Experiential Learning of Interdisciplinary Care Skills in Surgery Assessed From Student Reflections.

INTRODUCTION: Interdisciplinary healthcare collaboration improves patient outcomes, increases workplace satisfaction, and reduces costs. Our medical school utilizes an experiential learning tool for teaching interprofessionalism known as the Longitudinal Patient Project (LPP). Medical students are directed to identify a surgical patient to establish continuity with by observing them throughout preoperative, intraoperative, and postoperative periods, and follow-up with the patient after their procedure. Students then write reflections on their LPP experience. This study examines the LPP as an interprofessionalism teaching tool using qualitative analysis of student reflections. METHODS: NVivo 12 was used to code reflections. One researcher coded reflections for subject, depth, temporality, and confidence. Depth was assessed using Mezirow's Critical Reflection Theory, with students receiving titles of "content," "process," or "premise" reflectors based on the deepest level of reflection exhibited. Confidence was assessed by labeling reflective statements as "concrete" or "verbal." Data were coded by a second researcher for validation. Consensus was reached, the remainder of the dataset was updated to reflect codebook changes, and trends were assessed. RESULTS: Inter-rater agreement was 83%. All students achieved "content" level reflection. Ninety-seven percent of students reached "process" reflection. Ninety-three percent of students reached "premise" reflection. Students provided more concrete indicators of knowledge gained from the LPP than from prior experiences. Subjects included communication, team dynamics, patient impact, and student experience. Increased depth and breadth of reflection on communication and team dynamics were observed from the LPP. CONCLUSIONS: The LPP illustrates the importance of interdisciplinary care in surgery. Future iterations should emphasize the impact on patients and their families.

Comparison of the Perspectives of Medical Students and Residents on the Surgery Learning Environment.

BACKGROUND: The learning environment plays a critical role in learners' satisfaction and outcomes. However, we often lack insight into learners' perceptions and assessments of these environments. It can be difficult to discern learners' expectations, making their input critical. When medical students and surgery residents are asked to evaluate their teachers, what do they focus on? MATERIALS AND METHODS: Open-ended comments from medical students' evaluations of residents and attending surgeons and from residents' evaluations of attendings during the 2016-2017 academic year were analyzed. Content analysis was used, and codes derived from the data. A matrix of theme by learner role was created to distinguish differences between medical student and resident learners. Subthemes were grouped based on similarity into high-order themes. RESULTS: Two overarching themes were Creating a positive environment for learning by modeling professional behaviors and Intentionally engaging learners in training and educational opportunities. Medical students and residents made similar comments for the subthemes of appropriate demeanor, tone and dialog, respect, effective direct instruction, feedback, debriefing, giving appropriate levels of autonomy, and their expectations as team members on a service. Differences existed in the subthemes of punctuality, using evidence, clinical knowledge, efficiency, direct interactions with patients, learning outcomes, and career decisions. CONCLUSIONS: Faculty development efforts should target professional communication, execution of teaching skills, and relationships among surgeons, other providers, and patients. Attendings should make efforts to discuss their approach to clinical decision making and patient interactions and help residents and medical students voice their opinions and questions through trusting adult learner-teacher relationships.

Patient-Centered Outcome Spectrum: An Evidence-based Framework to Aid in Shared Decision-making.

