Isolated Pulmonary Embolism following Shoulder Arthroscopy

Pulmonary embolism (PE) following shoulder arthroscopy is a rare complication. We present a unique case report of a 43-year-old right-hand dominant female who developed a PE 41 days postoperatively with no associated upper or lower extremity DVT. The patient had minimal preoperative and intraoperative risk factors. Additionally, she had no thromboembolic symptoms postoperatively until 41 days following surgery when she developed sudden right-hand swelling, labored breathing, and abdominal pain. A stat pulmonary computed tomography (CT) angiogram of the chest revealed an acute PE in the right lower lobe, and subsequent extremity ultrasounds showed no upper or lower extremity deep vein thrombosis. After a thorough review of the literature, we present the first documented isolated PE following shoulder arthroscopy. Although rare, sudden development of an isolated PE is possible, and symptoms such as sudden hand swelling, trouble breathing, and systemic symptoms should be evaluated aggressively with a pulmonary CT angiogram given the fact that an extremity ultrasound may be negative for deep vein thrombosis

Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center.

BACKGROUND: Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression. METHODS: Data were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient. RESULTS: Among 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively. CONCLUSIONS: We demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked

The neurophysiology of intraoperative error: An EEG study of trainee surgeons during robotic-assisted surgery simulations.

Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential. Accurately characterizing the neurophysiology of surgeons during intraoperative error will help guide more accurate performance assessment and precision training for surgeons and other teleoperators. To better understand the neurophysiology of intraoperative error, we build and deploy a system for intraoperative error detection and electroencephalography (EEG) signal synchronization during robot-assisted surgery (RAS). We then examine the association between EEG data and detected errors. Our results suggest that there are significant EEG changes during intraoperative error that are detectable irrespective of surgical experience level

Single-Electron Electronics

Contains table of contents for Section 2, research goals and objectives, a summary of recent work and a list of publications.Joint Services Electronics Program Contract DAAHO4-95-1-0038U.S. Army Research Office Grant DAAHO4-94-G-011

Single-Electron Electronics

Contains table of contents for Section 2, research goals and objectives, summary of recent work and a list of publications.Joint Services Electronics Program Grant DAAL04-95-1-0038U.S. Army Research Office Grant DAAH04-94-G-011

Artificial Atoms

Contains research goals and objectives, reports on six research projects and a list of publications.Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038National Science Foundation Grant ECS 92-0342

Venous thromboembolism and intracranial hemorrhage after craniotomy for primary malignant brain tumors: a National Surgical Quality Improvement Program analysis

Venous thromboembolism (VTE), including deep venous thrombosis (DVT) and pulmonary embolism (PE), frequently complicates the postoperative course of primary malignant brain tumor patients. Thromboprophylactic anticoagulation is commonly used to prevent VTE at the risk of intracranial hemorrhage (ICH). We extracted all patients who underwent craniotomy for a primary malignant brain tumor from the National Surgical Quality Improvement Program (NSQIP) registry (2005–2015) to perform a time-to-event analysis and identify relevant predictors of DVT, PE, and ICH within 30 days after surgery. Among the 7376 identified patients, the complication rates were 2.6, 1.5, and 1.3% for DVT, PE, and ICH, respectively. VTE was the second-most common major complication and third-most common reason for readmission. ICH was the most common reason for reoperation. The increased risk of VTE extends beyond the period of hospitalization, especially for PE, whereas ICH occurred predominantly within the first days after surgery. Older age and higher BMI were overall predictors of VTE. Dependent functional status and longer operative times were predictive for VTE during hospitalization, but not for post-discharge events. Admission two or more days before surgery was predictive for DVT, but not for PE. Preoperative steroid usage and male gender were predictive for post-discharge DVT and PE, respectively. ICH was associated with various comorbidities and longer operative times. This multicenter study demonstrates distinct critical time periods for the development of thrombotic and hemorrhagic events after craniotomy. Furthermore, the VTE risk profile depends on the type of VTE (DVT vs. PE) and clinical setting (hospitalized vs. post-discharge patients)

