Biological basis of critical illness subclasses: from the bedside to the bench and back again.

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Relevance of CT for the detection of septic foci: diagnostic performance in a retrospective cohort of medical intensive care patients.

AIM: To analyse the diagnostic yield of computed tomography (CT) in septic patients from a medical intensive care unit (ICU). MATERIALS AND METHODS: A full-text search of the department's radiological information system (RIS) retrieved 227 body CT examinations undertaken to search for a septic focus in 2018 from medical ICU patients. CT reports were categorised according to the identified foci. Clinical and laboratory information was gathered. Data were analysed statistically using descriptive statistics, diagnostic test quality criteria, binomial tests and chi-square test. RESULTS: A total of 227 CT examinations from 165 septic patients detected 264 foci, which were distributed as follows: 58.3% (n=154/264) chest, 26.5% (n=70/264) abdomen, 5.3% (n=14/264) genitourinary system, and 9.8% (n=26/264) other body regions. In 15.9% (n=36/227) no focus was identified on CT. Based on CT reports, 37.5% (n=99/264) of foci were graded as certain, 18.9% (n=50/264) as likely, and 15.9% (n=42/264) as possible infectious sources. Septic foci were detected using CT with 75.8% sensitivity (95% confidence interval [CI] 69.6-81.9%) and 59.46% specificity (95% CI 42.9-76.1%). The positive predictive value was 90.6% (95% CI 86-95.2%), with a negative predictive value of 32.4% (95% CI 21-43.8%). CONCLUSION: The present results confirm that body CT is a suitable rule-in test for septic patients in medical intensive care, although it cannot reliably rule out a septic focus. Follow-up CT examinations may reveal a septic source in the further course of a patient's hospital stay.

Quick Sequential Organ Failure Assessment: Modifications for Identifying Maternal Morbidity and Mortality in Obstetrical Patients.

OBJECTIVE: Screening tools, including the Systemic Inflammatory Response Syndrome (SIRS) criteria and Sequential Organ Failure Assessment (SOFA) criteria, have not been validated in the pregnant population. We aimed to determine if pregnancy-specific modifications to the quick SOFA (qSOFA) can improve prediction of severe maternal morbidity in pregnant women with serious infections. STUDY DESIGN: We performed a retrospective cohort study of pregnant patients with severe infections admitted to a single institution from January 1, 2011, through December 31, 2017. The primary outcome was severe maternal morbidity, defined as a composite of adverse maternal outcomes: intensive care unit (ICU) admission for >48 hours, need for invasive monitoring (central line or arterial line), intubation, pharmacologic hemodynamic support (intravenous vasopressors or inotropes), and/or maternal death. A logistic regression was then applied and the resulting predictors were analyzed individually and in combination with receiver operating characteristic (ROC) curves to modify qSOFA for pregnancy, that is, qSOFA-P. RESULTS: Analysis of 104 pregnant patients with severe infections found that the standard qSOFA did not accurately predict severe maternal morbidity (ROC area under the curve [AUC] = 0.54, p = 0.49, sensitivity = 0.38, and specificity = 0.70). Pregnancy-specific modifications or "qSOFA-P" (respiratory rate [RR] ≥ 35 breaths/minute and systolic blood pressure [SBP] ≤ 85 mm Hg) significantly improved prediction of severe maternal morbidity (AUC = 0.77, p < 0.001, sensitivity = 0.79, and specificity = 0.74). CONCLUSION: The standard qSOFA is a poor screening tool in the prediction of severe maternal morbidity in pregnant patients with infections. A pregnancy-specific screening system, qSOFA-P, improved prediction of severe maternal morbidity in pregnant women with severe infections. Further prospective and large multicenter studies are needed to validate this scoring system in pregnant women. KEY POINTS: · Validated scoring systems for evaluating pregnant patients with sepsis are needed.. · Modifications to existing systems may improve the evaluation of pregnant patients with sepsis.. · The qSOFA-P (RR ≥ 35 breaths/minute and SBP ≤ 85 mm Hg) includes modifications to qSOFA, and improves the detection of patients who would develop severe maternal morbidity...

