PATIENTS AND METHODS: CGA data was collected from 249 Asian patients aged 70 years or older. Nutritional risk was assessed based on the Nutrition Screening Initiative (NSI) checklist. Univariate and multivariate logistic regression analyses were applied to assess the association between patient clinical factors together with domains within the CGA and moderate to high nutritional risk. Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating characteristics curve (AUC). Internal validation was performed using simulated datasets via bootstrapping.
RESULTS: Among the 249 patients, 184 (74%) had moderate to high nutritional risk. Multivariate logistic regression analysis identified stage 3-4 disease (Odds Ratio [OR] 2.54; 95% CI, 1.14-5.69), ECOG performance status of 2-4 (OR 3.04; 95% CI, 1.57-5.88), presence of depression (OR 5.99; 95% CI, 1.99-18.02) and haemoglobin levels <12 g/dL (OR 3.00; 95% CI 1.54-5.84) as significant independent factors associated with moderate to high nutritional risk. The model achieved good calibration (Hosmer-Lemeshow test's p = 0.17) and discrimination (AUC = 0.80). It retained good calibration and discrimination (bias-corrected AUC = 0.79) under internal validation.
CONCLUSION: Having advanced stage of cancer, poor performance status, depression and anaemia were found to be predictors of moderate to high nutritional risk. Early identification of patients with these risk factors will allow for nutritional interventions that may improve treatment tolerance, quality of life and survival outcomes.
OBJECTIVES: The objective of our study was to determine the efficacy of a single session of 20 min mindful breathing in alleviating multiple symptoms in palliative care.
METHODS: Adult palliative care in patients with at least one symptom scoring ≥5/10 based on the Edmonton Symptom Assessment Scale (ESAS) were recruited from September 2018 to December 2018. Recruited patients were randomly assigned to either 20 min mindful breathing and standard care or standard care alone.
RESULTS: Forty patients were randomly assigned to standard care plus a 20 min mindful breathing session (n=20) or standard care alone (n=20). There was statistically significant reduction of total ESAS score in the mindful breathing group compared with the control group at minute 20 (U=98, n 1 = n 2 = 20, mean rank 1 = 15.4, mean rank 2 = 25.6, median reduction 1 = 6.5, median reduction 2 = 1.5, z=-2.763, r=0.3, p=0.005).
CONCLUSION: Our results provided evidence that a single session of 20 min mindful breathing was effective in reducing multiple symptoms rapidly for palliative care patients.
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
Methods: We systematically searched PubMed, EMBASE and Web of Science for studies published from their starting dates to Aug 7, 2018. The sex-specific hazard ratios (HRs) and their pooled ratio (women vs men) of all-cause and CHD mortality associated with type 2 diabetes were obtained through an inverse variance-weighted random-effects meta-analysis. Subgroup analyses were used to explore the potential sources of heterogeneity.
Results: The 35 analyzed prospective cohort studies included 2 314 292 individuals, among whom 254 038 all-cause deaths occurred. The pooled women vs men ratio of the HRs for all-cause and CHD mortality were 1.17 (95% CI: 1.12-1.23, I2 = 81.6%) and 1.97 (95% CI: 1.49-2.61, I2 = 86.4%), respectively. The pooled estimate of the HR for all-cause mortality was approximately 1.30 in articles in which the duration of follow-up was longer than 10 years and 1.10 in articles in which the duration of follow-up was less than 10 years. The pooled HRs for all-cause mortality in patients with type 2 diabetes was 2.33 (95% CI: 2.02-2.69) in women and 1.91 (95% CI: 1.72-2.12) in men, compared with their healthy counterparts.
Conclusions: The effect of diabetes on all-cause and CHD mortality is approximately 17 and 97% greater, respectively, for women than for men.