Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort

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.

Evaluation of a Structured Review Process for Emergency Department Return Visits with Admission

BACKGROUND

Review of emergency department (ED) revisits with admission allows the identification of improvement opportunities. Applying a health equity lens to revisits may highlight potential disparities in care transitions. Universal definitions or practicable frameworks for these assessments are lacking. The authors aimed to develop a structured methodology for this quality assurance (QA) process, with a layered equity analysis.

METHODS

The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher's exact tests.

RESULTS

The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education.

CONCLUSION

The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.

Validation of SeptiCyte RAPID to Discriminate Sepsis from Non-Infectious Systemic Inflammation

(1) Background: SeptiCyte RAPID is a molecular test for discriminating sepsis from non-infectious systemic inflammation, and for estimating sepsis probabilities. The objective of this study was the clinical validation of SeptiCyte RAPID, based on testing retrospectively banked and prospectively collected patient samples. (2) Methods: The cartridge-based SeptiCyte RAPID test accepts a PAXgene blood RNA sample and provides sample-to-answer processing in ~1 h. The test output (SeptiScore, range 0-15) falls into four interpretation bands, with higher scores indicating higher probabilities of sepsis. Retrospective (N = 356) and prospective (N = 63) samples were tested from adult patients in ICU who either had the systemic inflammatory response syndrome (SIRS), or were suspected of having/diagnosed with sepsis. Patients were clinically evaluated by a panel of three expert physicians blinded to the SeptiCyte test results. Results were interpreted under either the Sepsis-2 or Sepsis-3 framework. (3) Results: Under the Sepsis-2 framework, SeptiCyte RAPID performance for the combined retrospective and prospective cohorts had Areas Under the ROC Curve (AUCs) ranging from 0.82 to 0.85, a negative predictive value of 0.91 (sensitivity 0.94) for SeptiScore Band 1 (score range 0.1-5.0; lowest risk of sepsis), and a positive predictive value of 0.81 (specificity 0.90) for SeptiScore Band 4 (score range 7.4-15; highest risk of sepsis). Performance estimates for the prospective cohort ranged from AUC 0.86-0.95. For physician-adjudicated sepsis cases that were blood culture (+) or blood, urine culture (+)(+), 43/48 (90%) of SeptiCyte scores fell in Bands 3 or 4. In multivariable analysis with up to 14 additional clinical variables, SeptiScore was the most important variable for sepsis diagnosis. A comparable performance was obtained for the majority of patients reanalyzed under the Sepsis-3 definition, although a subgroup of 16 patients was identified that was called septic under Sepsis-2 but not under Sepsis-3. (4) Conclusions: This study validates SeptiCyte RAPID for estimating sepsis probability, under both the Sepsis-2 and Sepsis-3 frameworks, for hospitalized patients on their first day of ICU admission.

Diagnostic uncertainty in infectious diseases: Advocacy for a nosological framework

Diagnostic uncertainty (DU) is frequent in infectious diseases (ID), being recorded in 10% to over 50% of patients. Herein, we show that in several fields of clinical practice, high rates of DU are constant over time. DUs are not taken into account in guidelines, as therapeutic propositions are based on an established diagnosis. Moreover, while other guidelines underline the need for rapid broad-spectrum antibiotic therapy for patients with sepsis, many clinical conditions mimic sepsis and lead to unnecessary antibiotic therapy. Considering DU, many studies have been carried out to look for relevant biomarkers of infections, which also attest to non-infectious diseases mimicking infections. Therefore, diagnosis is often primarily a hypothesis, and empirical antibiotic therapy should be reassessed when microbiological data are available. However, other than for urinary tract infections or unexpected primary bacteremia, the high frequency of sterile microbiological samples implies that DU remains central in follow-up, which does not facilitate clinical management or antibiotic optimization. The main way to resolve the therapeutic challenge of DU could be to precisely describe the latter through a consensual definition that would facilitate consideration of DU and its mandatory therapeutic implications. A consensual definition of DU would also clarify responsibility and accountability for physicians in the antimicrobial approval process and l provide an opportunity to instruct their students in this large field of medical practices and to productively conduct relevant research.

