Preoperative ketamine administration for prevention of postoperative neurocognitive disorders after major orthopedic surgery in elderly patients: A multicenter randomized blinded placebo-controlled trial.

BACKGROUND: Preventive anesthetic impact on the high rates of postoperative neurocognitive disorders in elderly patients is debated. The Prevention of postOperative Cognitive dysfunction by Ketamine (POCK) study aimed to assess the effect of ketamine on this condition. METHODS: This is a multicenter, randomized, double-blind, interventional study. Patients ≥60 years undergoing major orthopedic surgery were randomly assigned in a 1:1 ratio to receive preoperative ketamine 0.5 mg/kg as an intravenous bolus (n = 152) or placebo (n = 149) in random blocks stratified according to the study site, preoperative cognitive status and age. The primary outcome was the proportion of objective delayed neurocognitive recovery (dNR) defined as a decline of one or more neuropsychological assessment standard deviations on postoperative day 7. Secondary outcomes included a three-month incidence of objective postoperative neurocognitive disorder (POND), as well as delirium, anxiety, and symptoms of depression seven days and three months after surgery. RESULTS: Among 301 patients included, 292 (97%) completed the trial. Objective dNR occurred in 50 (38.8%) patients in the ketamine group and 54 (40.9%) patients in the placebo group (OR [95% CI] 0.92 [0.56;1.51], p = 0.73) on postoperative day 7. Incidence of objective POND three months after surgery did not differ significantly between the two groups nor did incidence of delirium, anxiety, apathy, and fatigue. Symptoms of depression were less frequent in the ketamine group three months after surgery (OR [95%CI] 0.34 [0.13-0.86]). CONCLUSIONS: A single preoperative bolus of intravenous ketamine does not prevent the occurrence of dNR or POND in elderly patients scheduled for major orthopedic surgery. (Clinicaltrials.gov NCT02892916.).

An immune signature of postoperative cognitive decline in elderly patients.

Postoperative cognitive decline (POCD) is the predominant complication affecting elderly patients following major surgery, yet its prediction and prevention remain challenging. Understanding biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This longitudinal study involving 26 elderly patients undergoing orthopedic surgery aimed to characterize the impact of peripheral immune cell responses to surgical trauma on POCD. Trajectory analyses of single-cell mass cytometry data highlighted early JAK/STAT signaling exacerbation and diminished MyD88 signaling post-surgery in patients who developed POCD. Further analyses integrating single-cell and plasma proteomic data collected before surgery with clinical variables yielded a sparse predictive model that accurately identified patients who would develop POCD (AUC = 0.80). The resulting POCD immune signature included one plasma protein and ten immune cell features, offering a concise list of biomarker candidates for developing point-of-care prognostic tests to personalize perioperative management of at-risk patients. The code and the data are documented and available at https://github.com/gregbellan/POCD . Teaser: Modeling immune cell responses and plasma proteomic data predicts postoperative cognitive decline.

Discovery of sparse, reliable omic biomarkers with Stabl.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Lung Ultrasound Score on Postoperative Day 1 Is Predictive of the Occurrence of Pulmonary Complications after Major Abdominal Surgery: A Multicenter Prospective Observational Study.

BACKGROUND: Postoperative pulmonary complications after major abdominal surgery are frequent and carry high morbidity and mortality. Early identification of patients at risk of pulmonary complications by lung ultrasound may allow the implementation of preemptive strategies. The authors hypothesized that lung ultrasound score would be associated with pulmonary postoperative complications. The main objective of the study was to evaluate the performance of lung ultrasound score on postoperative day 1 in predicting pulmonary complications after major abdominal surgery. Secondary objectives included the evaluation of other related measures for their potential prediction accuracy. METHODS: A total of 149 patients scheduled for major abdominal surgery were enrolled in a bicenter observational study. Lung ultrasound score was performed before the surgery and on days 1, 4, and 7 after surgery. Pulmonary complications occurring before postoperative day 10 were recorded. RESULTS: Lung ultrasound score on postoperative day 1 was higher in patients developing pulmonary complications before day 10 (median, 13; interquartile range, 8.25 to 18; vs. median, 10; interquartile range, 6.5 to 12; Mann-Whitney P = 0.002). The area under the curve for predicting postoperative pulmonary complications before day 10 was 0.65 (95% CI, 0.55 to 0.75; P = 0.003). Lung ultrasound score greater than 12 had a sensitivity of 0.54 (95% CI, 0.40 to 0.67), specificity of 0.77 (95% CI, 0.67 to 0.85), and negative predictive value of 0.74 (95% CI, 0.65 to 0.83). Lung ultrasound score greater than 17 had sensitivity of 0.33 (95% CI, 0.21 to 0.47), specificity of 0.95 (95% CI, 0.88 to 0.98), and positive predictive value of 0.78 (95% CI, 0.56 to 0.93). Anterolateral lung ultrasound score and composite scores using lung ultrasound score and other patient characteristics showed similar predictive accuracies. CONCLUSIONS: An elevated lung ultrasound score on postoperative day 1 is associated with the occurrence of pulmonary complications within the first 10 days after major abdominal surgery.

