Prediction of serious complications in patients with pulmonary thromboembolism and solid cancer: Validation of the EPIPHANY Index in a prospective cohort of patients from the PERSEO study

INTRODUCTION: There is currently no validated score capable of classifying cancer-associated pulmonary embolism (PE) in its full spectrum of severity. This study has validated the EPIPHANY Index, a new tool to predict serious complications in cancer patients with suspected or unsuspected PE. METHOD: The PERSEO Study prospectively recruited individuals with PE and active cancer or receiving antineoplastic therapy from 22 Spanish hospitals. The estimation of the relative frequency θ of complications based on the EPIPHANY Index categories was made using the Bayesian alternative for the binomial test. RESULTS: A total of 900 patients, who were diagnosed with PE between October 2017 and January 2020, were enrolled. The rate of serious complications at 15 days was 11.8%, 95% highest density interval [HDI], 9.8–14.1%. Of the EPIPHANY low-risk patients, 2.4% (95% HDI, 0.8–4.6%) had serious complications, as did 5.5% (95% HDI, 2.9–8.7%) of the moderate-risk participants and 21.0% (95% HDI, 17.0–24.0%) of those with high-risk episodes. The EPIPHANY Index was associated with overall survival (OS) in patients with different risk levels: median OS was 16.5, 14.4, and 4.4 months for those at low, intermediate, and high risk, respectively. Both the EPIPHANY Index and the Hestia criteria exhibited greater negative predictive value and a lower negative likelihood ratio than the remaining models. The incidence of bleeding at 6 months was 6.2% (95% HDI, 2.9–9.5%) in low/moderate-risk vs 12.7% (95% HDI, 10.1–15.4%) in high-risk (p-value = 0.037) episodes. Of the outpatients, serious complications at 15 days were recorded in 2.1% (95% HDI, 0.7–4.0%) of the cases with EPIPHANY low/intermediate-risk vs 5.3% (95% HDI, 1.7–11.8%) in high-risk cases. CONCLUSION: We have validated the EPIPHANY Index in patients with incidental or symptomatic cancer-related PE. This model can contribute to standardize decision-making in a scenario lacking quality evidence.