HIV Lipodystrophy Case Definition using Artificial Neural Network Modelling
Authors: Ioannidis John PA , Trikalinos Thomas A , Law Matthew , Carr Andrew ,   the HIV Lipodystrophy Case Definition Study Group, Carr A , Barr D , Cooper DA , Emery S , Grinspoon S , Ioannidis J , Lewis R , Law M , Lichtenstein K , Murray J , Pizzuti D , Powderly WG , Rozenbaum W , Schambelan M ,   Coordinating committee, Puls R , Emery S , Moore A , Miller J , Carr A ,   Investigators, Belloso WH , Ivalo SA , Clara LO , Barcan LA , Stern LD , Galich AM , Perman MI , Losso M , Duran A , Toibaro J , Baker D , Vale R , McFarlane R , MacLeod H , Kidd J , Genn B , Carr A , Fielden R , Mallal S , French M , Cain A , Skett J , Maxwell D , Mijch A , Hoy J , Pierce A , McCormick C , De Graaf B , Falutz J , Vatistas J , Dion L , Montaner J , Harris M , Phillips P , Montessori V , Valyi M , Stewart W , Walmsley S , Casciaro L , Lundgren J , Andersen O , Gronholdt A , Beguinot I , Mercié P , Chêne G , Reynes J , Cotte L , Rozenbaum W , Nait-Ighil L , Slama L , Nguyen TH , Rousselle C , Viard J-P , Roudière L , Maignan A , Burgard M , Mauss S , Schmutz G , Scholten S , Oka S , Fraser H , Ishihara M , Itoh K , Reiss P , van der Valk M , Leunissen P , Nievaard M , van EckSmit B , Kujik C can , Paton N , Peperstraete B , Karim F , Khim C Y , Ong S , Gatell J , Martinez E , Milinkovic A , Churchill D , Timaeus C , Maher T , Perry N , Bray A , Moyle G , Baldwin C , Higgs C , Reynolds B , Carpenter C , Bausserman L , Fiore T , DiSpigno M , Cohen C , Hellinger J , Foy K , Hubka S , Riccio B , El-Sadr W , Raghavan S , Chowdury N , de Vries B , Miller S , Hammer S , Crawford M , Chang S , Dobkin J , Quagliarello B , Gallagher D , Punyanitya M , Kessler H , Tenorio A , Kjos S , Falloon J , Lane HC , Rock D , Ehler L , Lichtenstein K , McClain T , Murphy R , Milne P , Powderly W , Aberg J , Klebert M , Conklin M , Ward D , Green L , Stearn B 
Published: April 2022

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.