Population Health Model Predicting the Long-Term Impact of Sotatercept on Morbidity and Mortality in Patients with Pulmonary Arterial Hypertension (PAH)

INTRODUCTION

Pulmonary arterial hypertension (PAH) is a rare, progressive disease associated with significant morbidity and mortality. The phase 3 STELLAR trial tested sotatercept plus background therapy (BGT) versus placebo plus BGT. BGT was comprised of mono-, double-, or triple-PAH targeted therapy. Building on STELLAR findings, we employed a population health model to assess the potential long-term clinical impact of sotatercept.

METHODS

Based on the well-established ESC/ERS 4-strata risk assessment approach, we developed a six-state Markov-type model (low risk, intermediate-low risk, intermediate-high risk, high risk, lung/heart-lung transplant, and death) to compare the clinical outcomes of sotatercept plus BGT versus BGT alone over a lifetime horizon. State-transition probabilities were obtained from STELLAR. Risk stratum-adjusted mortality and lung/heart-lung transplant probabilities were based on COMPERA PAH registry data, and the post-transplant mortality probability was obtained from existing literature. Model outcomes were discounted at 3% annually. Sensitivity analyses were conducted to examine model robustness.

RESULTS

In the base case, sotatercept plus BGT was associated with longer life expectancy from model baseline (16.5 vs 5.1 years) versus BGT alone, leading to 11.5 years gained per patient. Compared with BGT alone, sotatercept plus BGT was further associated with a gain in infused prostacyclin-free life years per patient, along with 683 PAH hospitalizations and 4 lung/heart-lung transplant avoided per 1000 patients.

CONCLUSIONS

According to this model, adding sotatercept to BGT increased life expectancy by roughly threefold among patients with PAH while reducing utilization of infused prostacyclin, PAH hospitalizations, and lung/heart-lung transplants. Real-world data are needed to confirm these findings.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT04576988 (STELLAR).

Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma

BACKGROUND

Combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CCA) is defined as a single nodule showing differentiation into HCC and intrahepatic cholangiocarcinoma and has a poor prognosis.

AIM

To develop a radiomics nomogram for predicting post-resection survival of patients with cHCC-CCA.

METHODS

Patients with pathologically diagnosed cHCC-CCA were randomly divided into training and validation sets. Radiomics features were extracted from portal venous phase computed tomography (CT) images using the least absolute shrinkage and selection operator Cox regression and random forest analysis. A nomogram integrating the radiomics score and clinical factors was developed using univariate analysis and multivariate Cox regression. Nomogram performance was assessed in terms of the C-index as well as calibration, decision, and survival curves.

RESULTS

CT and clinical data of 118 patients were included in the study. The radiomics score, vascular invasion, anatomical resection, total bilirubin level, and satellite lesions were found to be independent predictors of overall survival (OS) and were therefore included in an integrative nomogram. The nomogram was more strongly associated with OS (hazard ratio: 8.155, 95% confidence interval: 4.498-14.785, P < 0.001) than a model based on the radiomics score or only clinical factors. The area under the curve values for 1-year and 3-year OS in the training set were 0.878 and 0.875, respectively. Patients stratified as being at high risk of poor prognosis showed a significantly shorter median OS than those stratified as being at low risk (6.1 vs 81.6 mo, P < 0.001).

CONCLUSION

This nomogram may predict survival of cHCC-CCA patients after hepatectomy and therefore help identify those more likely to benefit from surgery.