Lessons of War: Turning Data Into Decisions

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds

A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

IMPORTANCE

Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

Inhibition of focal adhesion kinase 2 results in a macrophage polarization shift to M2 which attenuates local and systemic inflammation and reduces heterotopic ossification after polysystem extremity trauma

Introduction

Heterotopic ossification (HO) is a complex pathology often observed in combat injured casualties who have sustained severe, high energy polytraumatic extremity injuries. Once HO has developed, prophylactic therapies are limited outside of surgical excision. Tourniquet-induced ischemia injury (IR) exacerbates trauma-mediated musculoskeletal tissue injury, inflammation, osteogenic progenitor cell development and HO formation. Others have shown that focal adhesion kinase-2 (FAK2) plays a key role in regulating early inflammatory signaling events. Therefore, we hypothesized that targeting FAK2 prophylactically would mitigate extremity trauma induced IR inflammation and HO formation.

Methods

We tested whether the continuous infusion of a FAK2 inhibitor (Defactinib, PF-573228; 6.94 µg/kg/min for 14 days) can mitigate ectopic bone formation (HO) using an established blast-related extremity injury model involving femoral fracture, quadriceps crush injury, three hours of tourniquet-induced limb ischemia, and hindlimb amputation through the fracture site. Tissue inflammation, infiltrating cells, osteogenic progenitor cell content were assessed at POD-7. Micro-computed tomography imaging was used to quantify mature HO at POD-56.

Results

In comparison to vehicle control-treated rats, FAK2 administration resulted in no marked wound healing complications or weight loss. FAK2 treatment decreased HO by 43%. At POD-7, marked reductions in tissue proinflammatory gene expression and assayable osteogenic progenitor cells were measured, albeit no significant changes in expression patterns of angiogenic, chondrogenic and osteogenic genes. At the same timepoint, injured tissue from FAK-treated rats had fewer infiltrating cells. Additionally, gene expression analyses of tissue infiltrating cells resulted in a more measurable shift from an M1 inflammatory to an M2 anti-inflammatory macrophage phenotype in the FAK2 inhibitor-treated group.

Discussion

Our findings suggest that FAK2 inhibition may be a novel strategy to dampen trauma-induced inflammation and attenuate HO in patients at high risk as a consequence of severe musculoskeletal polytrauma.

Bayesian Statistics for Medical Devices: Progress Since 2010

The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.

Artificial intelligence and machine learning for hemorrhagic trauma care

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

Advanced Wound Diagnostics: Toward Transforming Wound Care into Precision Medicine

Significance: Nonhealing wounds are an ever-growing global pandemic, with mortality rates and management costs exceeding many common cancers. Although our understanding of the molecular and cellular factors driving wound healing continues to grow, standards for diagnosing and evaluating wounds remain largely subjective and experiential, whereas therapeutic strategies fail to consistently achieve closure and clinicians are challenged to deliver individualized care protocols. There is a need to apply precision medicine practices to wound care by developing evidence-based approaches, which are predictive, prescriptive, and personalized. Recent Advances: Recent developments in "advanced" wound diagnostics, namely biomarkers (proteases, acute phase reactants, volatile emissions, and more) and imaging systems (ultrasound, autofluorescence, spectral imaging, and optical coherence tomography), have begun to revolutionize our understanding of the molecular wound landscape and usher in a modern age of therapeutic strategies. Herein, biomarkers and imaging systems with the greatest evidence to support their potential clinical utility are reviewed. Critical Issues: Although many potential biomarkers have been identified and several imaging systems have been or are being developed, more high-quality randomized controlled trials are necessary to elucidate the currently questionable role that these tools are playing in altering healing dynamics or predicting wound closure within the clinical setting. Future Directions: The literature supports the need for the development of effective point-of-care wound assessment tools, such as a platform diagnostic array that is capable of measuring multiple biomarkers at once. These, along with advances in telemedicine, synthetic biology, and "smart" wearables, will pave the way for the transformation of wound care into a precision medicine. Clinical Trial Registration number: NCT03148977.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery

OBJECTIVE

To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing.

SUMMARY BACKGROUND DATA

Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes.

METHODS

Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing fourteen surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury.

RESULTS

The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and post-operative time points and are already seeing evidence of physiologic perturbation between timepoints.

