The development of highly active combination antiretroviral therapy (cART) has resulted in a remarkable decline in HIV-associated morbidity and mortality [1]. HIV has become a chronic disease with a life expectancy of the medication-compliant infected population approaching the uninfected population [2]. With the changing course of HIV infection, the pattern of HIV-associated neurocognitive disorders (HAND) has also altered [3]. The adverse consequences of HIV on the central nervous system have reduced in severity but are still prevalent as milder forms of neurocognitive impairment (NCI) [4, 5]. Even in milder forms, NCI is disabling and constitutes a public health issue due to its adverse effect on everyday functioning. NCI has been found to be associated with poor medication management [6], low self-efficacy for healthcare interactions [7], unemployment [8], poor health-related quality of life [9], and higher mortality [10].

The pathogenesis of NCI in people living with HIV (PLWH) is multifaceted, including direct viral replication, chronic inflammation, treatment-related adverse effects, comorbidities, and aging, and is not well recognized [11]. A number of risk factor associations with NCI have been studied in the HIV-infected population. However, the studies have generated inconsistent results. While some studies reported older age, female gender, Hispanic ethnicity, substance use, comorbidities (depression, hepatitis-C co-infection, metabolic disorders, and anemia), high viral load, and low CD4 T-cell counts to be positively associated with NCI in PLWH [12-21], others reported contradicting results [22-28]. The findings' inconsistencies may be due to methodological differences, including differences in study population and eligibility criteria, small sample size, differences in testing modalities, and a variable degree of control for confounding factors. There is still a need for a comprehensive assessment of etiological factors to develop an optimal prediction model for NCI in PLWH.

Neuropsychologic testing remains the “gold standard” of NCI diagnosis; however, it is time consuming, costly, and requires interpretation by a neuropsychologist. Thus, a brief and readily available screening tool is desirable in clinical practice. The current screening tools such as CogState battery, revised HIV dementia scale, and Montreal Cognitive Assessment are either robust in detecting more severe forms of impairment (e.g., HIV-associated dementia) [29] or did not have good screening accuracy for HAND [30]. Furthermore, these screening tools require neurocognitive testing for implementation. Given that demographic and clinical data can be readily collected, they may be valuable in developing user-friendly nomograms to predict NCI in PLWH. Nomograms are a user-friendly pictorial representation of complex statistical models that estimate individualized risk probabilities of particular events based on patient and disease characteristics. Nomograms have been widely studied and used in cancer research [31]. However, to our knowledge, they have not been utilized for neuropsychological assessments. Nomograms could potentially serve as adjunct procedures when determining whether a patient may require further neuropsychological assessments. Our study's first objective was to identify factors associated with NCI using a large and diverse sample of PLWH. The second objective of our study was to build a user-friendly predictive tool (nomogram) based on demographic, clinical, and behavioral variables to identify PLWH at risk of NCI.

A total of 1610 participants were enrolled in the CHARTER study; of these, 1,595 participants (99.1%) had complete baseline data on the outcome variable. After excluding 288 participants (18.0%) with missing information on covariates of interest, the final sample analyzed included 1,307 participants. Most participants were under 50 years of age (81.6%), males (77.4%), and non-Hispanic African Americans (47.4%) (Table S1). At baseline, 21.6% of participants had global T-scores under 40 (impaired), and 65.1% were using highly active antiretroviral therapy (HAART). Furthermore, among those who were older than 50 years old, 41.2% were neurocognitively impaired compared to 17.2% of those at or below 50 years of age (Table S1).

The Bayesian network analysis identified that neurocognitive impairment has a direct probabilistic relationship, in descending order of mutual information, with age, lifetime cocaine use, current employment status, difficulty in bathing, dressing, eating, or using the toilet, impaired use of hands, diagnosis of abnormally high cholesterol, current psychotropic medication use, presence of any AIDS-defining illness and lifetime history of stroke (Fig. 1).

Fig. (1). Markov Blanket model with mutual information between neurocognitive impairment (central node) and other covariates.

The number at the top (within each box) in Fig. (1) is the mutual information (predictive importance) between the outcome (neurocognitive impairment shown in the center) and each covariate. The middle number is the relative mutual information with regard to the child node (i.e., the amount of uncertainty reduced regarding the parent node by knowing the child node). The bottom number is the relative mutual information with regard to the parent node (i.e., the amount of uncertainty reduced regarding the child node by knowing the parent node). The arrows’ direction is dependent on computational complexity and does not reflect causality.

Among the variables included in the Markov blanket of the target variable (neurocognitive status), age provided the highest amount of mutual information (0.0333) and lifetime history of stroke the least (0.0088). Specifically, by knowing age, the uncertainty regarding neurocognitive status was reduced by 4.4% on average (Fig. 1). The K-fold cross-validation yielded an overall accuracy of 78%. The MB model generated for the younger participants (<50 years) showed that, except for history of stroke, all other predictors remained the same (Fig. S1) as the overall model, whereas the MB model for older participants (≥50 years) included the history of stroke but not four other variables (Fig. S2).

Results of the multiple logistic regression indicated age to have the strongest association with NCI. Specifically, the adjusted odds ratio of NCI among those above 50 years of age was 2.77 times (95% CI=1.99-3.85) those at or below 50 years of age (Table 1).

History of high cholesterol, current psychotropic drug use, history of stroke, history of any AIDS-defining illness, current difficulty eating, dressing, bathing, or using the toilet, and impaired use of hands were also positively associated with NCI (Table 1). Those employed and those who reported a history of cocaine use had lower odds of NCI (Table 1). Since age had the highest adjusted regression coefficient (β=1.02; equivalent to OR=2.77 after exponentiation), it was assigned 100 points in the nomogram (Fig. 2).

The points per variable are added together for each person and then converted into a probability of having NCI. For example, for a non-Hispanic white, aged below 50 years, with no employment, current use of psychotropic drugs, AIDS-defining illness, history of stroke, impaired use of hands and difficulty eating, dressing, bathing, or using the toilet and positive histories of high cholesterol and lifetime cocaine use will have total points of 146 converted to a probability of approximately 0.13. 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 (Fig. 3a), and there was excellent agreement between observed and predicted probabilities, as shown by the nomogram's calibration plot (Fig. 3b).

Fig. (2). Nomogram for predicting the probability of neurocognitive impairment among HIV-infected participants of the CNS HIV Antiretroviral Therapy Effects Research (CHARTER) study (n=1,307).

Fig. (3a). Receiver operating characteristic (ROC) curve; AUC= 0.7473.

Fig. (3b). Calibration plot of observed versus nomogram predicted probabilities.

Despite the availability of potent antiretroviral medications, mild to moderate neurocognitive impairment persists in PLWH [33]. There is limited agreement in the literature about the risk factors for NCI in PLWH. Our first objective was to use a large sample of HIV-infected patients with NCI status assessed through a comprehensive battery of neuropsychological tests to examine the factors associated with NCI. The search for an optimal screening tool for NCI in PLWH is still ongoing. Traditional neuropsychological test batteries are lengthy, time-consuming, and require trained psychometrists and thus may not be feasible for use in primary care clinics. We hypothesized that the probability of having HIV-related NCI during a clinic visit may be assessed using a predictive tool based on demographic, behavioral, and clinical factors and thus created a nomogram.

The results of both Bayesian network analysis and multivariable logistic regression demonstrated age to be the most important predictor of NCI (highest mutual information and highest adjusted odds ratios). As we used corrected (adjusted for age, education, and other variables) T-scores based on standardized normative data, the association specifically embodies changes beyond the normal age-related neurocognitive function. The association between age and NCI among PLWH is crucial as HIV has been found to lead to premature and accelerated aging [47]. However, as we did not use an HIV-negative control group, we cannot comment on the interaction between HIV status and age in association with NCI. Our finding regarding age is in line with a recent US-based study that found older age (>50 years) to be associated with weaker overall cognitive performance among PLWH [48]. Another recent systematic review of HIV-infected adults reported that the odds ratio (OR) of having NCI among older participants compared to their younger counterparts varied between 1.18 and 4.8 [49]. The T-scores correction might explain why education dropped out of the model and nomogram during analyses.

Interestingly, cocaine use was associated with lower odds of NCI (OR=0.46, 95% CI=0.33-0.63). As we had combined lifetime cocaine abuse and lifetime cocaine dependence to generate the cocaine use variable, we may have underestimated the findings in active drug users. A possible explanation is that some participants might have used cocaine only in the past, and this may account for the reversal of cocaine use effects. The literature exhibits mixed results regarding the association between NCI and cocaine use among PLWH. Meade et al. did not find any association between current (past three months) cocaine use and a global neurocognitive measure (Global Deficit Scores); however, they did find higher impairment in processing speed and executive functioning among cocaine users compared to non-users [24]. Similarly, Attonito et al. did not find any association between cocaine use and neurocognition among 370 HIV-positive participants living in Florida [50]. Interestingly, another study using CHARTER data combined lifetime and current use of cocaine to define cocaine use variable, and although it did not detect any association between global impairment and cocaine use, they did report a weak association between cocaine use and improved verbal fluency [51].

Although limited research has been done on the topic, the inverse association between employment and NCI found in our study is consistent with the literature. Blackstone et al. demonstrated higher unemployment among those with NCI, and Rabkin et al. found that impairment in executive functioning represented significant employment barriers in HIV-infected men [52, 53]. Not only may NCI render PLWH unable to be efficiently employed, but unemployment itself may also reduce neurocognitive ability, as being employed provides cognitive stimulation, facilitating enhanced cognitive functioning [54].

