our analysis on 6,877 patients with sepsis who were identified by the alert before initiation of antibiotic therapy. Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h. Improvements in mortality rate (4.5%, CI 0.8, 8.3%, adjusted absolute reduction) and organ failure were larger among those patients who were additionally flagged as high risk. Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.

Piloting a registry for paediatric sepsis: The PoRPoiSe study

AIM

To develop a model for a paediatric sepsis registry for use in emergency care settings. A regional study, in the UK, was undertaken to identify the most basic registry components which are desirable and feasible using the concept of a minimum viable product.

METHODS

Two-round survey of clinicians using a modified Delphi methodology in conjunction with a regional data collection project in three paediatric emergency departments across London.

RESULTS

The survey identified 34 desirable information items to be included in a registry. Fifteen of 34 items are currently feasible from our experience of data collection.

CONCLUSION

The development of a multi-centre paediatric sepsis registry sepsis may have several benefits but is currently extremely limited primarily because of technological fragmentation within our Health Service. Our findings have important implications for researchers wishing to plan sepsis surveillance programmes, locally and internationally.

The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupted Time Series Analysis

Background

Sepsis is one of the leading causes of preventable deaths in hospitals. This study presents the evaluation of a quality collaborative, which aimed to decrease sepsis-related hospital mortality.

Methods

The German Quality Network Sepsis (GQNS) offers quality reporting based on claims data, peer reviews, and support for establishing continuous quality management and staff education. This study evaluates the effects of participating in the GQNS during the intervention period (April 2016-June 2018) in comparison to a retrospective baseline (January 2014-March 2016). The primary outcome was all-cause risk-adjusted hospital mortality among cases with sepsis. Sepsis was identified by International Classification of Diseases (ICD) codes in claims data. A controlled time series analysis was conducted to analyze changes from the baseline to the intervention period comparing GQNS hospitals with the population of all German hospitals assessed via the national diagnosis-related groups (DRGs)-statistics. Tests were conducted using piecewise hierarchical models. Implementation processes and barriers were assessed by surveys of local leaders of quality improvement teams.

Results

Seventy-four hospitals participated, of which 17 were university hospitals and 18 were tertiary care facilities. Observed mortality was 43.5% during baseline period and 42.7% during intervention period. Interrupted time-series analyses did not show effects on course or level of risk-adjusted mortality of cases with sepsis compared to the national DRG-statistics after the beginning of the intervention period (p = 0.632 and p = 0.512, respectively). There was no significant mortality decrease in the subgroups of patients with septic shock or ventilation >24 h or predefined subgroups of hospitals. A standardized survey among 49 local quality improvement leaders in autumn of 2018 revealed that most hospitals did not succeed in implementing a continuous quality management program or relevant measures to improve early recognition and treatment of sepsis. Barriers perceived most commonly were lack of time (77.6%), staff shortage (59.2%), and lack of participation of relevant departments (38.8%).

Conclusion

As long as hospital-wide sepsis quality improvement efforts will not become a high priority for the hospital leadership by assuring adequate resources and involvement of all pertinent stakeholders, voluntary initiatives to improve the quality of sepsis care will remain prone to failure.

Should hospital-onset Adult Sepsis Event surveillance be routine… or even mandatory?

Hospital-onset sepsis accounts for 10%-15% of all sepsis cases and is associated with very high mortality rates, yet to date most hospitals have paid little attention to tracking its incidence and outcomes. This contrasts sharply with the substantial effort that hospitals and regulatory agencies spend tracking and reporting a limited subset of healthcare-associated infections. The recent development of the Center for Disease Control and Prevention's hospital-onset Adult Sepsis Event (ASE) definition, however, provides a validated and standardized mechanism for facilities to identify patients with nosocomial sepsis using routinely available electronic health record data. Recent data have demonstrated that hospital-onset ASE surveillance identifies many infections that are largely missed by current reportable healthcare-associated infections and that are associated with much higher mortality rates. Expanding the breadth of surveillance to include these highly consequential infections could help identify new targets for prevention and quality improvement and ultimately catalyze better outcomes for hospitalized patients. More work is needed, however, to characterize the preventability of hospital-onset ASE, develop and validate robust case-mix adjustment tools, and facilitate widespread uptake in hospitals with limited resources.

