Use of artificial intelligence in critical care: opportunities and obstacles.

BACKGROUND: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY: Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials.

Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials.

Persistent Acute Kidney Injury is Associated with Poor Outcomes and Increased Hospital Cost in Vascular Surgery.

BACKGROUND: Postoperative acute kidney injury (AKI) is common after major surgery and is associated with increased morbidity, mortality, and cost. Additionally, there are recent studies demonstrating that time to renal recovery may have a substantial impact on clinical outcomes. We hypothesized that patients with delayed renal recovery after major vascular surgery will have increased complications, mortality, and hospital cost. METHODS: A single-center retrospective cohort of patients undergoing nonemergent major vascular surgery between 6/1/2014 and 10/1/2020 was analyzed. Development of postoperative AKI (defined using Kidney Disease Improving Global Outcomes (KDIGO) criteria: >50% or > 0.3 mg/dl absolute increase in serum creatinine relative to reference after surgery and before discharge) was evaluated. Patients were divided into 3 groups: no AKI, rapidly reversed AKI (<48 hours), and persistent AKI (≥48 hours). Multivariable generalized linear models were used to evaluate the association between AKI groups and postoperative complications, 90-day mortality, and hospital cost. RESULTS: A total of 1,881 patients undergoing 1,980 vascular procedures were included. Thirty five percent of patients developed postoperative AKI. Patients with persistent AKI had longer intensive care unit and hospital stays, as well as more mechanical ventilation days. In multivariable logistic regression analysis, persistent AKI was a major predictor of 90-day mortality (odds ratio 4.1, 95% confidence interval 2.4-7.1). Adjusted average cost was higher for patients with any type of AKI. The incremental cost of having any AKI ranged from $3,700 to $9,100, even after adjustment for comorbidities and other postoperative complications. The adjusted average cost for patients stratified by type of AKI was higher among patients with persistent AKI compared to those with no or rapidly reversed AKI. CONCLUSIONS: Persistent AKI after vascular surgery is associated with increased complications, mortality, and cost. Strategies to prevent and aggressively treat AKI, specifically persistent AKI, in the perioperative setting are imperative to optimize care for this population.

Postoperative Overtriage to an Intensive Care Unit Is Associated With Low Value of Care.

OBJECTIVE: We test the hypothesis that for low-acuity surgical patients, postoperative intensive care unit (ICU) admission is associated with lower value of care compared with ward admission. BACKGROUND: Overtriaging low-acuity patients to ICU consumes valuable resources and may not confer better patient outcomes. Associations among postoperative overtriage, patient outcomes, costs, and value of care have not been previously reported. METHODS: In this longitudinal cohort study, postoperative ICU admissions were classified as overtriaged or appropriately triaged according to machine learning-based patient acuity assessments and requirements for immediate postoperative mechanical ventilation or vasopressor support. The nearest neighbors algorithm identified risk-matched control ward admissions. The primary outcome was value of care, calculated as inverse observed-to-expected mortality ratios divided by total costs. RESULTS: Acuity assessments had an area under the receiver operating characteristic curve of 0.92 in generating predictions for triage classifications. Of 8592 postoperative ICU admissions, 423 (4.9%) were overtriaged. These were matched with 2155 control ward admissions with similar comorbidities, incidence of emergent surgery, immediate postoperative vital signs, and do not resuscitate order placement and rescindment patterns. Compared with controls, overtraiged admissions did not have a lower incidence of any measured complications. Total costs for admission were $16.4K for overtriage and $15.9K for controls ( P =0.03). Value of care was lower for overtriaged admissions [2.9 (2.0-4.0)] compared with controls [24.2 (14.1-34.5), P <0.001]. CONCLUSIONS: Low-acuity postoperative patients who were overtriaged to ICUs had increased total costs, no improvements in outcomes, and received low-value care.

Moving toward a contemporary classification of drug-induced kidney disease.