Prevalence, Clinical Characteristics, and Outcomes of Sepsis Caused by Severe Acute Respiratory Syndrome Coronavirus 2 Versus Other Pathogens in Hospitalized Patients With COVID-19

The prevalence and causes of sepsis in patients hospitalized with COVID-19 are poorly characterized.

OBJECTIVES

To investigate the prevalence, clinical characteristics, and outcomes of sepsis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) versus other pathogens in patients hospitalized with COVID-19.

DESIGN SETTING AND PARTICIPANTS

Cross-sectional, retrospective chart review of 200 randomly selected patients hospitalized with COVID-19 at four Massachusetts hospitals between March 2020 and March 2021.

MAIN OUTCOMES AND MEASURES

The presence or absence of sepsis was determined per Sepsis-3 criteria (infection leading to an increase in Sequential Organ Failure Assessment score by ≥ 2 points above baseline). Sepsis episodes were assessed as caused by SARS-CoV-2, other pathogens, or both. Rates of organ dysfunction and in-hospital death were also assessed.

RESULTS

Sepsis was present in 65 of 200 COVID-19 hospitalizations (32.5%), of which 46 of 65 sepsis episodes (70.8%) were due to SARS-CoV-2 alone, 17 of 65 (26.2%) were due to both SARS-CoV-2 and non-SARS-CoV-2 infections, and two of 65 (3.1%) were due to bacterial infection alone. SARS-CoV-2-related organ dysfunction in patients with sepsis occurred a median of 1 day after admission (interquartile range, 0-2 d) and most often presented as respiratory (93.7%), neurologic (46.0%), and/or renal (39.7%) dysfunctions. In-hospital death occurred in 28 of 200 COVID-19 hospitalizations (14.0%), including two of 135 patients without sepsis (1.5%), 16 of 46 patients with sepsis (34.8%) due to SARS-CoV-2 alone, and 10 of 17 patients with sepsis (58.8%) due to both SARS-CoV-2 and bacterial pathogens.

CONCLUSIONS

Sepsis occurred in one in three patients hospitalized with COVID-19 and was primarily caused by SARS-CoV-2 itself, although bacterial infection also contributed in a quarter of sepsis cases. Mortality in COVID-19 patients with sepsis was high, especially in patients with mixed SARS-CoV-2 and bacterial sepsis. These findings affirm SARS-CoV-2 as an important cause of sepsis and highlight the need to improve surveillance, recognition, prevention, and treatment of both viral and bacterial sepsis in hospitalized patients with COVID-19.

Retrospective Identification of Infection in the Emergency Department: A Significant Challenge in Sepsis Clinical Trials

BACKGROUND

This study examined three methods for retrospectively identifying infection in emergency department (ED) patients: modified objective definitions of infection (MODI) from the CDC/NHSN, physician adjudication determination of infection, and ED treating physician behavior.

METHOD

This study used a subset of data from a prospective sepsis trial. We used Fleiss's Kappa to compare agreement between two physicians retrospectively adjudicating infection based on the patient's medical record, modified infection definition from the CDC/NHSN, and ED treating physician behavior.

RESULTS

Overall, there was similar agreement between physician adjudication of infection and MODI criteria (Kappa=0.59) compared to having two physicians independently identify infection through retrospective chart review (Kappa=0.58). ED treating physician behavior was a poorer proxy for infection when compared to the MODI criteria (0.41) and physician adjudication (Kappa = 0.50).

CONCLUSION

Retrospective identification of infection poses a significant challenge in sepsis clinical trials. Using modified definitions of infection provides a standardized, less time consuming, and equally effective means of identifying infection compared to having multiple physicians adjudicate a patient's chart.