Ground truth labels challenge the validity of sepsis consensus definitions in critical illness

BACKGROUND

Sepsis is the leading cause of death in the intensive care unit (ICU). Expediting its diagnosis, largely determined by clinical assessment, improves survival. Predictive and explanatory modelling of sepsis in the critically ill commonly bases both outcome definition and predictions on clinical criteria for consensus definitions of sepsis, leading to circularity. As a remedy, we collected ground truth labels for sepsis.

METHODS

In the Ground Truth for Sepsis Questionnaire (GTSQ), senior attending physicians in the ICU documented daily their opinion on each patient's condition regarding sepsis as a five-category working diagnosis and nine related items. Working diagnosis groups were described and compared and their SOFA-scores analyzed with a generalized linear mixed model. Agreement and discriminatory performance measures for clinical criteria of sepsis and GTSQ labels as reference class were derived.

RESULTS

We analyzed 7291 questionnaires and 761 complete encounters from the first survey year. Editing rates for all items were > 90%, and responses were consistent with current understanding of critical illness pathophysiology, including sepsis pathogenesis. Interrater agreement for presence and absence of sepsis was almost perfect but only slight for suspected infection. ICU mortality was 19.5% in encounters with SIRS as the "worst" working diagnosis compared to 5.9% with sepsis and 5.9% with severe sepsis without differences in admission and maximum SOFA. Compared to sepsis, proportions of GTSQs with SIRS plus acute organ dysfunction were equal and macrocirculatory abnormalities higher (p < 0.0001). SIRS proportionally ranked above sepsis in daily assessment of illness severity (p < 0.0001). Separate analyses of neurosurgical referrals revealed similar differences. Discriminatory performance of Sepsis-1/2 and Sepsis-3 compared to GTSQ labels was similar with sensitivities around 70% and specificities 92%. Essentially no difference between the prevalence of SIRS and SOFA ≥ 2 yielded sensitivities and specificities for detecting sepsis onset close to 55% and 83%, respectively.

CONCLUSIONS

GTSQ labels are a valid measure of sepsis in the ICU. They reveal suspicion of infection as an unclear clinical concept and refute an illness severity hierarchy in the SIRS-sepsis-severe sepsis spectrum. Ground truth challenges the accuracy of Sepsis-1/2 and Sepsis-3 in detecting sepsis onset. It is an indispensable intermediate step towards advancing diagnosis and therapy in the ICU and, potentially, other health care settings.

Association Between Implementation of the Severe Sepsis and Septic Shock Early Management Bundle Performance Measure and Outcomes in Patients With Suspected Sepsis in US Hospitals

IMPORTANCE

In October 2015, the Centers for Medicare & Medicaid Services began requiring US hospitals to report adherence to the Severe Sepsis and Septic Shock Early Management Bundle (SEP-1).

OBJECTIVE

To evaluate the association of SEP-1 implementation with sepsis treatment patterns and outcomes in diverse hospitals.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study with interrupted time-series analysis and logistic regression models was conducted among adults admitted to 114 hospitals from October 2013 to December 2017 with suspected sepsis (blood culture orders, ≥2 systemic inflammatory response syndrome criteria, and acute organ dysfunction) within 24 hours of hospital arrival. Data analysis was conducted from September 2020 to September 2021.

EXPOSURES

SEP-1 implementation in the fourth quarter (Q4) of 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was quarterly rates of risk-adjusted short-term mortality (in-hospital death or discharge to hospice). Secondary outcomes included lactate testing and administration of anti-methicillin-resistant Staphylococcus aureus (MRSA) or antipseudomonal β-lactam antibiotics within 24 hours of hospital arrival. Generalized estimating equations with robust sandwich variances were used to fit logistic regression models to assess for changes in level or trends in these outcomes, adjusting for baseline characteristics and severity of illness.

