Virologic and Immunologic Monitoring in HIV Care

Date of current publication: October 31, 2025
Lead authors: Shanmukha Priya Peddireddy, MBBS (Nuvance Health, New York, NY); Hector I. Ojeda-Martinez, MD; Christine A. Kerr, MD
Writing group: Rona M. Vail, MD, AAHIVS; Sanjiv S. Shah, MD, MPH, AAHIVS; Steven M. Fine, MD, PhD; Joseph P. McGowan, MD, FACP, FIDSA, AAHIVS; Samuel T. Merrick, MD, FIDSA; Asa E. Radix, MD, MPH, PhD, FACP, AAHIVS; Jessica Rodrigues, MPH, MS; Christopher J. Hoffmann, MD, MPH, MSc, FACP; Brianna L. Norton, DO, MPH; Charles J. Gonzalez, MD
Committee: Medical Care Criteria Committee
Date of original publication: June 1, 2016

Periodic laboratory tests are necessary to evaluate a patient’s response to antiretroviral therapy (ART). Monitoring HIV-1 RNA levels (viral load) to confirm appropriate response to treatment and durable viral suppression is the most accurate and meaningful measure of the effectiveness of ART Gale, et al. 2013. HIV suppression is essential to the health of the individual with HIV and to preventing HIV transmission through sex.

Regular immunologic monitoring in patients with consistently undetectable HIV viral loads and CD4 counts >200 cells/mm³ offers little utility in clinical practice today. Clinicians rarely use this information to guide decision-making for clinically stable, virologically suppressed patients.

The New York State Department of Health AIDS Institute has developed these evidence-based recommendations for ambulatory care of patients with HIV to accomplish the following:

• Guide clinicians in the use of HIV viral load testing at appropriate times and intervals to assess initial and ongoing ART responses.
• Clarify the appropriate use of immunologic (CD4 count) monitoring in the care of patients with HIV.

Note on “experienced” HIV care providers: The NYSDOH AI Clinical Guidelines Program defines an “experienced HIV care provider” as a practitioner who has been accorded HIV Specialist status by the American Academy of HIV Medicine. Nurse practitioners (NPs) and licensed midwives who provide clinical care to individuals with HIV in collaboration with a physician may be considered experienced HIV care providers if all other practice agreements are met; NPs with more than 3,600 hours of qualifying experience do not require collaboration with a physician (8 NYCRR 79-5:1; 10 NYCRR 85.36; 8 NYCRR 139-6900). Physician assistants who provide clinical care to individuals with HIV under the supervision of an HIV Specialist physician may also be considered experienced HIV care providers (10 NYCRR 94.2).

Laboratory monitoring can provide a consistent and objective measure of a successful and appropriate antiretroviral regimen. The optimal frequency of HIV-1 viral load and immunologic monitoring has been debated, but appropriate viral load monitoring can be useful for detecting virologic failure early, helping to reduce transmission by ensuring an undetectable viral load, and assessing adherence challenges. Too frequent laboratory monitoring can contribute to treatment fatigue, over-medicalization, and false positive results. Because viral load monitoring remains the most important method of ensuring continued viral load suppression, patients who are monitored at longer intervals should be carefully selected.

The decision to increase a patient’s monitoring interval should be based on length of viral suppression, CD4 count, use of ART for transmission prevention, and adherence to medical care, including visit attendance and retention in care. For select patients with an initial CD4 count ≥300 cells/mm³ and sustained viral suppression for ≥3 years the frequency of HIV viral load monitoring may be reduced to once annually Benator, et al. 2015. The reduction in viral load monitoring frequency does not necessarily correspond to a reduction in visit frequency and regular clinical encounters remain beneficial, offering opportunities for early identification of new health concerns and providing ongoing patient support and reassurance. Patients should continue to be evaluated by a clinician at least twice yearly for discussion of adherence challenges, evaluation for sexually transmitted infection (STI) screening, and management of comorbidities.

A retrospective study noted that the strongest predictor of virologic failure at 12 months was a missed or canceled appointment rather than the interval of follow-up Buscher, et al. 2013. HIV viral load testing once, twice, and ≥3 times within the first year of starting ART produced similar results Shen, et al. 2016. However, this and other similar studies Romih, et al. 2010; Reekie, et al. 2008 have significant limitations, including their retrospective nature and short follow-up periods. Until more definitive data are available, the decision to lengthen monitoring intervals for HIV RNA levels should be individualized based on length of viral suppression, CD4 count, use of ART for transmission prevention, and adherence to medical care, including visit attendance and retention in care.