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

OBJECTIVE

To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.

MATERIALS AND METHODS

PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.

RESULTS

The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.

DISCUSSION

Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.

CONCLUSIONS

Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

Diagnostic Challenges in Sepsis

Purpose of Review

Sepsis is a leading cause of death worldwide. Groundbreaking international collaborative efforts have culminated in the widely accepted surviving sepsis guidelines, with iterative improvements in management strategies and definitions providing important advances in care for patients. Key to the diagnosis of sepsis is identification of infection, and whilst the diagnostic criteria for sepsis is now clear, the diagnosis of infection remains a challenge and there is often discordance between clinician assessments for infection.

Recent Findings

We review the utility of common biochemical, microbiological and radiological tools employed by clinicians to diagnose infection and explore the difficulty of making a diagnosis of infection in severe inflammatory states through illustrative case reports. Finally, we discuss some of the novel and emerging approaches in diagnosis of infection and sepsis.

Summary

While prompt diagnosis and treatment of sepsis is essential to improve outcomes in sepsis, there remains no single tool to reliably identify or exclude infection. This contributes to unnecessary antimicrobial use that is harmful to individuals and populations. There is therefore a pressing need for novel solutions. Machine learning approaches using multiple diagnostic and clinical inputs may offer a potential solution but as yet these approaches remain experimental.

Simplified therapeutic guidelines: the main tool of antimicrobial stewardship programs associated with optimal antibiotic therapy V3

Hospitals regularly seek to upgrade their antimicrobial stewardship program (ASP). Our aim was to evaluate the impact of simplified therapeutic guidelines (STGs) compared to various established tools for ASP on the rate of optimal antibiotic therapy (OAT) and antibiotic consumption. Audits of antibiotic prescriptions were carried out over a 24-month period. Feedback information led to STGs (e.g., ≤ 15 drugs). The impact of STGs was based on the rate of OAT, defined as a diagnosis of the infectious disease in the patient's medical records associated with the corresponding therapy indicated in the STGs or in other guidelines. STGs were compared to five other means of ASP: internal or national guidelines, audit, information regarding antibiotic consumption and bacterial resistance, and restricted access to targeted antibiotics. Antibiotic consumption was measured in defined daily doses/1000 days of hospital stay, focusing on third-generation cephalosporins (TGC) and fluoroquinolones (FQ). Twenty-six hospitals were audited from April 2017 to June 2019. A total of 1,028 antibiotic prescriptions were analyzed, including 204 (20%) after STG implementation in seven hospitals. In multivariate analysis, OAT (n = 176, 17%) was associated with STGs, AOR 2.21 [1.51-3.22], and with three tools in place, 1.75 [1.24-2.48]. The relative variations of consumption of TGC and FQ for hospitals with or without STGs were - 13.1 vs. + 9.4% and - 18.5 vs. - 2.7%, respectively, from 2018 to 2019. STGs were more likely than other ASP tools to improve the rate of OAT and to reduce the consumption of antibiotics.

Sepsis in Home Health Care: Screening, Education, and Rapid Triage

BACKGROUND

A majority of sepsis cases originate in the home and community. Home health clinicians play an important role in the early identification and timely treatment of sepsis.

LOCAL PROBLEM

A home health care provider sought to prevent hospital readmissions due to sepsis by implementing a sepsis-screening protocol and quality improvement initiative.

METHODS

The provider conducted a retrospective chart review of 33 264 sepsis screens of 7242 patients.

INTERVENTIONS

A sepsis-screening protocol, clinician and patient/caregiver sepsis education, physician and emergency department communication, and emergency medical services collaboration procedure were implemented.

RESULTS

A majority (69.2%) of positive sepsis screens resulted in patients receiving early medical intervention and avoiding hospitalization.

CONCLUSIONS

Having a formal sepsis-screening program in place prompts home health clinicians to communicate the patient's symptoms to their primary care provider, which can positively impact hospital readmission rates and associated medical costs.