Drug-induced kidney disease (DIKD) accounts for about one-fourth of all cases of acute kidney injury (AKI) in hospitalized patients, especially in critically ill setting. There is no standard definition or classification system of DIKD. To address this, a phenotype definition of DIKD using expert consensus was introduced in 2015. Recently, a novel framework for DIKD classification was proposed that incorporated functional change and tissue damage biomarkers. Medications were stratified into four categories, including "dysfunction without damage," "damage without dysfunction," "both dysfunction and damage," and "neither dysfunction nor damage" using this novel framework along with predominant mechanism(s) of nephrotoxicity for drugs and drug classes. Here, we briefly describe mechanisms and provide examples of drugs/drug classes related to the categories in the proposed framework. In addition, the possible movement of a patient's kidney disease between certain categories in specific conditions is considered. Finally, opportunities and barriers to adoption of this framework for DIKD classification in real clinical practice are discussed. This new classification system allows congruencies for DIKD with the proposed categorization of AKI, offering clarity as well as consistency for clinicians and researchers.

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review.

OBJECTIVE: Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions. SUMMARY BACKGROUND DATA: Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (ie, resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms. METHODS: Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines. RESULTS: Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriche these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to intensive care units (ICUs) has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions. CONCLUSION: To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

Clinical Trajectories of Acute Kidney Injury in Surgical Sepsis: A Prospective Observational Study.

OBJECTIVE: To characterize endothelial function, inflammation, and immunosuppression in surgical patients with distinct clinical trajectories of AKI and to determine the impact of persistent kidney injury and renal non-recovery on clinical outcomes, resource utilization, and long-term disability and survival. SUMMARY OF BACKGROUND DATA: AKI is associated with increased healthcare costs and mortality. Trajectories that account for duration and recovery of AKI have not been described for sepsis patients, who are uniquely vulnerable to renal dysfunction. METHODS: This prospective observational study included 239 sepsis patients admitted and enrolled between January 2015 and July 2017. Kidney Disease: Improving Global Outcomes (KDIGO) and Acute Disease Quality Initiative (ADQI) criteria were used to classify subjects as having no AKI, rapidly reversed AKI, persistent AKI with renal recovery, or persistent AKI without renal recovery. Serial biomarker profiles, clinical outcomes, resource utilization, and long-term physical performance status and survival were compared among AKI trajectories. RESULTS: Sixty-two percent of the study population developed AKI. Only one-third of AKI episodes rapidly reversed within 48 hours; the remaining had persistent AKI, among which 57% did not have renal recovery by discharge. One-year survival and proportion of subjects fully active 1 year after sepsis was lowest among patients with persistent AKI compared with other groups. Long-term mortality hazard rates were 5-fold higher for persistent AKI without renal recovery compared with no AKI. CONCLUSIONS: Among critically ill surgical sepsis patients, persistent AKI and the absence of renal recovery are associated with distinct early and sustained immunologic and endothelial biomarker signatures and decreased long-term physical function and survival.

Transcriptomic responses from improved murine sepsis models can better mimic human surgical sepsis.

Historically, murine models of inflammation in biomedical research have been shown to minimally correlate with genomic expression patterns from blood leukocytes in humans. In 2019, our laboratory reported an improved surgical sepsis model of cecal ligation and puncture (CLP) that provides additional daily chronic stress (DCS), as well as adhering to the Minimum Quality Threshold in Pre-Clinical Sepsis Studies (MQTiPSS) guidelines. This model phenotypically recapitulates the persistent inflammation, immunosuppression, and catabolism syndrome observed in adult human surgical sepsis survivors. Whether these phenotypic similarities between septic humans and mice are replicated at the circulating blood leukocyte transcriptome has not been demonstrated. Our analysis, in contrast with previous findings, demonstrated that genome-wide expression in our new murine model more closely approximated human surgical sepsis patients, particularly in the more chronic phases of sepsis. Importantly, our new model of murine surgical sepsis with chronic stress did not reflect well gene expression patterns from humans with community-acquired sepsis. Our work indicates that improved preclinical murine sepsis modeling can better replicate both the phenotypic and transcriptomic responses to surgical sepsis, but cannot be extrapolated to other sepsis etiologies. Importantly, these improved models can be a useful adjunct to human-focused and artificial intelligence-based forms of research in order to improve septic patients' morbidity and mortality.

Early Biomarker Signatures in Surgical Sepsis.