RESULTS

The cohort included 117 510 patients (median [IQR] age, 67 years [55-78] years; 60 530 [51.5%] men and 56 980 [48.5%] women) with suspected sepsis. Lactate testing rates increased from 55.1% (95% CI, 53.9%-56.2%) in Q4 of 2013 to 76.7% (95% CI, 75.4%-78.0%) in Q4 of 2017, with a significant level change following SEP-1 implementation (odds ratio [OR], 1.34; 95% CI, 1.04-1.74). There were increases in use of anti-MRSA antibiotics (19.8% [95% CI, 18.9%-20.7%] in Q4 of 2013 to 26.3% [95% CI, 24.9%-27.7%] in Q4 of 2017) and antipseudomonal antibiotics (27.7% [95% CI, 26.7%-28.8%] in Q4 of 2013 to 40.5% [95% CI, 38.9%-42.0%] in Q4 of 2017), but these trends preceded SEP-1 and did not change with SEP-1 implementation. Unadjusted short-term mortality rates were similar in the pre-SEP-1 period (Q4 of 2013 through Q3 of 2015) vs the post-SEP-1 period (Q1 of 2016 through Q4 of 2017) (20.3% [95% CI, 20.0%-20.6%] vs 20.4% [95% CI, 20.1%-20.7%]), and SEP-1 implementation was not associated with changes in level (OR, 0.94; 95% CI, 0.68-1.29) or trend (OR, 1.00; 95% CI, 0.97-1.04) for risk-adjusted short-term mortality rates.

CONCLUSIONS AND RELEVANCE

In this cohort study, SEP-1 implementation was associated with an immediate increase in lactate testing rates, no change in already-increasing rates of broad-spectrum antibiotic use, and no change in short-term mortality rates for patients with suspected sepsis. Other approaches to decrease sepsis mortality may be warranted.

Epidemiology, Outcomes, and Trends of Patients With Sepsis and Opioid-Related Hospitalizations in U.S. Hospitals

OBJECTIVES

Widespread use and misuse of prescription and illicit opioids have exposed millions to health risks including serious infectious complications. Little is known, however, about the association between opioid use and sepsis.

DESIGN

Retrospective cohort study.

SETTING

About 373 U.S. hospitals.

PATIENTS

Adults hospitalized between January 2009 and September 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Sepsis was identified by clinical indicators of concurrent infection and organ dysfunction. Opioid-related hospitalizations were identified by the International Classification of Diseases, 9th Revision, Clinical Modification codes and/or inpatient orders for buprenorphine. Clinical characteristics and outcomes were compared by sepsis and opioid-related hospitalization status. The association between opioid-related hospitalization and all-cause, in-hospital mortality in patients with sepsis was assessed using mixed-effects logistic models to adjust for baseline characteristics and severity of illness.The cohort included 6,715,286 hospitalizations; 375,479 (5.6%) had sepsis, 130,399 (1.9%) had opioid-related hospitalizations, and 8,764 (0.1%) had both. Compared with sepsis patients without opioid-related hospitalizations (n = 366,715), sepsis patients with opioid-related hospitalizations (n = 8,764) were younger (mean 52.3 vs 66.9 yr) and healthier (mean Elixhauser score 5.4 vs 10.5), had more bloodstream infections from Gram-positive and fungal pathogens (68.9% vs 47.0% and 10.6% vs 6.4%, respectively), and had lower in-hospital mortality rates (10.6% vs 16.2%; adjusted odds ratio, 0.73; 95% CI, 0.60-0.79; p < 0.001 for all comparisons). Of 1,803 patients with opioid-related hospitalizations who died in-hospital, 928 (51.5%) had sepsis. Opioid-related hospitalizations accounted for 1.5% of all sepsis-associated deaths, including 5.7% of sepsis deaths among patients less than 50 years old. From 2009 to 2015, the proportion of sepsis hospitalizations that were opioid-related increased by 77% (95% CI, 40.7-123.5%).

CONCLUSIONS

Sepsis is an important cause of morbidity and mortality in patients with opioid-related hospitalizations, and opioid-related hospitalizations contribute disproportionately to sepsis-associated deaths among younger patients. In addition to ongoing efforts to combat the opioid crisis, public health agencies should focus on raising awareness about sepsis among patients who use opioids and their providers.

Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines

Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.

External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients

IMPORTANCE

The Epic Sepsis Model (ESM), a proprietary sepsis prediction model, is implemented at hundreds of US hospitals. The ESM's ability to identify patients with sepsis has not been adequately evaluated despite widespread use.