Major Cardiovascular Events After COVID-19, Event Rates Post-vaccination, Antiviral or Anti-inflammatory Therapy, and Temporal Trends: Rationale and Methodology of the Corona-VTE-Network Study

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with excess risk of cardiovascular and thrombotic events in the early post-infection period and during convalescence. Despite the progress in our understanding of cardiovascular complications, uncertainty persists with respect to more recent event rates, temporal trends, association between vaccination status and outcomes, and findings within vulnerable subgroups such as older adults (aged 65 years or older), or those undergoing hemodialysis. Sex-informed findings, including results among pregnant and breastfeeding women, as well as adjusted comparisons between male and female adults are similarly understudied. METHODS: Adult patients, aged ≥18 years, with polymerase chain reaction-confirmed COVID-19 who received inpatient or outpatient care at the participating centers of the registry are eligible for inclusion. A total of 10,000 patients have been included in this multicenter study, with Brigham and Women\u27s Hospital (Boston, MA) serving as the coordinating center. Other sites include Beth Israel Deaconess Medical Center, Anne Arundel Medical Center, University of Virginia Medical Center, University of Colorado Health System, and Thomas Jefferson University Health System. Data elements will be ascertained manually for accuracy. The two main outcomes are 1) a composite of venous or arterial thrombotic events, and 2) a composite of major cardiovascular events, defined as venous or arterial thrombosis, myocarditis or heart failure with inpatient treatment, new atrial fibrillation/flutter, or cardiovascular death. Clinical outcomes are adjudicated by independent physicians. Vaccination status and time of inclusion in the study will be ascertained for subgroup-specific analyses. Outcomes are pre-specified to be reported separately for hospitalized patients versus those who were initially receiving outpatient care. Outcomes will be reported at 30-day and 90-day follow-up. Data cleaning at the sites and the data coordinating center and outcomes adjudication process are in-progress. CONCLUSIONS: The CORONA-VTE-Network study will share contemporary information related to rates of cardiovascular and thrombotic events in patients with COVID-19 overall, as well as within key subgroups, including by time of inclusion, vaccination status, patients undergoing hemodialysis, the elderly, and sex-informed analyses such as comparison of women and men, or among pregnant and breastfeeding women

Low Absolute Risk of Thrombotic and Cardiovascular Events in Outpatient Pregnant Women with COVID-19

INTRODUCTION: Pregnancy may contribute to an excess risk of thrombotic or cardiovascular events. COVID-19 increases the risk of these events, although the risk is relatively limited among outpatients. We sought to determine whether outpatient pregnant women with COVID-19 are at a high risk for cardiovascular or thrombotic events. MATERIALS & METHODS: We analyzed pregnant outpatients with COVID-19 from the multicenter CORONA-VTE-Network registry. The main study outcomes were a composite of adjudicated venous or arterial thrombotic events, and a composite of adjudicated cardiovascular events. Events were assessed 90 days after the COVID-19 diagnosis and reported for non-pregnant women ≤45 years, and for men ≤45 years, as points of reference. RESULTS: Among 6585 outpatients, 169 were pregnant at diagnosis. By 90-day follow-up, two pregnant women during the third trimester had lower extremity venous thrombosis, one deep and one superficial vein thrombosis. The cumulative incidence of thrombotic events was 1.20 % (95 % confidence interval [CI]: 0.0 to 2.84 %). Respective rates were 0.47 % (95 % CI: 0.14 % to 0.79 %) among non-pregnant women, and 0.49 % (95 % CI: 0.06 % to 0.91 %) among men ≤45 years. No non-thrombotic cardiovascular events occurred in pregnant women. The rates of cardiovascular events were 0.53 % (95 % CI: 0.18 to 0.87) among non-pregnant women, and 0.68 % (95 % CI: 0.18 to 1.18) in men aged ≤45 years. CONCLUSIONS: Thrombotic and cardiovascular events are rare among outpatients with COVID-19. Although a higher event rate among outpatient pregnant women cannot be excluded, the absolute event rates are low and do not warrant population-wide cardiovascular interventions to optimize outcomes