Sepsis 2019: What Surgeons Need to Know

The definition of sepsis continues to be as dynamic as the management strategies used to treat this. Sepsis-3 has replaced the earlier systemic inflammatory response syndrome (SIRS)-based diagnoses with the rapid Sequential Organ Failure Assessment (SOFA) score assisting in predicting overall prognosis with regards to mortality. Surgeons have an important role in ensuring adequate source control while recognizing the threat of carbapenem-resistance in gram-negative organisms. Rapid diagnostic tests are being used increasingly for the early identification of multi-drug-resistant organisms (MDROs), with a key emphasis on the multidisciplinary alert of results. Novel, higher generation antibiotic agents have been developed for resistance in ESKCAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) organisms while surgeons have an important role in the prevention of spread. The Study to Optimize Peritoneal Infection Therapy (STOP-IT) trial has challenged the previous paradigm of length of antibiotic treatment whereas biomarkers such as procalcitonin are playing a prominent role in individualizing therapy. Several novel therapies for refractory septic shock, while still investigational, are gaining prominence rapidly (such as vitamin C) whereas others await further clinical trials. Management strategies presented as care bundles continue to be updated by the Surviving Sepsis Campaign, yet still remain controversial in its global adoption. We have broadened our temporal and epidemiologic perspective of sepsis by understanding it both as an acute, time-sensitive, life-threatening illness to a chronic condition that increases the risk of mortality up to five years post-discharge. Artificial intelligence, machine learning, and bedside scoring systems can assist the clinician in predicting post-operative sepsis. The public health role of the surgeon is key. This includes collaboration and multi-disciplinary antibiotic stewardship at a hospital level. It also requires controlling pharmaceutical sales and the unregulated dispensing of antibiotic agents globally through policy initiatives to control emerging resistance through prevention.

Appendicitis in Pregnancy: A Post-Hoc Analysis of an EAST Multicenter Study

Objective: To compare the presentation, management, and outcomes of appendicitis in pregnant and non-pregnant females of childbearing age (18-45 years). Methods: This was a post-hoc analysis of a prospectively collected database (January 2017-June 2018) from 28 centers in America. We compared pregnant and non-pregnant females' demographics, clinical presentation, laboratory data, imaging findings, management, and clinical outcomes. Results: Of the 3,597 subjects, 1,010 (28%) were of childbearing age, and 41 were pregnant: The mean age of the pregnant subjects was 30 ± 8 years at a median gestational age of 15 (range 10-23) weeks. The two groups had similar demographics and clinical presentation, but there were differences in management and outcomes. For example, in pregnant subjects, abdominal ultrasound scans (US) plus magnetic resonance imaging (MRI) was the most frequently used imaging method (41%) followed by MRI alone (29%), US alone (22%), computed tomography (CT) (5%), and no imaging (2%). Despite similar American Association for the Surgery of Trauma Emergency General Surgery Clinical and Imaging Grade at presentation, pregnant subjects were more likely to be treated with antibiotics alone (15% versus 4%; p = 0.008). Pregnant subjects were less likely to have simple appendicitis and were more likely to have complicated (perforated or gangrenous) appendicitis or a normal appendix. With the exception of index hospital length of stay, there were no significant differences between the groups in clinical outcomes at index hospitalization or at 30 days. Conclusion: Almost 1 in 20 women of childbearing age presenting with appendicitis is pregnant. Appendicitis most commonly affects women in early to mid-pregnancy. Compared with non-pregnant women of childbearing age, pregnant women presenting with appendicitis undergo non-operative management more often and are less likely to have simple appendicitis. Compared with non-pregnant patients, they have similar clinical outcomes at both index hospitalization and 30 days after discharge.

Association of Anti-Factor Xa-Guided Dosing of Enoxaparin With Venous Thromboembolism After Trauma

Importance

The efficacy of anti-factor Xa (anti-Xa)-guided dosing of thromboprophylaxis after trauma remains controversial.

Objective

To assess whether dosing of enoxaparin sodium based on peak anti-Xa levels is associated with the venous thromboembolism (VTE) rate after trauma.