INTRODUCTION: Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients. METHODS: This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naïve Bayes classifier predicted cluster labels in a validation cohort. RESULTS: The development cohort contained one cluster characterized by early organ dysfunction (cluster I, n = 18) and one cluster characterized by recovery (cluster II, n = 139). Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II (30 versus 16, P < 0.001) and SOFA scores (13 versus 5, P < 0.001), greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock (78% versus 17%, P < 0.001). Cluster I had higher mortality within 14 d of sepsis onset (11% versus 1.5%, P = 0.001) and within 1 y (44% versus 20%, P = 0.032), and higher incidence of chronic critical illness (61% versus 30%, P = 0.001). The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters. CONCLUSIONS: Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review.

OBJECTIVE: This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making. BACKGROUND: Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants. METHODS: PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods. RESULTS: Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses. CONCLUSIONS: Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury.

PURPOSE OF REVIEW: Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. RECENT FINDINGS: Among recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies. SUMMARY: Use of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.

Development of a Theory-Informed Behavior Change Intervention to Reduce Inappropriate Prescribing of Nephrotoxins and Renally Eliminated Drugs.

BACKGROUND: Goals of managing patients with acute kidney injury (AKI) are mitigating disease progression and ensuring safety while providing supportive care because no effective treatment exists. One strategy recommended in guidelines to meet these goals is optimizing medication management. Unfortunately, guideline implementation appears to be lacking as observed by the frequent occurrence of medication errors and adverse drug events. OBJECTIVE: To address this performance gap in the care of hospitalized patients receiving nephrotoxins and renally eliminated drugs, we sought to provide a potential intervention based on theory-informed behavior change. METHODS: Formative research with a qualitative analysis identifying what needs to change in patient care was completed by obtaining clinician opinion and expert opinion and reviewing the published literature. Frontline providers, including 8 physicians, 4 pharmacists, and a multiprofessional group of authors, provided insight into possible barriers to appropriate prescribing. Capability, Opportunity, Motivation and Behavior model and Theoretical Domain Framework were applied to characterize behavior change interventions and inform a potential implementation intervention for changing inappropriate prescribing behaviors. RESULTS: Lack of knowledge about appropriate drug management in patients at risk for adverse outcomes was provided as a major barrier. Other reported barriers included a lack of: (1) tools to assist with drug management, (2) motivation to make changes, (3) routinization, and (4) an accountable clinician. CONCLUSIONS AND RELEVANCE: Assigning a designated clinician to execute a stepwise, routine care process following the checklist provided is a recommended intervention to overcome barriers. The intended impact is behavior change that reduces inappropriate prescribing.

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension.

BACKGROUND: Models that predict postoperative complications often ignore important intraoperative events and physiological changes. This study tested the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications would improve when using both preoperative and intraoperative data input data compared with preoperative data alone. METHODS: This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a 5-y period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for seven postoperative complications and mortality occurring during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone; one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as area under the receiver operating characteristic curve), precision (expressed as area under the precision-recall curve), and reclassification indices. RESULTS: Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precision than models using preoperative data alone for predicting all seven postoperative complications (intensive care unit length of stay >48 h, mechanical ventilation >48 h, neurologic complications including delirium, cardiovascular complications, acute kidney injury, venous thromboembolism, and wound complications), and in-hospital mortality (accuracy: 88% versus 77%; area under the receiver operating characteristic curve: 0.93 versus 0.87; area under the precision-recall curve: 0.21 versus 0.15). Overall reclassification improvement was 2.4%-10.0% for complications and 11.2% for in-hospital mortality. CONCLUSIONS: Incorporating both preoperative and intraoperative data significantly increased the accuracy, discrimination, and precision of machine learning models predicting postoperative complications and mortality.

Intelligent, Autonomous Machines in Surgery.

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery.

OBJECTIVE: To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data. BACKGROUND: Postoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited. METHODS: In a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance. RESULTS: MySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85). CONCLUSIONS: We constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.

The Development of Chronic Critical Illness Determines Physical Function, Quality of Life, and Long-Term Survival Among Early Survivors of Sepsis in Surgical ICUs.