OBJECTIVE

To externally validate the ESM in the prediction of sepsis and evaluate its potential clinical value compared with usual care.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study was conducted among 27 697 patients aged 18 years or older admitted to Michigan Medicine, the academic health system of the University of Michigan, Ann Arbor, with 38 455 hospitalizations between December 6, 2018, and October 20, 2019.

EXPOSURE

The ESM score, calculated every 15 minutes.

MAIN OUTCOMES AND MEASURES

Sepsis, as defined by a composite of (1) the Centers for Disease Control and Prevention surveillance criteria and (2) International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic codes accompanied by 2 systemic inflammatory response syndrome criteria and 1 organ dysfunction criterion within 6 hours of one another. Model discrimination was assessed using the area under the receiver operating characteristic curve at the hospitalization level and with prediction horizons of 4, 8, 12, and 24 hours. Model calibration was evaluated with calibration plots. The potential clinical benefit associated with the ESM was assessed by evaluating the added benefit of the ESM score compared with contemporary clinical practice (based on timely administration of antibiotics). Alert fatigue was evaluated by comparing the clinical value of different alerting strategies.

RESULTS

We identified 27 697 patients who had 38 455 hospitalizations (21 904 women [57%]; median age, 56 years [interquartile range, 35-69 years]) meeting inclusion criteria, of whom sepsis occurred in 2552 (7%). The ESM had a hospitalization-level area under the receiver operating characteristic curve of 0.63 (95% CI, 0.62-0.64). The ESM identified 183 of 2552 patients with sepsis (7%) who did not receive timely administration of antibiotics, highlighting the low sensitivity of the ESM in comparison with contemporary clinical practice. The ESM also did not identify 1709 patients with sepsis (67%) despite generating alerts for an ESM score of 6 or higher for 6971 of all 38 455 hospitalized patients (18%), thus creating a large burden of alert fatigue.

CONCLUSIONS AND RELEVANCE

This external validation cohort study suggests that the ESM has poor discrimination and calibration in predicting the onset of sepsis. The widespread adoption of the ESM despite its poor performance raises fundamental concerns about sepsis management on a national level.

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning

BACKGROUND

Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adverse outcomes and improve the quality of care.

METHODS

Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train machine learning models (XGBoost and ElasticNet) to predict the occurrence of PSI 8 h prior to clinical suspicion. Prediction for PSI was benchmarked against PRISM-III.

RESULTS

Our model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI = [0.82, 0.85]), sensitivity of 0.73 [0.69, 0.74], and positive predictive value (PPV) of 0.36 [0.34, 0.36]. The PRISM-III conversely achieved a lower sensitivity of 0.19 [0.16, 0.22] and PPV of 0.30 [0.26, 0.34] at a cut-off of ≥ 10. The features with the most impact on the PSI prediction were maximum diastolic blood pressure prior to PSI prediction (mean SHAP = 3.4), height (mean SHAP = 3.2), and maximum temperature prior to PSI prediction (mean SHAP = 2.6).

CONCLUSION

A machine learning model using common features in the electronic medical records can predict the onset of serious infections in children with central venous lines at least 8 h prior to when a clinical team drew a blood culture.

Surveillance for Healthcare-Associated Infections: Hospital-Onset Adult Sepsis Events versus Current Reportable Conditions

BACKGROUND

U.S. hospitals are required by CMS to publicly report CLABSI, CAUTI, C.diffficile, MRSA bacteremia, and selected SSIs for benchmarking and pay-for-performance programs. It is unclear, however, to what extent these conditions capture the full breadth of serious healthcare-associated infections (HAIs). CDC's hospital-onset Adult Sepsis Event (HO-ASE) definition could facilitate more comprehensive and efficient surveillance for serious HAIs, but the overlap between HO-ASE and currently reportable HAIs is unknown.

METHODS

We retrospectively assessed the overlap between HO-ASEs and reportable HAIs among adults hospitalized between June 2015-June 2018 in 3 hospitals. Medical record reviews were conducted for 110 randomly selected HO-ASE cases to determine clinical correlates.

