An integrative model using flow cytometry identifies nosocomial infection after trauma

Open or Closed? Management of Skin Incisions After Emergency General Surgery Laparotomies

INTRODUCTION

We sought to determine if there was a relationship between skin management and surgical site infections (SSIs) among patients undergoing a laparotomy for emergency general surgery (EGS). We hypothesize that skin closure technique is not associated with SSI.

METHODS

We performed a retrospective review of adult patients (>18 y) who underwent an exploratory laparotomy for EGS conditions within 6 h of surgical consultation from 2015 to 2019. Patients whose fascia was not closed during the index operation were excluded. Patients were divided into groups: open skin (OS) and closed skin (CS). OS included negative pressure wound therapy or wet-to-dry gauze; CS included closure with staples or sutures. Our primary outcome was the rate of SSI.

RESULTS

The cohort comprised 388 patients: 42.3% OS (n = 164) and 57.7% CS (n = 224). The OS group had greater rates of systemic inflammatory response syndrome [SIRS] (54.9% versus 27.7%, P < 0.0001), hollow viscus perforation [HVP] (71.3% versus 20.5%, P < 0.0001), and peritoneal drains (51.2% versus 17.9%, P < 0.0001). Rates of OS management increased as wound class severity increased (0% [I] versus 12.2% [II] versus 15.9% [III] versus 72% [IV], P < 0.0001). The SSI rate for the cohort was 3.6% (n = 14); there was no difference in SSI rates (2.7% versus 4.9%, P = 0.3) between the CS or OS groups. Median length of stay was longer for the OS group (10 d versus 6.5 d, P < 0.0001). Independent predictors of OS management were SIRS (adjusted odds ratio [aOR] 1.72, 95% confidence interval [CI] 1.01-2.93, P = 0.04), HVP (aOR 2.03, 95% CI 1.09-3.8, P = 0.03), and class III/IV wounds (aOR 8.65, 95% CI 4.43-16.89, P < 0.0001).

CONCLUSIONS

OS management occurs more often in patients with SIRS, HVP, and dirty wounds after EGS laparotomies. However, we found no difference in SSI between groups, suggesting that skin closure can be considered in contaminated or dirty wounds.

The CD56-CD16+ NK cell subset in chronic infections

Long-term human diseases can shape the immune system, and natural killer (NK) cells have been documented to differentiate into distinct subsets specifically associated with chronic virus infections. One of these subsets found in large frequencies in HIV-1 are the CD56-CD16+ NK cells, and this population's association with chronic virus infections is the subject of this review. Human NK cells are classically defined by CD56 expression, yet increasing evidence supports the NK cell status of the CD56-CD16+ subset which we discuss herein. We then discuss the evidence linking CD56-CD16+ NK cells to chronic virus infections, and the potential immunological pathways that are altered by long-term infection that could be inducing the population's differentiation. An important aspect of NK cell regulation is their interaction with human leukocyte antigen (HLA) class-I molecules, and we highlight work that indicates both virus and genetic-mediated variations in HLA expression that have been linked to CD56-CD16+ NK cell frequencies. Finally, we offer a perspective on CD56-CD16+ NK cell function, taking into account recent work that implies the subset is comparable to CD56+CD16+ NK cell functionality in antibody-dependent cell cytotoxicity response, and the definition of CD56-CD16+ NK cell subpopulations with varying degranulation capacity against target cells.

Multidimensional machine learning models predicting outcomes after trauma

BACKGROUND

An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients.

METHODS

This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation.

RESULTS

A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data.

CONCLUSION

Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.