HIV Lipodystrophy Case Definition using Artificial Neural Network Modelling

Objective

A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy.

Methods

The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers were trained and validated. Results were compared against logistic regression models using the same information.

Results

Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under the ROC curve. Their average sensitivity and specificity were 72.4 and 71.2%, as compared with 73.0 and 62.9% for logistic regression, respectively (area under the ROC curve, 0.784 vs 0.748). The discriminating performance of the neural networks was largely unaffected when built excluding 13 parameters that patients may not have readily available. The average sensitivity and specificity of the neural networks remained the same when metabolic variables were also considered (total 60 items) without a clear advantage against logistic regression (overall accuracy 71.8%). The performance of networks considering also body composition variables was similar to that of logistic regression (overall accuracy 78.5% for both).

Conclusions

Neural networks may offer a means to improve the discriminating performance for HIV lipodystrophy, when only clinical data are available and a rapid approximate diagnostic decision is needed. In this context, information on metabolic parameters is apparently not helpful in improving the diagnosis of HIV lipodystrophy, unless imaging and body composition studies are also obtained.

Respiratory symptoms, lung function, and nasal cellularity in Indonesian wood workers: a dose-response analysis

OBJECTIVES

It was hypothesised that inflammation plays a dominant part in the respiratory effects of exposure to wood dust. The purpose of this study was to relate the nasal inflammatory responses of workers exposed to meranti wood dust to (a) levels of exposure, (b) respiratory symptoms and (c) respiratory function.

METHODS

A cross sectional study was carried out in 1997 in a woodworking plant that used mainly meranti, among 982 workers exposed to different concentrations of wood dust. Personal sampling (n=243) of inhalable dust measurements indicated mean exposure in specific jobs, and enabled classification of 930 workers in three exposure classes (<2, 2-5, and >5 mg/m(3)) based on job title. Questionnaires were used to screen respiratory symptoms in the entire population. Lung function was measured with two different techniques, conventional flow-volume curves and the forced oscillation technique. Nasal lavage was done to assess inflammation in the upper respiratory tract.

RESULTS

A negative trend between years of employment and most flow-volume variables was found in men, but not in women workers. Current exposure, however, was not related to spirometric outcomes, respiratory symptoms, or nasal cellularity. Some impedance variables were related to current exposure but also with better function at higher exposure.

CONCLUSIONS

Exposure to meranti wood dust did not cause an inflammation in the upper respiratory tract nor an increase of respiratory symptoms or decrease of lung function. These data do not corroborate the hypothesis that inflammation plays a part in airway obstruction induced by wood dust.