Implementation of the Early-Onset Sepsis Risk Calculator at a Community Level I Nursery.

The objective of this retrospective cohort study was to decrease the frequency of laboratory draws and the number of neonates receiving empiric antibiotics who are born to mothers with chorioamnionitis from 100% to 50% 6 months following implementation of the sepsis risk calculator (SRC) at a level 1 community nursery. Data were compared pre- and post-implementation of the SRC using the Fischer's exact test. The rate of intravenous (IV) antibiotic use decreased from 93% to 7% (P < .0001). The rate of blood culture collection decreased from 100% to 46% (P < .0001). With implementation of the SRC, administration of IV antibiotics, laboratory draws, and IV placement significantly decreased without increasing rates of early-onset sepsis in our patient population. Our study demonstrated that the SRC can be effectively and safely implemented at a level 1 community-based newborn nursery, resulting in a decrease in unnecessary medical treatment without negative patient outcomes.

A hypolipoprotein sepsis phenotype indicates reduced lipoprotein antioxidant capacity, increased endothelial dysfunction and organ failure, and worse clinical outcomes.

OBJECTIVE: Approximately one-third of sepsis patients experience poor outcomes including chronic critical illness (CCI, intensive care unit (ICU) stay > 14 days) or early death (in-hospital death within 14 days). We sought to characterize lipoprotein predictive ability for poor outcomes and contribution to sepsis heterogeneity. DESIGN: Prospective cohort study with independent replication cohort. SETTING: Emergency department and surgical ICU at two hospitals. PATIENTS: Sepsis patients presenting within 24 h. METHODS: Measures included cholesterol levels (total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C]), triglycerides, paraoxonase-1 (PON-1), and apolipoprotein A-I (Apo A-I) in the first 24 h. Inflammatory and endothelial markers, and sequential organ failure assessment (SOFA) scores were also measured. LASSO selection assessed predictive ability for outcomes. Unsupervised clustering was used to investigate the contribution of lipid variation to sepsis heterogeneity. MEASUREMENTS AND MAIN RESULTS: 172 patients were enrolled. Most (~ 67%, 114/172) rapidly recovered, while ~ 23% (41/172) developed CCI, and ~ 10% (17/172) had early death. ApoA-I, LDL-C, mechanical ventilation, vasopressor use, and Charlson Comorbidity Score were significant predictors of CCI/early death in LASSO models. Unsupervised clustering yielded two discernible phenotypes. The Hypolipoprotein phenotype was characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1), higher SOFA scores, and worse clinical outcomes (45% rapid recovery, 40% CCI, 16% early death; 28-day mortality, 21%). The Normolipoprotein cluster patients had higher cholesterol levels, less endothelial dysfunction, lower SOFA scores and better outcomes (79% rapid recovery, 15% CCI, 6% early death; 28-day mortality, 15%). Phenotypes were validated in an independent replication cohort (N = 86) with greater sepsis severity, which similarly demonstrated lower HDL-C, ApoA-I, and higher ICAM-1 in the Hypolipoprotein cluster and worse outcomes (46% rapid recovery, 23% CCI, 31% early death; 28-day mortality, 42%). Normolipoprotein patients in the replication cohort had better outcomes (55% rapid recovery, 32% CCI, 13% early death; 28-day mortality, 28%) Top features for cluster discrimination were HDL-C, ApoA-I, total SOFA score, total cholesterol level, and ICAM-1. CONCLUSIONS: Lipoproteins predicted poor sepsis outcomes. A Hypolipoprotein sepsis phenotype was identified and characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1) and organ failure, and worse clinical outcomes.

Diagnostic yield of routine daily blood culture in patients on veno-arterial extracorporeal membrane oxygenation.

