Determinants of HIV outpatient service utilization according to HIV parameters

Introduction: The increased life expectancy of HIV patients in the era of highly active antiretroviral therapy has had profound consequences for the healthcare systems that provide their care. It is useful to assess whether healthcare resources need to be adapted to the different stages of HIV infection or to patient characteristics [1]. To study how patient features influence utilization of out patient services, we retrospectively analyzed the electronic health record of HIV-positive patients who had followed day-care programs at the AIDS Center of the University of Palermo, Italy. Materials and Methods: 223 HIV-infected subjects were recruited and divided into two groups according to CD4 cell counts (117 with a CD4 count ≤500/mm3 and 106 with CD4 count ≥500/mm3). Data on age, gender, race, lifestyle habits (including educational level, drug abuse history, smoking status, alcohol consumption, sexual behaviour) BMI, HIV-RNA, CD4+ T-cell count, antiretroviral therapy (ART), comorbidities such as HCV co-infection, osteoporosis biomarker, dyslipidemia, diabetes, renal function and systolic and diastolic blood pressure were recorded in a purposely designed database and were analyzed in relation to AIN by uni- and multivariable logistic regression. Results: Table 1 shows the characteristics of enrolled patients; the average age of the recruited patients was 45.4±9.5 years. 163 individuals were male (73%), 26 were immigrants (12%) and 91 (40%) were treatment-naïve. Mean day care access for laboratory tests to evaluate stage of HIV and for treatment monitoring was 6.5 days for CD4 cell count measurements and 9.6 for HIV RNA/drug-resistance testing. When patients were stratified according to CD4 count, mean day care access for laboratory tests to evaluate HIV stage and to monitor treatment was negatively correlated with CD4 cell counts. Table 1 Selected characteristics of 223 HIV-infected patients Variable HIV with a CD4 count ≤500/mm3 n = 117 HIV with a CD4 count ≥500/mm3 n = 106 p Age years (mean and SD) .4 .8 ns Male/Female /33 /27 ns BMI (kg/m2) (mean and SD) .4 .6 ns Ethnicity (n) Caucasian /African /18 /8 ns High-risk sexual behaviors Heterosex/homosex/bisexual men /29/10 /22/20 ns CD4+ T-cell count (cells/∣ÌL) (mean) /// HIV-RNA copies/mL (mean) .728 .669 ns -Hydroxyvitamin D (ng/mL) .9 .1 ns Drug addiction (n) ns ARV therapy (<5 years) .014 ARV therapy (five-ten years) ns ARV therapy (>10 years) ns Mean day care access for laboratory tests CD4/Viral load .0/10.1 .2/9.4 .045/ns Conclusions: Only patients with CD4 counts ≤500/mm3 showed higher rates of healthcare utilization; these data may be useful for monitoring and revising implementation plans for the different phases of HIV disease

Alveolar&ndash;Arterial Gradient Is an Early Marker to Predict Severe Pneumonia in COVID-19 Patients

Background: One of the main challenges in the management of COVID-19 patients is to early assess and stratify them according to their risk of developing severe pneumonia. The alveolar&ndash;arterial oxygen gradient (D(A-a)O2) is defined as the difference between the alveolar and arteriolar concentration of oxygen, an accurate index of the ventilatory function. The aim of this study is to evaluate D(A-a)O2 as a marker for predicting severe pneumonia in COVID-19 patients, in comparison to the PaO2/FiO2. Methods: This retrospective, multicentric cohort study included COVID-19 patients admitted to two Italian hospitals between April and July 2020. Clinical and laboratory data were retrospectively collected at the time of hospital admission and during hospitalization. The presence of severe COVID-19 pneumonia was evaluated, as defined by the Infectious Diseases Society of America (IDSA) criteria for community-acquired pneumonia (CAP). Patients were divided in severe and non-severe groups. Results: Overall, 53 COVID-19 patients were included in the study: male were 30/53 (57%), and 10/53 (19%) had severe pneumonia. Patients with severe pneumonia reported dyspnea more often than non-severe patients (90% vs. 39.5%; p = 0.031). A history of chronic obstructive pulmonary disease (COPD) was recalled by 5/10 (50%) patients with severe pneumonia, and only in 6/43 (1.4%) of non-severe cases (p = 0.023). A ROC curve, for D(A-a)O2 &gt;60 mmHg in detecting severe pneumonia, showed an area under the curve (AUC) of 0.877 (95% CI: 0.675&ndash;1), while the AUC of PaO2/FiO2 &lt; 263 mmHg resulted 0.802 (95% CI: 0.544&ndash;1). D(A-a)O2 in comparison to PaO2/FiO2 had a higher sensibility (77.8% vs. 66.7%), positive predictive value (75% vs. 71.4%), negative predictive value (94% vs. 91%), and similar specificity (94.4% vs. 95.5%). Conclusions: Our study suggests that the D(A-a)O2 is more appropriate than PaO2/FiO2 to identify COVID-19 patients at risk of developing severe pneumonia early