CONCLUSIONS

This repository allows for longitudinal, state-of-the-art genomic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Evaluating the potential use of functional fibrosis to facilitate improved outcomes following volumetric muscle loss injury

Volumetric muscle loss (VML) was defined as the frank loss of skeletal muscle tissue with associated chronic functional deficits. Significant effort has been dedicated to developing approaches for treating VML injuries, most of which have focused on stimulating regeneration of the affected musculature via a variety of approaches (e.g., biomaterials). VML injury induces a prolonged inflammatory response which causes fibrotic tissue deposition and is thought to inhibit de novo myofiber regeneration despite observed improvements in functional outcomes (i.e., functional fibrosis; FF). Recent approaches have sought to attenuate inflammation and/or fibrosis as a means to create a permissive environment for regenerative therapies. However, there are currently no clinically available interventions capable of facilitating full restoration of form and function following VML injury; thus, an unmet clinical need exists for a near-term interventional strategy to treat affected patients. FF could serve as an alternative approach to facilitate improved functional outcomes following VML injuries. We sought to investigate whether intentionally exploiting the concept of FF (i.e., induction of a supraphysiological fibrotic response via the delivery of a polypropylene mesh combined with TGFβ) would enhance the function of the VML affected musculature. We found that FF treatment induces enhanced fibrotic tissue deposition within the VML defect as evidenced by histological and molecular analysis. FF-treated animals exhibit improved in vivo muscle function compared to untreated control animals at 8 weeks post-injury, thus substantiating the concept that FF could serve as an efficacious approach for facilitating improved functional outcomes following VML injury. STATEMENT OF SIGNIFICANCE: VML injuries result in long-term functional impairments and reduced quality of life for affected individuals, namely combat injured US Service members, and no clinical interventions can restore the form and function of the injured limb. Extensive efforts have been aimed at developing therapeutics to address this critical gap; unfortunately, most interventions facilitate only modest regeneration. Interestingly, improved muscle function has been observed in VML studies following treatment with a therapeutic, despite a lack of myogenic tissue formation; a phenomenon termed Functional Fibrosis (FF). Herein we exploited the concept of FF to enhance the function of VML affected musculature. This finding is significant in that the commercially available interventions used to induce FF can be translated into the clinic near-term, thus improving the standard of care for VML injuries.

Decision curve analysis to evaluate the clinical benefit of prediction models

There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination (area-under-the-curve [AUC] or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be "high enough" to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature.

Driving biology: The effect of standardized wound management on wound biomarker profiles

BACKGROUND

The timing of coverage of an open wound is based on heavily on clinical gestalt. DoD's Surgical Critical Care Initiative created a clinical decision support tool that predicts wound closure success using clinical and biomarker data. The military uses a regimented protocol consisting of serial washouts and debridements. While decisions around wound closure in civilian centers are subject to the same clinical parameters, preclosure wound management is, generally, much more variable. We hypothesized that the variability in management would affect local biomarker expression within these patients.

METHODS

We compared data from 116 wounds in 73 military patients (MP) to similar data from 88 wounds in 78 civilian patients (CP). We used Wilcoxon rank-sum tests to assess concentrations of 32 individual biomarkers taken from wound effluent. Along with differences in the debridement frequency, we focused on these local biomarkers in MP and CP at both the first washout and the washout performed just prior to attempted closure.

RESULTS

On average, CP waited longer from the time of injury to closure (21.9 days, vs. 11.6 days, p < 0.0001) but had a similar number of washouts (3.86 vs. 3.44, p = 0.52). When comparing the wound effluent between the two populations, they had marked biochemical differences both when comparing the results at the first washout and at the time of closure. However, in a subset of civilian patients whose average number of days between washouts was never more than 72 hours, these differences ceased to be significant for most variables.

CONCLUSION

There were significant differences in the baseline biochemical makeup of wounds in the CP and MP. These differences could be eliminated if both were treated under similar wound care paradigms. Variations in therapy affect not only outcomes but also the actual biochemical makeup of wounds.

LEVEL OF EVIDENCE

Therapeutic, level IV.

Random forest modeling can predict infectious complications following trauma laparotomy

BACKGROUND

Identifying clinical and biomarker profiles of trauma patients may facilitate the creation of models that predict postoperative complications. We sought to determine the utility of modeling for predicting severe sepsis (SS) and organ space infections (OSI) following laparotomy for abdominal trauma.

