METHODS: 220 patients underwent CT of the chest, abdomen and pelvis (CAP) using a standard FV protocol, and subsequently, a customised 1.0 mL/kg WBV protocol within one year. Both image sets were assessed for contrast enhancement using CT attenuation at selected regions-of-interest (ROIs). The visual image quality was evaluated by three radiologists using a 4-point Likert scale. Quantitative CT attenuation was correlated with the visual quality assessment to determine the HU's enhancement indicative of the image quality grades. Contrast media usage was calculated to estimate cost-savings from both protocols.
RESULTS: Mean patient age was 61 ± 14 years, and weight was 56.1 ± 8.7 kg. FV protocol produced higher contrast enhancement than WBV, p
METHODS: A total of 224 patients were recruited. An optimised CT protocol was applied using 100 kV and 1 mL/kg of contrast media dosing. The image quality and radiation dose exposure of this CT protocol were compared to those of a standard 120 kV, 80 mL fixed volume protocol. The radiation dose information and CT Hounsfield units were recorded. The signal-to-noise ratio, contrast-to-noise ratio (CNR) and figure of merit (FOM) were used as comparison metrics. The images were assessed for contrast opacification and visual quality by two radiologists. The renal function, contrast media volume and cost were also evaluated.
RESULTS: The median effective dose was lowered by 16% in the optimised protocol, while the arterial phase images achieved significantly higher CNR and FOM. The radiologists' evaluation showed more than 97% absolute agreement with no significant differences in image quality. No significant differences were found in the pre- and post-CT estimated glomerular filtration rate. However, contrast media usage was significantly reduced by 1,680 mL, with an overall cost savings of USD 421 in the optimised protocol.
CONCLUSION: The optimised weight-based protocol is cost-efficient and lowers radiation dose while maintaining overall contrast enhancement and image quality.
METHODOLOGY: Records of patients diagnosed with tuberculosis from 1st January 2018 to 30th September 2019 were retrieved. Sociodemographic and clinical data were extracted. Treatment outcomes and all-cause mortality were recorded at 1 year after diagnosis. Univariate, multivariate, and stepwise regression were used to determine the factors associated with all-cause mortality.
RESULTS: Four-hundred and seventy-one patients were reviewed. The mean age was 46.6 ± 19.7 years. The all-cause mortality rate at one year of diagnosis was 15.3%. Factors identified were age [aOR 1.026 (95% CI: 1.004-1.049)], chronic kidney disease [aOR 3.269 (1.508-7.088)], HIV positive status [aOR 4.743 (1.505-14.953)], active cancer [aOR 5.758 (1.605-20.652)], liver disease [aOR 6.220 (1.028-37.621)], and moderate to advanced chest X-ray findings [aOR 3.851 (1.033-14.354)].
CONCLUSIONS: On average, one in seven patients diagnosed with TB died within a year in a Malaysian tertiary hospital. Identification of this vulnerable group using the associated factors found in this study may help to reduce the risk of mortality through early intervention strategies.
METHODS: A pilot cluster randomized controlled trial (cRCT) with qualitative interviews was conducted. Each primary care doctor was considered a cluster and randomized to either the control (usual practice) or intervention (DeSSBack) group. Patient outcomes including Roland-Morris Disability Questionnaire (RMDQ), Hospital Anxiety and Depression Scale, and a 10-point pain rating scale were measured at baseline and 2-month postintervention. The doctors in the intervention group were interviewed to explore feasibility and acceptability of using DeSSBack.
RESULTS: Thirty-six patients with nonspecific LBP participated in this study (intervention n = 23; control n = 13). Fidelity was poor among patients but good among doctors. The RMDQ and anxiety score had medium effect sizes of 0.718 and 0.480, respectively. The effect sizes for pain score (0.070) and depression score were small (0.087). There was appreciable acceptability and satisfaction with use of DeSSBack, as it was helpful in facilitating thorough and standardized management, providing appropriate treatment plans based on risk stratification, improving consultation time, empowering patient-centred care, and easy to use.
CONCLUSIONS: A future cRCT to evaluate the effectiveness of DeSSBack is feasible to be conducted in a primary care setting with minor modifications. DeSSBack was found useful by doctors and can be improved to enhance efficiency.
TRIAL REGISTRATION: The protocol of the cluster randomized controlled trial was registered at ClinicalTrials.gov (NCT04959669).
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
METHODS: We prospectively followed 100 patients (50:50 cuffed and non-cuffed PICC) and compared CRBSI rate between these groups. Daily review and similar catheter care were performed until a PICC-related complication, completion of therapy, death or defined end-of-study date necessitate removal. CRBSI was confirmed in each case by demonstrating concordance between isolates colonizing the PICC at the time of infection and from peripheral blood cultures.
RESULTS: A total of 50 cuffed PICC were placed for 1864 catheter-days. Of these, 12 patients (24%) developed infection, for which 5 patients (10%) had a CRBSI for a rate of 2.7 per 1000 catheter-days. Another 50 tunnelled non-cuffed PICCs were placed for 2057 catheter-days. Of these, 7 patients (14%) developed infection, for which 3 patients (6%) had a CRBSI. for a rate of 1.5 per 1000 catheter-days. The mean time to development of infection is 24 days in cuffed and 19 days in non-cuffed groups. The mean duration of utilization was significantly longer in non-cuffed than in cuffed group (43 days in non-cuffed vs 37 days in cuffed group, p = 0.008).
CONCLUSIONS: Cuffed PICC does not further reduce the rate of local or bloodstream infection. Tunnelled non-cuffed PICC is shown to be as effective if not better at reducing risk of CRBSI and providing longer catheter dwell time compared to cuffed PICC.
METHODS: This cross-sectional study of women who underwent DBT and ABUS from December 2019 to March 2022 included opportunistic and targeted screening cases, as well as symptomatic women. Breast density, Breast Imaging Reporting and Data System categories and histopathology reports were collected and compared. The PPV3 (proportion of examinations with abnormal findings that resulted in a tissue diagnosis of cancer), biopsy rate (percentage of biopsies performed) and cancer detection yield (number of malignancies found by the diagnostic test given to the study sample) were calculated.
RESULTS: A total of 1089 ABUS examinations were performed (age range: 29-85 y, mean: 51.9 y). Among these were 909 screening (83.5%) and 180 diagnostic (16.5%) examinations. A total of 579 biopsies were performed on 407 patients, with a biopsy rate of 53.2%. There were 100 (9.2%) malignant lesions, 30 (5.2%) atypical/B3 lesions and 414 (71.5%) benign cases. In 9 cases (0.08%), ABUS alone detected malignancies, and in 19 cases (1.7%), DBT alone detected malignancies. The PPV3 in the screening group was 14.6%.
CONCLUSION: ABUS is useful as an adjunct to DBT in the opportunistic screening and diagnostic setting.