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

IntroductionThere is currently no validated score capable of classifying cancer-associated pulmonary embolism (PE) in its full spectrum of severity. This study has validated the EPIHANY Index, a new tool to predict serious complications in cancer patients with suspected or unsuspected PE. MethodThe PERSEO Study prospectively recruited individuals with PE and cancer from 22 Spanish hospitals. The estimation of the relative frequency {theta} of complications based on the EPIPHANY Index categories was made using the Bayesian alternative for the binomial test. ResultsNine hundred patients diagnosed with PE between 2017/2020 were recruited. 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 correlated with overall survival. 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, 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. ConclusionWe 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. SummaryWe have validated the EPIPHANY Index in patients with acute, incidental, or symptomatic cancer-related PE. This predictive model of complications can contribute to standardize decision-making in a scenario lacking quality evidence.