Impact of goal-directed hemodynamic therapy on perioperative outcomes in head and neck free flap surgery: A before-and-after pilot study.

Background: Free flap reconstruction for head and neck cancer is associated with a high risk of perioperative complications. One of the modifiable risk factors associated with perioperative morbidity is intraoperative hypotension (IOH). The main aim of this pilot study is to determine if the intraoperative use of goal-directed hemodynamic therapy (GDHT) is associated with a reduction in the number of IOH events in this population. Methods: A before-and-after study design. The patients who had intraoperative GDHT were compared to patients from a previous period before the implementation of GDHT. The primary outcome was the number of IOH episodes defined as five or more successive minutes with a mean arterial pressure <65 mmHg. The secondary outcomes included major postoperative morbidity and 30-day mortality. Results: A total of 414 patients were included. These were divided into two groups. The control group (n = 346; January 1, 2018, to December 31, 2019), and the monitored group (n = 68; January 1, 2020, to May 1, 2021). The median intraoperative administered fluid volume was similar between the control and monitored groups (2250 interquartile range [IQR] [1607-3050] vs. 2210 IQR [1700-2807] mL). The monitored group was found to have an increased use of norepinephrine and dobutamine (respectively, 1.2% vs. 5.9% and 2.4% vs. 30.9%; p < 0.05). When adjusting for confounders (comorbidities, estimated blood loss, and duration of anesthesia) the incidence rate ratio (95% confidence interval) of number of IOH events was 0.94 (0.86-1.03), p = 0.24. The rate of postoperative flap and medical complications did not differ between the two groups. Conclusions: Even though the use of vasopressors/inotropes was higher in the monitored group, the number of IOH episodes and postoperative morbidity and mortality were similar between the two groups. Further change in hemodynamic management will require the use of specific blood pressure targets in the GDHT fluid algorithm.

Mechanical Ventilation, Past, Present, and Future.

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure. The earliest MV machines for prolonged ventilatory support and oxygenation were large and cumbersome. They required a significant amount of skills and expertise to operate. These early devices had limited capabilities, battery, power, safety features, alarms, and therefore these often caused harm to patients. Moreover, the physiology of MV was modified when mechanical ventilators moved from negative pressure to positive pressure mechanisms. Monitoring systems were also very limited and therefore the risks related to MV support were difficult to quantify, predict and timely detect for individual patients who were necessarily young with few comorbidities. Technology and devices designed to use tracheostomies versus endotracheal intubation evolved in the last century too and these are currently much more reliable. In the present, positive pressure MV is more sophisticated and widely used for extensive period of time. Modern ventilators use mostly positive pressure systems and are much smaller, more portable than their predecessors, and they are much easier to operate. They can also be programmed to provide different levels of support based on evolving physiological concepts allowing lung-protective ventilation. Monitoring systems are more sophisticated and knowledge related to the physiology of MV is improved. Patients are also more complex and elderly compared to the past. MV experts are informed about risks related to prolonged or aggressive ventilation modalities and settings. One of the most significant advances in MV has been protective lung ventilation, diaphragm protective ventilation including noninvasive ventilation (NIV). Health care professionals are familiar with the use of MV and in many countries, respiratory therapists have been trained for the exclusive purpose of providing safe and professional respiratory support to critically ill patients. Analgo-sedation drugs and techniques are improved, and more sedative drugs are available and this has an impact on recovery, weaning, and overall patients' outcome. Looking toward the future, MV is likely to continue to evolve and improve alongside monitoring techniques and sedatives. There is increasing precision in monitoring global "patient-ventilator" interactions: structure and analysis (asynchrony, desynchrony, etc). One area of development is the use of artificial intelligence (AI) in ventilator technology. AI can be used to monitor patients in real-time, and it can predict when a patient is likely to experience respiratory distress. This allows medical professionals to intervene before a crisis occurs, improving patient outcomes and reducing the need for emergency intervention. This specific area of development is intended as "personalized ventilation." It involves tailoring the ventilator settings to the individual patient, based on their physiology and the specific condition they are being treated for. This approach has the potential to improve patient outcomes by optimizing ventilation and reducing the risk of harm. In conclusion, MV has come a long way since its inception, and it continues to play a critical role in anesthesia and in CCM settings. Advances in technology have made MV safer, more effective, affordable, and more widely available. As technology continues to improve, more advanced and personalized MV will become available, leading to better patients' outcomes and quality of life for those in need.