OBJECTIVES: This study sought to examine mortality, health-related quality of life, and physical function among sepsis survivors who developed chronic critical illness. DESIGN: Single-institution, prospective, longitudinal, observational cohort study assessing 12-month outcomes. SETTING: Two surgical/trauma ICUs at an academic tertiary medical and level 1 trauma center. PATIENTS: Adult critically ill patients that survived 14 days or longer after sepsis onset. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Baseline patient characteristics and function, sepsis severity, and clinical outcomes of the index hospitalization were collected. Follow-up physical function (short physical performance battery; Zubrod; hand grip strength) and health-related quality of life (EuroQol-5D-3L, Short Form-36) were measured at 3, 6, and 12 months. Hospital-free days and mortality were determined at 12 months. We compared differences in long-term outcomes between subjects who developed chronic critical illness (≥ 14 ICU days with persistent organ dysfunction) versus those with rapid recovery. The cohort consisted of 173 sepsis patients; 63 (36%) developed chronic critical illness and 110 (64%) exhibited rapid recovery. Baseline physical function and health-related quality of life did not differ between groups. Those who developed chronic critical illness had significantly fewer hospital-free days (196 ± 148 vs 321 ± 65; p < 0.0001) and reduced survival at 12-months compared with rapid recovery subjects (54% vs 92%; p < 0.0001). At 3- and 6-month follow-up, chronic critical illness patients had significantly lower physical function (3 mo: short physical performance battery, Zubrod, and hand grip; 6 mo: short physical performance battery, Zubrod) and health-related quality of life (3- and 6-mo: EuroQol-5D-3L) compared with patients who rapidly recovered. By 12-month follow-up, chronic critical illness patients had significantly lower physical function and health-related quality of life on all measures. CONCLUSIONS: Surgical patients who develop chronic critical illness after sepsis exhibit high healthcare resource utilization and ultimately suffer dismal long-term clinical, functional, and health-related quality of life outcomes. Further understanding of the mechanisms driving the development and persistence of chronic critical illness will be necessary to improve long-term outcomes after sepsis.

Old Mice Demonstrate Organ Dysfunction as well as Prolonged Inflammation, Immunosuppression, and Weight Loss in a Modified Surgical Sepsis Model.

OBJECTIVES: Our goal was to "reverse translate" the human response to surgical sepsis into the mouse by modifying a widely adopted murine intra-abdominal sepsis model to engender a phenotype that conforms to current sepsis definitions and follows the most recent expert recommendations for animal preclinical sepsis research. Furthermore, we aimed to create a model that allows the study of aging on the long-term host response to sepsis. DESIGN: Experimental study. SETTING: Research laboratory. SUBJECTS: Young (3-5 mo) and old (18-22 mo) C57BL/6j mice. INTERVENTIONS: Mice received no intervention or were subjected to polymicrobial sepsis with cecal ligation and puncture followed by fluid resuscitation, analgesia, and antibiotics. Subsets of mice received daily chronic stress after cecal ligation and puncture for 14 days. Additionally, modifications were made to ensure that "Minimum Quality Threshold in Pre-Clinical Sepsis Studies" recommendations were followed. MEASUREMENTS AND MAIN RESULTS: Old mice exhibited increased mortality following both cecal ligation and puncture and cecal ligation and puncture + daily chronic stress when compared with young mice. Old mice developed marked hepatic and/or renal dysfunction, supported by elevations in plasma aspartate aminotransferase, blood urea nitrogen, and creatinine, 8 and 24 hours following cecal ligation and puncture. Similar to human sepsis, old mice demonstrated low-grade systemic inflammation 14 days after cecal ligation and puncture + daily chronic stress and evidence of immunosuppression, as determined by increased serum concentrations of multiple pro- and anti-inflammatory cytokines and chemokines when compared with young septic mice. In addition, old mice demonstrated expansion of myeloid-derived suppressor cell populations and sustained weight loss following cecal ligation and puncture + daily chronic stress, again similar to the human condition. CONCLUSIONS: The results indicate that this murine cecal ligation and puncture + daily chronic stress model of surgical sepsis in old mice adhered to current Minimum Quality Threshold in Pre-Clinical Sepsis Studies guidelines and met Sepsis-3 criteria. In addition, it effectively created a state of persistent inflammation, immunosuppression, and weight loss, thought to be a key aspect of chronic sepsis pathobiology and increasingly more prevalent after human sepsis.