RESULTS

Amongst 282,441 hospitalized patients, 2,301 (0.8%) met HO-ASE criteria and 1,260 (0.4%) had reportable HAIs. In-hospital mortality rates were higher with HO-ASEs than reportable HAIs (28.6% vs 12.9%). Mortality rates for HO-ASE missed by reportable HAIs were substantially higher than mortality rates for reportable HAIs missed by HO-ASE (28.1% vs 6.3%). Reportable HAIs were only present in 334/2,301 (14.5%) HO-ASEs, most commonly CLABSIs (6.0% of HO-ASEs), C.difficile (5.0%), and CAUTI (3.0%). On medical record review, most HO-ASEs were caused by pneumonia (39.1%, of which only 34.9% were ventilator-associated), bloodstream infections (17.4%, of which only 10.5% were central line-associated), non-C.difficile intra-abdominal infections (14.5%), urinary infections (7.3%, of which 87.5% were catheter-associated), and skin/soft tissue infections (6.4%).

CONCLUSIONS

CDC's HO-ASE definition detects many serious nosocomial infections missed by currently reportable HAIs. HO-ASE surveillance could increase the efficiency and clinical significance of surveillance while identifying new targets for prevention.

Prevalence and Clinical Characteristics of Patients With Sepsis Discharge Diagnosis Codes and Short Lengths of Stay in U.S. Hospitals

Objectives:

Some patients diagnosed with sepsis have very brief hospitalizations. Understanding the prevalence and clinical characteristics of these patients may provide insight into how sepsis diagnoses are being applied as well as the breadth of illnesses encompassed by current sepsis definitions.

Design:

Retrospective observational study.

Setting:

One-hundred ten U.S. hospitals in the Cerner HealthFacts dataset (primary cohort) and four hospitals in Eastern Massachusetts (secondary cohort used for detailed medical record reviews).

Patients:

Adults hospitalized from April 2016 to December 2017.

Interventions:

None.

Measurements and Main Results:

We identified hospitalizations with International Classification of Diseases, 10th Edition codes for sepsis (including sepsis, septicemia, severe sepsis, and septic shock) and compared “short stay sepsis” patients (defined as discharge alive within 3 d) versus nonshort stay sepsis patients using detailed electronic health record data. In the Cerner cohort, 67,733 patients had sepsis discharge diagnosis codes, including 6,918 (10.2%) with short stays. Compared with nonshort stay sepsis patients, short stay patients were younger (median age 60 vs 67 yr) and had fewer comorbidities (median Elixhauser score 5 vs 13), lower rates of positive blood cultures (8.2% vs 24.1%), lower rates of ICU admission (6.2% vs 31.6%), and less frequently had severe sepsis/septic shock codes (13.5% vs 36.6%). Almost all short stay and nonshort stay sepsis patients met systemic inflammatory response syndrome criteria at admission (84.5% and 87.5%, respectively); 47.2% of those with short stays had Sequential Organ Failure Assessment scores of 2 or greater at admission versus 73.2% of those with longer stays. Findings were similar in the secondary four-hospital cohort. Medical record reviews demonstrated that physicians commonly diagnosed sepsis based on the presence of systemic inflammatory response syndrome criteria, elevated lactates, or positive blood cultures without concurrent organ dysfunction.

Conclusions:

In this large U.S. cohort, one in 10 patients coded for sepsis were discharged alive within 3 days. Although most short stay patients met systemic inflammatory response syndrome criteria, they met Sepsis-3 criteria less than half the time. Our findings underscore the incomplete uptake of Sepsis-3 definitions, the breadth of illness severities encompassed by both traditional and new sepsis definitions, and the possibility that some patients with sepsis recover very rapidly.

Association of a Care Bundle for Early Sepsis Management With Mortality Among Patients With Hospital-Onset or Community-Onset Sepsis

IMPORTANCE

The Early Management Bundle for Severe Sepsis/Septic Shock (SEP-1) is a quality metric based on a care bundle for early sepsis management. Published evidence on the association of SEP-1 with mortality is mixed and largely excludes cases of hospital-onset sepsis.

OBJECTIVE

To assess the association of the SEP-1 bundle with mortality and organ dysfunction in cohorts with hospital-onset or community-onset sepsis.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used data from 4 University of California hospitals from October 1, 2014, to October 1, 2017. Adult inpatients with a diagnosis consistent with sepsis or disseminated infection and laboratory or vital signs meeting the Sepsis-3 (Third International Consensus Definitions for Sepsis and Septic Shock) criteria were divided into community-onset sepsis and hospital-onset sepsis cohorts based on whether time 0 of sepsis occurred after arrival in the emergency department or an inpatient area. Data were analyzed from April to October 2019. Additional analyses were performed from December 2019 to January 2020.