Clinical phenotypes of cardiogenic shock survivors: insights into late host responses and long-term outcomes.

AIMS: An elevated risk of adverse events persists for years in cardiogenic shock (CS) survivors with high mortality rate and physical/mental disability. This study aims to link clinical CS-survivor phenotypes with distinct late host-response patterns at intensive care unit (ICU) discharge and long-term outcomes using model-based clustering. METHODS AND RESULTS: In the original prospective, observational, international French and European Outcome Registry in Intensive Care Units (FROG-ICU) study, ICU patients with CS on admission were identified (N = 228). Among them, 173 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct CS-survivor phenotypes at ICU discharge using 15 readily available clinical and laboratory variables. The primary endpoint was 1 year of mortality after ICU discharge. Secondary endpoints were readmission and physical/mental disability [short form-36 questionnaire (SF-36) score] within 1 year after ICU discharge. Two distinct phenotypes at ICU discharge were identified (A and B). Patients in Phenotype B (38%) were more anaemic and had higher circulating levels of lactate, sustained kidney injury, and persistent elevation in plasma markers of inflammation, myocardial fibrosis, and endothelial dysfunction compared with Phenotype A. They had also a higher rate of non-ischaemic origin of CS and right ventricular dysfunction on admission. CS survivors in Phenotype B had higher 1 year of mortality compared with Phenotype A (P = 0.045, Kaplan-Meier analysis). When adjusted for traditional risk factors (i.e. age, severity of illness, and duration of ICU stay), Phenotype B was independently associated with 1 year of mortality [adjusted hazard ratio = 2.83 (95% confidence interval 1.21-6.60); P = 0.016]. There was a significantly lower physical quality of life in Phenotype B patients at 3 months (i.e. SF-36 physical component score). CONCLUSIONS: A phenotype with sustained inflammation, myocardial fibrosis, and endothelial dysfunction at ICU discharge was identified from readily available data and was independently associated with poor long-term outcomes in CS survivors.

An international RAND/UCLA expert panel to determine the optimal diagnosis and management of burn inhalation injury.

BACKGROUND: Burn inhalation injury (BII) is a major cause of burn-related mortality and morbidity. Despite published practice guidelines, no consensus exists for the best strategies regarding diagnosis and management of BII. A modified DELPHI study using the RAND/UCLA (University of California, Los Angeles) Appropriateness Method (RAM) systematically analysed the opinions of an expert panel. Expert opinion was combined with available evidence to determine what constitutes appropriate and inappropriate judgement in the diagnosis and management of BII. METHODS: A 15-person multidisciplinary panel comprised anaesthetists, intensivists and plastic surgeons involved in the clinical management of major burn patients adopted a modified Delphi approach using the RAM method. They rated the appropriateness of statements describing diagnostic and management options for BII on a Likert scale. A modified final survey comprising 140 statements was completed, subdivided into history and physical examination (20), investigations (39), airway management (5), systemic toxicity (23), invasive mechanical ventilation (29) and pharmacotherapy (24). Median appropriateness ratings and the disagreement index (DI) were calculated to classify statements as appropriate, uncertain, or inappropriate. RESULTS: Of 140 statements, 74 were rated as appropriate, 40 as uncertain and 26 as inappropriate. Initial intubation with ≥ 8.0 mm endotracheal tubes, lung protective ventilatory strategies, initial bronchoscopic lavage, serial bronchoscopic lavage for severe BII, nebulised heparin and salbutamol administration for moderate-severe BII and N-acetylcysteine for moderate BII were rated appropriate. Non-protective ventilatory strategies, high-frequency oscillatory ventilation, high-frequency percussive ventilation, prophylactic systemic antibiotics and corticosteroids were rated inappropriate. Experts disagreed (DI ≥ 1) on six statements, classified uncertain: the use of flexible fiberoptic bronchoscopy to guide fluid requirements (DI = 1.52), intubation with endotracheal tubes of internal diameter < 8.0 mm (DI = 1.19), use of airway pressure release ventilation modality (DI = 1.19) and nebulised 5000IU heparin, N-acetylcysteine and salbutamol for mild BII (DI = 1.52, 1.70, 1.36, respectively). CONCLUSIONS: Burns experts mostly agreed on appropriate and inappropriate diagnostic and management criteria of BII as in published guidance. Uncertainty exists as to the optimal diagnosis and management of differing grades of severity of BII. Future research should investigate the accuracy of bronchoscopic grading of BII, the value of bronchial lavage in differing severity groups and the effectiveness of nebulised therapies in different severities of BII.