METHODS

Clinical and molecular biomarker data were collected prospectively from patients undergoing exploratory laparotomy for abdominal trauma at a Level I trauma center between 2014 and 2017. Machine learning algorithms were used to develop models predicting SS and OSI. Random forest (RF) was performed, and features were selected using backward elimination. The SS model was trained on 117 records and validated using the leave-one-out method on the remaining 15 records. The OSI model was trained on 113 records and validated on the remaining 19. Models were assessed using areas under the curve.

RESULTS

One hundred thirty-two patients were included (median age, 30 years [23-42 years], 68.9% penetrating injury, median Injury Severity Score of 18 [10-27]). Of these, 10.6% (14 of 132) developed SS and 13.6% (18 of 132) developed OSI. The final RF model resulted in five variables for SS (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, interleukin-6, and eotaxin) and four variables for OSI (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, and interleukin-8). The RF models predicted SS and OSI with areas under the curve of 0.798 and 0.774, respectively.

CONCLUSION

Random forests with RFE can help identify clinical and biomarker profiles predictive of SS and OSI after trauma laparotomy. Once validated, these models could be used as clinical decision support tools for earlier detection and treatment of infectious complications following injury.

LEVEL OF EVIDENCE

Prognostic, level III.

Prediction of venous thromboembolism using clinical and serum biomarker data from a military cohort of trauma patients

INTRODUCTION

Venous thromboembolism (VTE) is a frequent complication of trauma associated with high mortality and morbidity. Clinicians lack appropriate tools for stratifying trauma patients for VTE, thus have yet to be able to predict when to intervene. We aimed to compare random forest (RF) and logistic regression (LR) predictive modelling for VTE using (1) clinical measures alone, (2) serum biomarkers alone and (3) clinical measures plus serum biomarkers.

METHODS

Data were collected from 73 military casualties with at least one extremity wound and prospectively enrolled in an observational study between 2007 and 2012. Clinical and serum cytokine data were collected. Modelling was performed with RF and LR based on the presence or absence of deep vein thrombosis (DVT) and/or pulmonary embolism (PE). For comparison, LR was also performed on the final variables from the RF model. Sensitivity/specificity and area under the curve (AUC) were reported.

RESULTS

Of the 73 patients (median Injury Severity Score=16), nine (12.3%) developed VTE, four (5.5%) with DVT, four (5.5%) with PE, and one (1.4%) with both DVT and PE. In all sets of predictive models, RF outperformed LR. The best RF model generated with clinical and serum biomarkers included five variables (interleukin-15, monokine induced by gamma, vascular endothelial growth factor, total blood products at resuscitation and presence of soft tissue injury) and had an AUC of 0.946, sensitivity of 0.992 and specificity of 0.838.

CONCLUSIONS

VTE may be predicted by clinical and molecular biomarkers in trauma patients. This will allow the development of clinical decision support tools which can help inform the management of high-risk patients for VTE.

Prototype of a Military Medic Smartphone Medical Graphical User Interface for Use by Medics in Deployed Environments

Introduction

Prompt and effective combat casualty care is essential for decreasing morbidity and mortality during military operations. Similarly, accurate documentation of injuries and treatments enables quality care, both in the immediate postinjury phase and the longer-term recovery. This article describes efforts to prototype a Military Medic Smartphone (MMS) for use by combat medics and other health care providers who work in austere environments.

Materials and Methods

The MMS design builds on previous electronic health record systems and is based on observations of medic workflows. It provides several functions including a compact yet efficient physiologic monitor, a communications device for telemedicine, a portable reference library, and a recorder of casualty care data from the point of injury rearward to advanced echelons of care. Apps and devices communicate using an open architecture to support different sensors and future expansions.

Results

The prototype MMS was field tested during live exercises to generate qualitative feedback from potential users, which provided significant guidance for future enhancements.

Conclusions

The widespread deployment of this type of device will enable more effective health care, limit the impact of battlefield injuries, and save lives.

Extracellular Mitochondrial DNA and N-Formyl Peptides in Trauma and Critical Illness: A Systematic Review

OBJECTIVES

Extracellular mitochondrial DNA and N-formyl peptides released following tissue damage may contribute to systemic inflammation through stimulation of the innate immune system. In this review, we evaluate existing in vivo human data regarding a role for mitochondrial DNA and N-formyl peptides in producing systemic inflammation in trauma and critical illness, investigate the utility of these molecules in risk prediction and clinical decision support, and provide suggestions for standardization of future research.

