The Effect of the COVID-19 Pandemic on Academic Research Gender Disparities in Radiology.

RATIONALE AND OBJECTIVES: Gender disparities have long existed in radiology. The COVID-19 pandemic disrupted research activities worldwide and have impacted gender disparities across medical specialties. This study investigates the effect of the COVID-19 pandemic on gender disparities in radiology academic authorship. MATERIALS AND METHODS: A retrospective observational study was conducted using data from 110 843 global and 23 977 US radiology articles. The gender of authors was determined using an automated gender inference tool. Descriptive statistics were applied to explore authorship changes overall globally, in the US as well as across countries and states. RESULTS: Female first-authorship increased globally from 16.9% to 17.6% (p < 0.001), and in the US, from 19.0% to 19.6% (p = 0.19) in the peri-COVID period. The combined female percentage increased from 19.7% to 20.0% globally (p = 0.021), and from 20.2% to 21.1% in the US (p = 0.006). Country-level analysis revealed significant increases in female authorship in Colombia, Denmark, Egypt, France, India, and Japan, while New Zealand demonstrated a decrease in female authorship. In the US, Florida, Indiana, Louisiana, Massachusetts, and Ohio experienced increases in female authorship, whereas South Carolina demonstrated a decrease in female authorship. CONCLUSION: In contrast with other medical fields, the study demonstrates that radiology experienced a slight increase in female authorship in radiology research globally and in the US during the COVID period. While the pandemic may have influenced these findings, further research is needed to establish regional causal relationships and identify best practices for promoting gender equity in radiology research.

Machine learning prediction of hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the United States.

BACKGROUND AND PURPOSE: Predicting patient recovery and discharge disposition following mechanical thrombectomy remains a challenge in patients with ischemic stroke. Machine learning offers a promising prognostication approach assisting in personalized post-thrombectomy care plans and resource allocation. As a large national database, National Inpatient Sample (NIS), contain valuable insights amenable to data-mining. The study aimed to develop and evaluate ML models predicting hospital discharge disposition with a focus on demographic, socioeconomic and hospital characteristics. MATERIALS AND METHODS: The NIS dataset (2006-2019) was used, including 4956 patients diagnosed with ischemic stroke who underwent thrombectomy. Demographics, hospital characteristics, and Elixhauser comorbidity indices were recorded. Feature extraction, processing, and selection were performed using Python, with Maximum Relevance - Minimum Redundancy (MRMR) applied for dimensionality reduction. ML models were developed and benchmarked prior to interpretation of the best model using Shapley Additive exPlanations (SHAP). RESULTS: The multilayer perceptron model outperformed others and achieved an AUROC of 0.81, accuracy of 77 %, F1-score of 0.48, precision of 0.64, and recall of 0.54. SHAP analysis identified the most important features for predicting discharge disposition as dysphagia and dysarthria, NIHSS, age, primary payer (Medicare), cerebral edema, fluid and electrolyte disorders, complicated hypertension, primary payer (private insurance), intracranial hemorrhage, and thrombectomy alone. CONCLUSION: Machine learning modeling of NIS database shows potential in predicting hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the NIS database. Insights gained from SHAP interpretation can inform targeted interventions and care plans, ultimately enhancing patient outcomes and resource allocation.

Operator assessment versus core laboratory adjudication of recanalization following endovascular treatment of acute ischemic stroke: a systematic review and meta-analysis.

BACKGROUND: Successful recanalization after endovascular thrombectomy serves as the primary endpoint in clinical trials and is a crucial predictor of long-term outcomes. Radiographic outcomes for various interventions have been shown to vary based on the type of interpreter, including the site interventionalist compared with an independent reader. OBJECTIVE: To compare angiographic outcomes in stroke thrombectomy procedures based on the type of reader. METHODS: A systematic literature search was conducted in Medline, EMBASE, Scopus, and Web-of-Science through February 2022. We included primary studies that reported core laboratory-read and operator angiographic outcomes after mechanical thrombectomy for ischemic stroke. Furthermore, study-defined successful recanalization data were collected. RESULTS: Eight studies were included with 4797 patients, 51.2% of whom were male. Four thousand, four hundred and thirty-one patients had core readings, and 4211 had operator readings. Study-defined successful recanalization was significantly higher for operator (84%, 3543/4211) examinations than for core laboratory-read (78.4%, 3476/4431) examinations (p<0.001; OR=1.462, 95% CI 1.175 to 1.819). The modified Thrombolysis in Cerebral Infarction (mTICI) scale score of ≥2 b was higher for operator (85%, 3341/3929) examinations than for core laboratory-read (78.6%, 3107/3952) examinations (p<0.001; OR=1.349, 95% CI 1.071 to 1.701). mTICI 3 was significantly higher for operator (54.6%, 1000/1832) examinations than for core laboratory-read (39.9%, 731/1832) examinations (p<0.001; OR=1.823, 95% CI 1.598 to 2.081). CONCLUSION: Operator angiographic reads are statistically significantly higher than core laboratory-read readings following stroke thrombectomy, especially for complete recanalization. These differences should be considered when interpreting reports of angiographic outcomes after thrombectomy.