Validation of reference gene stability for miRNA quantification by reverse transcription quantitative PCR in the peripheral blood of patients with COVID-19 critical illness.

The COVID-19 pandemic has created an urgency to study the host gene response that leads to variable clinical presentations of the disease, particularly the critical illness response. miRNAs have been implicated in the mechanism of host immune dysregulation and thus hold potential as biomarkers and/or therapeutic agents with clinical application. Hence, further analyses of their altered expression in COVID-19 is warranted. An important basis for this is identifying appropriate reference genes for high quality expression analysis studies. In the current report, NanoString technology was used to study the expression of 798 miRNAs in the peripheral blood of 24 critically ill patients, 12 had COVID-19 and 12 were COVID-19 negative. A list of potentially stable candidate reference genes was generated that included ten miRNAs. The top six were analyzed using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in a total of 41 patients so as to apply standard computational algorithms for validating reference genes, namely geNorm, NormFinder, BestKeeper and RefFinder. There was general agreement among all four algorithms in the ranking of four stable miRNAs: miR-186-5p, miR-148b-3p, miR-194-5p and miR-448. A detailed analysis of their output rankings led to the conclusion that miR-186-5p and miR-148b-3p are appropriate reference genes for miRNA expression studies using PaxGene tubes in the peripheral blood of patients critically ill with COVID-19 disease.

Distinct host-response signatures in circulatory shock: a narrative review.

Circulatory shock is defined syndromically as hypotension associated with tissue hypoperfusion and often subcategorized according to hemodynamic profile (e.g., distributive, cardiogenic, hypovolemic) and etiology (e.g., infection, myocardial infarction, trauma, among others). These shock subgroups are generally considered homogeneous entities in research and clinical practice. This current definition fails to consider the complex pathophysiology of shock and the influence of patient heterogeneity. Recent translational evidence highlights previously under-appreciated heterogeneity regarding the underlying pathways with distinct host-response patterns in circulatory shock syndromes. This heterogeneity may confound the interpretation of trial results as a given treatment may preferentially impact distinct subgroups. Re-analyzing results of major 'neutral' treatment trials from the perspective of biological mechanisms (i.e., host-response signatures) may reveal treatment effects in subgroups of patients that share treatable traits (i.e., specific biological signatures that portend a predictable response to a given treatment). In this review, we discuss the emerging literature suggesting the existence of distinct biomarker-based host-response patterns of circulatory shock syndrome independent of etiology or hemodynamic profile. We further review responses to newly prescribed treatments in the intensive care unit designed to personalize treatments (biomarker-driven or endotype-driven patient selection in support of future clinical trials).

Cardiogenic shock: a major challenge for the clinical trialist.

PURPOSE OF REVIEW: Cardiogenic shock (CS) results in persistently high short-term mortality and a lack of evidence-based therapies. Several trials of novel interventions have failed to show an improvement in clinical outcomes despite promising preclinical and physiologic principles. In this review, we highlight the challenges of CS trials and provide suggestions for the optimization and harmonization of their design. RECENT FINDINGS: CS clinical trials have been plagued by slow or incomplete enrolment, heterogeneous or nonrepresentative patient cohorts, and neutral results. To achieve meaningful, practice-changing results in CS clinical trials, an accurate CS definition, a pragmatic staging of its severity for appropriate patient selection, an improvement in informed consent process, and the use of patient-centered outcomes are required. Future optimizations include the use of predictive enrichment using host response biomarkers to unravel the biological heterogeneity of the CS syndrome and identify subphenotypes most likely to benefit from individualized treatment to allow a personalized medicine approach. SUMMARY: Accurate characterization of CS severity and its pathophysiology are crucial to unravel heterogeneity and identify the patients most likely to benefit from a tested treatment. Implementation of biomarker-stratified adaptive clinical trial designs (i.e., biomarker or subphenotype-based therapy) might provide important insights into treatment effects.

Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival.

BACKGROUND: Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches. METHODS: Plasma gas chromatography-liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women's Hospital, Boston, USA). Seven ML approaches were trained to differentiate survivors from non-survivors. Model performance predicting 28 day mortality was assessed through internal cross-validation, and innate top-feature (metabolite) selection and rankings were compared across the 7 ML approaches and with conventional statistical methods (logistic regression). Metabolites were consensus ranked by a summary, ensemble ML ranking procedure weighing their contribution to mortality risk prediction across multiple ML models. RESULTS: Median (IQR) patient age was 58 (47, 62) years, 45% were women, and median (IQR) SOFA score was 9 (6, 12). Mortality at 28 days was 42%. The models' specificity ranged from 0.619 to 0.821. Partial least squares regression-discriminant analysis and nearest shrunken centroids prioritized the greatest number of metabolites identified by at least one other method. Penalized logistic regression demonstrated top-feature results that were consistent with many ML methods. Across the plasma metabolome, the 13 metabolites with the strongest linkage to mortality defined through an ensemble ML importance score included lactate, bilirubin, kynurenine, glycochenodeoxycholate, phenylalanine, and others. Four of these top 13 metabolites (3-hydroxyisobutyrate, indoleacetate, fucose, and glycolithocholate sulfate) have not been previously associated with sepsis survival. Many of the prioritized metabolites are constituents of the tryptophan, pyruvate, phenylalanine, pentose phosphate, and bile acid pathways. CONCLUSIONS: We identified metabolites linked with sepsis survival, some confirming prior observations, and others representing new associations. The application of ensemble ML feature-ranking tools to metabolomic data may represent a promising statistical platform to support biologic target discovery.

Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort.

BACKGROUND: Late mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis. METHODS: In the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655). Among them, 467 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct sepsis-survivors clinical classes using readily available data at ICU discharge. The primary endpoint was one-year mortality after ICU discharge. RESULTS: At ICU discharge, two distinct subtypes were identified (A and B) using 15 readily available clinical and biological variables. Patients assigned to subtype B (48% of the studied population) had more impaired cardiovascular and kidney functions, hematological disorders and inflammation at ICU discharge than subtype A. Sepsis-survivors in subtype B had significantly higher one-year mortality compared to subtype A (respectively, 34% vs 16%, p < 0.001). When adjusted for standard long-term risk factors (e.g., age, comorbidities, severity of illness, renal function and duration of ICU stay), subtype B was independently associated with increased one-year mortality (adjusted hazard ratio (HR) = 1.74 (95% CI 1.16-2.60); p = 0.006). CONCLUSIONS: A subtype with sustained organ failure and inflammation at ICU discharge can be identified from routine clinical and laboratory data and is independently associated with poor long-term outcome in sepsis-survivors. Trial registration NCT01367093; https://clinicaltrials.gov/ct2/show/NCT01367093 .

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.

BACKGROUND: Proposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS. METHODS: We included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to three clusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In-hospital mortality and 1-year mortality were analyzed using logistic regression and Cox proportional-hazards models, respectively. RESULTS: We included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in-hospital mortality across subgroups. In-hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1-year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01). CONCLUSIONS: We observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in-hospital and 1-year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.

Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.

Advances in the Staging and Phenotyping of Cardiogenic Shock: Part 1 of 2.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated. This paper is part 1 of a 2-part state-of-the-art review. In this first article, we consider the context for clinical staging and stratification in CS with a focus on established severity staging systems for CS and their use for risk stratification and clinical care. We describe the use of staging for predicting outcomes in populations with or at risk for CS, including risk modifiers that provide more nuanced risk stratification, and highlight how these approaches may allow individualized care.