Impact of preoperative uni- or multimodal prehabilitation on postoperative morbidity: meta-analysis.

BACKGROUND: Postoperative complications occur in up to 43% of patients after surgery, resulting in increased morbidity and economic burden. Prehabilitation has the potential to increase patients' preoperative health status and thereby improve postoperative outcomes. However, reported results of prehabilitation are contradictory. The objective of this systematic review is to evaluate the effects of prehabilitation on postoperative outcomes (postoperative complications, hospital length of stay, pain at postoperative day 1) in patients undergoing elective surgery. METHODS: The authors performed a systematic review and meta-analysis of RCTs published between January 2006 and June 2023 comparing prehabilitation programmes lasting ≥14 days to 'standard of care' (SOC) and reporting postoperative complications according to the Clavien-Dindo classification. Database searches were conducted in PubMed, CINAHL, EMBASE, PsycINFO. The primary outcome examined was the effect of uni- or multimodal prehabilitation on 30-day complications. Secondary outcomes were length of ICU and hospital stay (LOS) and reported pain scores. RESULTS: Twenty-five studies (including 2090 patients randomized in a 1:1 ratio) met the inclusion criteria. Average methodological study quality was moderate. There was no difference between prehabilitation and SOC groups in regard to occurrence of postoperative complications (OR = 1.02, 95% c.i. 0.93 to 1.13; P = 0.10; I2 = 34%), total hospital LOS (-0.13 days; 95% c.i. -0.56 to 0.28; P = 0.53; I2 = 21%) or reported postoperative pain. The ICU LOS was significantly shorter in the prehabilitation group (-0.57 days; 95% c.i. -1.10 to -0.04; P = 0.03; I2 = 46%). Separate comparison of uni- and multimodal prehabilitation showed no difference for either intervention. CONCLUSION: Prehabilitation reduces ICU LOS compared with SOC in elective surgery patients but has no effect on overall complication rates or total LOS, regardless of modality. Prehabilitation programs need standardization and specific targeting of those patients most likely to benefit.

Perioperative ketamine administration to prevent delirium and neurocognitive disorders after surgery: a systematic review and meta-analysis.

BACKGROUND: Surgery induces high rates of cognitive disorders, persisting for up to 12 months in elderly adults. This review aimed to assess the currently debated preventive effect of perioperative ketamine on postoperative delirium and postoperative neurocognitive disorders (POND). MATERIALS AND METHODS: Systematic review and meta-analysis including all randomized controlled trials investigating the effects of perioperative ketamine administration in adult patients compared to placebo or no intervention on postoperative delirium and/or POND between January 2007 and April 2022. Database searches were conducted in PubMed, Medline, Embase, Scopus, and Central. Random effects models were used to pool overall estimates. The GRADE approach was used to assess the quality of the evidence. RESULTS: From 1379 records screened, 14 randomized controlled trials with 1618 patients randomized met our inclusion criteria with a high level of consensus among reviewers, amongst whom 50% were at low-moderate risk of bias. There was no between-group difference in postoperative delirium [8 trials, 1265 patients, odds ratio (OR) 0.93, 95% CI (0.51-1.70), I2 =28%] and POND [5 trials, 494 patients, OR 0.52, 95% CI (0.15-1.80); I2 =78%]. There was no significant between-group difference in postoperative psychological adverse effects, level of pain, hospital length of stay, or mortality. Between-group subgroup analyses showed no difference in delirium or POND incidence according to surgical setting, ketamine dose, mode of administration, combination or not with other drug(s), and assessment timing or definition of cognitive disorders. CONCLUSION: Perioperative ketamine does not prevent postoperative delirium or POND. Significant study heterogeneity suggests that standardized measures for POND assessment and a specific focus on patients at high risk for POND should be used to improve the comparability of future studies.

Outcomes After Endovascular Therapy With Procedural Sedation vs General Anesthesia in Patients With Acute Ischemic Stroke: The AMETIS Randomized Clinical Trial.

