The AROC annual report: the state of rehabilitation in Australia in 2009

This is the fifth comprehensive annual report describing discharge episodes from subacute inpatient rehabilitation programs provided by facilities that are members of the Australasian Rehabilitation Outcomes Centre (AROC)1

Simple SVM based whole-genome segmentation

We present a support vector machine (SVM) based framework for DNA segmentation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction. Experiments demonstrate our approach has significantly better performance than other methods on both tasks

Comparison of retinal nerve fiber layer and macular thickness for discriminating primary open-angle glaucoma and normal-tension glaucoma using optical coherence tomography

Purpose The aim of this study was to evaluate the discrimination capabilities of macular and peripapillary retinal nerve fiber layer (pRNFL) thickness parameters as measured using spectral domain optical coherence tomography (SD-OCT) between primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG). Methods A total of 90 subjects were enrolled: 30 healthy subjects, 30 subjects with POAG and 30 subjects with NTG, consecutively. Retinal nerve fiber layer thickness, macular thickness and volume measurements were obtained with circular and radial SD-OCT scans. All parameters were compared between groups using an analysis of variance test. Areas under receiver-operating characteristic (AROC) curves with sensitivities at specificities greater than or equal to 90 per cent were generated to compare discrimination capabilities of various parameters between POAG and NTG. Results Macular thickness and volume measurements were the highest in normal subjects, followed by NTG and POAG (p < 0.05). Average retinal nerve fiber layer thickness had perfect discrimination for normal-POAG (AROC: 1.000; sensitivity: 100 per cent) and near perfect discrimination for normal-NTG (AROC: 0.979; sensitivity: 93 per cent) as well as NTG-POAG pairs (AROC: 0.900; sensitivity: 60 per cent). Inferior outer macular thickness (IOMT) and total volume were the best macular thickness and volume parameters having similar AROCs and sensitivities between normal and POAG (IOMT, AROC: 0.987; sensitivity: 92 per cent and total volume, AROC: 0.997; sensitivity: 97 per cent), normal and NTG (IOMT, AROC: 0.862, sensitivity: 47 per cent and total volume, AROC: 0.898, sensitivity: 67 per cent) and also between NTG and POAG (IOMT, AROC: 0.910, sensitivity: 53 per cent and total volume, AROC: 0.922, sensitivity: 77 per cent). In each comparison group, there was no statistically significant difference in AROCs between average retinal nerve fiber layer and inferior outer macular thickness, as well as total volume. Conclusions The macular parameters offer comparable performance to pRNFL parameters for the discrimination of NTG and POAG. Average retinal nerve fiber layer thickness, total macular volume and inferior outer macular thickness were the best SD-OCT parameters with superior discriminating capabilities

A systematic review and meta-analysis of the diagnostic accuracy of the Phototest for cognitive impairment and dementia

The recently developed Phototest is a simple, easy and very brief test for detecting cognitive impairment or dementia. Objective: To evaluate the diagnostic accuracy of the Phototest for detecting cognitive impairment or dementia. METHODS: We used a manually created database to search for studies evaluating the Phototest diagnostic yield and performed an initial meta-analysis to determine sensitivity (Sn) and specificity (Sp) of diagnostic parameters. We also performed a second meta-analysis of individual participant data. RESULTS: In total, 6 studies were included in the meta-analysis. For dementia, Sn was 0.85 (95% CI, 0.82-0.88) and Sp 0.87 (95% CI, 0.85-0.99); for cognitive impairment, Sn was 0.80 (95% CI, 0.77-0.92) and Sp 0.88 (95% CI, 0.86-0.90). In the individual data meta-analysis, 1565 subjects were included, where best cut-off points for dementia and for cognitive impairment were 26/27 (Sn=0.89 (95% CI 0.85-0.91), Sp=0.84 (95% CI, 0.82-0.91)) and 28/29 (Sn=0.79 (95% CI, 0.76-0.81), Sp=0.88 (95% CI, 0.86-0.90)), respectively. CONCLUSION: Phototest has good diagnostic accuracy for dementia and cognitive impairment. It is brief, simple and can be used in illiterate persons. This makes it suitable for use in primary care settings and/or in subjects with low educational level.Phototest é um teste simples, fácil e muito rápido para detecção de comprometimento cognitivo e demência recentemente desenvolvido. Objetivo: Avaliar a acurácia diagnostica do Phototest para detecção de comprometimento cognitivo e demência. MÉTODOS: Nós usamos um banco de dados manualmente criado para estudos que avaliassem a capacidade diagnóstica do Phototest e realizamos uma meta-análise para determinar a sensibilidade (Sn) e especificidade (Ep) dos parâmetros diagnósticos. Nós também realizamos uma segunda meta-análise dos dados individuais dos participantes. RESULTADOS: Um total de seis estudos foram incluídos na meta-análise. Para demência a Sn foi 0.85 (95% CI, 0,82-0,88) e Ep 0,87 (95% CI, 0,85-0,99); para comprometimento cognitivo a Sn foi 0,80 (95% CI, 0,77-0,92) e Sp 0,88 (95% CI, 0,86-0,90). Na meta-análise de dados individuais, 1565 foram incluídos, os melhores escores de corte para demência e para comprometimento cognitivo foram 26/27 (Sn=0,89 (95% CI 0,85-0,91), Ep=0,84 (95% CI, 0,82-0,91)) e 28/29 (Sn=0,79 (95% CI, 0,76-0,81), Ep=0,88 (95% CI, 0,86-0,90)), respectivamente. CONCLUSÃO: Photest tem boa acurácia diagnostica para demência e comprometimento cognitivo. É breve, simples e pode ser usado em pessoas analfabetas. Tornando-o apropriado para o uso em cuidados primários e/ou sujeitos com baixo nível educacional.Fil: Carnero Pardo, Cristobal. Hospital Universitario Virgen de Las Nieves; España. Fidyan Neurocenter; EspañaFil: Lopez Alcalde Samuel. Fidyan Neurocenter; España. Hospital Universitario Virgen de Las Nieves; EspañaFil: Allegri, Ricardo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas ; ArgentinaFil: Russo, María Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas ; Argentin