Assessment of a Cellular Host Response Test as a Sepsis Diagnostic for Those With Suspected Infection in the Emergency Department

Supplemental Digital Content is available in the text.

Objectives:

Sepsis is a common cause of morbidity and mortality. A reliable, rapid, and early indicator can help improve efficiency of care and outcomes. To assess the IntelliSep test, a novel in vitro diagnostic that quantifies the state of immune activation by measuring the biophysical properties of leukocytes, as a rapid diagnostic for sepsis and a measure of severity of illness, as defined by Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation-II scores and the need for hospitalization.

Design, Setting, SUBJECTS:

Adult patients presenting to two emergency departments in Baton Rouge, LA, with signs of infection (two of four systemic inflammatory response syndrome criteria, with at least one being aberration of temperature or WBC count) or suspicion of infection (a clinician order for culture of a body fluid), were prospectively enrolled. Sepsis status, per Sepsis-3 criteria, was determined through a 3-tiered retrospective and blinded adjudication process consisting of objective review, site-level clinician review, and final determination by independent physician adjudicators.

MEASUREMENTS AND MAIN RESULTS:

Of 266 patients in the final analysis, those with sepsis had higher IntelliSep Index (median = 6.9; interquartile range, 6.1–7.6) than those adjudicated as not septic (median = 4.7; interquartile range, 3.7–5.9; p < 0.001), with an area under the receiver operating characteristic curve of 0.89 and 0.83 when compared with unanimous and forced adjudication standards, respectively. Patients with higher IntelliSep Index had higher Sequential Organ Failure Assessment (3 [interquartile range, 1–5] vs 1 [interquartile range, 0–2]; p < 0.001) and Acute Physiology and Chronic Health Evaluation-II (7 [interquartile range, 3.5–11.5] vs 5 [interquartile range, 2–9]; p < 0.05) and were more likely to be admitted to the hospital (83.6% vs 48.3%; p < 0.001) compared with those with lower IntelliSep Index.

CONCLUSIONS:

In patients presenting to the emergency department with signs or suspicion of infection, the IntelliSep Index is a promising tool for the rapid diagnosis and risk stratification for sepsis.

Serial measurement of pancreatic stone protein for the early detection of sepsis in intensive care unit patients: a prospective multicentric study

Background

The early recognition and management of sepsis improves outcomes. Biomarkers may help in identifying earlier sub-clinical signs of sepsis. We explored the potential of serial measurements of C-reactive protein (CRP), procalcitonin (PCT) and pancreatic stone protein (PSP) for the early recognition of sepsis in patients hospitalized in the intensive care unit (ICU).

Methods

This was a multicentric international prospective observational clinical study conducted in 14 ICUs in France, Switzerland, Italy, and the United Kingdom. Adult ICU patients at risk of nosocomial sepsis were included. A biomarker-blinded adjudication committee identified sepsis events and the days on which they began. The association of clinical sepsis diagnoses with the trajectories of PSP, CRP, and PCT in the 3 days preceding these diagnoses of sepsis were tested for markers of early sepsis detection. The performance of the biomarkers in sepsis diagnosis was assessed by receiver operating characteristic (ROC) analysis.

Results

Of the 243 patients included, 53 developed nosocomial sepsis after a median of 6 days (interquartile range, 3–8 days). Clinical sepsis diagnosis was associated with an increase in biomarkers value over the 3 days preceding this diagnosis [PSP (p = 0.003), PCT (p = 0.025) and CRP (p = 0.009)]. PSP started to increase 5 days before the clinical diagnosis of sepsis, PCT 3 and CRP 2 days, respectively. The area under the ROC curve at the time of clinical sepsis was similar for all markers (PSP, 0.75; CRP, 0.77; PCT, 0.75).