BACKGROUND: Bloodstream infections (BSIs) are frequent on veno-arterial extracorporeal membrane oxygenation (V-A ECMO). Performing routine blood cultures (BCs) may identify early paucisymptomatic BSIs. We investigated the contribution of systematic daily BCs to detect BSIs on V-A ECMO. METHODS: This was a retrospective study including all adult patients requiring V-A ECMO and surviving more than 24 h. Our protocol included routine daily BCs, from V-A ECMO insertion up to 5 days after withdrawal; other BCs were performed on-demand. RESULTS: On the 150 V-A ECMO included, 2146 BCs were performed (1162 routine and 984 on-demand BCs); 190 (9%) were positive, including 68 contaminants. Fifty-one (4%) routine BCs revealed BSIs; meanwhile, 71 (7%) on-demand BCs revealed BSIs (p = 0.005). Performing routine BCs was negatively associated with BSIs diagnosis (OR 0.55, 95% CI [0.38; 0.81], p = 0.002). However, 16 (31%) BSIs diagnosed by routine BCs would have been missed by on-demand BCs. Independent variables for BSIs diagnosis after routine BCs were: V-A ECMO for cardiac graft failure (OR 2.43, 95% CI [1.20; 4.92], p = 0.013) and sampling with on-going antimicrobial therapy (OR 2.15, 95% CI [1.08; 4.27], p = 0.029) or renal replacement therapy (OR 2.05, 95% CI [1.10; 3.81], p = 0.008). Without these three conditions, only two BSIs diagnosed with routine BCs would have been missed by on-demand BCs sampling. CONCLUSIONS: Although routine daily BCs are less effective than on-demand BCs and expose to contamination and inappropriate antimicrobial therapy, a policy restricted to on-demand BCs would omit a significant proportion of BSIs. This argues for a tailored approach to routine daily BCs on V-A ECMO, based on risk factors for positivity.

Monocyte distribution width (MDW) performance as an early sepsis indicator in the emergency department: comparison with CRP and procalcitonin in a multicenter international European prospective study.

BACKGROUND: Early sepsis diagnosis has emerged as one of the main challenges in the emergency room. Measurement of sepsis biomarkers is largely used in current practice to improve the diagnosis accuracy. Monocyte distribution width (MDW) is a recent new sepsis biomarker, available as part of the complete blood count with differential. The objective was to evaluate the performance of MDW for the detection of sepsis in the emergency department (ED) and to compare to procalcitonin (PCT) and C-reactive protein (CRP). METHODS: Subjects whose initial evaluation included a complete blood count were enrolled consecutively in 2 EDs in France and Spain and categorized per Sepsis-2 and Sepsis-3 criteria. The performance of MDW for sepsis detection was compared to that of procalcitonin (PCT) and C-reactive protein (CRP). RESULTS: A total of 1,517 patients were analyzed: 837 men and 680 women, mean age 61 ± 19 years, 260 (17.1%) categorized as Sepsis-2 and 144 patients (9.5%) as Sepsis-3. The AUCs [95% confidence interval] for the diagnosis of Sepsis-2 were 0.81 [0.78-0.84] and 0.86 [0.84-0.88] for MDW and MDW combined with WBC, respectively. For Sepsis-3, MDW performance was 0.82 [0.79-0.85]. The performance of MDW combined with WBC for Sepsis-2 in a subgroup of patients with low sepsis pretest probability was 0.90 [0.84-0.95]. The AUC for sepsis detection using MDW combined with WBC was similar to CRP alone (0.85 [0.83-0.87]) and exceeded that of PCT. Combining the biomarkers did not improve the AUC. Compared to normal MDW, abnormal MDW increased the odds of Sepsis-2 by factor of 5.5 [4.2-7.1, 95% CI] and Sepsis-3 by 7.6 [5.1-11.3, 95% CI]. CONCLUSIONS: MDW in combination with WBC has the diagnostic accuracy to detect sepsis, particularly when assessed in patients with lower pretest sepsis probability. We suggest the use of MDW as a systematic screening test, used together with qSOFA score to improve the accuracy of sepsis diagnosis in the emergency department. Trial Registration ClinicalTrials.gov (NCT03588325).

Limits of the Glasgow Coma Scale When Assessing for Sepsis in Allogeneic Hematopoietic Cell Transplant Recipients.

BACKGROUND: The well-documented association between acute mental status changes and sepsis development and progression makes acute mental status an attractive factor for sepsis screening tools. However, the usefulness of acute mental status within these criteria is limited to the frequency and accuracy of its capture. The Glasgow Coma Scale (GCS) score-the acute mental status indicator in many clinical sepsis criteria-is infrequently captured among allogeneic hematopoietic cell transplant recipients with suspected infections, and its ability to serve as an indicator of acute mental status among this high-risk population is unknown. OBJECTIVE: We evaluated the GCS score as an indicator of acute mental status during the 24 hours after suspected infection onset among allogeneic hematopoietic cell transplant recipients. METHODS: Using data from the first 100 days posttransplant for patients transplanted at a single center between September 2010 and July 2017, we evaluated the GCS score as an indicator of documented acute mental status during the 24 hours after suspected infection onset. From all inpatients with suspected infections, we randomly selected a cohort based on previously published estimates of GCS score frequency among hematopoietic cell transplant recipients with suspected infections and performed chart review to ascertain documentation of clinical acute mental status within the 24 hours after suspected infection onset. RESULTS: A total of 773 patients had ≥1 suspected infections and experienced 1,655 suspected infections during follow-up-625 of which had an accompanying GCS score. Among the randomly selected cohort of 100 persons with suspected infection, 28 were accompanied with documented acute mental status, including 18 without a recorded GCS. In relation to documented acute mental status, the GCS had moderate to high sensitivity and high specificity. DISCUSSION: These data indicate that, among allogeneic hematopoietic cell transplant recipients with suspected infections, the GCS scores are infrequently collected and have a moderate sensitivity. If sepsis screening tools inclusive of acute mental status changes are to be used, nursing teams need to increase measurement of GCS scores among high sepsis risk patients or identify a standard alternative indicator.

Comparison of culture-negative and culture-positive sepsis or septic shock: a systematic review and meta-analysis.

BACKGROUND: Mortality and other clinical outcomes between culture-negative and culture-positive septic patients have been documented inconsistently and are very controversial. A systematic review and meta-analysis was performed to compare the clinical outcomes of culture-negative and culture-positive sepsis or septic shock. METHODS: We searched the PubMed, Cochrane and Embase databases for studies from inception to the 1st of January 2021. We included studies involving patients with sepsis or septic shock. All authors reported our primary outcome of all-cause mortality and clearly compared culture-negative versus culture-positive patients with clinically relevant secondary outcomes (ICU length of stay, hospital length of stay, mechanical ventilation requirements, mechanical ventilation duration and renal replacement requirements). Results were expressed as odds ratio (OR) and mean difference (MD) with accompanying 95% confidence interval (CI). RESULTS: Seven studies including 22,655 patients were included. The primary outcome of this meta-analysis showed that there was no statistically significant difference in the all-cause mortality between two groups (OR = 0.95; 95% CI, 0.88 to 1.01; P = 0.12; Chi-2 = 30.71; I2 = 80%). Secondary outcomes demonstrated that there was no statistically significant difference in the ICU length of stay (MD = - 0.19;95% CI, - 0.42 to 0.04; P = 0.10;Chi-2 = 5.73; I2 = 48%), mechanical ventilation requirements (OR = 1.02; 95% CI, 0.94 to 1.11; P = 0.61; Chi2 = 6.32; I2 = 53%) and renal replacement requirements (OR = 0.82; 95% CI, 0.67 to 1.01; P = 0.06; Chi-2 = 1.21; I2 = 0%) between two groups. The hospital length of stay of culture-positive group was longer than that of the culture-negative group (MD = - 3.48;95% CI, - 4.34 to - 2.63; P < 0.00001;Chi-2 = 1.03; I2 = 0%). The mechanical ventilation duration of culture-positive group was longer than that of the culture-negative group (MD = - 0.64;95% CI, - 0.88 to - 0.4; P < 0.00001;Chi-2 = 4.86; I2 = 38%). CONCLUSIONS: Culture positivity or negativity was not associated with mortality of sepsis or septic shock patients. Furthermore, culture-positive septic patients had similar ICU length of stay, mechanical ventilation requirements and renal replacement requirements as those culture-negative patients. The hospital length of stay and mechanical ventilation duration of culture-positive septic patients were both longer than that of the culture-negative patients. Further large-scale studies are still required to confirm these results.