EXPOSURES

Administration of SEP-1 and 4 individual bundle components (serum lactate level testing, blood culture, broad-spectrum intravenous antibiotic treatment, and intravenous fluid treatment).

MAIN OUTCOMES AND MEASURES

The primary outcome was in-hospital mortality. The secondary outcome was days requiring vasopressor support, measured as vasopressor days.

RESULTS

Among the 6404 patient encounters identified (3535 men [55.2%]; mean [SD] age, 64.0 [18.2] years), 2296 patients (35.9%) had hospital-onset sepsis. Among 4108 patients (64.1%) with community-onset sepsis, serum lactate level testing within 3 hours of time 0 was associated with reduced mortality (absolute difference, -7.61%; 95% CI, -14.70% to -0.54%). Blood culture (absolute difference, -1.10 days; 95% CI, -1.85 to -0.34 days) and broad-spectrum intravenous antibiotic treatment (absolute difference, -0.62 days; 95% CI, -1.02 to -0.22 days) were associated with fewer vasopressor days. Among patients with hospital-onset sepsis, broad-spectrum intravenous antibiotic treatment was the only bundle component significantly associated with any improved outcome (mortality difference, -5.20%; 95% CI, -9.84% to -0.56%). Care that was adherent to the complete SEP-1 bundle was associated with increased vasopressor days in patients with community-onset sepsis (absolute difference, 0.31 days; 95% CI, 0.11-0.51 days) but was not significantly associated with reduced mortality in either cohort (absolute difference, -0.07%; 95% CI, -3.02% to 2.88% in community-onset; absolute difference, -0.42%; 95% CI, -6.77% to 5.93% in hospital-onset).

CONCLUSIONS AND RELEVANCE

SEP-1-adherent care was not associated with improved outcomes of sepsis. Although multiple components of SEP-1 were associated with reduced mortality or decreased days of vasopressor therapy for patients who presented with sepsis in the emergency department, only broad-spectrum intravenous antibiotic treatment was associated with reduced mortality when time 0 occurred in an inpatient unit. Current sepsis quality metrics may need refinement.

Essential role of laboratory physicians in transformation of laboratory practice and management to a value-based patient-centric model

The laboratory is a vital part of the continuum of patient care. In fact, there are few programs in the healthcare system that do not rely on ready access and availability of complex diagnostic laboratory services. The existing transactional model of laboratory "medical practice" will not be able to meet the needs of the healthcare system as it rapidly shifts toward value-based care and precision medicine, which demands that practice be based on total system indicators, clinical effectiveness, and patient outcomes. Laboratory "value" will no longer be focused primarily on internal testing quality and efficiencies but rather on the relative cost of diagnostic testing compared to direct improvement in clinical and system outcomes. The medical laboratory as a "business" focused on operational efficiency and cost-controls must transform to become an essential clinical service that is a tightly integrated equal partner in direct patient care. We would argue that this paradigm shift would not be necessary if laboratory services had remained a "patient-centric" medical practice throughout the last few decades. This review is focused on the essential role of laboratory physicians in transforming laboratory practice and management to a value-based patient-centric model. Value-based practice is necessary not only to meet the challenges of the new precision medicine world order but also to bring about sustainable healthcare service delivery.

Agreement of claims-based methods for identifying sepsis with clinical criteria in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort

BACKGROUND

Claims-based algorithms are commonly used to identify sepsis in health services research because the laboratory features required to define clinical criteria may not be available in administrative data.

METHODS

We evaluated claims-based sepsis algorithms among adults in the US aged ≥65 years with Medicare health insurance enrolled in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Suspected infections from baseline (2003-2007) through December 31, 2012 were analyzed. Two claims-based algorithms were evaluated: (1) infection plus organ dysfunction diagnoses or sepsis diagnoses (Medicare-Implicit/Explicit) and (2) Centers for Medicare and Medicaid Services Severe Sepsis/Septic Shock Measure diagnoses (Medicare-CMS). Three classifications based on clinical criteria were used as standards for comparison: (1) the sepsis-related organ failure assessment (SOFA) score (REGARDS-SOFA), (2) "quick" SOFA (REGARDS-qSOFA), and (3) Centers for Disease Control and Prevention electronic health record criteria (REGARDS-EHR).