Adjusting ROC curve for Covariates with AROC R package

The ability of a medical test to differentiate between diseased and non-diseased states is of vital importance and must be screened by statistical analysis for reliability and improvement. The receiver operating characteristic (ROC) curve remains a popular method of marker analysis, disease screening and diagnosis. Covariates in this field related to the subject’s characteristics are incorporated in the analysis to avoid bias. The covariate adjusted ROC (AROC) curve was proposed as a method of incorporation. The AROC R-package was recently released and brings various methods of estimation based on multiple authors work. The aim of this study was to explore the AROC package functionality and usability using real data noting its possible limitations. The main methods of the package were capable of incorporating different and multiple variables, both categorical and continuous, in the AROC curve estimation. When tested for the same data, AROC curves are generated with no statistical differences, regardless of method. The package offers a variety of methods to estimate the AROC curve complemented with predictive checks and pooled ROC estimation. The package offers a way to conduct a more thorough ROC and AROC analysis, making it available for any R user.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

To eat or not to eat? Indicators for reduced food intake in 91,245 patients hospitalized on nutritionDays 2006-2014 in 56 countries worldwide: A descriptive analysis

Background: Inadequate nutrition during hospitalization is strongly associated with poor patient outcome, but ensuring adequate food intake is not a priority in clinical routine worldwide. This lack of priority results in inadequate and unbalanced food intake in patients and huge amounts of wasted food. Objectives: We evaluate the main factors that are associated with reduced meal intake in hospitalized patients and the differences between geographical regions. Design: We conducted a descriptive analysis of data from 9 consecutive, annual, and cross-sectional nutritionDay samples (2006-2014) in a total of 91,245 adult patients in 6668 wards in 2584 hospitals in 56 countries. A general estimation equation methodology was used to develop a model for meal intake, and P-value thresholding was used for model selection. Results: The proportion of patients who ate a full meal varied widely (24.7-61.5%) across world regions. The factors that were most strongly associated with reduced food intake on nutritionDay were reduced intake during the previous week (OR: 0.20; 95% CI: 0.17, 0.22), confinement to bed (OR: 0.49; 95% CI: 0.44, 0.55), female sex (OR: 0.53; 95% CI: 0.5, 0.56), younger age (OR: 0.74; 95% CI: 0.64, 0.85) and older age (OR: 0.80; 95% CI: 0.74; 0.88), and low body mass index (OR: 0.84; 95% CI: 0.79, 0.90). The pattern of associated factors was homogenous across world regions. Conclusions: A set of factors that are associated with full meal intake was identified and is applicable to patients hospitalized in any region of the world. Thus, the likelihood for reduced food intake is easily estimated through access to patient characteristics, independent of world regions, and enables the easy personalization of food provision

Anthropometric indices as novel markers of risk in type 2 diabetes mellitus (T2DM) among Nigerian adults in Zamfara State

Body mass index (BMI) has traditionally been used as an indicator of body size measure and composition. Although, other measures of adiposity of the abdomen such as waist circumference (WC), waist-hip ratio (WHR), neck circumference (NC) have been suggested as being superior to BMI in predicting disease outcome. This study was designed to compare different anthropometric variables in term of their ability to predict type 2 diabetes mellitus (T2DM). This was a case-control study in 240 participants involving 120 verified T2DM cases and 120 non-diabetics as control. Age, gender and anthropometric data were collected from each participant. Logistic regression models were used with areas under the receiver operating characteristic (AROC) curve to compare the variables predictive statistics. The AROC of WHR to identify T2DM patients was 0.678 (P&lt;0.05), with sensitivity 62.5% of and specificity of 60.8%. The AROC for average arm circumference (AAC) model is 0.649 with sensitivity of 55.8% followed by BMI model (AROC 0.635) and WC model (AROC 0.600) (P&lt;0.05). Hip circumference (HC) (AROC 0.508) and NC (AROC 0.492) models were not significant predictors of T2DM. Subjects of ≥60 years, AAC value ≥32.6 cm, BMI value ≥ 30 kg/m2, and WHR value ≥ 0.93 were at significantly (P&lt;0.05) higher odds of developing T2DM than lower subjects with lower values. There were no significant differences (P&gt;0.05) in the mean HC and NC values between the diabetic and non-diabetic subjects. The non-diabetic subjects have significantly (P&gt;0.05) higher mean height value than the diabetic subjects. Measures of generalized and central obesity were significantly associated with increased risk of developing T2DM. This study revealed that WHR can predict type 2 diabetes mellitus risk more accurately than other anthropometric measures and can thus be helpful in predicting patients with future occurrence of diabetes and providing necessary intervention