Design, Setting, and Participants

Retrospective review of 950 consecutive adults admitted to a single level I trauma intensive care unit for more than 48 hours from December 1, 2014, through March 31, 2017. Within 24 hours of admission, these trauma patients were screened with the Greenfield Risk Assessment Profile (RAP) (possible score range, 0-46). Patients younger than 18 years and those with VTE on admission were excluded, resulting in a study population of 792 patients.

Exposures

The control group received fixed doses of either heparin sodium, 5000 U 3 times a day, or enoxaparin sodium, 30 mg twice a day. The adjustment cohort initially received enoxaparin sodium, 30 mg twice a day. A peak anti-Xa level was drawn 4 hours after the third dose. If the anti-Xa level was 0.2 IU/mL or higher, no adjustment was made. If the anti-Xa level was less than 0.2 IU/mL, each dose was increased by 10 mg. The process was repeated up to a maximum dose of 60 mg twice a day.

Main Outcomes and Measures

Rates of VTE were measured. Venous duplex ultrasonography and computed tomographic angiography were used for diagnosis.

Results

The study population comprised 792 patients with a mean (SD) age of 46 (19) years and was composed of 598 men (75.5%). The control group comprised 570 patients, was older, and had a longer time to thromboprophylaxis initiation. The adjustment group consisted of 222 patients, was more severely injured, and had a longer hospital length of stay. The mean (SD) RAP scores were 9 (4) for the control group and 9 (5) for the adjustment group (P = .28). The VTE rates were similar for both groups (34 patients [6.0%] vs 15 [6.8%]; P = .68). Prophylactic anti-Xa levels were reached in 119 patients (53.6%) in the adjustment group. No difference in VTE rates was observed between those who became prophylactic and those who did not (7 patients [5.9%] vs 8 [7.8%]; P = .58). To control for confounders, 132 patients receiving standard fixed-dose enoxaparin were propensity matched to 84 patients receiving dose-adjusted enoxaparin. The VTE rates remained similar between the control and adjustment groups (3 patients [2.3%] vs 3 [3.6%]; P = .57).

Conclusions and Relevance

Rates of VTE were not reduced with anti-Xa-guided dosing, and almost half of the patients never reached prophylactic anti-Xa levels; achieving those levels did not decrease VTE rates. Thus, other targets, such as platelets, may be necessary to optimize thromboprophylaxis after trauma.

Validating the ATLS Shock Classification for Predicting Death, Transfusion, or Urgent Intervention

BACKGROUND

The Advanced Trauma Life Support (ATLS) shock classification has been accepted as the conceptual framework for clinicians caring for trauma patients. We sought to validate its ability to predict mortality, blood transfusion, and urgent intervention.

MATERIALS AND METHODS

We performed a retrospective review of trauma patients using the 2014 National Trauma Data Bank. Using initial vital signs data, patients were categorized into shock class based on the ATLS program. Rates for urgent blood transfusion, urgent operative intervention, and mortality were compared between classes.

RESULTS

630,635 subjects were included for analysis. Classes 1, 2, 3, and 4 included 312,404, 17,133, 31, and 43 patients, respectively. 300,754 patients did not meet criteria for any ATLS shock class. Of the patients in class 1 shock, 2653 died (0.9%), 3123 (1.0%) were transfused blood products, and 7115 (2.3%) underwent an urgent procedure. In class 2, 219 (1.3%) died, 387 (2.3%) were transfused, and 1575 (9.2%) underwent intervention. In class 3, 7 (22.6%) died, 10 (32.3%) were transfused, and 13 (41.9%) underwent intervention. In class 4, 15 (34.9%) died, 19 (44.2%) were transfused, and 23 (53.5%) underwent intervention. For uncategorized patients, 21,356 (7.1%) died, 15,168 (5.0%) were transfused, and 23,844 (7.9%) underwent intervention.

CONCLUSIONS

Almost half of trauma patients do not meet criteria for any ATLS shock class. Uncategorized patients had a higher mortality (7.1%) than patients in classes 1 and 2 (0.9% and 1.3%, respectively). Classes 3 and 4 only accounted for 0.005% and 0.007%, respectively, of patients. The ATLS classification system does not help identify many patients in severe shock.

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes.

