The Inter-Mammary Sticky Roll: A Novel Technique for Securing a Doppler Ultrasonic Probe to the Precordium for Venous Air Embolism Detection.

Venous air embolism is a devastating and potentially life-threatening complication that can occur during neurosurgical procedures. We report the development and use of the "inter-mammary sticky roll," a technique to reliably secure a precordial Doppler ultrasonic probe to the chest wall during neurosurgical cases that require lateral decubitus positioning. We have found that this noninvasive technique is safe, and effectively facilitates a constant Doppler signal with no additional risk to the patient

Potent spinal parenchymal AAV9-mediated gene delivery by subpial injection in adult rats and pigs.

Effective in vivo use of adeno-associated virus (AAV)-based vectors to achieve gene-specific silencing or upregulation in the central nervous system has been limited by the inability to provide more than limited deep parenchymal expression in adult animals using delivery routes with the most clinical relevance (intravenous or intrathecal). Here, we demonstrate that the spinal pia membrane represents the primary barrier limiting effective AAV9 penetration into the spinal parenchyma after intrathecal AAV9 delivery. We develop a novel subpial AAV9 delivery technique and AAV9-dextran formulation. We use these in adult rats and pigs to show (i) potent spinal parenchymal transgene expression in white and gray matter including neurons, glial and endothelial cells after single bolus subpial AAV9 delivery; (ii) delivery to almost all apparent descending motor axons throughout the length of the spinal cord after cervical or thoracic subpial AAV9 injection; (iii) potent retrograde transgene expression in brain motor centers (motor cortex and brain stem); and (iv) the relative safety of this approach by defining normal neurological function for up to 6 months after AAV9 delivery. Thus, subpial delivery of AAV9 enables gene-based therapies with a wide range of potential experimental and clinical utilizations in adult animals and human patients

Glioblastoma Mimicking Viral Encephalitis Responds to Acyclovir: A Case Series and Literature Review

Viral encephalitis and glioblastoma are both relatively rare conditions with poor prognoses. While the clinical and radiographic presentations of these diseases are often distinctly different, viral encephalitis can sometimes masquerade as glioblastoma. Rarely, glioblastoma can also be misdiagnosed as viral encephalitis. In some cases where a high-grade glioma was initially diagnosed as viral encephalitis, antiviral administration has proven effective for relieving early symptoms. We present three cases in which patients presented with symptoms and radiographic findings suggestive of viral encephalitis and experienced dramatic clinical improvement following treatment with acyclovir, only to later be diagnosed with glioblastoma in the region of suspected encephalitis and ultimately succumb to tumor progression

Preoperative predictions of in-hospital mortality using electronic medical record data

Background: Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient's medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores. Methods: Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC). Results: We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time. Conclusions: Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time

An Automated Machine Learning-based Model Predicts Postoperative Mortality Using Readily-Extractable Preoperative Electronic Health Record Data

Background Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. Methods We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. Results Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955). Conclusions This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period

Perioperative Vision Loss in Cervical Spinal Surgery.

STUDY DESIGN: Retrospective multicenter case series. OBJECTIVE: To assess the rate of perioperative vision loss following cervical spinal surgery. METHODS: Medical records for 17 625 patients from 21 high-volume surgical centers from the AOSpine North America Clinical Research Network who received cervical spine surgery (levels from C2 to C7) between January 1, 2005, and December 31, 2011, inclusive, were reviewed to identify occurrences of vision loss following surgery. RESULTS: Of the 17 625 patients in the registry, there were 13 946 patients assessed for the complication of blindness. There were 9591 cases that involved only anterior surgical approaches; the remaining 4355 cases were posterior and/or circumferential fusions. There were no cases of blindness or vision loss in the postoperative period reported during the sampling period. CONCLUSIONS: Perioperative vision loss following cervical spinal surgery is exceedingly rare

Preoperative predictions of in-hospital mortality using electronic medical record data

Background: Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient's medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores. Methods: Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC). Results: We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time. Conclusions: Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time

AIAA Design, Build, Fly Team - MULLET Competition Aircraft 2021-2022

MULLET, the Medical Unmanned Low-Level Electric Transport, is Embry-Riddle Aeronautical University Daytona Beach’s aircraft for the 2021–2022 AIAA Design, Build, Fly competition. This UAV was designed to perform four missions, including a ground mission and three flight missions. Mission 1 is a deployment flight that demonstrates the aircraft’s flight capability; Mission 2 is a staging flight for the transportation of vaccine syringes; Mission 3 is a delivery flight for the transportation and deployment of vaccine vial packages; and the Ground Mission is a demonstration of the ability to rapidly prepare the aircraft for flight. The aircraft was designed, manufactured, and flown by a team of 40 undergraduate aerospace engineering students. The design process comprised three phases: conceptual, preliminary, and detail design. Initially, the conceptual design focused on analyzing the requirements with a scoring analysis to select the optimal payload that maximized the mission scores. After the aircraft and subsystem configurations were selected, the weight, wing, tail, and propulsion system were sized during the preliminary design. A detail design then focused on the aircraft’s structural characteristics and systems integration. The manufacturing process followed with the goal of fabricating the aircraft to the designed specifications and weight. A detailed schedule was developed and was continuously refined to manufacture each aircraft iteration in a timely manner, enabling rapid prototyping throughout the design, build, and fly process. Finally, a testing plan was established to evaluate a series of test objectives essential to the aircraft’s mission performance