OBJECTIVE: Our objective was to develop an alternate construct for reporting anticipated outcomes after emergency general surgery (EGS) that presents risk in terms of a composite measure. BACKGROUND: Currently available prediction tools generate risk outputs for discrete as opposed to composite measures of postoperative outcomes. A construct to synthesize multiple discrete estimates into a global understanding of a patient's likely postoperative health status is lacking and could augment shared decision-making conversations. METHODS: Using the 2012 to 2014 American College of Surgeons National Surgical Quality Improvement Program Participant Use File, we developed the Patient-Centered Outcomes Spectrum (PCOS) for patients ≥65 years old who underwent an EGS operation. The PCOS defines 3 exclusive types of global outcomes (good, intermediate, and bad outcomes) and allows patients to be prospectively stratified by both their EGS diagnosis and preoperative surgical risk profile. RESULTS: Of the patients in our study population, 13,330 (46.4%) experienced a 30-day postoperative course considered a good outcome. Conversely, 3791 (13.2%) of study patients experienced a bad outcome. The remainder of patients (11,617; 40.4%) were classified as experiencing an intermediate outcome. The incidence of good, intermediate, and bad outcomes was 69.7%, 28.2%, and 2.1% for low-risk patients, and 22.0%, 48.9%, and 29.1% for high-risk patients. Diagnosis-specific PCOS constructs are also provided. CONCLUSIONS: Consistent with the goals of shared decision-making, the PCOS provides an evidence-based construct based upon a composite outcome measure for patients and providers as they weigh the risks of undergoing EGS.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data. MATERIALS AND METHODS: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Temporal validation was undertaken to ensure the models' temporal generalizability. Additionally, analyses to assess the variable importance were conducted. RESULTS: Both models demonstrated impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of over 0.8. Additionally, they showed considerable accuracy, with macro-F1 scores exceeding or near 0.7, in assessing injuries in the areas of the chest and head. We showed in our variable importance analysis that the most important features in the model have strong face validity in determining clinically relevant trauma injuries. DISCUSSION: The CUI-based model achieves comparable performance, if not higher, compared to the free-text-based model, with reduced complexity. Furthermore, integrating structured EHR data improves performance, particularly when the text modalities are insufficiently indicative. CONCLUSIONS: Our multi-modal, multiclass models can provide accurate stratification of trauma injury severity and clinically relevant interpretations.

Disaster Research/Evaluation Frameworks, Part 1: An Overview-RETRACTED.

The goals of conducting disaster research are to obtain information to: (1) decrease the human, environmental, and economic losses; (2) decrease morbidity; (3) decrease pain and suffering; and (4) enhance the recovery of the affected population. Two principal, but inter-related, branches of disaster research are: (1) Epidemiological; and (2) Interventional. In response to the need for the discipline of disaster health to build its science on data that are generalizable and comparable, a set of five Frameworks have been developed to structure the information and research of the health aspects of disasters: (1) Conceptual; (2) Longitudinal; (3) Transectional Societal; (4) Relief-Recovery; and (5) Risk-Reduction. These Frameworks provide a standardized format for studying and comparing the epidemiology of disasters as well as evaluating the interventions (responses) provided prior to, during, and following a disaster, especially as they relate to the health status of the people affected or at-risk. Critical to all five Frameworks is the inclusion of standardized definitions of the terms used to describe factors that lead to and affect the occurrence and severity of a disaster. The Conceptual Framework describes the progression of a hazard that becomes an event, which causes structural damage and a decrease or loss of function (functional damage), that, in turn, produces needs that lead to a disaster. The Longitudinal Framework describes this chronological progression as phases in order of their appearance in time, even though some of them occur concurrently. In order to study and compare the effects of an event on the complex amalgam that constitutes a society, the essential functions of a society have been deconstructed into 13 Basic Societal Systems that comprise the Transectional Societal Framework. These diverse, but inter-related Basic Societal Systems interface with each other through a 14th system called Coordination and Control. Epidemiological research studies the relationships and occurrences that influence and result from a disaster. Interventional research involves the evaluation of interventions, whether they are directed at relief, recovery, hazard mitigation, capacity building, or performance. The Relief-Recovery and Risk-Reduction Frameworks are based on a Disaster Logic Model. The Relief-Recovery Framework provides the structure necessary to systematically evaluate specific interventions provided during the Relief and Recovery phases of a disaster. The Risk-Reduction Framework details the processes involved in mitigating the risk that a hazard will produce a destructive event and/or that capacity building will augment the resilience of a community to the consequences of such an event. It incorporates a cascade of risks that lead from the presence of a hazard to the development of a disaster. Risk is described as the likelihood that each of the steps leading from a hazard to a disaster will take place; it also includes the probable consequences of the occurrence of each of the elements in the Conceptual Framework. The Conceptual, Longitudinal, and Transectional Societal Frameworks are useful in epidemiological research, i.e., the study of the incidence of, and factors influencing events and disasters. The Relief-Recovery and Risk-Reduction Frameworks are added to the Conceptual, Longitudinal, and Transectional Societal Frameworks for conducting and reporting of interventional research/evaluations. Examples of the application of these Frameworks are provided.

Which hospital-acquired conditions matter the most in trauma? An evidence-based approach for prioritizing trauma program improvement.

BACKGROUND: Prevention of hospital-acquired conditions (HACs) is a focus of trauma center quality improvement. The relative contributions of various HACs to postinjury hospital outcomes are unclear. We sought to quantify and compare the impacts of six HACs on early clinical outcomes and resource utilization in hospitalized trauma patients. METHODS: Adult patients from the 2013 to 2016 American College of Surgeons Trauma Quality Improvement Program Participant Use Data Files who required 5 days or longer of hospitalization and had an Injury Severity Score of 9 or greater were included. Multiple imputation with chained equations was used for observations with missing data. The frequencies of six HACs and five adverse outcomes were determined. Multivariable Poisson regression with log link and robust error variance was used to produce relative risk estimates, adjusting for patient-, hospital-, and injury-related factors. Risk-adjusted population attributable fractions estimates were derived for each HAC-outcome pair, with the adjusted population attributable fraction estimate for a given HAC-outcome pair representing the estimated percentage decrease in adverse outcome that would be expected if exposure to the HAC had been prevented. RESULTS: A total of 529,856 patients requiring 5 days or longer of hospitalization were included. The incidences of HACs were as follows: pneumonia, 5.2%; urinary tract infection, 3.4%; venous thromboembolism, 3.3%; surgical site infection, 1.3%; pressure ulcer, 1.3%; and central line-associated blood stream infection, 0.2%. Pneumonia demonstrated the strongest association with in-hospital outcomes and resource utilization. Prevention of pneumonia in our cohort would have resulted in estimated reductions of the following: 22.1% for end organ dysfunction, 7.8% for mortality, 8.7% for prolonged hospitalization, 7.1% for prolonged intensive care unit stay, and 6.8% for need for mechanical ventilation. The impact of other HACs was comparatively small. CONCLUSION: We describe a method for comparing the contributions of HACs to outcomes of hospitalized trauma patients. Our findings suggest that trauma program improvement efforts should prioritize pneumonia prevention. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.

Simulation-Based Medical Education: Development of an Assessment Tool for Novice Use.

BACKGROUND: Simulation-based medical education, an educational model in which students engage in simulated patient scenarios, improves performance. However, assessment tools including the Oxford Non-Technical Skills (NOTECHS) scale require expert assessors. We modified this tool for novice use. METHODS: Medical students participated in 5 nontechnical simulations. The NOTECHS scale was modified to allow for novice evaluation. Three novices and 2 experts assessed performance, with intraclass correlation used to assess validity. RESULTS: Twenty-two learners participated in the simulations. Novice reviewers had moderate to excellent correlation among evaluations (0.66 < intraclass correlation coefficients [ICC] < 0.95). Novice and expert reviewers had moderate to good correlation among evaluations (0.51 < ICC < 0.88). DISCUSSION: The modified NOTECHS scales can be utilized by novices to evaluate simulation performance. Novice assessment correlates with expert review. These tools may encourage the use of simulation-based medical education.

"Rhythm of the Night": Medical student reflections on overnight trauma call.

INTRODUCTION: On their surgical clerkship, students reflected on their experience on a traditional overnight call. We explored whether perceived learning experiences differ between students who identify surgical specialties as their career compared to those who do not. METHODS: Medical students participated in traditional call at a Level 1 Trauma Center and submitted guided reflections. Reflections were coded using thematic analysis. We used Epistemic Network Analysis to compare codes based on matched specialty. RESULTS: 418 students submitted reflections between 2016 and 2019.95% learned something on call they could not have during daily services. Students who matched in surgical specialties connected more call learning experiences to formation of student agency through individualized teaching experiences and awareness of stamina. CONCLUSIONS: Most students found participation in overnight call valuable, and professed increased awareness of their skills and future residency duties. Results indicate students who matched in surgical specialties had more formative experiences on call.

