Utilization of Absolute Monocyte Counts to Predict Cardiovascular Events in People Living With HIV

M Bogorodskaya; A Lyass; TF Mahoney; LH Borowsky; P Sen; FK Swirski; S Srinivasa; CT Longenecker; JM Massaro; RB D'Agostino Sr.; VA Triant

Disclosures

HIV Medicine. 2021;22(4):314-320. 

In This Article

Methods

Study Design/Data Source

We identified patients in the Partners HIV Cohort, a prospective observational clinical care cohort derived from the Partners HealthCare System Research Patient Data Registry (RPDR), a centralized clinical data registry containing comprehensive demographic and clinical information for patients seen at Brigham and Women's Hospital (BWH) since 1996 or Massachusetts General Hospital (MGH) since 1992. Data are derived from several sources, including hospital billing systems and a clinical data repository, and data on demographics, encounters, diagnoses and laboratories are available. The diagnosis of HIV was based on an individual having at least three encounters (inpatient or outpatient) with International Classification of Diseases (ICD)-9-CM coding of either 042 or ICD-10-CM coding of B20. ICD codes are international medical classifications for diseases, injuries, health encounters and inpatient procedures which are used for payment reimbursement and national surveillance research in the United States.

Eligibility Criteria

Patients with at least two encounters between 1 January 2000 and 31 December 2017 and at least one peripheral absolute monocyte value were included. The start of observation was the earliest date after 1 January 2000 that lipid laboratory measurements were available. Patients were followed until they developed a CVE, died or were censored on 31 December 2017. As peripheral monocytes have a short turnover of 1–7 days[17,18] and can fluctuate significantly with time of day, medication use, acute infections and changes in physiological stress,[18] patients with certain medical conditions and certain medications were excluded. Patients who were under 18 years of age at the start of observation or had any diagnosis of malignancy, autoimmune disorder (including systemic lupus erythematosus, rheumatoid arthritis, mixed connective tissue disease, systemic sclerosis, psoriatic arthritis, ankylosing spondylitis, Sjögren's syndrome, sarcoidosis, inflammatory bowel disease, or any type of vasculitis), had a solid organ or haematopoietic bone marrow transplant, or had a CVE (acute, fatal or non-fatal stroke or MI) prior to the start of observation were excluded. All monocyte values associated with white blood cell (WBC) values > 11.0 × 103/μL (the upper limit of normal at our institution's laboratory), monocyte values associated with inpatient hospitalization, and monocyte values ± 5 SD from the mean[19] were excluded. These exclusions were applied to all monocyte values included in the average of the baseline monocyte count. The exclusions were to avoid elevations in AMCs due to acute infections or acute episodes of physiological stress.

Predictor Variables

Measured total WBC count and AMC were obtained from RPDR laboratory data. Additional baseline variables obtained from the RPDR included data on traditional cardiac risk factors (hypertension, diabetes, dyslipidaemia), CD4 cell count, HIV VL, and use of ART. Smoking status was ascertained by the application of a validated natural language processing-based algorithm.[20] All variables, unless mentioned otherwise, were obtained at or prior to baseline monocyte count. For HIV VL, CD4 cell count, nadir CD4 cell count, total cholesterol and blood pressure, the value obtained closest to the baseline date was used. Time on ART was also calculated prior to baseline. Clinical diagnoses (hypertension, diabetes, dyslipidaemia, congestive heart failure, peripheral vascular disease and hepatitis C) were obtained using ICD-9-CM and ICD-10-CM codes. Use of medications was represented as current medication as of baseline. To account for the variability of AMC, baseline AMC was defined as the average of all outpatient monocyte counts a year before and after the baseline date.

Ascertainment of Outcomes

The primary outcome was major adverse CVEs, defined as fatal or non-fatal acute MI or acute ischaemic stroke. Acute MI and ischaemic stroke events were determined by ICD coding and included all patients with ICD-9-CM code 410 or ICD-10-CM code I21 (acute MI) and ICD-9-CM code 433–434 or ICD-10-CM code I63 (acute ischemic stroke), and all subtypes. In cases of recurrent CVE, only the first event after start of observation was counted in the analysis.

Statistical Analysis

Demographic and clinical characteristics were compared between those with and without incident CVE using two-sample t-test for continuous variables and χ 2 test for categorical variables. Univariate Cox proportional hazards model was used to assess the relationship between quartiles of AMC and incident CVEs. Then, a stepwise multivariate Cox proportional hazards model was run, using a set of demographic and clinical characteristics to predict incident CVE (age, sex, race, total cholesterol, high-density lipoprotein, diabetes, systolic blood pressure, hypertension treatment, smoking and statin medication use). Predictors from this model significant at 0.10 alpha level were then included as covariates in a stepwise Cox proportional hazards model assessing the relationship between quartiles of AMCs and CVEs, additionally adjusting for CD4 cell count and VL. Due to a change in the absolute monocyte laboratory test and reference range in 2008, analysis was also internally adjusted for the time period (before vs. after 2008). All analyses were performed using SAS 9.4 (Cary, NC).

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