Studies do not indicate the appropriate interval for viral suppression monitoring for ongoing transmission prevention. Data indicate that the linked sexual transmission of HIV in serodiscordant couples in which the partner with HIV maintains sustained viral suppression is negligible Rodger, et al. 2016. Based on this information, those with HIV may rely on ART as a strategy to prevent viral transmission to an uninfected partner.

Table 1, below, provides a guide for monitoring HIV RNA levels and CD4 counts.

Plasma HIV-1 RNA level (viral load): Plasma levels of viral RNA have been shown to correlate with clinical outcomes, including overall mortality, and measurement of HIV RNA levels provides the most precise means of establishing whether a response to antiretroviral therapy (ART) has occurred HIV Surrogate Marker Collaborative Group 2000; Thiebaut, et al. 2000; Murray, et al. 1999; Marschner, et al. 1998; Mellors, et al. 1997.

HIV RNA levels should be obtained from all patients at baseline Porter, et al. 2015; Behrens, et al. 2014; Molina, et al. 2013; Tarwater, et al. 2004; Gulick, et al. 2003; Wu, et al. 2003.

For patients beginning ART, or those changing therapy as a result of virologic failure, HIV RNA should be measured at 4 weeks after initiation of ART and should decrease by at least 1 log (10-fold) in the presence of effective therapy Haubrich, et al. 2011 (see Appendix: Interpretation of HIV Viral Load). For patients who do not have background antiretroviral resistance, an undetectable viral load (<50 copies/mL) is usually achieved within 3 months. Patients with a baseline HIV viral load >100,000 copies/mL can be expected to achieve an undetectable viral load within 6 months of effective treatment.

An absent or incomplete viral load response to ART should raise concerns about poor adherence to ART and viral resistance Townsend, et al. 2009; Baxter, et al. 2000. If a patient has less than a 1.0 log₁₀ reduction in viral load after 4 weeks, or if the viral load remains >200 copies/mL after 12 weeks, evaluate for potential issues such as drug resistance, adherence challenges, drug-drug interactions, or drug absorption problems DHHS(b) 2025; Pyngottu, et al. 2021. The reduction in viral load will be more rapid with INSTI-containing regimens than with other regimens. At 4 weeks, INSTI-containing regimens should reduce viral load by 2 to 3 logs. If the reduction is slower (e.g., 1.5- to 2-log drop within 6 weeks), check the patient’s genotype test results and discuss adherence or other possible causes for the less robust response, such as drug-drug interactions Venter, et al. 2019; Stephan, et al. 2014.

Blips: Patients on previously suppressive ART with newly detectable HIV RNA levels of 50 to 500 copies/mL may be experiencing low-level transient viremia (“blip”) and not virologic failure. A blip by definition means that the viral load is again below the level of quantification on repeat testing performed promptly after a detectable result in someone previously suppressed. Persistent elevation, even at low levels, warrants further investigation. Acute concurrent illness and/or recent vaccination may cause this transient rise; however, studies have suggested that low-level transient viremia represents random biologic and statistical variation or false elevations of viral load resulting from laboratory processing Lee, et al. 2006; Nettles, et al. 2005. Isolated blips may not be indicative of risk of virologic failure; however, consecutive blips have been associated with an elevated risk of treatment failure Pernas, et al. 2016. Retesting should be performed within 4 weeks to differentiate low-level transient viremia (a blip) from sustained viremia and possible virologic failure.

The risk of virologic rebound (breakthrough) increases when values are >500 copies/mL Grennan, et al. 2012. However, ART should not be changed based on a single viral load elevation. Both low-level viremia (LLV; 50 to 999 copies/mL) and transient viral blips are associated with an increased risk of subsequent virologic failure. A European multicenter cohort study found that individuals experiencing blips had a 70% higher risk virologic failure and those with LLV had a 120% higher risk of virologic failure than participants with sustained viral suppression Elvstam, et al. 2023. Persistent LLV may lead to the development of drug resistance mutations. In the aforementioned study, 35% of individuals with LLV had at least 1 resistance mutation detected within 90 days Elvstam, et al. 2023.

Intermittent viremia, including blips, has also been linked to increased levels of inflammatory markers such as sCD14 and D-dimer. These markers are associated with monocyte activation and pathway coagulation, suggesting that even transient increases in viral load may contribute to systemic inflammation Funderburg, et al. 2024. LLV has also been associated with higher all-cause mortality. A study reported that individuals with LLV had more than double the risk of death than those with sustained suppression. Additionally, LLV in the range of 200 to 999 copies/mL was linked to an increased risk of serious non-AIDS events Elvstam, et al. 2021.

Advances in molecular detection technology have led to the development of HIV nucleic acid tests that are highly sensitive and more reliable than earlier versions. Real-time polymerase chain reaction (PCR) technology has been widely adopted for HIV-1 RNA quantification, but new technologies are continually emerging and being adapted to viral detection and quantification. The currently available U.S. Food and Drug Administration (FDA)-approved HIV-1 viral load tests that use real-time PCR technology offer a larger dynamic range of quantification than early-version viral load tests. The lower and upper limits of quantification of the currently available U.S. Food and Drug Administration (FDA)-approved HIV-1 viral load tests are shown in Table 3, below. Several different HIV viral load tests have been developed, and 4 are currently approved for use in the United States. Dried blood sampling is a newer technology that may provide a rapid and efficient method for sample collection as it becomes more available for point-of-care evaluation and medication adjustment and for settings without phlebotomy access. Comparative studies have demonstrated consistent results between plasma samples collected by staff and dried blood samples obtained by patients despite potential variability in collection techniques Sahu, et al. 2024. This technology continues to evolve and is not widely available.

HIV testing is conducted using assays from multiple manufacturers, each with a specific lower limit of quantification. The upper limit of quantification typically ranges up to 10,000,000 copies/mL, depending on the assay used. All currently available viral load assays quantify the level of cell-free virus in plasma and are approved by the FDA for monitoring response to ART, tracking viral suppression, and detecting treatment failure. As mentioned above, successful ART with first-line, INSTI-containing regimens should decrease the viral load by at least 2 logs (>100-fold) within 4 weeks, with the viral load decreasing below the limit of detection within 6 months DHHS(a) 2025, and reductions in viral load may be slower for patients taking non–INSTI-containing regimens. Cohort studies strongly suggest that patients with viral loads <50 copies/mL have more sustained viral suppression than patients with viral loads between 50 and 400 copies/mL Elvstam, et al. 2023; Fleming, et al. 2019; Joya, et al. 2019. Assays that can detect <50 copies/mL are recommended for determining prolonged viral suppression and for monitoring patients who are on ART.

Lymphocyte subsets (CD4 count): CD4 lymphocyte count is used to evaluate immunologic staging, predict the risk of clinical progression, and make decisions regarding opportunistic infection prophylaxis Lopez Bernaldo de Quiros, et al. 2001; El-Sadr, et al. 2000. Low CD4 counts can be seen in other disease processes and should therefore not be used to diagnose HIV infection. Although CD4 count was used historically to establish a threshold for initiating ART, current guidelines in New York State recommend ART for all patients with HIV regardless of CD4 count. For patients who may not be ready to initiate ART, CD4 count can be used to guide discussions between patient and care provider regarding the urgency of initiating ART.

Although a CD4 count should be obtained at baseline Moore and Keruly 2007; Oldfield, et al. 1998; Havlir, et al. 1996; Schneider, et al. 1992; Fischl, et al. 1988, clinicians are unlikely to use it to guide clinical decision-making in practice for virologically suppressed patients once their CD4 count remains above 200 cells/mm³. However, for those infected with HIV-2 or HIV-1 variants that cannot be accurately quantified, such as group O isolates Berger, et al. 2020, using viral load assays, CD4 count remains the most effective tool for monitoring disease progression.

Although a significant CD4 count increase often occurs among patients treated with effective ART, the absence of such an increase should not be interpreted as treatment failure if the viral load declines appropriately. ART regimens are generally not changed in patients with undetectable viral loads who experience immunologic failure, although patients should remain on appropriate prophylaxis for opportunistic infections based on CD4 count. One study of a cohort of more than 62,000 individuals in New York City over 1.9 years of observation reported that in those who entered the cohort with a CD4 count ≥350 cells/mm³, there was a >90% likelihood of sustaining a CD4 count >200 cells/mm³ during that period of time Myers, et al. 2016. Reassuringly, other data suggest that in patients with sustained viral suppression and CD4 counts between 100 and 200 cells/mm³, the risk of pneumocystis pneumonia is very low even in the absence of prophylaxis Chaiwarith, et al. 2013; Mocroft, et al. 2010; D'Egidio, et al. 2007.

Lack of correlation between viral load and CD4 count response is particularly common among patients aged ≥50 years Sabin, et al. 2008; Gras, et al. 2007 and patients with low initial CD4 counts (<100 cells/mm³) Kelley, et al. 2009; Moore and Keruly 2007; Garcia, et al. 2004.