DATA SOURCES

PubMed, Embase (1971-2017).

STUDY SELECTION

Studies measuring extracellular mitochondrial DNA and/or N-formyl peptides in acutely ill patients.

DATA EXTRACTION

Fifty-four studies were analyzed. Data extracted included article characteristics, methods, results, and performance in clinical prediction.

DATA SYNTHESIS

The most common patient types investigated were trauma (19 studies) and sepsis (eight). In studies comparing patient mitochondrial DNA or N-formyl peptide levels to healthy controls, 38 (90.5%) reported significantly elevated mitochondrial DNA levels in patients at first reported time point, as did the one study making this comparison for N-formyl peptides. Nine studies (81.8%) reported significantly elevated plasma/serum mitochondrial DNA levels in at least one time point in patients who developed inflammatory complications of their primary pathology compared with patients without inflammatory complications. For the ability of mitochondrial DNA to predict complications or outcomes, the area under the curve was 0.7 or greater in 84.6% of receiver operating characteristic curves, and 92.9% of odds, adjusted odds, risk, and hazard ratios were statistically significant.

CONCLUSIONS

Extracellular mitochondrial DNA levels are elevated early in patients' hospital courses in many acute illnesses and are higher in patients who develop inflammatory complications. Elevated mitochondrial DNA levels may be clinically useful in risk prediction and clinical decision support systems. Further research is needed to determine the role of extracellular N-formyl peptides in systemic inflammation and their possible clinical utility.

The Uniformed Services University's Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the Critically Ill

Precision medicine endeavors to leverage all available medical data in pursuit of individualized diagnostic and therapeutic plans to improve patient outcomes in a cost-effective manner. Its promise in the field of critical care remains incompletely realized. The Department of Defense has a vested interest in advancing precision medicine for those sent into harm's way and specifically seeks means of individualizing care in the context of complex and highly dynamic combat clinical decision environments. Building on legacy research efforts conducted during the Afghanistan and Iraq conflicts, the Uniformed Service University (USU) launched the Surgical Critical Care Initiative (SC2i) in 2013 to develop clinical- and biomarker-driven Clinical Decision Support Systems (CDSS), with the goals of improving both patient-specific outcomes and resource utilization for conditions with a high risk of morbidity or mortality. Despite technical and regulatory challenges, this military-civilian partnership is beginning to deliver on the promise of personalized care, organizing and analyzing sizable, real-time medical data sets to support complex clinical decision-making across critical and surgical care disciplines. We present the SC2i experience as a generalizable template for the national integration of federal and non-federal research databanks to foster critical and surgical care precision medicine.

Preclosure spectroscopic differences between healed and dehisced traumatic wounds

BACKGROUND

The complexity and severity of traumatic wounds in military and civilian trauma demands improved wound assessment, before, during, and after treatment. Here, we explore the potential of 3 charge-coupled device (3CCD) imaging values to distinguish between traumatic wounds that heal following closure and those that fail. Previous studies demonstrate that normalized 3CCD imaging values exhibit a high correlation with oxygen saturation and allow for comparison of values between diverse clinical settings, including utilizing different equipment and lighting.

METHODS

We screened 119 patients at Walter Reed National Military Medical Center and at Grady Memorial Hospital with at least one traumatic extremity wound of ≥ 75 cm2. We collected images of each wound during each débridement surgery for a total of 66 patients. An in-house written computer application selected a region of interest in the images, separated the pixel color values, calculated relative values, and normalized them. We followed patients until the enrolled wounds were surgically closed, quantifying the number of wounds that dehisced (defined as wound failure or infection requiring return to the operating room after closure) or healed.

RESULTS

Wound failure occurred in 20% (19 of 96) of traumatic wounds. Normalized intensity values for patients with wounds that healed successfully were, on average, significantly different from values for patients with wounds that failed (p ≤ 0.05). Simple thresholding models and partial least squares discriminant analysis models performed poorly. However, a hierarchical cluster analysis model created with 17 variables including 3CCD data, wound surface area, and time from injury predicts wound failure with 76.9% sensitivity, 76.5% specificity, 76.6% accuracy, and a diagnostic odds ratio of 10.8 (95% confidence interval: 2.6-45.9).

