Clinical outcome, recanalization success, and time metrics in drip-and-ship vs. drive-the-doctor: A retrospective analysis of the HEI-LU-Stroke registry.

Purpose: This study aimed at comparing clinical outcome, recanalization success and time metrics in the "drip and ship" (DS) vs. "drive the doctor" (DD) concept in a comparable setting. Methods: This is a retrospective analysis of thrombectomy registries of a comprehensive stroke center (CSC) and a thrombectomy-capable stroke center (TSC). Patients, who were transferred from the TSC to the CSC, were classified as DS. Patients treated at the TSC by an interventionalist transferred from the CSC were classified as DD. Good outcome was defined as mRS 0-2 or equivalent to premorbid mRS at discharge. Recanalization (TICI 2b-3 or equivalent) and time metrics were compared in both groups. Results: In total, 295 patients were included, of which 116 (39.3%) were treated in the DS concept and 179 (60.7%) in the DD concept. Good clinical outcome was similarly achieved in DS and DD (DS 25.0% vs. DD 31.3%, P = 0.293). mRS on discharge (DS median 4, DD median 4, P = 0.686), NIHSS improvement (DS median 4, DD median 5, P = 0.582) and NIHSS on discharge (DS median 9, DD median 7, P = 0.231) were similar in both groups. Successful reperfusion was achieved similarly in DS (75.9%) and DD as well (81.0%, P = 0.375). Time from onset to reperfusion (median DS 379 vs. DD 286 min, P = 0.076) and time from initial imaging to reperfusion were longer in DS compared to DD (median DS 246 vs. DD 162 min, P < 0.001). Conclusion: The DD concept is time saving while achieving similar clinical outcome and recanalization results.

Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial.

OBJECTIVE: The aim of this study was to determine the feasibility of using machine learning to establish the need for preclinical airway management for injured patients based on a standardized emergency dataset. METHODS: A registry-based, retrospective analysis was conducted of adult trauma patients who were treated by physician-staffed emergency medical services in southwestern Germany between 2018 and 2020. The primary outcome was to assess the feasibility of using the random forest (RF) and Naive Bayes (NB) machine learning algorithms to predict the need for preclinical airway management. The secondary outcome was to use a principal component analysis to determine the attributes that can be used and advanced for future model development. RESULTS: In total, 25,556 adults with multiple injuries were identified, including 1,451 patients (5.7%) who required airway management. Key attributes were auscultation, injury pattern, oxygen therapy, thoracic drainage, noninvasive ventilation, catecholamines, pelvic sling, colloid infusion, initial vital signs, preemergency status, and shock index. The area under the receiver operating characteristics curve was between 0.96 (RF; 95% confidence interval [CI], 0.96-0.97) and 0.93 (NB; 95% CI, 0.92-0.93; P<0.01). For the prediction of airway management, RF yielded a higher precision-recall area than NB (0.83 [95% CI, 0.8-0.85] vs. 0.66 [95% CI, 0.61-0.72], respectively; P<0.01). CONCLUSION: To predict the need for preclinical airway management in injured patients, attributes that are commonly recorded in standardized datasets can be used with machine learning. In future models, the RF algorithm could be used because it has robust prediction accuracy.

Enhancement characteristics and impact on image quality of two gadolinium chelates at equimolar doses for time-resolved 3-Tesla MR-angiography of the calf station.

PURPOSE: To compare enhancement characteristics and image quality of two macrocyclic gadolinium chelates, gadoterate meglumine and gadobutrol, in low-dose, time-resolved MRA of the calf station. MATERIALS AND METHODS: 100 consecutive patients with peripheral arterial disease (stages II-IV) were retrospectively analysed. Fifty patients were included in each group - 32 men and 18 women for gadobutrol (mean age 67 years) and 34 men, 16 women for gadoterate meglumine (mean age 64 years). 0.03 mmol/kg bw of either gadobutrol or gadoterate meglumine was injected. Gadobutrol was diluted 1 ∶ 1 with normal saline (0.9% NaCl) to provide similar injection volume and bolus geometry compared to the undiluted 0.5 M dose of gadoterate meglumine. Signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR) and image quality were analysed and compared between the two groups. RESULTS: Mean SNR ranged from 83.0 ± 46.7 (peroneal artery) to 96.4 ± 64.5 (anterior tibial artery) for gadobutrol, and from 37.6 ± 13.8 (peroneal artery) to 45.3 ± 16.4 (anterior tibial artery) for the gadoterate meglumine group (p<0.0001). CNR values ranged from 30.1 ± 20.1 (peroneal artery) to 37.6 ± 26.0 (anterior tibial artery) for gadobutrol and from 14.9 ± 8.0 (peroneal artery) to 18.6 ± 16.4 (anterior tibial artery) for gadoterate meglumine (p<0.0001). No significant difference in image quality was found except for the peroneal arteries (p = 0.006 and p = 0.04). Interreader agreement was excellent (kappa 0.87-0.93). CONCLUSION: The significantly better enhancement as assessed by SNR and CNR provided by gadobutrol compared to gadoterate meglumine does not translate into substantial differences in image quality in an equimolar, low-dose, time-resolved MRA protocol of the calves.