0° vs. 180° CT localiser: The effect of vertical off-centring, phantom positioning and tube voltage on dose optimisation in multidetector computed tomography.

INTRODUCTION: Patient positioning is an essential consideration for the optimisation of radiation dose during CT examinations. The study objectives seek to explore the effects of vertical off-centring, localiser direction (0° and 180°), and phantom positioning (supine and prone) on radiation dose, using three different tube voltages in multidetector computed tomography (MDCT) imaging. METHODS: The trunk of a PBU-60 anthropomorphic phantom was imaged using a Discovery CT750 HD - 128 slice (GE Healthcare). Images employing 0° and 180° localisers were acquired in supine and prone orientation for each combination of vertical off-centring (±100, ±60 and ±30 mm) and different tube voltages (80, 120 and 140 kVp), using the system's automatic tube current modulation (ATCM) function. The displayed volume CT dose index (CTDIvol ) and dose length product (DLP) were recorded. RESULTS: With incremental table off-centring of ±100 mm, the dose at 120 kVp in the supine position ranged from 63% to 196% (0° localiser) and from 66% to 191% (180° localiser) as compared to iso-centre. While in the prone position, the dose ranged from 62% to 195% (0° localiser); and 62% to 193% (180° localiser), with a notable dose increase at higher tube voltages. Dose variation and vertical off-centring showed a significant relationship for both 0° and 180° localisers (r = 0.94 and 0.96, respectively, P < 0.001). The CTDIvol variation between supine and prone phantom positions at ±100 mm off-centring was 0.22 mGy (2.9%), and 0.19 mGy (2.3%) when the 0° and 180 ° localisers were utilised, respectively. CONCLUSIONS: Phantom off-centring and localiser direction evidenced large dose variation. It is recommended that the 0° localiser is employed during CT examinations, in order to minimise the potential additional radiation dose which may result from off-centring and the use of lower tube voltages where clinically appropriate.

Characterising within-hospital SARS-CoV-2 transmission events: a retrospective analysis integrating epidemiological and viral genomic data from a UK tertiary care setting across two pandemic waves

Structured abstractO_ST_ABSObjectivesC_ST_ABSTo characterise within-hospital SARS-CoV-2 transmission across two waves of the COVID-19 pandemic. DesignA retrospective Bayesian modelling study to reconstruct transmission chains amongst 2181 patients and healthcare workers using combined viral genomic and epidemiological data. SettingA large UK NHS Trust with over 1400 beds and employing approximately 17,000 staff. Participants780 patients and 522 staff testing SARS-CoV-2 positive between 1st March 2020 and 25th July 2020 (Wave 1); and 580 patients and 299 staff testing SARS-CoV-2 positive between 30th November 2020 and 24th January 2021 (Wave 2). Main outcome measuresTransmission pairs including who-infected-whom; location of transmission events in hospital; number of secondary cases from each individual, including differences in onward transmission from community and hospital onset patient cases. ResultsStaff-to-staff transmission was estimated to be the most frequent transmission type during Wave 1 (31.6% of observed hospital-acquired infections; 95% CI 26.9 to 35.8%), decreasing to 12.9% (95% CI 9.5 to 15.9%) in Wave 2. Patient-to-patient transmissions increased from 27.1% in Wave 1 (95% CI 23.3 to 31.4%) to 52.1% (95% CI 48.0 to 57.1%) in Wave 2, to become the predominant transmission type. Over 50% of hospital-acquired infections were concentrated in 8/120 locations in Wave 1 and 10/93 locations in Wave 2. Approximately 40% to 50% of hospital-onset patient cases resulted in onward transmission compared to less than 4% of definite community-acquired cases. ConclusionsPrevention and control measures that evolved during the COVID-19 pandemic may have had a significant impact on reducing infections between healthcare workers, but were insufficient during the second wave to prevent a high number of patient-to-patient transmissions. As hospital-acquired cases appeared to drive most onward transmissions, more frequent and rapid identification and isolation of these cases will be required to break hospital transmission chains in subsequent pandemic waves.