Phlebotomine Sand Flies (Diptera: Psychodidae) of the Province of Chaco, Argentina

- The phlebotomine sandfl ies of the province of Chaco, Argentina, are poorly known, with reports from more than 40 years or captures related with outbreaks of leishmaniasis. In here, Mycropygomyia peresi (Mangabeira) is reported for the fi rst time in Argentina, extending the known dstribution of Migonemyia migonei (Fran??a), Evandromyia sallesi (Galv??o & Coutinho), Mycropygomyia quinquefer (Dyar), Brumptomyia brumpti (Larousse) y Nemapalpus spp to the province of Chaco. Mg. migonei, together with Nyssomyia neivai (Pinto), Evandromyia cortelezzii (Br??thes), and Psathyromyia shannoni (Dyar) also captured in Chaco, were incriminated as vectors of Leishmania in Argentina

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis UsingInvasive Angiography as Reference Standard

Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. To compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence-quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography, and to assess downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. This study entailed a retrospective post-hoc analysis of the derivation cohort of the prospective 23-center CREDENENCE trial. The study included 301 patients [mean age 64.4±10.2 years; 88 female, 213 male] recruited from 2014 to 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥50%) stenosis in 54%, including severe (≥70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified non-obstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p\u3c.001) than MPI for predicting ≥50% stenosis by QCA (0.88 vs 0.66), ≥70% stenosis by QCA (0.92 vs 0.81), and FFR \u3c0.80 (0.90 vs 0.71). AI-QCT ≥50% and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AI-QCT in all patients and those showing ≥70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing. ClinicalTrials.gov NCT02173275

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs