STUDY DESIGN: Systematic Review of Literature.
METHODS: PubMed, the Cochrane Library, and SCOPUS databases were searched through November 2019.
RESULTS: Eight studies (1,924 patients) met criteria (age range: 28-70.9 years, body mass index range: 21.9-37 kg/m2 , and AHI range: 0.5-62 events/hour). Five studies compared ODI and AHI simultaneously, and three had a week to months between assessments. Sensitivities ranged from 32% to 98.5%, whereas specificities ranged from 47.7% to 98%. Significant heterogeneity was present; however, for studies reporting data for a 4% ODI ≥ 15 events/hour, the specificity for diagnosing OSA ranged from 75% to 98%, and only one study reported the positive predictive value, which was 97%. Direct ODI and AHI comparisons were not made because of different hypopnea scoring, different oxygen desaturation categories, and different criteria for grading OSA severity.
CONCLUSION: Significant heterogeneity exists in studies comparing ODI and AHI. Based on currently published studies, consideration should be given for diagnosing adult OSA with a 4% ODI of ≥ 15 events/hour and for recommending further evaluation for diagnosing OSA with a 4% ODI ≥ 10 events/hour. Screening with oximetry may be indicated for the detection of OSA in select patients. Further study is needed before a definitive recommendation can be made. Laryngoscope, 131:440-447, 2021.
METHODS: A total of 2,461 secondary school students aged 12-17 years from 19 schools in Sarawak participated in the study. Questionnaire was used to obtain socio-demographic data, parental history of hypertension, and self-reported physical activity. Anthropometric and blood pressure measurements were taken. Data was entered and analysed using SPSS version 23.0.
RESULTS: The prevalence of adolescents with elevated blood pressure, overweight, central obesity, and overfat were 30.1%, 24.3%, 13.5%, and 6.7%, respectively. Multivariate logistic regression demonstrated the predictors significantly associated with elevated blood pressure among respondents: overweight (adjusted odds ratio=3.144), being male (adjusted odds ratio=3.073), being Chinese (adjusted odds ratio=2.321) or Iban (adjusted odds ratio=1.578), central obesity (adjusted odds ratio=2.145), being overfat (adjusted odds ratio=1.885), and being an older adolescent (adjusted odds ratio=1.109). Parental history of hypertension, locality, and physical activity showed no significant associations.
CONCLUSION: The obesity epidemic must be tackled at community and school levels by health education and regulation of school canteen foods.
METHOD: This retrospective study included patients with major trauma injuries reported to a trauma centre of Hospital Sultanah Aminah over a 6-year period from 2011 and 2017. Model validation was examined using the measures of discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The Hosmer-Lemeshow (H-L) goodness-of-fit test was used to examine calibration capabilities. The predictive validity of both MTOS-TRISS and NTrD-TRISS models were further evaluated by incorporating parameters such as the New Injury Severity Scale and the Injury Severity Score.
RESULTS: Total patients of 3788 (3434 blunt and 354 penetrating injuries) with average age of 37 years (standard deviation of 16 years) were included in this study. All MTOS-TRISS and NTrD-TRISS models examined in this study showed adequate discriminative ability with AUCs ranged from 0.86 to 0.89 for patients with blunt trauma mechanism and 0.89 to 0.99 for patients with penetrating trauma mechanism. The H-L goodness-of-fit test indicated the NTrD-TRISS model calibrated as good as the MTOS-TRISS model for patients with blunt trauma mechanism.
CONCLUSION: For patients with blunt trauma mechanism, both the MTOS-TRISS and NTrD-TRISS models showed good discrimination and calibration performances. Discrimination performance for the NTrD-TRISS model was revealed to be as good as the MTOS-TRISS model specifically for patients with penetrating trauma mechanism. Overall, this validation study has ascertained the discrimination and calibration performances of the NTrD-TRISS model to be as good as the MTOS-TRISS model particularly for patients with blunt trauma mechanism.
METHODS: In this prospective multicentre study, consecutive CKD patients (n = 154) undergoing routine clinical cardiac magnetic resonance (CMR) imaging were compared with patients with hypertensive (HTN, n = 163) and hypertrophic cardiomyopathy (HCM, n = 158), and normotensive controls (n = 133).
RESULTS: Native T1 was significantly higher in all patient groups, whereas native T2 in CKD only (p
METHODS: Clinico-pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorized as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n = 1000), and bias-corrected indices of model performance were computed.
RESULTS: Potentially malignant disorders were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias-corrected discrimination and calibration (Dxy = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%.
CONCLUSION: The "Newcastle Nomogram" has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient-specific clinico-pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision-making during diagnosis and management of PMD.
METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior.
RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region.
CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.
OBJECTIVE: To investigate the accuracy of anthropometric indices as a screening tool for predicting MetS among apparently healthy individuals in Karachi, Pakistan.
METHODS: A community-based cross-sectional survey was conducted in Karachi, Pakistan, from February 2022 to August 2022. A total of 1,065 apparently healthy individuals aged 25 years and above were included. MetS was diagnosed using International Diabetes Federation guidelines. Anthropometric indices were defined based on body mass index (BMI), neck circumference (NC), mid-upper arm circumference (MUAC), waist circumference (WC), waist to height ratio (WHtR), conicity index, reciprocal ponderal index (RPI), body shape index (BSI), and visceral adiposity index (VAI). The analysis involved the utilization of Pearson's correlation test and independent t-test to examine inferential statistics. The receiver operating characteristic (ROC) analysis was also applied to evaluate the predictive capacities of various anthropometric indices regarding metabolic risk factors. Moreover, the area under the curve (AUC) was computed, and the chosen anthropometric indices' optimal cutoff values were determined.
RESULTS: All anthropometric indices, except for RPI in males and BSI in females, were significantly higher in MetS than those without MetS. VAI [AUC 0.820 (95% CI 0.78-0.86)], WC [AUC 0.751 (95% CI 0.72-0.79)], WHtR [AUC 0.732 (95% CI 0.69-0.77)], and BMI [AUC 0.708 (95% CI 0.66-0.75)] had significantly higher AUC for predicting MetS in males, whereas VAI [AUC 0.693 (95% CI 0.64-0.75)], WHtR [AUC 0.649 (95% CI 0.59-0.70)], WC [AUC 0.646 (95% CI 0.59-0.61)], BMI [AUC 0.641 (95% CI 0.59-0.69)], and MUAC [AUC 0.626 (95% CI 0.57-0.68)] had significantly higher AUC for predicting MetS in females. The AUC of NC for males was 0.656 (95% CI 0.61-0.70), while that for females was 0.580 (95% CI 0.52-0.64). The optimal cutoff points for all anthropometric indices exhibited a high degree of sensitivity and specificity in predicting the onset of MetS.
CONCLUSION: BMI, WC, WHtR, and VAI were the most important anthropometric predictors for MetS in apparently healthy individuals of Pakistan, while BSI was found to be the weakest indicator.
METHODS AND ANALYSIS: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk.
ETHICS AND DISSEMINATION: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).