Myeloid-derived suppressor cell function and epigenetic expression evolves over time after surgical sepsis.

BACKGROUND: Sepsis is an increasingly significant challenge throughout the world as one of the major causes of patient morbidity and mortality. Central to the host immunologic response to sepsis is the increase in circulating myeloid-derived suppressor cells (MDSCs), which have been demonstrated to be present and independently associated with poor long-term clinical outcomes. MDSCs are plastic cells and potentially modifiable, particularly through epigenetic interventions. The objective of this study was to determine how the suppressive phenotype of MDSCs evolves after sepsis in surgical ICU patients, as well as to identify epigenetic differences in MDSCs that may explain these changes. METHODS: Circulating MDSCs from 267 survivors of surgical sepsis were phenotyped at various intervals over 6 weeks, and highly enriched MDSCs from 23 of these samples were co-cultured with CD3/CD28-stimulated autologous T cells. microRNA expression from enriched MDSCs was also identified. RESULTS: We observed that MDSC numbers remain significantly elevated in hospitalized sepsis survivors for at least 6 weeks after their infection. However, only MDSCs obtained at and beyond 14 days post-sepsis significantly suppressed T lymphocyte proliferation and IL-2 production. These same MDSCs displayed unique epigenetic (miRNA) expression patterns compared to earlier time points. CONCLUSIONS: We conclude that in sepsis survivors, immature myeloid cell numbers are increased but the immune suppressive function specific to MDSCs develops over time, and this is associated with a specific epigenome. These findings may explain the chronic and persistent immune suppression seen in these subjects.

External Validation of the qSOFA Score in Emergency Department Patients With Pneumonia.

BACKGROUND: Pneumonia is the leading cause of sepsis. In 2016, the 3rd International Consensus Conference for Sepsis released the Quick Sepsis-Related Organ Failure Assessment (qSOFA) to identify risk for poor outcomes in sepsis. OBJECTIVE: We sought to externally validate qSOFA in emergency department (ED) patients with pneumonia and compare the accuracy of qSOFA to systemic inflammatory response syndrome score (SIRS), Confusion, Respiratory Rate and Blood Pressure (CRB), Confusion, Respiratory Rate, Blood Pressure and Age (CRB-65), and DS CRB-65, which is based on the CRB-65 score and includes two additional items-presence of underlying comorbid disease and blood oxygen saturation. METHODS: A subgroup analysis of U.S. Critical Illness and Injury Trials Group (USCIITG-Lung Injury Prevention Study [LIPS]; ClinicalTrials.gov ID: NCT00889772) prospective cohort. The primary outcome was in-hospital mortality. Secondary outcomes were measures of intensive care unit (ICU) utilization. Sensitivity, specificity, and area under the curve (AUC) were reported. RESULTS: From March to August 2009, 5584 patients were enrolled; 713 met inclusion criteria. Median age was 61 years (interquartile range 49-75 years). SIRS criteria had the highest sensitivity for death (89%) and lowest specificity (25%), while CRB had the highest specificity (88%) and lowest sensitivity (31%), followed by qSOFA (80% and 53%, respectively). This trend was maintained for the secondary outcomes. There was no significant difference in the AUC for death using qSOFA (AUC 0.75; 95% confidence interval [CI] 0.66-0.84), SIRS (AUC 0.70; 95% CI 0.61-0.78), CRB (AUC 0.71; 95% CI 0.62-0.80), CRB-65 (AUC 0.71; 95% CI 0.63-0.80), and DS CRB-65 (AUC 0.73; 95% CI 0.64-0.82). CONCLUSIONS: In this multicenter observational study of ED patients hospitalized with pneumonia, we found no significant differences between qSOFA and SIRS for predicting in-hospital death. In addition, several popular pneumonia-specific severity scores performed nearly identically to qSOFA score in predicting death and ICU utilization. Validation is needed in a larger sample.