Comparison and interpretability of machine learning models to predict severity of chest injury.

OBJECTIVE: Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting. MATERIALS AND METHODS: A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net (EN) regularization, extreme gradient boosted (XGB) machines, and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations. RESULTS: Of 8952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." DISCUSSION: The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. CONCLUSION: NLP and ML methods to populate trauma registries for quality analyses are feasible.

Prediction of severe chest injury using natural language processing from the electronic health record.

INTRODUCTION: Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may enable automated injury scoring. We hypothesize that NLP with machine learning can discriminate between cases of severe and non-severe injury to the thorax after trauma. METHODS: Clinical documents from a trauma center were examined between 2014 and 2018. Severe chest injury was defined as a thorax abbreviated injury score (AIS) >2 and served as the reference standard for supervised learning. Free text unigrams and concept unique identifiers (CUIs) from the Unified Medical Language Systems (UMLS) were extracted from clinical documents collected at one hour, four hours, and eight hours after patient arrival to the emergency department. Logistic regression models with elastic net regularization were tuned to maximize area under the receiver operating characteristic curve (AUROC) using 10-fold cross-validation on the training dataset (80%) and tested on a hold-out 20% dataset. RESULTS: There were 6,891 traumas that met inclusion criteria. The complete data corpus consisted of 473,694 documents. Models trained using the first hour of data had a mean AUROC of 0.88 (95%CI [0.86, 0.89]); model discrimination and reclassification from the first hour significantly improved after eight hours with a mean AUROC of 0.94 (95%CI [0.93, 0.95]). Performance of models using CUIs were similar to unigrams (p>0.05). Models demonstrated excellent clinical face validity. CONCLUSIONS: Both CUIs and unigrams demonstrated excellent discrimination in predicting severity of chest injury using the first eight hours of clinical documents. Our model demonstrates that automated anatomical injury scoring is feasible and may be used for aggregation of data for trauma research and quality programs.

Safety in Allowing Residents to Independently Perform Appendectomy: A Retrospective Review.

BACKGROUND: In an era of competency-based education and concern about graduating resident readiness for practice, early resident autonomy and the ability to safely teach junior residents is becoming increasingly important. In this study, we aimed to understand the effect of "teaching resident" (2 residents operating under the supervision of an attending physician) appendectomy cases on outcomes. STUDY DESIGN: We performed a single-center retrospective review of 928 patients who underwent appendectomy within the University of Wisconsin hospital system, from October 2014 to December 2017. We examined how 2 residents (compared with 1 resident with an attending) attempting a case affected operation time, surgical site infection (SSI) rate, conversion to open rate, postoperative CT scanning, and readmission rate, while controlling for sex, age, American Society of Anesthesiologists (ASA) class, BMI, previous lower abdominal surgery, acuity, perforation, and presence of a junior attending. RESULTS: We identified 597 1-resident cases and 331 2-resident or "teaching resident" cases. We performed multiple logistic regression to assess teaching resident cases as a predictor of postoperative outcomes. There were no significant differences in postoperative surgical site infection (superficial or organ space) odds ratio (OR) = 0.83 (95% CI, 0.47, 1.45); p = 0.51, conversion to open OR = 1.10 (95% CI, 0.46, 2.60); p = 0.84, postoperative CT scanning OR = 0.82 (95% CI, 0.48, 1.35); p = 0.42, or readmission within 30 days OR = 0.76 (95% CI, 0.40, 1.44); p = 0.40. However, teaching resident operative times were more likely to be classified as prolonged OR = 1.44 (95% CI, 1.03, 2.01); p = 0.03. CONCLUSIONS: Senior surgical trainees can safely supervise more junior trainees performing appendectomy procedures, and training programs should encourage faculty to allow residents to not only manage operative appendicitis as independently as possible, but to supervise junior residents in the intraoperative management of appendicitis.

