Early Detection and Management of Venous Thrombosis in Skull Base Surgery: Role of Routine Doppler Ultrasound Monitoring.

BACKGROUND: Venous thromboembolism (VTE), encompassing deep venous thrombosis (DVT) and pulmonary embolism (PE), causes postoperative morbidity and mortality in neurosurgical patients. The use of pharmacological prophylaxis for DVT prevention in the immediate postoperative period carries increased risk of intracranial hemorrhage, especially after skull base surgeries. OBJECTIVE: To investigate the impact of routine Doppler ultrasound monitoring in prevention and tiered management of VTE after skull base surgery. METHODS: We retrospectively analyzed a large cohort of consecutive adult patients who were prospectively and uniformly managed with routine monitoring by Doppler ultrasound for DVT after resection of a skull base tumor. RESULTS: A total of 389 patients who underwent 459 surgeries for intracranial tumor resection were analyzed. Skull base meningioma was the most common pathology. Forty-four (9.59%) postoperative VTEs were detected: 9 (1.96%) with PE with or without DVT and 35 (7.63%) with DVT alone. Four cases of subsegmental PE were diagnosed without evidence of lower extremity DVT, possibly in the setting of peripherally inserted central catheters maintenance. One patient had a preoperative proximal DVT and underwent a prophylactic inferior vena cava filter but expired from PE after discharge. Prior history of VTE (risk ratio [RR] 5.13; 95% CI 2.76-7.18; P < .01), anesthesia duration (RR 1.14; 95% CI 1.03-1.27; P = .02), and blood transfusion (RR 1.95; 95% CI 1.01-3.37; P = .04) were associated with VTE development on multivariate analysis. CONCLUSION: Routine postoperative venous ultrasound monitoring detects asymptomatic DVT guiding management. This is an alternative strategy to prescribing pharmacological VTE prophylaxis immediately after lengthy surgeries for intracranial tumors. Peripherally inserted central catheters were associated with subsegmental PE.

Evaluation of simulation models in neurosurgical training according to face, content, and construct validity: a systematic review.

BACKGROUND: Neurosurgical training has been traditionally based on an apprenticeship model. However, restrictions on clinical exposure reduce trainees' operative experience. Simulation models may allow for a more efficient, feasible, and time-effective acquisition of skills. Our objectives were to use face, content, and construct validity to review the use of simulation models in neurosurgical education. METHODS: PubMed, Web of Science, and Scopus were queried for eligible studies. After excluding duplicates, 1204 studies were screened. Eighteen studies were included in the final review. RESULTS: Neurosurgical skills assessed included aneurysm clipping (n = 6), craniotomy and burr hole drilling (n = 2), tumour resection (n = 4), and vessel suturing (n = 3). All studies assessed face validity, 11 assessed content, and 6 assessed construct validity. Animal models (n = 5), synthetic models (n = 7), and VR models (n = 6) were assessed. In face validation, all studies rated visual realism favourably, but haptic realism was key limitation. The synthetic models ranked a high median tactile realism (4 out of 5) compared to other models. Assessment of content validity showed positive findings for anatomical and procedural education, but the models provided more benefit to the novice than the experienced group. The cadaver models were perceived to be the most anatomically realistic by study participants. Construct validity showed a statistically significant proficiency increase among the junior group compared to the senior group across all modalities. CONCLUSION: Our review highlights evidence on the feasibility of implementing simulation models in neurosurgical training. Studies should include predictive validity to assess future skill on an individual on whom the same procedure will be administered. This study shows that future neurosurgical training systems call for surgical simulation and objectively validated models.

Cost-Effectiveness of Antibiotic-Eluting Envelope for Prevention of Cardiac Implantable Electronic Device Infections in Heart Failure.

BACKGROUND: Use of an antibiotic-eluting envelope (AEE) during cardiac implantable electronic device procedures reduces infection risk but increases procedural costs. We aim to estimate the cost-effectiveness of AEE use during cardiac implantable electronic device procedures among patients with heart failure. METHODS: A state-transition cohort model of heart failure patients undergoing cardiac implantable electronic device implantation or generator replacement was developed with input parameters estimated from randomized trials, registries, surveys, and claims data. Effectiveness was estimated from the World-Wide Randomized Antibiotic Envelope Infection Prevention Trial. AEE was assumed to cost $953 per unit. The model projected mortality, quality-adjusted life-years, costs, and the incremental cost-effectiveness ratio of AEE use compared with usual care from a US healthcare sector perspective over a lifetime horizon. We assumed a cost-effectiveness threshold of $100 000 per quality-adjusted life-year gained. RESULTS: Compared with usual care, AEE use in initial implantations produced an incremental cost-effectiveness ratio of $112 000 per quality-adjusted life-year gained (39% probability of being cost-effective). In generator replacement procedures, AEE use produced an incremental cost-effectiveness ratio of $54 000 per quality-adjusted life-year gained (84% probability of being cost-effective). Results were sensitive to the underlying rate of infection, cost of the AEE, and durability of AEE effectiveness. CONCLUSIONS: Universal AEE use for cardiac implantable electronic device procedures in patients with heart failure with reduced ejection fraction is unlikely to be cost-effective, reinforcing the need for individualized risk assessment to guide uptake of the AEE in clinical practice. Selective use in patients at increased risk of infection, such as those undergoing generator replacement procedures, is more likely to meet health system value benchmarks.