Importance: General anesthesia and procedural sedation are common practice for mechanical thrombectomy in acute ischemic stroke. However, risks and benefits of each strategy are unclear. Objective: To determine whether general anesthesia or procedural sedation for anterior circulation large-vessel occlusion acute ischemic stroke thrombectomy are associated with a difference in periprocedural complications and 3-month functional outcome. Design, Setting, and Participants: This open-label, blinded end point randomized clinical trial was conducted between August 2017 and February 2020, with final follow-up in May 2020, at 10 centers in France. Adults with occlusion of the intracranial internal carotid artery and/or the proximal middle cerebral artery treated with thrombectomy were enrolled. Interventions: Patients were assigned to receive general anesthesia with tracheal intubation (n = 135) or procedural sedation (n = 138). Main Outcomes and Measures: The prespecified primary composite outcome was functional independence (a score of 0 to 2 on the modified Rankin Scale, which ranges from 0 [no neurologic disability] to 6 [death]) at 90 days and absence of major periprocedural complications (procedure-related serious adverse events, pneumonia, myocardial infarction, cardiogenic acute pulmonary edema, or malignant stroke) at 7 days. Results: Among 273 patients evaluable for the primary outcome in the modified intention-to-treat population, 142 (52.0%) were women, and the mean (SD) age was 71.6 (13.8) years. The primary outcome occurred in 38 of 135 patients (28.2%) assigned to general anesthesia and in 50 of 138 patients (36.2%) assigned to procedural sedation (absolute difference, 8.1 percentage points; 95% CI, -2.3 to 19.1; P = .15). At 90 days, the rate of patients achieving functional independence was 33.3% (45 of 135) with general anesthesia and 39.1% (54 of 138) with procedural sedation (relative risk, 1.18; 95% CI, 0.86-1.61; P = .32). The rate of patients without major periprocedural complications at 7 days was 65.9% (89 of 135) with general anesthesia and 67.4% (93 of 138) with procedural sedation (relative risk, 1.02; 95% CI, 0.86-1.21; P = .80). Conclusions and Relevance: In patients treated with mechanical thrombectomy for anterior circulation acute ischemic stroke, general anesthesia and procedural sedation were associated with similar rates of functional independence and major periprocedural complications. Trial Registration: ClinicalTrials.gov Identifier: NCT03229148.

High Mobility Group Box 1 (HMGB1): Potential Target in Sepsis-Associated Encephalopathy.

Sepsis-associated encephalopathy (SAE) remains a challenge for intensivists that is exacerbated by lack of an effective diagnostic tool and an unambiguous definition to properly identify SAE patients. Risk factors for SAE development include age, genetic factors as well as pre-existing neuropsychiatric conditions. Sepsis due to certain infection sites/origins might be more prone to encephalopathy development than other cases. Currently, ICU management of SAE is mainly based on non-pharmacological support. Pre-clinical studies have described the role of the alarmin high mobility group box 1 (HMGB1) in the complex pathogenesis of SAE. Although there are limited data available about the role of HMGB1 in neuroinflammation following sepsis, it has been implicated in other neurologic disorders, where its translocation from the nucleus to the extracellular space has been found to trigger neuroinflammatory reactions and disrupt the blood-brain barrier. Negating the inflammatory cascade, by targeting HMGB1, may be a strategy to complement non-pharmacologic interventions directed against encephalopathy. This review describes inflammatory cascades implicating HMGB1 and strategies for its use to mitigate sepsis-induced encephalopathy.

Gastric Cross-Sectional Area to Predict Gastric Intolerance in Critically Ill Patients: The Sono-ICU Prospective Observational Bicenter Study.

To evaluate the correlation between gastric cross-sectional area (GCSA) and the occurrence of gastric intolerance in critically ill patients within 24 hours of the measurement. DESIGN: Two-center prospective observational study. SETTING: Two academic ICUs in France between June 2020 and August 2021. PATIENTS: All surgical intubated ICU patients greater than or equal to 18 years old receiving enteral feeding for greater than 12 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Forty-four patients were included, 11 (25%) of whom presented digestive intolerance. Primary outcome was assessment of the association between GCSA and the occurrence of gastric intolerance within 24 hours of the measurement. GCSA value was significantly higher in patients with upper digestive intolerance compared to those without (553 mm2 [interquartile range (IQR), 500-649 mm2] vs 970 mm2 [IQR, 777-1,047]; p < 0.001, respectively). The optimal threshold for predicting upper digestive intolerance was 720 mm2 (area under the receiver operating characteristic curve 0.86; positive predictive value 62.5%; negative predictive value 96.4%; sensibility 0.91; and specificity 0.81). Multivariate analysis (weighted by propensity score), including known risk factors, showed that GCSA above the 720 mm2 threshold was independently associated with the occurrence of upper digestive intolerance (odds ratio, 1.85; 1.37-2.49; p < 0.0002). Measurement quality was "good" (i.e., liver, aorta, superior mesenteric vein, and pancreas were all visualized) in 81% of cases. CONCLUSIONS: Measurement of GCSA by ultrasound would allow prediction of gastric intolerance in critically ill patients. This should be confirmed by a prospective score validation and interventional trials.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data.

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.

OBJECTIVE: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established. BACKGROUND: Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity. METHODS: Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state. RESULTS: The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery. CONCLUSIONS: Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

Upcoming and urgent challenges in critical care research based on COVID-19 pandemic experience.

While the coronavirus disease 2019 (COVID-19) pandemic placed a heavy burden on healthcare systems worldwide, it also induced urgent mobilisation of research teams to develop treatments preventing or curing the disease and its consequences. It has, therefore, challenged critical care research to rapidly focus on specific fields while forcing critical care physicians to make difficult ethical decisions. This narrative review aims to summarise critical care research -from organisation to research fields- in this pandemic setting and to highlight opportunities to improve research efficiency in the future, based on what is learned from COVID-19. This pressure on research revealed, i.e., (i) the need to harmonise regulatory processes between countries, allowing simplified organisation of international research networks to improve their efficiency in answering large-scale questions; (ii) the importance of developing translational research from which therapeutic innovations can emerge; (iii) the need for improved triage and predictive scores to rationalise admission to the intensive care unit. In this context, key areas for future critical care research and better pandemic preparedness are artificial intelligence applied to healthcare, characterisation of long-term symptoms, and ethical considerations. Such collaborative research efforts should involve groups from both high and low-to-middle income countries to propose worldwide solutions. As a conclusion, stress tests on healthcare organisations should be viewed as opportunities to design new research frameworks and strategies. Worldwide availability of research networks ready to operate is essential to be prepared for next pandemics. Importantly, researchers and physicians should prioritise realistic and ethical goals for both clinical care and research.

Anesthesia for digestive tract endoscopy.

PURPOSE OF REVIEW: Nonoperating room anesthesia for digestive tract endoscopy has its own specificities and requires practical training. Monitoring devices, anesthetic drugs, understanding of procedures and management of complications are critical aspects. RECENT FINDINGS: New data are available regarding risk factors for intra- and postoperative complications (based on anesthesia registries), airway management, new anesthetic drugs, techniques of administration and management of advances in interventional endoscopy procedures. SUMMARY: Digestive tract endoscopy is a common procedure that takes place outside the operating room most of the time and has become more and more complex due to advanced invasive procedures. Prior evaluation of the patient's comorbidities and a good understanding of the objectives and constraints of the endoscopic procedures are required. Assessing the risk of gastric content aspiration is critical for determining appropriate anesthetic protocols. The availability of adequate monitoring (capnographs adapted to spontaneous ventilation, bispectral index), devices for administration of anesthetic/sedative agents (target-controlled infusion) and oxygenation (high flow nasal oxygenation) guarantees the quality of sedation and patient' safety during endoscopic procedures. Knowledge of the specificities of each interventional endoscopic procedure (endoscopic retrograde cholangiopancreatography, submucosal dissection) allows preventing complications during anesthesia.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Risk factors for prolonged time to hospital discharge after ambulatory cholecystectomy under general anaesthesia. A retrospective cohort study.

BACKGROUND: Although predictive models have already integrated demographic factors and comorbidities as risk factors for a prolonged hospital stay, factors related to anaesthesia management in ambulatory surgery have not been yet characterized. This study aims to identify anaesthetic factors associated with a prolonged discharge time in ambulatory surgery. METHODS: All clinical records of patients who underwent ambulatory cholecystectomy in a French University Hospital (Hôpital Saint Antoine, Paris) between January 1st, 2012 and December 31st, 2018 were retrospectively reviewed. The primary endpoint was the discharge time, defined as the time between the end of surgery and discharge. A multivariable Cox proportional-hazards model was fitted to investigate the factors associated with a prolonged discharge time. RESULTS: Five hundred and thirty-five (535) patients were included. The median time for discharge was 150 min (interquartile range - IQR [129-192]). A bivariable analysis highlighted a positive correlation between discharge timeline and the doses-weight of ketamine and sufentanil. In the multivariable Cox proportional hazards model analysis, the anaesthesia-related factors independently associated with prolonged discharge time were the dose-weight of ketamine in interaction with the dose weight of sufentanil (HR 0.10 per increment of 0.1 mg/kg of ketamine or 0.2 µg/kg of sufentanil, CI 95% [0.01-0.61], p = 0.013) and the non-use of a non-steroidal anti-inflammatory drug (NSAID) (HR 0.81 [0.67-0.98], p = 0.034). Twenty patients (4%) had unscheduled hospitalization following surgery. CONCLUSION: Anaesthesia management, namely the use of ketamine and the non-use of NSAID, affects time to hospital discharge.

Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.

OBJECTIVE: The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

A Peripheral Immune Signature of Labor Induction.

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.