Sepsis Subclasses: A Framework for Development and Interpretation.

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.

Comparison of Sepsis Definitions as Automated Criteria.

OBJECTIVES: Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. DESIGN: Retrospective analysis of data extracted from electronic health record. SETTING: Single, tertiary-care center in St. Louis, MO. PATIENTS: Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%). CONCLUSIONS: The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.

Sepsis and Septic Shock Definitions in Patients With Cancer Admitted in ICU.

INTRODUCTION: In 2016, a new definition of sepsis and septic shock was adopted. Some studies based on the general population demonstrated that the Sequential Organ Failure Assessment (SOFA) score is more accurate than the systemic inflammatory response syndrome (SIRS) criteria to predict hospital mortality of infected patients requiring intensive care. PATIENTS AND METHOD: We have analyzed all the records of patients with cancer admitted for a suspected infection between January 1, 2013, and December 31, 2016, in our oncological intensive care unit (ICU). Sequential Organ Failure Assessment score and quick SOFA (qSOFA) score as well as SIRS criteria were calculated. We analyzed the accuracy of each score to predict hospital mortality in the setting of the new and old definitions of septic shock. RESULTS: Our study includes 241 patients with a solid tumor and 112 with a hematological malignancy. The hospital mortality rate is 37% (68% in patients with septic shock according to the new definition and 60% according to old definition) between 2013 and 2016. To predict hospital mortality, the SOFA score has an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.68-0.79), the qSOFA of 0.65 (95% CI, 0.59-0.70), and the SIRS criteria of 0.58 (95% CI, 0.52-0.63). In multivariate analysis, a higher SOFA score or a higher qSOFA score indicates poor prognosis: odds ratio (OR) per 1-point increase by 1.28 (95% CI, 1.18-1.39) and 1.48 (95% CI, 1.04-2.11), respectively. Complete remission is a good prognostic factor for hospital mortality: OR 0.39 (95% CI, 0.22-0.67). CONCLUSION: The new definition of sepsis and septic shock is applicable in an ICU oncological population with the same reliability as in the general population. The SOFA score is more accurate than qSOFA and SIRS criteria to predict hospital mortality.

Validation of Inflammopathic, Adaptive, and Coagulopathic Sepsis Endotypes in Coronavirus Disease 2019.

OBJECTIVES: Complex critical syndromes like sepsis and coronavirus disease 2019 may be composed of underling "endotypes," which may respond differently to treatment. The aim of this study was to test whether a previously defined bacterial sepsis endotypes classifier recapitulates the same clinical and immunological endotypes in coronavirus disease 2019. DESIGN: Prospective single-center observational cohort study. SETTING: Patients were enrolled in Athens, Greece, and blood was shipped to Inflammatix (Burlingame, CA) for analysis. PATIENTS: Adult patients within 24 hours of hospital admission with coronavirus disease 2019 confirmed by polymerase chain reaction and chest radiography. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We studied 97 patients with coronavirus disease 2019, of which 50 went on to severe respiratory failure (SRF) and 16 died. We applied a previously defined 33-messenger RNA classifier to assign endotype (Inflammopathic, Adaptive, or Coagulopathic) to each patient. We tested endotype status against other clinical parameters including laboratory values, severity scores, and outcomes. Patients were assigned as Inflammopathic (29%), Adaptive (44%), or Coagulopathic (27%), similar to our prior study in bacterial sepsis. Adaptive patients had lower rates of SRF and no deaths. Coagulopathic and Inflammopathic endotypes had 42% and 18% mortality rates, respectively. The Coagulopathic group showed highest d-dimers, and the Inflammopathic group showed highest C-reactive protein and interleukin-6 levels. CONCLUSIONS: Our predefined 33-messenger RNA endotypes classifier recapitulated immune phenotypes in viral sepsis (coronavirus disease 2019) despite its prior training and validation only in bacterial sepsis. Further work should focus on continued validation of the endotypes and their interaction with immunomodulatory therapy.

Classification of invasive bloodstream infections and Plasmodium falciparum malaria using autoantibodies as biomarkers.