Cost-effectiveness of Dapagliflozin for the Treatment of Heart Failure With Reduced Ejection Fraction.

Importance: Heart failure with reduced ejection fraction produces substantial morbidity, mortality, and health care costs. Dapagliflozin is the first sodium-glucose cotransporter 2 inhibitor approved for the treatment of heart failure with reduced ejection fraction. Objective: To examine the cost-effectiveness of adding dapagliflozin to guideline-directed medical therapy for heart failure with reduced ejection fraction in patients with or without diabetes. Design, Setting, and Participants: This economic evaluation developed and used a Markov cohort model that compared dapagliflozin and guideline-directed medical therapy with guideline-directed medical therapy alone in a hypothetical cohort of US adults with similar clinical characteristics as participants of the Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction (DAPA-HF) trial. Dapagliflozin was assumed to cost $4192 annually. Nonparametric modeling was used to estimate long-term survival. Deterministic and probabilistic sensitivity analyses examined the impact of parameter uncertainty. Data were analyzed between September 2019 and January 2021. Main Outcomes and Measures: Lifetime incremental cost-effectiveness ratio in 2020 US dollars per quality-adjusted life-year (QALY) gained. Results: The simulated cohort had a starting age of 66 years, and 41.8% had diabetes at baseline. Median (interquartile range) survival in the guideline-directed medical therapy arm was 6.8 (3.5-11.3) years. Dapagliflozin was projected to add 0.63 (95% uncertainty interval [UI], 0.25-1.15) QALYs at an incremental lifetime cost of $42 800 (95% UI, $37 100-$50 300), for an incremental cost-effectiveness ratio of $68 300 per QALY gained (95% UI, $54 600-$117 600 per QALY gained; cost-effective in 94% of probabilistic simulations at a threshold of $100 000 per QALY gained). Findings were similar in individuals with or without diabetes but were sensitive to drug cost. Conclusions and Relevance: In this study, adding dapagliflozin to guideline-directed medical therapy was projected to improve long-term clinical outcomes in patients with heart failure with reduced ejection fraction and be cost-effective at current US prices. Scalable strategies for improving uptake of dapagliflozin may improve long-term outcomes in patients with heart failure with reduced ejection fraction.

Patterns of Interaction Between Diffuse Low-Grade Glioma and Pregnancy: An Institutional Case Series.

BACKGROUND: The occurrence of pregnancy in patients with low-grade glioma (LGG) constitutes a unique therapeutic challenge. Owing to the rarity of cases, there is a dearth of information in existing literature. METHODS: We retrospectively identified all patients with a diagnosis of LGG and pregnancy at some point during their illness. Clinical course and obstetrical outcomes were reviewed. A volumetric analysis of tumor growth rate in association with pregnancy was performed. RESULTS: Of 15 women identified, 13 (86.7%) had a prepregnancy LGG diagnosis. Of the 2 patients in whom LGG was diagnosed during pregnancy, one underwent upfront surgery, and the other had watchful waiting with resection after 60 weeks. Nine patients (60.0%) remained asymptomatic during pregnancy, while 5 (33.3%) experienced recurrence of seizures. There was one case of transformation of an astrocytoma to glioblastoma during the third trimester, which was resected emergently. In 10 cases, progression occurred after pregnancy at a median interval of 24.2 months (interquartile range 6.6-37.5 months), with progression within 6 months of delivery in 2 cases. Mean (SD) growth rate during pregnancy was 7.8 (22.2) mm/year compared with 0.62 (1.12) mm/year before pregnancy and 0.29 (1.18) mm/year after pregnancy; the difference did not reach statistical significance (P = 0.306). CONCLUSIONS: Pregnancy was associated with clinical deterioration in one third of patients. No significant change in growth rate was identified. Time to progression and malignant dedifferentiation were unaffected. Patients with LGG wishing to pursue pregnancy should be counseled regarding the risk of complications, and if pregnancy is pursued, close neurological and obstetrical follow-up is recommended.

Deep learning in rare disease. Detection of tubers in tuberous sclerosis complex.

OBJECTIVE: To develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep learning standalone application. METHODS: T2 and FLAIR axial images with and without tubers were extracted from MRIs of patients with tuberous sclerosis complex (TSC) and controls, respectively. We trained three different convolutional neural network (CNN) architectures on a training dataset and selected the one with the lowest binary cross-entropy loss in the validation dataset, which was evaluated on the testing dataset. We visualized image regions most relevant for classification with gradient-weighted class activation maps (Grad-CAM) and saliency maps. RESULTS: 114 patients with TSC and 114 controls were divided into a training set, a validation set, and a testing set. The InceptionV3 CNN architecture performed best in the validation set and was evaluated in the testing set with the following results: sensitivity: 0.95, specificity: 0.95, positive predictive value: 0.94, negative predictive value: 0.95, F1-score: 0.95, accuracy: 0.95, and area under the curve: 0.99. Grad-CAM and saliency maps showed that tubers resided in regions most relevant for image classification within each image. A stand-alone trained deep learning App was able to classify images using local computers with various operating systems. CONCLUSION: This study shows that deep learning algorithms are able to detect tubers in selected MRI images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder.

Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma.

PURPOSE: The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others. MATERIALS AND METHODS: Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports. RESULTS: In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (ρ = 0.904; P < .001) but not with the frequency distribution of the variables of interest (ρ = 0.179; P = .52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents. CONCLUSION: This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining.