RESEARCH ARTICLE


Development of a Nomogram-Based Tool to Predict Neurocognitive Impairment Among HIV-positive Charter Participants



Zaeema Naveed1, Howard S. Fox2, Christopher S. Wichman3, Pamela May2, Christine M. Arcari1, Jane Meza3, Steven Totusek2, Lorena Baccaglini1, *
1 Department of Epidemiology, University of Nebraska Medical Center, Omaha, Nebraska, USA
2 Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
3 Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA


© 2021 Naveed et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at Department of Epidemiology, University of Nebraska Medical Center, Omaha, Nebraska, USA;
Tel: 402-552-6634; E-mail: lbaccaglini2@gmail.com


Abstract

Background:

Despite the widespread use of combination antiretroviral therapy (cART), HIV-associated neurocognitive impairment (NCI) persists in people living with HIV (PLWH). Studies have generated inconsistent results regarding etiological factors for NCI in PLWH. Furthermore, a user-friendly and readily available predictive tool is desirable in clinical practice to screen PLWH for NCI.

Objective:

This study aimed to identify factors associated with NCI using a large and diverse sample of PLWH and build a nomogram based on demographic, clinical, and behavioral variables.

Methods:

We performed Bayesian network analysis using a supervised learning technique with the Markov Blanket (MB) algorithm. Logistic regression was also conducted to obtain the adjusted regression coefficients to construct the nomogram.

Results:

Among 1,307 participants, 21.6% were neurocognitively impaired. During the MB analysis, age provided the highest amount of mutual information (0.0333). Logistic regression also showed that old age (>50 vs. ≤50 years) had the strongest association (OR=2.77, 95% CI=1.99-3.85) with NCI. The highest possible points on the nomogram were 626, translated to a nomogram-predicted probability of NCI to be approximately 0.95. The receiver operating characteristic (ROC) curve's concordance index was 0.75, and the nomogram's calibration plot exhibited an excellent agreement between observed and predicted probabilities.

Conclusion:

The nomogram used variables that can be easily measured in clinical settings and, thus, easy to implement within a clinic or web-interface platform. The nomogram may help clinicians screen for patients with a high probability of having NCI and thus needing a comprehensive neurocognitive assessment for early diagnosis and appropriate management.

Keywords: HIV, Neurocognition, Nomogram, Risk factors, Screening, Markov Blanket.