Conclusions

While the diagnostic accuracy of PSP, CRP and PCT for sepsis were similar in this cohort, serial PSP measurement demonstrated an increase of this marker the days preceding the onset of signs necessary to clinical diagnose sepsis. This observation justifies further evaluation of the potential clinical benefit of serial PSP measurement in the management of critically ill patients developing nosocomial sepsis.

Trial registration The study has been registered at ClinicalTrials.gov (no. NCT03474809), on March 16, 2018. https://www.clinicaltrials.gov/ct2/show/NCT03474809?term=NCT03474809&draw=2&rank=1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03576-8.

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.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Host Diagnostic Biomarkers of Infection in the ICU: Where Are We and Where Are We Going?

Purpose of Review

Early identification of infection in the critically ill patient and initiation of appropriate treatment is key to reducing morbidity and mortality. On the other hand, the indiscriminate use of antimicrobials leads to harms, many of which may be exaggerated in the critically ill population. The current method of diagnosing infection in the intensive care unit relies heavily on clinical gestalt; however, this approach is plagued by biases. Therefore, a reliable, independent biomarker holds promise in the accurate determination of infection. We discuss currently used host biomarkers used in the intensive care unit and review new and emerging approaches to biomarker discovery.

Recent Findings

White cell count (including total white cell count, left shift, and the neutrophil-leucocyte ratio), C-reactive protein, and procalcitonin are the most common host diagnostic biomarkers for sepsis used in current clinical practice. However, their utility in the initial diagnosis of infection, and their role in the subsequent decision to commence treatment, remains limited. Novel approaches to biomarker discovery that are currently being investigated include combination biomarkers, host 'sepsis signatures' based on differential gene expression, site-specific biomarkers, biomechanical assays, and incorporation of new and pre-existing host biomarkers into machine learning algorithms.

Summary

To date, no single reliable independent biomarker of infection exists. Whilst new approaches to biomarker discovery hold promise, their clinical utility may be limited if previous mistakes that have afflicted sepsis biomarker research continue to be repeated.

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.

Ventilator-Associated Tracheobronchitis: To Treat or Not to Treat?

Ventilator-associated tracheobronchitis (VAT) is an infection commonly affecting mechanically ventilated intubated patients. Several studies suggest that VAT is associated with increased duration of mechanical ventilation (MV) and length of intensive care unit (ICU) stay, and a presumptive increase in healthcare costs. Uncertainties remain, however, regarding the cost/benefit balance of VAT treatment. The aim of this narrative review is to discuss the two fundamental and inter-related dilemmas regarding VAT, i.e., (i) how to diagnose VAT? and (ii) should we treat VAT? If yes, should we treat all cases or only selected ones? How should we treat in terms of antibiotic choice, route, treatment duration?

Biomarkers of Infection and Sepsis

The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.

Valuing antibiotics: The role of the hospital clinician

The global public health threat of antibiotic-resistant infections as well as the lack of new treatments in clinical development is a critical issue. Reasons for this include diminished commercial incentives for pharmaceutical companies to develop new antibiotics, which part-reflects a shift in antibiotic marketing paradigm from broad deployment to targeted therapy in relatively small patient populations. Such changes are encouraged by antimicrobial stewardship (AMS). Other factors include a lack of recognition in the traditional assessment of new antibiotics by regulators, health technology assessors and payers of the broad range of benefits of new agents, particularly their value to health care, economies and society. Recognising the seriousness of the situation, there have been recent changes and proposals by regulators for modification of the assessment process to accommodate a broader range of acceptable data supporting new drug applications. There is also increasing recognition by some payers of the societal benefit of new antibiotics and the need for financial incentives for those developing high-priority antibiotics. However, progress is slow, with recent publications focusing on industry and strategic perspectives rather than clinical implications. In this opinion piece, we therefore focus on clinicians and the practical steps they can take to drive and contribute to increasing awareness and understanding of the value of antibiotics. This includes identifying and gathering appropriate alternative data sources, educating on AMS and prescribing habits, and contributing to international antibiotic susceptibility surveillance models.