Transient paralysis from carbon dioxide angiography in a patient after four-vessel endovascular thoracoabdominal aortic aneurysm repair.

Contrast angiography with carbon dioxide (CO2) is frequently used in patients with renal dysfunction or iodinated contrast allergies, as CO2 is nonallergenic, nontoxic, and rapidly absorbed in the blood. However, when delivered intra-arterially, there is a possibility that CO2 may create a vapor lock with resultant transient ischemia. We describe a case of suspected CO2 embolus to the iliolumbar artery after iliac artery stenting resulting in immediate loss of bilateral lower extremity motor and sensory function. After placement of a spinal drain and elevation of mean arterial blood pressure, the patient had complete return of sensation with improvement in motor function.

Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis.

OBJECTIVE: Previous reports of deep learning-assisted assessment of Mayo endoscopic subscore (MES) in ulcerative colitis have only explored the ability to distinguish disease remission (MES 0/1) from severe disease (MES 2/3) or inactive disease (MES 0) from active disease (MES 1-3). We sought to explore the utility of deep learning models in the automated grading of each individual MES in ulcerative colitis. METHODS: In this retrospective study, a total of 777 representative still images of endoscopies from 777 patients with clinically active ulcerative colitis were graded using the MES by two physicians. Each image was assigned an MES of 1, 2, or 3. A 101-layer convolutional neural network model was trained and validated on 90% of the data, while 10% was left for a holdout test set. Model discrimination was assessed by calculating the area under the curve (AUC) of a receiver operating characteristic as well as standard measures of accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: In the holdout test set, the final model classified MES 3 disease with an AUC of 0.96, MES 2 disease with an AUC of 0.86, and MES 1 disease with an AUC 0.89. Overall accuracy was 77.2%. Across MES 1, 2, and 3, average specificity was 85.7%, average sensitivity was 72.4%, average PPV was 77.7%, and the average NPV was 87.0%. CONCLUSION: We have demonstrated a deep learning model was able to robustly classify individual grades of endoscopic disease severity among patients with ulcerative colitis.

Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma.

BACKGROUND: Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to prognostication rely upon conventional statistical methods such as Cox proportional hazards (CPH) models and have suboptimal predictive ability. Given the success of deep learning approaches in other specialties, we sought to develop and compare deep learning models with CPH models for the prediction of 5-year survival in pediatric GU-RMS patients. METHODS: Patients less than 20 years of age with GU-RMS were identified within the Surveillance, Epidemiology, and End Results (SEER) database (1998-2011). Deep neural networks (DNN) were trained and tested on an 80/20 split of the dataset in a 5-fold cross-validated fashion. Multivariable CPH models were developed in parallel. The primary outcomes were 5-year overall survival (OS) and disease-specific survival (DSS). Variables used for prediction were age, sex, race, primary site, histology, degree of tumor extension, tumor size, receipt of surgery, and receipt of radiation. Receiver operating characteristic curve analysis was conducted, and DNN models were tested for calibration. RESULTS: 277 patients were included. The area under the curve (AUC) for the DNN models was 0.93 for OS and 0.91 for DSS. AUC for the CPH models was 0.82 for OS and 0.84 for DSS. The DNN models were well-calibrated: OS model (slope = 1.02, intercept = -0.06) and DSS model (slope = 0.79, intercept = 0.21). CONCLUSIONS: A deep learning-based model demonstrated excellent performance, superior to that of CPH models, in the prediction of pediatric GU-RMS survival. Deep learning approaches may enable improved prognostication for patients with rare cancers.

Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer.

PURPOSE: When exploring survival outcomes for patients with bladder cancer, most studies rely on conventional statistical methods such as proportional hazards models. Given the successful application of machine learning to handle big data in many disciplines outside of medicine, we sought to determine if machine learning could be used to improve our ability to predict survival in bladder cancer patients. We compare the performance of artificial neural networks (ANN), a type of machine learning algorithm, with that of multivariable Cox proportional hazards (CPH) models in the prediction of 5-year disease-specific survival (DSS) and overall survival (OS) in patients with bladder cancer. SUBJECTS AND METHODS: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 program database was queried to identify adult patients with bladder cancer diagnosed between 1995 and 2010, yielding 161,227 patients who met our inclusion criteria. ANNs were trained and tested on an 80/20 split of the dataset. Multivariable CPH models were developed in parallel. Variables used for prediction included age, sex, race, grade, SEER stage, tumor size, lymph node involvement, degree of extension, and surgery received. The primary outcomes were 5-year DSS and 5-year OS. Receiver operating characteristic curve analysis was conducted, and ANN models were tested for calibration. RESULTS: The area under the curve for the ANN models was 0.81 for the OS model and 0.80 for the DSS model. Area under the curve for the CPH models was 0.70 for OS and 0.81 for DSS. The ANN OS model achieved a calibration slope of 1.03 and a calibration intercept of -0.04, while the ANN DSS model achieved a calibration slope of 0.99 and a calibration intercept of -0.04. CONCLUSIONS: Machine learning algorithms can improve our ability to predict bladder cancer prognosis. Compared to CPH models, ANNs predicted OS more accurately and DSS with similar accuracy. Given the inherent limitations of administrative datasets, machine learning may allow for optimal interpretation of the complex data they contain.

Decline in health status of extremely premature neonates with patent ductus arteriosus after second course of medical therapy.

INTRODUCTION: Many neonatal centers offer surgical ligation of patent ductus arteriosus (PDA) after two failed courses of pharmacologic therapy. This study compares health status of extremely premature (< 28 weeks gestation) neonates who failed medical therapy at the time of their second course of medical treatment versus operation. MATERIALS AND METHODS: A retrospective chart review was performed on neonates born at less than 28 weeks gestation who underwent PDA ligation after two rounds of medical therapy over a 7.5-year period. Measurements of health status at the time of the second course of medical therapy and the time of operation were compared. RESULTS: Neonates (n = 34) required less fraction of inspired oxygen (33.5 ± 12.9% vs. 48.5 ± 24%, p < 0.0001), had lower mean airway pressure (7.5 ± 1.9 vs. 9.1 ± 2.4 mm Hg, p < 0.0001), and were less likely to require vasopressor support (16.7 vs. 60%, p = 0.0126) at the time of the start of second course than at surgery. CONCLUSION: Our study suggests that extremely premature neonates show a decline in cardiopulmonary reserve between a second course of medical therapy and surgical intervention.

Apuntes sobre la historia de la medicina en Tarija / Es

La presente obra esta conformada de tres cuerpos que a menera de capítulos abarcan los siguientes aspectos: en el capítulo primero se hace una reminiscencia de los principales centros hospitalarios de Tarija, su historia y circunstancias; el capítulo segundo comprende las enfermedades dominantes en la historia y las epidemias que la población sufrió; el capítulo tercero reseña la vida de prestigiosos médicos en Tarija, de los cuales fue posible recabar algunos datos. Como complemento se incluye un cuerpo de anexos documentales