Preventing Falls in Older Californians: State of the Art

In February 2003, the Foundation convened over 150 leaders in academic, legislative, community-based services, consumer advocates, aging network, housing, public health, public safety, and other leaders who worked for two days on a statewide blueprint on fall prevention.  In preparation for the convening, a Preconference White Paper was created and used to build the blueprint.  The California Blueprint describes state-of-the-art approaches to reducing the risks of falls, and the challenges to implementing fall prevention in California.  One of the top recommendations from this blueprint was the creation of a coordination center that could serve as a statewide resource and lead efforts in fall prevention.  This recommendation eventually led to the creation of the Fall Prevention Center of Excellence (FPCE)

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

1H‐magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single‐voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation

TIPIT: A randomised controlled trial of thyroxine in preterm infants under 28 weeks gestation: Magnetic Resonance Imaging and Magnetic Resonance Angiography protocol

<p>Abstract </p> <p>Background</p> <p>Infants born at extreme prematurity are at high risk of developmental disability. A major risk factor for disability is having a low level of thyroid hormone described as hypothyroxinaemia, which is recognised to be a frequent phenomenon in these infants. Derangements of critical thyroid function during the sensitive window in prematurity when early development occurs, may have a range of long term effects for brain development. Further research in preterm infants using neuroimaging techniques will increase our understanding of the specificity of the effects of hypothyroxinaemia on the developing foetal brain. This is an explanatory double blinded randomised controlled trial which is aimed to assess the effect of thyroid hormone supplementation on brain size, key brain structures, extent of myelination, white matter integrity and vessel morphology, somatic growth and the hypothalamic-pituitary-adrenal axis.</p> <p>Methods</p> <p>The study is a multi-centred double blinded randomised controlled trial of thyroid hormone supplementation in babies born below 28 weeks' gestation. All infants will receive either levothyroxine or placebo until 32 weeks corrected gestational age. The primary outcomes will be width of the sub-arachnoid space measured using cranial ultrasound and head circumference at 36 weeks corrected gestational age. The secondary outcomes will be thyroid hormone concentrations, the hypothalamic pituitary axis status and auxological data between birth and expected date of delivery; thyroid gland volume, brain size, volumes of key brain structures, extent of myelination and brain vessel morphology at expected date of delivery and markers of morbidity which include duration of mechanical ventilation and/or oxygen requirement and chronic lung disease.</p> <p><b>Trial registration</b></p> <p>Current Controlled Trials ISRCTN89493983</p

Metabolite selection for machine learning in childhood brain tumour classification

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. A new automated quality control method was developed, which trained machine learning classifiers using QR results