BACKGROUND: Appropriate screening strategies are needed to cost effectively identify patients with undiagnosed and untreated left ventricular systolic dysfunction (LVSD). AIM: To investigate the cost-benefit of screening high-risk patients in primary-care for LVSD (EF<40%) using various screening strategies. METHODS: Patients considered at high-risk of developing LVSD were recruited from three primary-care practices. Patients with known LVSD were excluded. Echocardiography, electrocardiography and blood tests were performed blinded to an NT-proBNP result. Logistic regression (LR) and receiver-operating characteristic analysis were used to assess the univariate and multivariable utility of NT-proBNP, QRS duration, symptoms and evidence of myocardial infarction (MI) to detect LVSD. RESULTS: 427 patients were assessed. 7.5% had undiagnosed LVSD. NT-proBNP, QRS, symptoms and MI were independent predictors of LVSD (p<0.014) and the resultant LR-model had an area-under-the-curve of 0.89 (0.84-0.94) and specificity of 54% (51-79%) at a sensitivity of 100%. The LR-model avoided 24.1% (18.1- 48.3%) of the cost and 50.1% (44.1-74.3%) of the echocardiograms compared to screening using echocardiography alone. CONCLUSIONS: Screening high-risk groups in primary-care increases the pick-up rate for undiagnosed LVSD and using an LR-model combining NT-proBNP, QRS, symptoms and evidence of MI has significant cost benefits.
BACKGROUND: Appropriate screening strategies are needed to cost effectively identify patients with undiagnosed and untreated left ventricular systolic dysfunction (LVSD). AIM: To investigate the cost-benefit of screening high-risk patients in primary-care for LVSD (EF<40%) using various screening strategies. METHODS:Patients considered at high-risk of developing LVSD were recruited from three primary-care practices. Patients with known LVSD were excluded. Echocardiography, electrocardiography and blood tests were performed blinded to an NT-proBNP result. Logistic regression (LR) and receiver-operating characteristic analysis were used to assess the univariate and multivariable utility of NT-proBNP, QRS duration, symptoms and evidence of myocardial infarction (MI) to detect LVSD. RESULTS: 427 patients were assessed. 7.5% had undiagnosed LVSD. NT-proBNP, QRS, symptoms and MI were independent predictors of LVSD (p<0.014) and the resultant LR-model had an area-under-the-curve of 0.89 (0.84-0.94) and specificity of 54% (51-79%) at a sensitivity of 100%. The LR-model avoided 24.1% (18.1- 48.3%) of the cost and 50.1% (44.1-74.3%) of the echocardiograms compared to screening using echocardiography alone. CONCLUSIONS: Screening high-risk groups in primary-care increases the pick-up rate for undiagnosed LVSD and using an LR-model combining NT-proBNP, QRS, symptoms and evidence of MI has significant cost benefits.
Authors: Ronald A Booth; Stephen A Hill; Andrew Don-Wauchope; P Lina Santaguida; Mark Oremus; Robert McKelvie; Cynthia Balion; Judy A Brown; Usman Ali; Amy Bustamam; Nazmul Sohel; Parminder Raina Journal: Heart Fail Rev Date: 2014-08 Impact factor: 4.214