A better understanding of disease-specific biomarker profiles during acute infections could guide the development of innovative diagnostic methods to differentiate between malaria and alternative causes of fever. We investigated autoantibody (AAb) profiles in febrile children (≤ 5 years) admitted to a hospital in rural Ghana. Serum samples from 30 children with a bacterial bloodstream infection and 35 children with Plasmodium falciparum malaria were analyzed using protein microarrays (Protoplex Immune Response Assay, ThermoFisher). A variable selection algorithm was applied to identify the smallest set of AAbs showing the best performance to classify malaria and bacteremia patients. The selection procedure identified 8 AAbs of which IFNGR2 and FBXW5 were selected in repeated model run. The classification error was 22%, which was mainly due to non-Typhi Salmonella (NTS) diagnoses being misclassified as malaria. Likewise, a cluster analysis grouped patients with NTS and malaria together, but separated malaria from non-NTS infections. Both current and recent malaria are a risk factor for NTS, therefore, a better understanding about the function of AAb in disease-specific immune responses is required in order to support their application for diagnostic purposes.

Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence of neutropenia to identify BSI. METHODS: We included patients 0-18 years of age at cancer diagnosis or HSCT between January 2009 and November 2018. Eligible blood cultures were those with no previous blood culture (regardless of result) within 7 days. The primary outcome was BSI. Four machine learning algorithms were used: elastic net, support vector machine and two implementations of gradient boosting machine (GBM and XGBoost). Model training and evaluation were performed using temporally disjoint training (60%), validation (20%) and test (20%) sets. The best model was compared to neutropenia alone in the test set. RESULTS: Of 11,183 eligible blood cultures, 624 (5.6%) were positive. The best model in the validation set was GBM, which achieved an area-under-the-receiver-operator-curve (AUROC) of 0.74 in the test set. Among the 2236 in the test set, the number of false positives and specificity of GBM vs. neutropenia were 508 vs. 592 and 0.76 vs. 0.72 respectively. Among 139 test set BSIs, six (4.3%) non-neutropenic patients were identified by GBM. All received antibiotics prior to culture result availability. CONCLUSIONS: We developed a machine learning algorithm to classify BSI. GBM achieved an AUROC of 0.74 and identified 4.3% additional true cases in the test set. The machine learning algorithm did not perform substantially better than using presence of neutropenia alone to predict BSI.

Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received.

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock. Risk trajectories diverge into four clusters following early prediction of septic shock, stratifying by outcome: the highest-risk and lowest-risk groups have a 76.5% and 10.4% prevalence of septic shock, and 43% and 18% mortality, respectively. These clusters differ also in treatments received and median time to shock onset. Analyses reveal the existence of a rapid (30-60 min) transition in risk at the time of threshold crossing. We hypothesize that this transition occurs as a result of the failure of compensatory biological systems to cope with infection, resulting in a bifurcation of low to high risk. Such a collapse, we believe, represents the true onset of septic shock. Thus, this rapid elevation in risk represents a potential new data-driven definition of septic shock.

The value of neutrophil gelatinase-associated lipocalin and citrullinated alpha enolase peptide-1 antibody in diagnosis, classification, and prognosis for patients with sepsis.