RESULTS

There were 2217 suspected infections among 9522 participants included in the current study. The total number of suspected infections classified as sepsis was 468 for Medicare-Implicit/Explicit, 249 for Medicare-CMS, 541 for REGARDS-SOFA, 185 for REGARDS-qSOFA, and 331 for REGARDS-EHR. The overall agreement between Medicare-Implicit/Explicit and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR was 77, 79, and 81%, respectively, sensitivity was 46, 53, and 57%, and specificity was 87, 82, and 85%. Comparing Medicare-CMS and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR, agreement was 77, 87, and 85%, respectively, sensitivity was 27, 41, and 36%, and specificity was 94, 92, and 93%. Events meeting the REGARDS-SOFA classification had a lower 90-day mortality rate (140.7 per 100 person-years) compared with the Medicare-CMS (296.1 per 100 person-years), REGARDS-qSOFA (238.6 per 100 person-years), Medicare-Implicit/Explicit (219.4 per 100 person-years), and REGARDS-EHR classifications (201.8 per 100 person-years).

CONCLUSION

Claims-based sepsis algorithms have high agreement and specificity but low sensitivity when compared with clinical criteria. Both claims-based algorithms identified a patient population with similar 90-day mortality rates as compared with classifications based on qSOFA and EHR criteria but higher mortality relative to SOFA criteria.

Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data

BACKGROUND

Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.

METHODS

A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review.

RESULTS

In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.

CONCLUSIONS

A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

Sepsis trends: increasing incidence and decreasing mortality, or changing denominator?

Numerous studies suggest that the incidence of sepsis has been steadily increasing over the past several decades while mortality rates are falling. However, reliably assessing trends in sepsis epidemiology is challenging due to changing diagnosis and coding practices over time. Ongoing efforts by clinicians, administrators, policy makers, and patient advocates to increase sepsis awareness, screening, and recognition are leading to more patients being labeled with sepsis. Subjective clinical definitions and heterogeneous presentations also allow for wide discretion in diagnosing sepsis rather than specific infections alone or non-specific syndromes. These factors create a potential ascertainment bias whereby the inclusion of less severely ill patients in sepsis case counts over time leads to a perceived increase in sepsis incidence and decrease in sepsis mortality rates. Analyses that rely on administrative data alone are further confounded by changing coding practices in response to new policies, financial incentives, and efforts to improve documentation. An alternate strategy for measuring sepsis incidence, outcomes, and trends is to use objective and consistent clinical criteria rather than administrative codes or registries to identify sepsis. This is feasible using data routinely found in electronic health record systems, such as blood culture draws and sustained courses of antibiotics to identify infection and laboratory values, vasopressors, and mechanical ventilation to measure acute organ dysfunction. Recent surveillance studies using this approach suggest that sepsis incidence and mortality rates have been essentially stable over the past decade. In this review, we summarize the major epidemiologic studies of sepsis trends, potential biases in these analyses, and the recent change in the surveillance paradigm toward using objective clinical data from electronic health records to more accurately characterize sepsis trends.

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.

Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions

Supplemental Digital Content is available in the text.

To develop and evaluate a novel strategy that automates the retrospective identification of sepsis using electronic health record data.

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.

Epidemiology of Hospital-Onset Versus Community-Onset Sepsis in U.S. Hospitals and Association With Mortality: A Retrospective Analysis Using Electronic Clinical Data

OBJECTIVES

Prior studies have reported that hospital-onset sepsis is associated with higher mortality rates than community-onset sepsis. Most studies, however, have used inconsistent case-finding methods and applied limited risk-adjustment for potential confounders. We used consistent sepsis criteria and detailed electronic clinical data to elucidate the epidemiology and mortality associated with hospital-onset sepsis.

DESIGN

Retrospective cohort study.

