Multi-Biomarker Prediction Models for Multiple Infection Episodes Following Blunt Trauma

Identifying biomarkers deciphering sepsis from trauma-induced sterile inflammation and trauma-induced sepsis

Objective

The purpose of this study was to identify a panel of biomarkers for distinguishing early stage sepsis patients from non-infected trauma patients.

Background

Accurate differentiation between trauma-induced sterile inflammation and real infective sepsis poses a complex life-threatening medical challenge because of their common symptoms albeit diverging clinical implications, namely different therapies. The timely and accurate identification of sepsis in trauma patients is therefore vital to ensure prompt and tailored medical interventions (provision of adequate antimicrobial agents and if possible eradication of infective foci) that can ultimately lead to improved therapeutic management and patient outcome. The adequate withholding of antimicrobials in trauma patients without sepsis is also important in aspects of both patient and environmental perspective.

Methods

In this proof-of-concept study, we employed advanced technologies, including Matrix-Assisted Laser Desorption/Ionization (MALDI) and multiplex antibody arrays (MAA) to identify a panel of biomarkers distinguishing actual sepsis from trauma-induced sterile inflammation.

Results

By comparing patient groups (controls, infected and non-infected trauma and septic shock patients under mechanical ventilation) at different time points, we uncovered distinct protein patterns associated with early trauma-induced sterile inflammation on the one hand and sepsis on the other hand. SYT13 and IL1F10 emerged as potential early sepsis biomarkers, while reduced levels of A2M were indicative of both trauma-induced inflammation and sepsis conditions. Additionally, higher levels of TREM1 were associated at a later stage in trauma patients. Furthermore, enrichment analyses revealed differences in the inflammatory response between trauma-induced inflammation and sepsis, with proteins related to complement and coagulation cascades being elevated whereas proteins relevant to focal adhesion were diminished in sepsis.

Conclusions

Our findings, therefore, suggest that a combination of biomarkers is needed for the development of novel diagnostic approaches deciphering trauma-induced sterile inflammation from actual infective sepsis.

Improving liver transplant outcomes with transplant-omics and network biology

PURPOSE OF REVIEW

Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the 'transplant-ome' - the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients.

RECENT FINDINGS

In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period.

SUMMARY

The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS

ABSTRACT

Introduction: Despite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a compelling challenge during recovery. A personalized medicine approach for accurate prediction of BSIs before they occur would contribute to prevention efforts and improve patient outcomes. Methods: We analyzed the blood transcriptome of severely burned (total burn surface area [TBSA] ≥20%) patients in the multicenter Inflammation and Host Response to Injury ("Glue Grant") cohort. Our study included 82 pediatric (aged <16 years) patients, with blood samples at least 3 days before the observed BSI episode. We applied the least absolute shrinkage and selection operator (LASSO) machine-learning algorithm to select a panel of biomarkers predictive of BSI outcome. Results: We developed a panel of 10 probe sets corresponding to six annotated genes ( ARG2 [ arginase 2 ], CPT1A [ carnitine palmitoyltransferase 1A ], FYB [ FYN binding protein ], ITCH [ itchy E3 ubiquitin protein ligase ], MACF1 [ microtubule actin crosslinking factor 1 ], and SSH2 [ slingshot protein phosphatase 2 ]), two uncharacterized ( LOC101928635 , LOC101929599 ), and two unannotated regions. Our multibiomarker panel model yielded highly accurate prediction (area under the receiver operating characteristic curve, 0.938; 95% confidence interval [CI], 0.881-0.981) compared with models with TBSA (0.708; 95% CI, 0.588-0.824) or TBSA and inhalation injury status (0.792; 95% CI, 0.676-0.892). A model combining the multibiomarker panel with TBSA and inhalation injury status further improved prediction (0.978; 95% CI, 0.941-1.000). Conclusions: The multibiomarker panel model yielded a highly accurate prediction of BSIs before their onset. Knowing patients' risk profile early will guide clinicians to take rapid preventive measures for limiting infections, promote antibiotic stewardship that may aid in alleviating the current antibiotic resistance crisis, shorten hospital length of stay and burden on health care resources, reduce health care costs, and significantly improve patients' outcomes. In addition, the biomarkers' identity and molecular functions may contribute to developing novel preventive interventions.

Immune modulation after traumatic brain injury

Traumatic brain injury (TBI) induces instant activation of innate immunity in brain tissue, followed by a systematization of the inflammatory response. The subsequent response, evolved to limit an overwhelming systemic inflammatory response and to induce healing, involves the autonomic nervous system, hormonal systems, and the regulation of immune cells. This physiological response induces an immunosuppression and tolerance state that promotes to the occurrence of secondary infections. This review describes the immunological consequences of TBI and highlights potential novel therapeutic approaches using immune modulation to restore homeostasis between the nervous system and innate immunity.

Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review

STUDY OBJECTIVE

Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research.

METHODS

We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers.

RESULTS

Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed.

CONCLUSION

This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.