We examined the blood concentrations of neutrophil gelatinase-associated lipocalin (NGAL) and citrullinated alpha enolase peptide-1 (CEP-1) antibody in sepsis patients to evaluate their potential diagnostic, classified and prognostic utility together with C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6).Sixty-nine patients admitted at the emergency department with sepsis were studied, on admission, their demographic and clinical information were recorded. Blood levels of CRP, PCT, IL-6, NGAL, and CEP-1 antibody were measured. Relationships between sequential [sepsis-related] organ failure assessment score and blood biomarkers, between acute physiology and chronic health evaluation II score and blood biomarkers were investigated. Additionally, the mutual correlation among CRP, PCT, IL-6, NGAL, and CEP-1 antibody were investigated. Diagnostic and predictive values for clinical outcomes for biomarkers were assessed by receiver operator characteristic curve.Sixty-nine participants (38 sepsis, 31 septic shock) were compared with 40 healthy controls. The levels of CRP, PCT, IL-6, and NGAL were significantly higher in sepsis patients ([59.49 ± 48.88]; 0.71, [0.13-11.72]; 60.46, [33.26-201.20]; 265.61, [185.79-500.96], respectively) compared with healthy controls ([2.05 ± 1.85]; 0.02, [0.02-0.03]; 12.08, [7.22-16.84]; 19.73, [7.66-34.39], respectively) (P < .001). CRP, PCT, IL-6, and NGAL had better discriminatory performance with an area under the receiver operator characteristic curve (AUC) of (0.98; 0.98; 0.90; 0.97, respectively), 95% confidence interval (CI) = ([0.95; 1.00]; [0.96; 1.00]; [0.84; 0.96]; [0.94; 1.00], respectively) (P < .001), with a cut off value of (8.02 mg/L [Se = 88.40%, Sp = 100.00%]; 0.06 ng/mL [Se = 94.20%, Sp = 75.00%]; 30.63 pg/mL [Se = 78.30%, Sp = 95.00%]; 95.72 ng/mL [Se = 99.00%, Sp = 92.00%], respectively). Between the sepsis group and septic shock group, PCT and NGAL were significantly higher in septic shock group (2.44, [0.49-20.36]; 294.65 [203.34-1262.47], respectively) compared with sepsis group (0.41, [0.11-2.63]; 219.94, [146.38-385.24], respectively) (P < .05). Between survivors group and nonsurvivors group, PCT was obviously elevated in nonsurvivors group (2.47, [0.70-12.49]) compare with survivors group (0.41, [0.11-8.16]) (P < .05), with an AUC of 0.69, 95% CI = (0.57; 0.81) (P < .05), while CEP-1 antibody was decreased in nonsurvivors group (14.03, [4.94-17.17]) contrast to survivors group (18.78, [8.08-39.72]) (P < .05), with an AUC of 0.67, 95% CI = (0.54; 0.80) (P < .05). Additionally, CEP-1 antibody demonstrated a negative correlation with either sequential [sepsis-related] organ failure assessment score (r = -0.31, P < .05) or PCT (r = -0.27, P < .05).As CRP, PCT, and IL-6, NGAL was valuable in sepsis diagnosis. With a classificatory value, PCT and NGAL correlated with the degree severity of sepsis. PCT and CEP-1 antibody were meaningful in sepsis prognosis. CEP-1 antibody may be a protective factor for sepsis.

TNM: a simple classification system for complicated intra-abdominal sepsis after acute appendicitis.

BACKGROUND: Delayed diagnosis in case of acute appendicitis (AA) could lead to complicated intra-abdominal sepsis (IAS). Grading systems are not commonly employed in the clinical practice, because they are too complicated or too specific. Therefore, we suggest grading the severity of complicated IAS after AA with a simple system: TNM, an acronym borrowed by cancer staging where T indicates temperature, N neutrophils, and M multiple organ failure (MOF). This prospective observational study evaluates the predictive value of the TNM score on mortality of patients with complicated IAS after AA. METHODS: Sixty-eight patients with complicated IAS after AA were treated. Three classes of attributes were chosen: temperature (T), neutrophils count (N), and MOF (M). After defining the categories T (T0-T4), N (N0-N3) and M (M0-M2), these were grouped in stages (0-IV). Variables analyzed for their possible relation to death were age, sex, temperature, neutrophils count, preoperative organ failure, immunocompromised status, stage (0-IV). Odds ratios were calculated in a univariate and multivariate analysis. RESULTS: TNM staging was: one patient stage 0; 16 patients at stage I; 26 patients at stage II; 16 patients at stage III; nine patients at stage IV. Death occurred in 15 patients (22%). Neutrophil count, preoperative organ failure, immunocompromised status, stages III-IV were potential predictors of postoperative death in univariate analysis; only stage IV was significant independent predictor of postoperative mortality in multivariate analysis. CONCLUSIONS: TNM classification is very easy to use; it helps to define the mortality risk and is useful to objectively compare patients with sepsis.