CONCLUSIONS

Imaging using 3CCD technology may provide a non-invasive and cost-effective method of aiding surgeons in deciding if wounds are ready for closure and could potentially decrease the number of required débridements and hospital days. The process may be automated to provide real-time feedback in the operating room and clinic. The low cost and small size of the cameras makes this technology attractive for austere and shipboard environments where space and weight are at a premium.

Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review

Background

Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds.

Objective

The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview.

Methods

A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models.

Results

The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015.

Conclusions

The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

Assessing Risk and Related Complications after Reversal of Combat-Associated Ostomies

BACKGROUND

During the past decade of conflict, numerous patients with combat-associated injuries required the formation of an ostomy. However, outcomes in those patients undergoing ostomy reversal have yet to be analyzed. We review the experience and identify risk factors for complications after ostomy reversal in a series of patients with combat injuries at our military treatment facility.

STUDY DESIGN

A retrospective review of patients with combat-associated injuries managed with a diverting ostomy who underwent ostomy reversal at our military treatment facility during a 13-year period. Demographic and clinical data were collected for all patients and postoperative complications were identified. Multivariate analysis was performed to identify independent risk factors for complications after reversal. Complication rates were calculated for 90-day periods of time after ostomy creation and best-fit curve analysis was conducted.

RESULTS

Ninety-nine patients were identified who underwent ostomy reversal. Forty patients (40.4%) suffered a post-reversal complication. On multivariate analysis, older age (odds ratio 1.11/y; p = 0.038), severe perineal injury indication for diversion (odds ratio 4.37; p = 0.028), and increased time interval between ostomy creation and reversal (odds ratio 1.005/d; p = 0.037), were independently associated with postoperative complications. A cubic regression best fit quarterly complication rate data (R² 0.526; p < 0.001) and calculates a minimum complication rate for reversal 90 to 180 days after ostomy creation.

CONCLUSIONS

Ostomy reversal in patients wounded in combat is a major undertaking with a high complication rate. The finding of a shorter interval from ostomy creation to reversal independently associated with a reduction in complications provides a modifiable risk factor to guide future practice and potentially reduce complications. Our modeling suggests reversal in the 3- to 6-month time frame can have the lowest rate of complications. Future research to reduce complications is indicated, especially in older patients with perineal wounds.

Local perivascular adiponectin associates with lower extremity vascular operative wound complications

BACKGROUND

Wound complication rates after lower extremity vascular operative procedures stand as high as 40% and represent a major cause of morbidity, mortality, and cost. In view of increasing recognition of adipose tissue involvement in homeostasis and the response to injury, we hypothesized that adipose phenotype is linked to operative wound outcomes.

METHODS

Clinical history, peripheral blood, and subcutaneous and perivascular adipose tissue were prospectively collected at the time of operation in patients undergoing lower extremity revascularization and lower extremity amputations. Nine biologic mediators (adiponectin; interleukin [IL]-1β, IL-6, and IL-8; leptin; monocyte chemoattractant protein-1; plasminogen activator inhibitor-1; resistin; and tumor necrosis factor) were assayed in the adipose tissues and plasma. The 30-day wound complications were captured in real time. Logarithmic transformation of mediator levels was performed based on positively skewed, non-Gaussian distribution, and data were compared using the Student t test. Bonferroni correction was used for multiple comparisons.

RESULTS

Sixty-six patients undergoing lower extremity revascularization or lower extremity amputations for severe peripheral arterial disease were enrolled. The 30-day follow-up was 92.4%. In total, 19 (29%) patients developed wound complications. Patients who developed wound complications had elevated perivascular adiponectin levels (mean ± standard error, 2,372.45 ± 648.64 ng/mL vs 832.53 ± 180.54 ng/mL, P = .004). Perivascular IL-1β levels were lower among patients with wound dehiscence (0.41 ± 0.004 pg/mL vs 0.73 ± 0.09 pg/mL, P = .001).

CONCLUSION

Local adipose tissue mediator levels at the time of operation demonstrate a previously undescribed compartment-specific relationship to wound outcomes in patients undergoing lower extremity vascular operative procedures. These associations provide fertile directives for defining the mechanisms underlying the pathogenesis of wound complications and their prevention.