Absolute CD4 counts are calculated values that may fluctuate widely. The calculation is made by multiplying the total white blood cell count (in thousands) by the percentage of total lymphocytes and then by the percentage of CD4 lymphocytes. Therefore, any change in one of these three parameters will cause the absolute CD4 count to vary. CD4 percentage is a direct measurement and more reliable than the calculated absolute CD4 value, especially over time. A stable CD4 percentage, even when fluctuations occur in the absolute CD4 count, can reassure both the patient and the clinician that immunologic stability is present.

Some factors that can cause these fluctuations include sex, age, race, drugs (zidovudine, cephalosporins, cancer chemotherapy, nicotine, interferon, and corticosteroids), anti-lymphocyte antibodies, and splenectomy. Differences in reagents and equipment both within a laboratory and between laboratories may further contribute to variations in CD4 counts. There is also interlaboratory variation of normal range.

Date of current publication: August 8, 2023
Lead authors: Jessica Rodrigues, MS; Jessica M. Atrio, MD, MSc; and Johanna L. Gribble, MA
Writing group: Steven M. Fine, MD, PhD; Rona M. Vail, MD; Samuel T. Merrick, MD; Asa E. Radix, MD, MPH, PhD; Christopher J. Hoffmann, MD, MPH; Charles J. Gonzalez, MD
Committee: Medical Care Criteria Committee
Date of original publication: August 8, 2023

Throughout its guidelines, the New York State Department of Health (NYSDOH) AIDS Institute (AI) Clinical Guidelines Program recommends “shared decision-making,” an individualized process central to patient-centered care. With shared decision-making, clinicians and patients engage in meaningful dialogue to arrive at an informed, collaborative decision about a patient’s health, care, and treatment planning. The approach to shared decision-making described here applies to recommendations included in all program guidelines. The included elements are drawn from a comprehensive review of multiple sources and similar  attempts to define shared decision-making, including the Institute of Medicine’s original description [Institute of Medicine 2001]. For more information, a variety of informative resources and suggested readings are included at the end of the discussion.

The benefits to patients that have been associated with a shared decision-making approach include:

• Decreased anxiety [Niburski, et al. 2020; Stalnikowicz and Brezis 2020]
• Increased trust in clinicians [Acree, et al. 2020; Groot, et al. 2020; Stalnikowicz and Brezis 2020]
• Improved engagement in preventive care [McNulty, et al. 2022; Scalia, et al. 2022; Bertakis and Azari 2011]
• Improved treatment adherence, clinical outcomes, and satisfaction with care [Crawford, et al. 2021; Bertakis and Azari 2011; Robinson, et al. 2008]
• Increased knowledge, confidence, empowerment, and self-efficacy [Chen, et al. 2021; Coronado-Vázquez, et al. 2020; Niburski, et al. 2020]

Collaborative care: Shared decision-making is an approach to healthcare delivery that respects a patient’s autonomy in responding to a clinician’s recommendations and facilitates dynamic, personalized, and collaborative care. Through this process, a clinician engages a patient in an open and respectful dialogue to elicit the patient’s knowledge, experience, healthcare goals, daily routine, lifestyle, support system, cultural and personal identity, and attitudes toward behavior, treatment, and risk. With this information and the clinician’s clinical expertise, the patient and clinician can collaborate to identify, evaluate, and choose from among available healthcare options [Coulter and Collins 2011]. This process emphasizes the importance of a patient’s values, preferences, needs, social context, and lived experience in evaluating the known benefits, risks, and limitations of a clinician’s recommendations for screening, prevention, treatment, and follow-up. As a result, shared decision-making also respects a patient’s autonomy, agency, and capacity in defining and managing their healthcare goals. Building a clinician-patient relationship rooted in shared decision-making can help clinicians engage in productive discussions with patients whose decisions may not align with optimal health outcomes. Fostering open and honest dialogue to understand a patient’s motivations while suspending judgment to reduce harm and explore alternatives is particularly vital when a patient chooses to engage in practices that may exacerbate or complicate health conditions [Halperin, et al. 2007].

Options: Implicit in the shared decision-making process is the recognition that the “right” healthcare decisions are those made by informed patients and clinicians working toward patient-centered and defined healthcare goals. When multiple options are available, shared decision-making encourages thoughtful discussion of the potential benefits and potential harms of all options, which may include doing nothing or waiting. This approach also acknowledges that efficacy may not be the most important factor in a patient’s preferences and choices [Sewell, et al. 2021].

