ABSTRACT
AIMS AND OBJECTIVES: Individual alpha frequency (IAF) is a biomarker of neurophysiological functioning. The IAF-guided repetitive transcranial magnetic stimulation (α-rTMS) is increasingly explored in diverse neurological conditions. However, there is limited data on the efficacy and safety of α-rTMS in children with autism spectrum disorder (ASD). MATERIALS AND METHODS: The IAF, childhood autism rating scale (CARS), Pediatric Quality of Life Inventory 4.0 (PedsQLTM 4.0), and semi-structured interview data of patients that received 19 α-rTMS sessions (4 weeks) were aggregated and analysed using paired student t-test and descriptive method. RESULTS: Data were retrieved from 28 patients (26 males, aged 3-9years (mean ± SD age: 6.1 ± 1.8years)). The post-α-rTMS data shows a significant improvement in IAF (9.4 Hz; p ≤ 0.025) towards 10 Hz. The CARS and PedsQLTM 4.0 surveys indicate that patients' ASD symptoms and quality of life improved significantly. Specifically, reports from semi-structured interviews suggest improved sleep trouble - the most significant comorbidity. The experiences of minor side effects such as hyperactivity resolved within two hours following α-rTMS sessions. CONCLUSION: This study presents evidence on the efficacy and safety of α-rTMS in improving ASD symptoms, quality of life and comorbid sleep troubles in children. However, these findings should be interpreted as preliminary pending the presentation of double-blind, randomised clinical trials.
ABSTRACT
Public hospital spending consumes a large share of government expenditure in many countries. The large cost variability observed between hospitals and also between patients in the same hospital has fueled the belief that consumption of a significant portion of this funding may result in no clinical benefit to patients, thus representing waste. Accurate identification of the main hospital cost drivers and relating them quantitatively to the observed cost variability is a necessary step towards identifying and reducing waste. This study identifies prime cost drivers in a typical, mid-sized Australian hospital and classifies them as sources of cost variability that are either warranted or not warranted-and therefore contributing to waste. An essential step is dimension reduction using Principal Component Analysis to pre-process the data by separating out the low value 'noise' from otherwise valuable information. Crucially, the study then adjusts for possible co-linearity of different cost drivers by the use of the sparse group lasso technique. This ensures reliability of the findings and represents a novel and powerful approach to analysing hospital costs. Our statistical model included 32 potential cost predictors with a sample size of over 50,000 hospital admissions. The proportion of cost variability potentially not clinically warranted was estimated at 33.7%. Given the financial footprint involved, once the findings are extrapolated nationwide, this estimation has far-reaching significance for health funding policy.