National Risk Factors for Child Maltreatment after Trauma: Failure to Prevent

The purpose of this study was to identify the risk factors for hospital readmission for child maltreatment after trauma, including admissions across different hospitals nationwide. The Nationwide Readmissions Database for 2010-2014 was queried for all patients younger than 18 years admitted for trauma. The primary outcome was readmission for child maltreatment. The secondary outcome was readmission for maltreatment presenting to a hospital different than the index admission hospital. A subgroup analysis was performed on patients without a diagnosis of maltreatment during the index admission. Multivariable logistic regression was performed for each outcome. There were 608,744 admissions identified and 44,569 (7.32%) involved maltreatment at the index admission. Readmission for maltreatment was found in 1,948 (0.32%) patients and 368 (18.89%) presented to a different hospital. The highest risk for readmission for maltreatment was found in patients with maltreatment identified at the index admission (odds ratios (OR) 9.48 [8.35-10.76]). The strongest risk factor for presentation to a different hospital was found with the lowest median household income quartile (OR 3.50 [2.63-4.67]). The subgroup analysis identified 647 (0.11%) children with readmission for maltreatment that was missed during the index admission. The strongest risk factor for this outcome was Injury Severity Score > 15 (OR 3.29 [2.68-4.03]). This study demonstrates that a significant portion of admissions for trauma in children and teenagers could be misrepresented as not involving maltreatment. These index admissions could be the only chance for intervention for child maltreatment. Identifying these at-risk individuals is critical to prevention efforts.

Susceptibility-directed anticoagulation after pancreas transplantation: A single-center retrospective study

Pancreas transplant achieves consistent long-term euglycemia in type 1 diabetes. Allograft thrombosis (AT) causes the majority of early graft failure. We compared outcomes of four anticoagulation regimens administered to 95 simultaneous kidney-pancreas or isolated pancreas transplanted between 1/1/2015 and 11/20/2018. Early postoperative anticoagulation regimens included the following: none, subcutaneous heparin/aspirin, with or without dextran, and heparin infusion. The regimens were empirically selected based on each surgeon's assessment of hemostasis of the operative field and personal preference. A sonographic-based global scoring system of AT is presented. The 47-month recipients and graft survival were 95% and 86%, respectively. Recipients with or without AT had similar survival. Five and four grafts were lost due to death and AT, respectively. Outcomes of prophylaxis regimens correlated with intensity of anticoagulation. Compared with no anticoagulation, an increase in hemorrhagic complications occurred exclusively with iv heparin. The higher arterial AT score found in regimens lacking antiplatelet therapy highlights the importance of early antiaggregants therapy. Abnormal fibrinolysis was associated with an increase in AT score. Platelet dysfunction, warm ischemia time, and enteric drainage were predictive of AT and, along with other known risk factors, were incorporated into an algorithm that matches intensity of early postoperative anticoagulation to the thrombotic risk.

Use of Telemedicine in Surgical Education: A Seven-Year Experience

Uniformity in surgical education is challenging because surgical experience is based on rotation assignments. With work hour restrictions, the likelihood of residents being exposed to rare or unusual cases is diminished. Telemedicine may create a new learning paradigm for surgical education and supplement exposure for rare or unusual cases. A retrospective review (2010–2016) of teleconferences involving trauma centers worldwide was conducted. Participating hospitals included centers from underdeveloped countries to first world nations. Trauma cases were discussed among surgeons with different levels of experience and resource availability. Data collected included types of cases, anatomic injury patterns, hospital location, and the number of telemedicine centers and viewers participating. Seventy-three hospitals in 64 cities, spanning 27 countries, participated in 276 telemedicine grand round conferences. Cases discussed included penetrating trauma (47%), blunt trauma (42%), and blast injury (4%). The anatomic regions included were the thorax (28%), abdomen (26%), thoracoabdominal region (13%), neck (7%), and pelvis (6%). The most common injury discussed was vascular in nature (18%), followed by the lung, liver, diaphragm, and heart. The most common vascular lesion was in the aorta (18%), followed by the iliac vessels (8%) and the vena cava (7%). Telemedicine is a valuable tool, allowing the dissemination of diverse experiences. Most cases presented evaluated rare injuries or complex surgical approaches, which are not commonly seen on trauma sites. Learning different approaches in the management of complex trauma will make surgeons more prepared to deal with challenging cases.

Use of Telemedicine in Surgical Education: A Seven-Year Experience

Uniformity in surgical education is challenging because surgical experience is based on rotation assignments. With work hour restrictions, the likelihood of residents being exposed to rare or unusual cases is diminished. Telemedicine may create a new learning paradigm for surgical education and supplement exposure for rare or unusual cases. A retrospective review (2010-2016) of teleconferences involving trauma centers worldwide was conducted. Participating hospitals included centers from underdeveloped countries to first world nations. Trauma cases were discussed among surgeons with different levels of experience and resource availability. Data collected included types of cases, anatomic injury patterns, hospital location, and the number of telemedicine centers and viewers participating. Seventy-three hospitals in 64 cities, spanning 27 countries, participated in 276 telemedicine grand round conferences. Cases discussed included penetrating trauma (47%), blunt trauma (42%), and blast injury (4%). The anatomic regions included were the thorax (28%), abdomen (26%), thoracoabdominal region (13%), neck (7%), and pelvis (6%). The most common injury discussed was vascular in nature (18%), followed by the lung, liver, diaphragm, and heart. The most common vascular lesion was in the aorta (18%), followed by the iliac vessels (8%) and the vena cava (7%). Telemedicine is a valuable tool, allowing the dissemination of diverse experiences. Most cases presented evaluated rare injuries or complex surgical approaches, which are not commonly seen on trauma sites. Learning different approaches in the management of complex trauma will make surgeons more prepared to deal with challenging cases.

Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation

The purpose of this study was to use natural language processing of physician documentation to predict mortality in patients admitted to the surgical intensive care unit (SICU). The Multiparameter Intelligent Monitoring in Intensive Care III database was used to obtain SICU stays with six different severity of illness scores. Natural language processing was performed on the physician notes. Classifiers for predicting mortality were created. One classifier used only the physician notes, one used only the severity of illness scores, and one used the physician notes with severity of injury scores. There were 3838 SICU stays identified during the study period and 5.4 per cent ended with mortality. The classifier trained with physician notes with severity of injury scores performed with the highest area under the curve (0.88 ± 0.05) and accuracy (94.6 ± 1.1%). The most important variable was the Oxford Acute Severity of Illness Score (16.0%). The most important terms were "dilated" (4.3%) and "hemorrhage" (3.7%). This study demonstrates the novel use of artificial intelligence to process physician documentation to predict mortality in the SICU. The classifiers were able to detect the subtle nuances in physician vernacular that predict mortality. These nuances provided improved performance in predicting mortality over physiologic parameters alone.

Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation

The purpose of this study was to use natural language processing of physician documentation to predict mortality in patients admitted to the surgical intensive care unit (SICU). The Multiparameter Intelligent Monitoring in Intensive Care III database was used to obtain SICU stays with six different severity of illness scores. Natural language processing was performed on the physician notes. Classifiers for predicting mortality were created. One classifier used only the physician notes, one used only the severity of illness scores, and one used the physician notes with severity of injury scores. There were 3838 SICU stays identified during the study period and 5.4 per cent ended with mortality. The classifier trained with physician notes with severity of injury scores performed with the highest area under the curve (0.88 ± 0.05) and accuracy (94.6 ± 1.1%). The most important variable was the Oxford Acute Severity of Illness Score (16.0%). The most important terms were “dilated” (4.3%) and “hemorrhage” (3.7%). This study demonstrates the novel use of artificial intelligence to process physician documentation to predict mortality in the SICU. The classifiers were able to detect the subtle nuances in physician vernacular that predict mortality. These nuances provided improved performance in predicting mortality over physiologic parameters alone.

Novel Method Suggests Global Superiority of Short-Duration Antibiotics for Intra-abdominal Infections

Desirability of outcome ranking and response adjusted for duration of antibiotic risk (DOOR/RADAR) are novel and innovative methods of evaluating data in antibiotic trials. We analyzed data from a noninferiority trial of short-course antimicrobial therapy for intra-abdominal infection (STOP-IT), and results suggest global superiority of short-duration therapy for intra-abdominal infections.