Proficiency development for graduating medical students, using skills-level-appropriate mastery learning versus traditional learning for chest tube placement: Assessing anxiety, confidence, and performance.

BACKGROUND: Mastery learning is an effective educational tool to assess basic procedural skill proficiency and may also be beneficial for more complex skills along the continuum of surgical training. In addition, anxiety and confidence have effects on cognitive and decision-making performance, both in educational and clinical settings. This study evaluates anxiety and confidence in a skills-level-appropriate mastery learning module for chest tube insertion in graduating medical students. METHODS: A 2-week intern preparatory course was held 2 consecutive years, with 10 and 14 students, respectively. Learners completed a pretest on day 1, didactic session and supervised deliberate practice followed by a Posttest on day 4, and a retention test on day 10. Year one used a traditional educational methodology, and year two provided for remediation as per mastery learning methodology. The chest tube scoring checklist was validated by faculty trauma surgeons to reflect an intern-appropriate skills level. Before and after each test, learners reported state anxiety. Immediately after each test, learners also completed a confidence scale. RESULTS: No learners in either year achieved mastery on the pretest. A total of 40% of the learners achieved the mastery standard on the posttest in year one. All (100%) of the learners achieved the mastery standard after the posttest in year two. Overall, after state anxiety decreased significantly in both years, confidence increased significantly in year two. CONCLUSION: A skills-level-appropriate mastery learning module resulted in higher performance and increased confidence compared with a traditional education model for chest tube placement for incoming surgical interns.

Dielectric properties of human normal, malignant and cirrhotic liver tissue: in vivo and ex vivo measurements from 0.5 to 20 GHz using a precision open-ended coaxial probe.

Hepatic malignancies have historically been treated with surgical resection. Due to the shortcomings of this technique, there is interest in other, less invasive, treatment modalities, such as microwave hepatic ablation. Crucial to the development of this technique is the accurate knowledge of the dielectric properties of human liver tissue at microwave frequencies. To this end, we characterized the dielectric properties of in vivo and ex vivo normal, malignant and cirrhotic human liver tissues from 0.5 to 20 GHz. Analysis of our data at 915 MHz and 2.45 GHz indicates that the dielectric properties of ex vivo malignant liver tissue are 19 to 30% higher than normal tissue. The differences in the dielectric properties of in vivo malignant and normal liver tissue are not statistically significant (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 16% higher than normal). Also, the dielectric properties of in vivo normal liver tissue at 915 MHz and 2.45 GHz are 16 to 43% higher than ex vivo. No statistically significant differences were found between the dielectric properties of in vivo and ex vivo malignant tissue (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 28% higher than normal). We report the one-pole Cole-Cole parameters for ex vivo normal, malignant and cirrhotic liver tissue in this frequency range. We observe that wideband dielectric properties of in vivo liver tissue are different from the wideband dielectric properties of ex vivo liver tissue, and that the in vivo data cannot be represented in terms of a Cole-Cole model. Further work is needed to uncover the mechanisms responsible for the observed wideband trends in the in vivo liver data.

Finite-element analysis of ex vivo and in vivo hepatic cryoablation.

Cryoablation is a widely used method for the treatment of nonresectable primary and metastatic liver tumors. A model that can accurately predict the size of a cryolesion may allow more effective treatment of tumor, while sparing normal liver tissue. We generated a computer model of tissue cryoablation using the finite-element method (FEM). In our model, we considered the heat transfer mechanism inside the cryoprobe and also cryoprobe surfaces so our model could incorporate the effect of heat transfer along the cryoprobe from the environment at room temperature. The modeling of the phase shift from liquid to solid was a key factor in the accurate development of this model. The model was verified initially in an ex vivo liver model. Temperature history at three locations around one cryoprobe and between two cryoprobes was measured. The comparison between the ex vivo result and the FEM modeling result at each location showed a good match, where the maximum difference was within the error range acquired in the experiment (< 5 degrees C). The FEM model prediction of the lesion size was within 0.7 mm of experimental results. We then validated our FEM in an in vivo experimental porcine model. We considered blood perfusion in conjunction with blood viscosity depending on temperature. The in vivo iceball size was smaller than the ex vivo iceball size due to blood perfusion as predicted in our model. The FEM results predicted this size within 0.1-mm error. The FEM model we report can accurately predict the extent of cryoablation in the liver.

