Biomedical subjects

HIV Lipodystrophy Case Definition Study Group

Publications and source records attributed to HIV Lipodystrophy Case Definition Study Group.

3 recordsLinked to original sources

PubMed2003

An objective lipodystrophy severity grading scale derived from the lipodystrophy case definition score.

HIV lipodystrophy can be objectively diagnosed using a score derived from the 10 parameters in the HIV lipodystrophy case definition (LDCD). Lipodystrophy severity remains subjectively determined by physical examination and patient assessment. Regional dual-energy x-ray absorptiometry (DEXA) and single-slice abdominal computed tomography (CT) scanning are objective but are gender-dependent body composition measures. The LDCD score may provide a means of generating an objective and lipodystrophy grading/severity scale applicable to both men and women. Total and regional clinical lipodystrophy severity scores (generated using the HIV Outpatient Study [HOPS] scale: nil (0), mild (1), moderate (2), and severe (3) lipoatrophy or fat accumulation in 8 body regions) were correlated with objective measures of LD (LDCD score, DEXA, abdominal CT) and metabolic (lipid, glycemic, acid-base) parameters known to correlate significantly with lipodystrophy severity. Analysis was based on 417 lipodystrophic adults and 371 controls recruited to the HIV LDCD study. Correlation coefficients were used to compare physician and patient assessments (rPhysician, rPatient) with objective LD measures and metabolic parameters. The strongest objective correlate of total clinical lipodystrophy severity was the LDCD score (rPhysician = 0.641 [95% CI, 0.584-0.698]; rPatient = 0.620 [95% CI, 0.561-0.678]), whereas the strongest imaging correlate (trunk:limb fat ratio on DEXA) was significantly lower (rPhysician = 0.483 [95% CI, 0.420-0.546]; rPatient = 0.475 [95% CI, 0.412-0.538]; P < 0.001). The LDCD score also yielded significantly greater correlations with 7 of the 8 metabolic parameters than did clinical lipodystrophy severity scores. Based on quartiles of physician-rated severity, the LDCD scores were categorized to allow for rating of lipodystrophy as absent (LDCD score < 0), grade 1 (0-9.9), grade 2 (10-14.9), grade 3 (15-22.9), and grade 4 (>or=23). In conclusion, the LDCD score is the best objective measure of lipodystrophy severity and, in contrast to DEXA and CT, it is also gender independent. Subjective assessment of lipodystrophy severity could possibly be abandoned in cross-sectional studies. The LDCD score and its derived lipodystrophy grading scale merit prospective evaluation.

Adult↗

PubMed2003

An objective case definition of lipodystrophy in HIV-infected adults: a case-control study.

BACKGROUND: Lipodystrophy (peripheral lipoatrophy, central fat accumulation, and lipomatosis) is a common and disfiguring problem in adult patients with HIV-1 infection on antiretrovirals. However, an objective, validated definition of the disorder does not exist. We aimed to develop an objective, sensitive, specific, and broadly applicable case definition of HIV lipodystrophy. METHODS: In a case-control study, 1081 consecutive, HIV-infected, adult outpatients (261 [15%] women) without active AIDS were recruited from 32 sites worldwide. We classed patients with at least one moderate or severe subjective lipodystrophic feature, identified by lipodystrophy-specific physical examination and patient questionnaire, and apparent to both doctor and patient as cases (n=417). We classed patients with no such feature as controls (n=371), and patients without a clear diagnosis as non-assigned. We used objective clinical, metabolic, and body composition measurements to construct a logistic regression model with a subset of randomly selected cases and controls. The model was validated in the remaining patients. FINDINGS: A model including age, sex, duration of HIV infection, HIV disease stage, waist to hip ratio, anion gap, serum HDL cholesterol concentration, trunk to peripheral fat ratio, percentage leg fat, and intra-abdominal to extra-abdominal fat ratio had 79% (95% CI 70-85) sensitivity and 80% (95% CI 71-87) specificity for diagnosis of lipodystrophy. Models that incorporated only clinical, or only clinical and metabolic variables had lower sensitivity and specificity than the inclusive model. Models for lipoatrophy, fat accumulation, and lipomatosis could not be developed since pure phenotypes occurred in fewer than 10% of patients with clinical diagnoses of these disorders. INTERPRETATION: Our objective case definition of HIV-associated lipodystrophy should improve assessment of lipodystrophy prevalence, risk factors, and pathogenesis; prevention and treatment approaches; and assist in diagnosis.

Adult↗

PubMed2003

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.

Absorptiometry, Photon↗