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Supplementary Material 1: Sample qualitative interview schedule for EIP clinical team members

Supplementary Material 2: COREQ (Consolidated criteria for Reporting Qualitative research) Checklist

Background Severe acute respiratory coronavirus 2 (SARS-CoV-2) was first recorded in Ireland in February 2020. Several studies have explored the association between age and SARS-CoV-2, showing that there were poorer outcomes in older people. Our objective was to evaluate the impact of age on outcomes such as hospital length of stay, mortality, and re-hospitalisation. Methods We performed a single-centre, retrospective observational cohort study, using an electronic microbiology database of recorded index admissions of SARS-CoV-2 positive patients aged 65 years and older during SARS-CoV-2 wave one (March 1st to May 31st 2020). PCR testing of nasopharyngeal and/or sputum samples was used to confirm positivity. Our clinical outcomes measured included hospital length of stay, mortality and re-admission rate within 6 months. Results 153 patients 65 years and above were admitted. The male to female ratio was 1.3 with 90% admitted medically. 79 patients were aged between 65–79 years; 84 patients ≥80 years; and 12 patients ≥90 years. Mortality was 25%, 31% and 42%, respectively. Median length of stay remained 14 days for ages 65–89 rising to 17.5 days for those ≥90 years. Re-hospitalisation rates at 6 months were similar for ages 65–79 and 89–89 years at 42% and 40%, respectively. One patient (14%) over 90 years was re-hospitalised. Conclusion SARS-CoV-2 has disproportionately impacted on general medical services treating older hospitalised people. In our centre, mortality for patients ≥65 years was 28.1% which compared favourably with 35.6% internationally as outlined by Victor et al. (2020) based on Spanish data. Treatment of SARS-CoV-2 is not futile in older patients with 58% of nonagenarians and 69% of octogenarians surviving, however re-hospitalisation rates are high at 40%. A targeted approach to discharge support via integrated care may ameliorate this.

PurposeIn order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose.MethodsSeven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient’s 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunn’s test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics.ResultsDL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 – 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ±250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314).ConclusionsThe autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.