Clinician awareness: The collaborative process of shared decision-making is enhanced by a clinician’s ability to demonstrate empathic interest in the patient, avoid stigmatizing language, employ cultural humility, recognize systemic barriers to equitable outcomes, and practice strategies of self-awareness and mitigation against implicit personal biases [Parish, et al. 2019].

Caveats: It is important for clinicians to recognize and be sensitive to the inherent power and influence they maintain throughout their interactions with patients. A clinician’s identity and community affiliations may influence their ability to navigate the shared decision-making process and develop a therapeutic alliance with the patient and may affect the treatment plan [KFF 2023; Greenwood, et al. 2020]. Furthermore, institutional policy and regional legislation, such as requirements for parental consent for gender-affirming care for transgender people or insurance coverage for sexual health care, may infringe upon a patient’s ability to access preventive- or treatment-related care [Sewell, et al. 2021].

Health equity: Adapting a shared decision-making approach that supports diverse populations is necessary to achieve more equitable and inclusive health outcomes [Castaneda-Guarderas, et al. 2016]. For instance, clinicians may need to incorporate cultural- and community-specific considerations into discussions with women, gender-diverse individuals, and young people concerning their sexual behaviors, fertility intentions, and pregnancy or lactation status. Shared decision-making offers an opportunity to build trust among marginalized and disenfranchised communities by validating their symptoms, values, and lived experience. Furthermore, it can allow for improved consistency in patient screening and assessment of prevention options and treatment plans, which can reduce the influence of social constructs and implicit bias [Castaneda-Guarderas, et al. 2016].

Clinician bias has been associated with health disparities and can have profoundly negative effects [FitzGerald and Hurst 2017; Hall, et al. 2015]. It is often challenging for clinicians to recognize and set aside personal biases and to address biases with peers and colleagues. Consciously or unconsciously, negative or stigmatizing assumptions are often made about patient characteristics, such as race, ethnicity, gender, sexual orientation, mental health, and substance use [Avery, et al. 2019; van Boekel, et al. 2013; Livingston, et al. 2012]. With its emphasis on eliciting patient information, a shared decision-making approach encourages clinicians to inquire about patients’ lived experiences rather than making assumptions and to recognize the influence of that experience in healthcare decision-making.

Stigma: Stigma may prevent individuals from seeking or receiving treatment and harm reduction services [Tsai, et al. 2019]. Among people with HIV, stigma and medical mistrust remain significant barriers to healthcare utilization, HIV diagnosis, and medication adherence and can affect disease outcomes [Turan, et al. 2017; Chambers, et al. 2015], and stigma among clinicians against people who use substances has been well-documented [Stone, et al. 2021; Tsai, et al. 2019; van Boekel, et al. 2013]. Sexual and reproductive health, including strategies to prevent HIV transmission, acquisition, and progression, may be subject to stigma, bias, social influence, and violence.

In addition to the references cited below, the following resources and suggested reading may be useful to clinicians.

References

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Avery JD, Taylor KE, Kast KA, et al. Attitudes toward individuals with mental illness and substance use disorders among resident physicians. Prim Care Companion CNS Disord 2019;21(1):18m02382. [PMID: 30620451]

Bertakis KD, Azari R. Patient-centered care is associated with decreased health care utilization. J Am Board Fam Med 2011;24(3):229-39. [PMID: 21551394]

Castaneda-Guarderas A, Glassberg J, Grudzen CR, et al. Shared decision making with vulnerable populations in the emergency department. Acad Emerg Med 2016;23(12):1410-16. [PMID: 27860022]

Chambers LA, Rueda S, Baker DN, et al. Stigma, HIV and health: a qualitative synthesis. BMC Public Health 2015;15:848. [PMID: 26334626]

Chen CH, Kang YN, Chiu PY, et al. Effectiveness of shared decision-making intervention in patients with lumbar degenerative diseases: a randomized controlled trial. Patient Educ Couns 2021;104(10):2498-2504. [PMID: 33741234]

Coronado-Vázquez V, Canet-Fajas C, Delgado-Marroquín MT, et al. Interventions to facilitate shared decision-making using decision aids with patients in primary health care: a systematic review. Medicine (Baltimore) 2020;99(32):e21389. [PMID: 32769870]

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Robinson JH, Callister LC, Berry JA, et al. Patient-centered care and adherence: definitions and applications to improve outcomes. J Am Acad Nurse Pract 2008;20(12):600-607. [PMID: 19120591]

Scalia P, Durand MA, Elwyn G. Shared decision-making interventions: an overview and a meta-analysis of their impact on vaccine uptake. J Intern Med 2022;291(4):408-25. [PMID: 34700363]

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