Prosentient Collection:

VERMONT non-optimised: Feasibility of angiography-derived vFFR using baseline diagnostic catheter images

2026-01-01T00:00:00Z

Title: VERMONT non-optimised: Feasibility of angiography-derived vFFR using baseline diagnostic catheter images Authors: Akrawi, D.; Kadappu, K.; Xu, J.; Naguib Badie, T. Y.; Gibbs, O.; Kachwalla, H.; Nguyen, P. T.; Kurup, R.; Premawardhana, U.; Lo, S.; Huang, J.; Tran, H.; Soosapilla, K.; O'Loughlin, A.; Hennessy, A.; Femia, G. Abstract: Background: Vessel-fractional-flow-reserve (vFFR) estimates coronary physiology from the three-dimensional reconstruction of two angiographic projections using computational fluid dynamics. Although its diagnostic accuracy using optimised angiographic acquisitions is well established, evidence supporting its use with baseline diagnostic catheter images remains limited. Aims: To evaluate the diagnostic performance of real-time vFFR derived from baseline diagnostic catheter images against wire-based FFR, and to compare its performance with vFFR computed from optimised angiographic projections. Methods: VERMONT Non-Optimised was a prospective, single-centre, blinded study in which real-time vFFR derived from both baseline diagnostic and optimised images were measured and compared with simultaneous wire-based FFR. A wire-based FFR of ? 0.80 defined a physiologically significant lesion. Results: In 195 patients with 205 intermediate lesions, 56 (27.3%) lesions were excluded from vFFR analysis. vFFR derived from baseline diagnostic images demonstrated an AUC of 0.91 (95% CI,0.87?0.96) for detecting lesions with FFR ? 0.80, achieving 94% sensitivity, 75% specificity, a negative predictive value of 96%, and a positive predictive value of 67%. Baseline diagnostic and optimised vFFR were strongly correlated (R = 0.87,p < 0.001), with a mean bias of ?0.0075 0.0490 and an intraclass correlation coefficient of 0.93 (95% CI,0.90?0.95), indicating excellent agreement. Conclusion: Real-time vFFR derived from judiciously selected baseline diagnostic catheter images demonstrated strong overall accuracy and high sensitivity for detecting physiologically significant lesions, with similar diagnostic performance to vFFR derived from optimised images. These findings support the use of vFFR as a reliable screening tool for intermediate lesions in both prospective and retrospective settings. 2026

Urticaria and Angioedema: When East Meets West?and the Middle

2026-01-01T00:00:00Z

Title: Urticaria and Angioedema: When East Meets West?and the Middle Authors: Peter, J.; Jindal, A.; Del Giacco, S.; Fomina, D.; Katelaris, C. H.; Hide, M.; Li, P. H.; Longhurst, H. J.; Metz, M.; Zhao, Z.; Shamji, M. H.; Torres, M. J.

Time toxicity associated with treatment of metastatic or unresectable gastro-oesophageal cancers in the second-line setting

2026-01-01T00:00:00Z

Title: Time toxicity associated with treatment of metastatic or unresectable gastro-oesophageal cancers in the second-line setting Authors: Nguyen, R. H.; Tang, J.; Nindra, U.; Roohullah, A.; Yoon, R.; Tognela, A.; Lim, S. H. S.; Asghari, R.; Chua, W.; Ng, W. Abstract: Purpose: Patients with metastatic or unresectable gastro-oesophageal cancers (mGECs) have poor prognoses and often face high symptom burdens and rates of disease-related complications. Second-line treatments offer modest survival gains, which need to be balanced with treatment toxicities. Time toxicity (TT) is increasingly recognised as a hidden toxicity of cancer therapy, and thus, this study aimed to quantify TT to patients undergoing second-line treatment for mGECs. Methods: This was a retrospective cohort study across three major hospitals in Sydney, Australia. Records were reviewed for all patients who received second-line systemic therapy for mGECs over 10 years. TT was defined as the number of days patients spent physically interacting with the healthcare system. Results: Eighty patients were identified, with the majority male (83%) and a median age of 64 years. The median time on second-line treatment was 2.4 months, and the median overall survival from the commencement of second-line treatment was 5.8 months. Patients spent a median 25% of days in physical contact with healthcare, of which 20% were planned encounters (e.g. clinic appointments, scheduled investigations, and treatment days). TT was lower in patients who remained on second-line treatment for more than 2 months versus those on treatment less than 2 months (29% vs. 23%, p < 0.001). One in eight patients died within 30 days of receiving second-line treatment. Conclusion: Patients on second-line treatment for mGEC spent 1 in 4 days in contact with healthcare, and 30-day mortality following systemic treatment was high. These findings may guide decisions and informed consent surrounding second-line treatment in mGECs. The Author(s) 2026.

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI): Prediction of Inpatient Admission From Emergency Departments Using Ensemble Machine Learning

2026-01-01T00:00:00Z

Title: The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI): Prediction of Inpatient Admission From Emergency Departments Using Ensemble Machine Learning Authors: Dinh, M.; Corbett, E.; Salmon, E.; Ahmed Khan, S.; Moore, N.; Brahmapuram, S.; Seimon, R.; Derrick, N.; Berendsen Russell, S.; Ngo, T. T.; Koprinska, I. Abstract: Objective: Use artificial intelligence (AI) to extend the Sydney triage to admission risk tool (START) and improve prediction of emergency department (ED) patient disposition. Methods: The study was conducted at an inner-city tertiary referral hospital ED. Adult (age ? 16 years) presentations from 1 January 2023 to 30 June 2025 were included. Participants were excluded if dead on ED arrival or left ED prior to completing treatment. The primary outcome was admission to an inpatient ward. A sequential ensemble modelling approach was used. To predict patient disposition, the original START was combined (stacked) with vital signs, blood results and CT imaging orders using a gradient boosting decision tree algorithm (XGBoost) and a pre-trained transformer model for clinical free text. Results: 162,915 cases were analysed with 27.31% overall inpatient admission rate. The final stacked meta-XGBoost model had an area under receiver operating curve (AUROC) of 0.88 (95% CI: 0.88, 0.89) with overall weighted accuracy of 0.84 (95% CI: 0.84, 0.85) and F1 score of 0.83 (95% CI: 0.83, 0.84) in the testing dataset. The model was adequately calibrated with R2 of 0.92 (95% CI: 0.67, 0.99) with a drop-off in correlation at the highest predicted probability ranges (> 0.80). After classifying inpatient stays < 24 h as potential discharges, a sensitivity analysis demonstrated AUROC for the final model of 0.89 (95% CI: 0.88, 0.89). Conclusions: An ensemble machine learning model was developed to accurately predict patient disposition from ED using structured and unstructured data. Prototype development and prospective evaluation of START-AI are required to assess model performance in clinical settings. 2026 Australasian College for Emergency Medicine.