We ascertained difficulty in daily living by combining difficulty in bathing, dressing, eating, or using the toilet and found a positive association with NCI. The finding is not surprising, as an ample body of literature indicates that HIV-associated neurocognitive disorders are a significant risk factor for everyday functioning decline [6-8, 55]. Our result that Hispanics and non-Hispanic Whites have higher adjusted odds of NCI than non-Hispanic African Americans is generally in contrast with prior reports of higher impairment among African Americans [56-58]. However, prior studies differ from ours regarding the included samples (gender or ART use specific) and NCI measures. Our finding may be sample dependent as it was a multicenter study and had voluntary participation; however, stratification by individual sites showed similar findings across sites (results not shown). Heaton et al., in a longitudinal analysis using CHARTER data, also found higher neurocognitive decline (RR=2.35) among Hispanics compared to non-Hispanics (Whites and African Americans combined). Previous studies have demonstrated that Hispanic adults are diagnosed late for HIV and thus have a delayed HIV care initiation [59, 60], which may be an explanation of our finding.

Among the comorbidities, we found impaired use of hands, abnormally high cholesterol, current psychotropic drug use, presence of any AIDS-defining illness, and lifetime history of stroke to be positively associated with NCI. The specific reasons for hand impairment were not available. Hand impairment may result from HIV-induced brain injury, aging, external trauma, systemic disease, or other CNS infections and vascular diseases [61-64]. Thus, in some participants with hand impairment, poor motor-domain performance may not truly represent NCI and may need further evaluation. Previous studies have consistently found hypercholesterolemia to be positively associated with NCI in PLWH. A recent longitudinal study using CHARTER data found that participants with declining cognition exhibited a higher baseline cholesterol/HDL ratio compared to patients with stably normal cognition [65]. Another longitudinal study established elevated cholesterol to be an independent risk factor for cognitive decline in ART-adherent HIV-infected men [66]. The most plausible explanation for this association may be HIV-induced endothelial dysfunction leading to cholesterol oxidation and thus sub-clinical cerebrovascular damage [67-69]. Psychotropic drug use may commonly indicate a diagnosis of anxiety, depression, psychosis, or sleep disorders, and hence an association with poor cognition. Although the literature is limited for other conditions, depression has been amply studied and, according to a recent comprehensive review, was associated positively with NCI in PLWH [70]. Depression may act directly or indirectly through poor ARV adherence to affect neurocognition in PLWH [70]. The presence of AIDS-defining comorbidities indicates higher severity of HIV infection and is rather plausibly associated with NCI. In addition to HIV infection acting itself, certain AIDS-defining infections such as HIV-related encephalopathy, disseminated histoplasmosis, disseminated mycobacterium avium complex, and progressive multifocal leukoencephalopathy may directly affect the brain leading to impairment [71]. The positive association between AIDS-defining comorbidities and HIV disease severity may explain why, when this variable was included in the Bayesian or logistic regression models, other variables such as HIV duration and the viral load dropped out.

Specialists have recommended conducting routine and regular screening for NCI for early detection, treatment adjustment, and management in PLWH. However, most screening tools are based on neurocognitive testing that requires clinicians to be suitably trained in its administration and interpretation. Furthermore, currently available screening tools are generally unable to detect milder forms of NCI [72, 73]. We created a nomogram based on easily recordable demographic, clinical, and behavioral factors to serve as a user-friendly screening tool and predict NCI in PLWH. There is one study by Cysique et al. that attempted to develop a screening algorithm with demographic and clinical factors using support vector machine methodology [74] using a sample of 97 HIV-infected individuals on CART and with advanced HIV infection. The study included age, current CD4 cell count, past central nervous system HIV‐related diseases, and current treatment duration in the algorithm. However, they did not convert their algorithm into a ready-to-use screening tool. Another study by Muñoz-Moreno et al. used a tree-structured approach and created four classification models to predict NCI in PLWH and identified age, employment status, CD4 cell count, highest viral load, comorbidities, HIV duration, and duration of current treatment as potential predictors [75]. However, their statistical approach (classification and regression trees) differed from our Bayesian network analysis.

Our study had some limitations. Although exclusion criteria were kept to a minimum to maximize generalizability, this was a clinic-based and volunteer sample. The proposed nomogram needs to be validated in other PLWH samples. This study is one of its kind, conducted at six centers in the US with information available on a wide range of demographic, behavioral, clinical, and laboratory measures and NCI status assessed through a comprehensive neuropsychological test battery. Some individuals with medical pathologies other than NCI could have ADL impairment. Thus, a disadvantage of including ADLs as a predictor of NCI is a potentially higher number of false positives during the screening nomogram. However, an advantage is that more individuals with NCI would have been false negatives had ADLs not been included as a predictor.

In conclusion, we investigated factors associated with NCI in PLWH and developed a novel nomogram to predict NCI in the HIV-infected population. The nomogram used variables that can be easily measured in clinical settings and, thus, easy to implement within a clinic or web-interface platform. Our goal with such a tool is to help clinicians predict specific patients who might have a high probability of NCI and be further evaluated by a comprehensive neuropsychological examination resulting in timely diagnosis and appropriate management. Future research should focus on external validation of the nomogram in different populations to assess broader applicability. Distinct nomograms may also be developed for specific patient subgroups such as older and younger patients.