Taser dart-to-heart distance that causes ventricular fibrillation in pigs.

Electromuscular incapacitating devices (EMDs), such as Tasers, deliver high current, short duration pulses that cause muscular contractions and temporarily incapacitate the human subject. Some reports suggest that EMDs can kill. To help answer the question, "Can the EMD directly cause ventricular fibrillation (VF)?", ten tests were conducted to measure the dart-to-heart distance that causes VF in anesthetized pigs [mass = 64 kg +/- 6.67 standard deviation (SD)] for the most common X26 Taser. The dart-to-heart distance that caused VF was 17 mm +/- 6.48 (SD) for the first VF event and 13.7 mm +/- 6.79 (SD) for the average of the successive VF events. The result shows that when the stimulation dart is close enough to the heart, X26 Taser current will directly trigger VF in pigs. Echocardiography of erect humans shows skin-to-heart distances from 10 to 57 mm (dart-to-heart distances of 1-48 mm). These results suggest that the probability of a dart on the body landing in 1 cm2 over the ventricle and causing VF is 0.000172.

Current status of liver tumor ablation devices.

The liver is a common site of disease for both primary and metastatic cancer. Since most patients have a disease that is not amenable to surgical resection, tumor ablation modalities are increasingly being used for treatment of liver cancer. This review describes the current status of ablative technologies used as alternatives for resection, clinical experience with these technologies, currently available devices and design rules for the development of new devices and the improvement of existing ones. It focuses on probe design for radiofrequency ablation, microwave ablation and cryoablation, and compares the advantages and disadvantages of each ablation modality.

Number of rib fractures thresholds independently predict worse outcomes in older patients with blunt trauma.

BACKGROUND: There have been conflicting reports regarding whether the number of rib fractures sustained in blunt trauma is associated independently with worse patient outcomes. We sought to investigate this risk-adjusted relationship among the lesser-studied population of older adults. METHODS: A retrospective review of the National Trauma Data Bank was performed for patients with blunt trauma who were ≥65 years old and had rib fractures between 2009 and 2012 (N = 67,695). Control data were collected for age, sex, injury severity score, injury mechanism, 24 comorbidities, and number of rib fractures. Outcome data included hospital mortality, hospital and intensive care unit durations of stay, duration of mechanical ventilation, and the occurrence of pneumonia. Multiple logistic and linear regression analyses were performed. RESULTS: Sustaining ≥5 rib fractures was associated with increased intensive care unit admission (odds ratio: 1.14, P < .001) and hospital duration of stay (relative duration: 105%, P < .001). Sustaining ≥7 rib fractures was associated with an increased incidence of pneumonia (odds ratio: 1.32, P < .001) and intensive care unit duration of stay (relative duration: 122%, P < .001). Sustaining ≥8 rib fractures was associated with increased mortality (odds ratio: 1.51, P < .001) and duration of mechanical ventilation (relative duration: 117%, P < .001). CONCLUSION: In older patients with trauma, sustaining at least 5 rib fractures is a significant predictor of worse outcomes independent of patient characteristics, comorbidities, and trauma burden.

A floating sleeve antenna yields localized hepatic microwave ablation.

We report a novel coaxial antenna for hepatic microwave ablation. This device uses a floating sleeve, that is, a metal conductor electrically isolated from the outer connector of the antenna coaxial body, to achieve a highly localized specific absorption rate pattern that is independent of insertion depth. This floating sleeve coaxial dipole antenna has low power reflection in the 2.4-GHz IMS band. Ex vivo experiments confirm our numerical simulation results. Index Terms-Ablation, coaxial aperture antennas, finite element methods, floating sleeve, microwave heating.