Publications

Pulmonary arterial hypertension (PAH) carries high morbidity and mortality despite available treatment options. In severe PAH, right ventricular (RV) diastolic pressure overload leads to interventricular septal bowing, hindering of left ventricular diastolic filling and reduced cardiac output (CO). Some animal studies suggest that pacing may mitigate this effect. We hypothesized that eliminating late diastole via ventricular pacing could improve CO in human subjects with severe PAH. Using minimal to no sedation, we performed transvenous acute biventricular (BiV) pacing and right heart catheterization in six patients with symptomatic PAH. Hemodynamic measurements were taken at baseline and during BiV pacing at various 20-ms intervals of V-V timing. We compared baseline CO to (1) CO while pacing the RV first by 80 ms (mimicking RV-only pacing), and then to (2) CO during pacing at the V-V timing that resulted in the highest CO. All participants were female, PASP 74 ± 14 mmHg, QRS duration 104 ± 20 ms. Compared with baseline, the CO decreased when the RV was paced first by 80 ms (7.2 ± 1.0 vs. 6.2 ± 1.1 L/min, p = 0.028). Pacing with optimal V-V timing produced CO similar to baseline (7.2 ± 1.0 vs. 7.4 ± 1.4, p = 0.92). Two patients (33%) met the predefined endpoint of a 15% increase in CO during pacing at the optimal V-V timing. In symptomatic PAH, V-V optimized acute BiV pacing does not consistently improve CO. However, acute BiV pacing did improve CO in a subset of this cohort. Further research is needed to identify predictors of response to cardiac resynchronization therapy in this population.

BACKGROUND

Individualized selection of antiretroviral (ARV) therapy is complex, considering drug resistance, comorbidities, drug-drug interactions, and other factors. HIV-ASSIST (www.hivassist.com) is a free, online tool that provides ARV decision support. HIV-ASSIST synthesizes patient and virus-specific attributes to rank ARV combinations based upon a composite objective of achieving viral suppression and maximizing tolerability.

OBJECTIVE

To evaluate concordance of HIV-ASSIST recommendations with ARV selections of experienced HIV clinicians.

DESIGN

Retrospective cohort study.

PATIENTS

New and established patients at the Johns Hopkins Bartlett HIV Clinic and San Francisco Veterans Affairs HIV Clinic completing clinic visits were included. Chart reviews were conducted of the most recent clinic visit to generate HIV-ASSIST recommendations, which were compared to prescribed regimens.

MAIN MEASURES

For each provider-prescribed regimen, we assessed its corresponding HIV-ASSIST "weighted score" (scale of 0 to 10 +, scores of < 2.0 are preferred), rank within HIV-ASSIST's ordered listing of ARV regimens, and concordance with the top five HIV-ASSIST ranked outputs.

KEY RESULTS

Among 106 patients (16% female), 23 (22%) were ARV-naïve. HIV-ASSIST outputs for ARV-naïve patients were 100% concordant with prescribed regimens (median rank 1 [IQR 1-3], median weighted score 1.1 [IQR 1-1.2]). For 18 (17%) ARV-experienced patients with ongoing viremia, HIV-ASSIST outputs were 89% concordant with prescribed regimens (median rank 2 [IQR 1-3], median weighted score 1 [IQR 1-1.2]). For 65 (61.3%) patients that were suppressed on a current ARV regimen, HIV-ASSIST recommendations were concordant 88% of the time (median rank 1 [IQR 1-1], median weighted score 1.1 [IQR 1-1.6]). In 18% of cases, HIV-ASSIST weighted score suggested that the prescribed regimen would be considered "less preferred" (score > 2.0) than other available alternatives.

CONCLUSION

HIV-ASSIST is an educational decision support tool that provides ARV recommendations concordant with experienced HIV providers from two major academic centers for a diverse set of patient scenarios.

Proteins are effector molecules that mediate the functions of genes and modulate comorbidities, behaviors and drug treatments. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.