Development of a multivariable prediction model for anal high-grade squamous intraepithelial lesions in persons living with HIV in Puerto Rico: a cross-sectional study

Lancet Reg Health Am. 2023 Jan:17:100382. doi: 10.1016/j.lana.2022.100382. Epub 2022 Nov 4.

Abstract

Background: Persons living with HIV (PLWH) are at high risk of developing anal high-grade squamous intraepithelial lesions (HSIL). We aimed to develop a prediction model for anal HSIL based on individual characteristics of PLWH.

Methods: Cross-sectional study of PLWH aged ≥21 years who attended the Anal Neoplasia Clinic of the University of Puerto Rico Comprehensive Cancer Center from 2016 to 2022. The primary outcome was biopsy-confirmed anal HSIL. For each sex, relations between potential predictors and HSIL were examined using univariate (ULRM) and multivariable (MLRM) logistic regression models. Risk modelling was performed with MLRM and validated with bootstrapping techniques. The area under the ROC Curves (AUC) was estimated with 95% CI.

Findings: HSIL was detected among 45.11% of patients, 68.48% were males, and 59.42% were ≥45 aged. Multivariable analysis showed that, in women, the only significant predictor for HSIL was having a previous abnormal anal cytology (p = 0.01). In men, significant predictors for HSIL were having a previous abnormal anal cytology (p < 0.001) and a history of infection with any gonorrhoea (p = 0.002). Other suggestive predictors for HSIL among women were obesity and smoking. No association between smoking and HSIL among men was observed (p < 0.05). The AUC estimated among women (0.732, 95% CI: 0.651-0.811) was higher than in men (0.689, 95% CI: 0.629-0.748).

Interpretation: Our results support that the inclusion of individual characteristics into the prediction model will adequately predict the presence of HSIL in PLWH.

Funding: This work was supported by the NCI (Grants #U54CA096297, #R25CA240120), the NIGMS (Grant #U54GM133807), and the NIMHD (Grant #U54MD007587).

Keywords: Anal HSIL; Early detection; Hispanics; Persons living with HIV; Prediction model.