Background: In this study we investigated subjective measures of sleepiness and related our findings to dimensions of affect, fatigue, emotion, mood and quality of life based on a hypothetical multidimensional model of sleepiness. Methods: Patients referred to a sleep clinic were assessed regarding their excessive daytime sleepiness (EDS), sleep complaints, routine and symptoms. Age, gender and body mass index (BMI), the Epworth Sleepiness Scale (ESS), the Stanford Sleepiness Scale (SSS), the Samn-Perelli fatigue Scale (SPS), the Global Vigor and Affect Scale (GVS and GAS, respectively), the Hospital Anxiety and Depression Scale (HADS-A and HADS-D, respectively), and the Positive and Negative Affect Schedule (PAS and NAS, respectively) scores Conclusions: A model of sleepiness that assesses dimensions of fatigue and anxiety could explain the symptom of subjective sleepiness better than the isolated use of the ESS.

Objectives To assess the feasibility and reliability of the use of artificial intelligence post-processing to calculate the RV:LV diameter ratio on computed tomography pulmonary angiography (CTPA) and to investigate its prognostic value in patients with acute PE. Methods Single-centre, retrospective study of 101 consecutive patients with CTPA-proven acute PE. RV and LV volumes were segmented on 1-mm contrast-enhanced axial slices and maximal ventricular diameters were derived for RV:LV ratio using automated post-processing software (IMBIO LLC, USA) and compared to manual analysis in two observers, via intraclass coefficient correlation analysis. Each CTPA report was analysed for mention of the RV:LV ratio and compared to the automated RV:LV ratio. Thirty-day all-cause mortality post-CTPA was recorded. Results Automated RV:LV analysis was feasible in 87% (n = 88). RV:LV ratios ranged from 0.67 to 2.43, with 64% (n = 65) > 1.0. There was very strong agreement between manual and automated RV:LV ratios (ICC = 0.83, 0.77-0.88). The use of automated analysis led to a change in risk stratification in 45% of patients (n = 40). The AUC of the automated measurement for the prediction of all-cause 30-day mortality was 0.77 (95% CI: 0.62-0.99). ConclusionThe RV:LV ratio on CTPA can be reliably measured automatically in the majority of real-world cases of acute PE, with perfect reproducibility. The routine use of this automated analysis in clinical practice would add important prognostic information in patients with acute PE. Key Points • Automated calculation of the right ventricle to left ventricle ratio was feasible in the majority of patients and demonstrated perfect intraobserver variability. • Automated analysis would have added important prognostic information and altered risk stratification in the majority of patients. • The optimal cut-off value for the automated right ventricle to left ventricle ratio was 1.18, with a sensitivity of 100% and specificity of 54% for the prediction of 30-day mortality.

No abstract

No abstract