Evaluation of Biomarkers in Critical Care and Perioperative Medicine: A Clinician's Overview of Traditional Statistical Methods and Machine Learning Algorithms

Interest in developing and using novel biomarkers in critical care and perioperative medicine is increasing. Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians. To improve scientific rigor, the proper application and reporting of traditional and emerging statistical methods (e.g., machine learning) of biomarker studies is required. This Readers' Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative medicine.

Management of severe thermal burns in the acute phase in adults and children.

OBJECTIVES: To provide recommendations to facilitate the management of severe thermal burns during the acute phase in adults and children. DESIGN: A committee of 20 experts was asked to produce recommendations in six fields of burn management, namely, (1) assessment, admission to specialised burns centres, and telemedicine; (2) haemodynamic management; (3) airway management and smoke inhalation; (4) anaesthesia and analgesia; (5) burn wound treatments; and (6) other treatments. At the start of the recommendation-formulation process, a formal conflict-of-interest policy was developed and enforced throughout the process. The entire process was conducted independently of any industry funding. The experts drew up a list of questions that were formulated according to the PICO model (Population, Intervention, Comparison, and Outcomes). Two bibliography experts per field analysed the literature published from January 2000 onwards using predefined keywords according to PRISMA recommendations. The quality of data from the selected literature was assessed using GRADE® methodology. Due to the current paucity of sufficiently powered studies regarding hard outcomes (i.e. mortality), the recommendations are based on expert opinion. RESULTS: The SFAR guidelines panel generated 24 statements regarding the management of acute burn injuries in adults and children. After two scoring rounds and one amendment, strong agreement was reached for all recommendations. CONCLUSION: Substantial agreement was reached among a large cohort of experts regarding numerous strong recommendations to optimise the management of acute burn injuries in adults and children.

PenKid measurement at admission is associated with outcome in severely ill burn patients.

BACKGROUND: Proenkephalin A 119-159 (penKid) has been proposed as a sensitive biomarker of renal function. This study evaluated the association of concentrations of plasma penKid with death and risk of acute kidney injury (AKI) in severely ill burn patients. METHODS: A prospective observational study in two centers with severely ill adult burn patients was conducted. The inclusion criteria were total body surface area (TBSA) burns >15%, with burn injury occurring <72 h before intensive care unit (ICU) admission and plasma sample taken at admission. The primary endpoint was 90-day mortality. The secondary endpoints were AKI and a combined endpoint of 90-day mortality and/or AKI. Mortality was also evaluated in the sub-group of patients with sub-clinical AKI, defined as a patient without AKI but with elevated penKid. RESULTS: A total of 113 consecutive patients were enrolled. The median age was 48 years (Interquartile range [IQR] 33-64), the median burn TBSA was 35% (IQR 25-53), and 90-day mortality was 31.9%. Thirty-one percent of the patients had AKI, and 41.6% of patients had the combined endpoint. There was a stepwise decrease in survival from patients without AKI, sub-AKI, and with AKI (survival rate 90.0% [95% CI 82.7-97.9], 66.7% [95% CI 48.1-92.4], and 31.4% [95% CI 19.3-51.3], respectively, p < 0.001). Plasma penKid concentration was significantly higher in non-survivors compared to survivors (86.9 pmol/L [IQR 53.3-166.1] versus 52.9 pmol/L [IQR 37.1-70.7]; p = 0.0001) and in patients with AKI compared to patients without AKI (86.4 pmol/L [IQR 56.5-153.4] versus 52.5 pmol/L [IQR 35.5-71.2]; p < 0.001). Penkid provided added value on top of serum creatinine (Screat) and Sepsis Related Organ Failure Assessment (SOFA) scores to predict 90-day mortality (combined c-index of 0.738 versus 0.707; p = 0.024 and 0.787 versus 0.752; p < 0.001). CONCLUSIONS: Plasma penKid concentration at admission was associated with an increased risk of death in burn patients. PenKid has additional prognostic value on top of Screat and SOFA to predict 90-day mortality.