SETTING

136 U.S. hospitals in the Cerner HealthFacts dataset.

PATIENTS

Adults hospitalized in 2009-2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We identified sepsis using Centers for Disease Control and Prevention Adult Sepsis Event criteria and estimated the risk of in-hospital death for hospital-onset sepsis versus community-onset sepsis using logistic regression models. In patients admitted without community-onset sepsis, we estimated risk of death associated with hospital-onset sepsis using Cox regression models with sepsis as a time-varying covariate. Models were adjusted for baseline characteristics and severity of illness. Among 2.2 million hospitalizations, there were 95,154 sepsis cases: 83,620 (87.9%) community-onset sepsis and 11,534 (12.1%) hospital-onset sepsis (0.5% of hospitalized cohort). Compared to community-onset sepsis, hospital-onset sepsis patients were younger (median 66 vs 68 yr) but had more comorbidities (median Elixhauser score 14 vs 11), higher Sequential Organ Failure Assessment scores (median 4 vs 3), higher ICU admission rates (61% vs 44%), longer hospital length of stay (median 19 vs 8 d), and higher in-hospital mortality (33% vs 17%) (p < 0.001 for all comparisons). On multivariate analysis, hospital-onset sepsis was associated with higher mortality versus community-onset sepsis (odds ratio, 2.1; 95% CI, 2.0-2.2) and patients admitted without sepsis (hazard ratio, 3.0; 95% CI, 2.9-3.2).

CONCLUSIONS

Hospital-onset sepsis complicated one in 200 hospitalizations and accounted for one in eight sepsis cases, with one in three patients dying in-hospital. Hospital-onset sepsis preferentially afflicted ill patients but even after risk-adjustment, they were twice as likely to die as community-onset sepsis patients; in patients admitted without sepsis, hospital-onset sepsis tripled the risk of death. Hospital-onset sepsis is an important target for surveillance, prevention, and quality improvement initiatives.

Prevalence, Underlying Causes, and Preventability of Sepsis-Associated Mortality in US Acute Care Hospitals

IMPORTANCE

Sepsis is present in many hospitalizations that culminate in death. The contribution of sepsis to these deaths, and the extent to which they are preventable, is unknown.

OBJECTIVE

To estimate the prevalence, underlying causes, and preventability of sepsis-associated mortality in acute care hospitals.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study in which a retrospective medical record review was conducted of 568 randomly selected adults admitted to 6 US academic and community hospitals from January 1, 2014, to December 31, 2015, who died in the hospital or were discharged to hospice and not readmitted. Medical records were reviewed from January 1, 2017, to March 31, 2018.

MAIN OUTCOMES AND MEASURES

Clinicians reviewed cases for sepsis during hospitalization using Sepsis-3 criteria, hospice-qualifying criteria on admission, immediate and underlying causes of death, and suboptimal sepsis-related care such as inappropriate or delayed antibiotics, inadequate source control, or other medical errors. The preventability of each sepsis-associated death was rated on a 6-point Likert scale.

RESULTS

The study cohort included 568 patients (289 [50.9%] men; mean [SD] age, 70.5 [16.1] years) who died in the hospital or were discharged to hospice. Sepsis was present in 300 hospitalizations (52.8%; 95% CI, 48.6%-57.0%) and was the immediate cause of death in 198 cases (34.9%; 95% CI, 30.9%-38.9%). The next most common immediate causes of death were progressive cancer (92 [16.2%]) and heart failure (39 [6.9%]). The most common underlying causes of death in patients with sepsis were solid cancer (63 of 300 [21.0%]), chronic heart disease (46 of 300 [15.3%]), hematologic cancer (31 of 300 [10.3%]), dementia (29 of 300 [9.7%]), and chronic lung disease (27 of 300 [9.0%]). Hospice-qualifying conditions were present on admission in 121 of 300 sepsis-associated deaths (40.3%; 95% CI 34.7%-46.1%), most commonly end-stage cancer. Suboptimal care, most commonly delays in antibiotics, was identified in 68 of 300 sepsis-associated deaths (22.7%). However, only 11 sepsis-associated deaths (3.7%) were judged definitely or moderately likely preventable; another 25 sepsis-associated deaths (8.3%) were considered possibly preventable.

CONCLUSIONS AND RELEVANCE

In this cohort from 6 US hospitals, sepsis was the most common immediate cause of death. However, most underlying causes of death were related to severe chronic comorbidities and most sepsis-associated deaths were unlikely to be preventable through better hospital-based care. Further innovations in the prevention and care of underlying conditions may be necessary before a major reduction in sepsis-associated deaths can be achieved.