Intermediate-high-risk pulmonary embolism (PE) is characterized by right ventricular (RV) dysfunction and elevated circulating cardiac troponin levels despite apparent hemodynamic stability at... Show moreIntermediate-high-risk pulmonary embolism (PE) is characterized by right ventricular (RV) dysfunction and elevated circulating cardiac troponin levels despite apparent hemodynamic stability at presentation. In these patients, full-dose systemic thrombolysis reduced the risk of hemodynamic decompensation or death but increased the risk of life-threatening bleeding. Reduced-dose thrombolysis may be capable of improving safety while maintaining reperfusion efficacy. The Pulmonary Embolism International THrOmbolysis (PEITHO)-3 study (ClinicalTrials.gov Identifier: NCT04430569) is a randomized, placebo-controlled, double-blind, multicenter, multinational trial with long-term follow-up. We will compare the efficacy and safety of a reduced-dose alteplase regimen with standard heparin anticoagulation. Patients with intermediate-high-risk PE will also fulfill at least one clinical criterion of severity: systolic blood pressure <= 110mm Hg, respiratory rate >20 breaths/min, or history of heart failure. The primary efficacy outcome is the composite of all-cause death, hemodynamic decompensation, or PE recurrence within 30 days of randomization. Key secondary outcomes, to be included in hierarchical analysis, are fatal or GUSTO severe or life-threatening bleeding; net clinical benefit (primary efficacy outcome plus severe or life-threatening bleeding); and all-cause death, all within 30 days. All outcomes will be adjudicated by an independent committee. Further outcomes include PE-related death, hemodynamic decompensation, or stroke within 30 days; dyspnea, functional limitation, or RV dysfunction at 6 months and 2 years; and utilization of health care resources within 30 days and 2 years. The study is planned to enroll 650 patients. The results are expected to have a major impact on risk-adjusted treatment of acute PE and inform guideline recommendations. Show less
It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of... Show moreIt is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy. Show less
