title: Validation of an echo-Doppler decision model to predict left ventricular filling pressure in patients with heart failure independently of ejection fraction.
abstract: OBJECTIVE
  To test a decision model for non-invasive estimation of left ventricular filling pressure (LVFP) in patients with left ventricular (LV) dysfunction and a wide range of ejection fractions (EF).
  
  
  RESULTS
  In patients with LV dysfunction (n = 270; EF = 42 +/- 16%), classification and regression tree (CART) analysis was used to generate a model for the prediction of elevated LVFP, defined as pulmonary capillary wedge pressure (PCWP) >15 mmHg, in a derivation cohort (n = 178). At each step of the decision tree, nodes including single or multiple criteria connected by Boolean operators were tested to achieve the best information entropy gain. Averaged mitral-to-myocardial early velocities ratio (E/e') > or =13 OR E-wave deceleration time <150 ms was closely associated with elevated LVFP. Alternatively, prediction of PCWP >15 mmHg needed the following criteria to be satisfied: (i) intermediate E/e' (13 > E/e' > 8); (ii) left atrial volume index >40 mL/m(2) OR ratio of mitral E-wave and colour M-mode propagation velocity >2 OR difference in duration of pulmonary vein and mitral flow at atrial contraction >30 ms; (iii) estimated pulmonary artery systolic pressure >35 mmHg. Patients were correctly allocated according to PCWP with an 87% sensitivity and a 90% specificity. Compared with the best single parameter estimating LVFP, a 17% relative increase in accuracy was achieved in patients with EF >50%. The model was prospectively validated in a testing group (n = 92): 80% sensitivity, 78% specificity.
  
  
  CONCLUSIONS
  This sequential testing is useful to non-invasively predict LVFP in patients with LV dysfunction, especially in those with preserved EF.
authors.affiliation: Cardiovascular Diseases Unit 1, Cardiac, Thoracic and Vascular Department, University of Pisa, Via Paradisa, 2, Pisa 56124, Italy. f.dini@ao-pisa.toscana.it
authors.forename: Frank Lloyd
authors.initials: FL
authors.lastname: Dini
authors.forename: Piercarlo
authors.initials: P
authors.lastname: Ballo
authors.forename: Luigi
authors.initials: L
authors.lastname: Badano
authors.forename: Paolo
authors.initials: P
authors.lastname: Barbier
authors.forename: Piersilvio
authors.initials: P
authors.lastname: Chella
authors.forename: Umberto
authors.initials: U
authors.lastname: Conti
authors.forename: Salvatore Mario
authors.initials: SM
authors.lastname: De Tommasi
authors.forename: Maurizio
authors.initials: M
authors.lastname: Galderisi
authors.forename: Stefano
authors.initials: S
authors.lastname: Ghio
authors.forename: Enrico
authors.initials: E
authors.lastname: Magagnini
authors.forename: Andrea
authors.initials: A
authors.lastname: Pieroni
authors.forename: Andrea
authors.initials: A
authors.lastname: Rossi
authors.forename: Cesare
authors.initials: C
authors.lastname: Rusconi
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Temporelli
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003661:Decision Support Techniques; D003663:Decision Trees; D015150:Echocardiography, Doppler; D005260:Female; D006333:Heart Failure / Q000000981:diagnostic imaging / Q000473:pathology*; D006352:Heart Ventricles / Q000000981:diagnostic imaging / Q000473:pathology*; D006439:Hemodynamics; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D013997:Time Factors; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging* / Q000473:pathology; D016277:Ventricular Function, Left