title: Influence of predictive modeling in implementing optimal heart failure therapy.
abstract: BACKGROUND
  A gap remains between evidence-based guidelines in the treatment of heart failure (HF) and current pharmacologic and device therapy. The Seattle Heart Failure Model (SHFM) is an accurate predictive tool that allows the clinician to quantitatively assess the influence of pharmacologic and device therapy on HF. The authors hypothesized that graphically demonstrating the improvement in survival using such a tool may well modify physician practice behavior.
  
  
  METHODS
  The authors examined 50 randomly selected patients from 10 primary care physicians having HF with a left ventricular ejection fraction <40%. Twenty-one data elements were entered into the SHFM to create a survival estimate before and after implementation of interventions known to be beneficial, both pharmacologic (addition of angiotensin-converting enzyme/angiotensin receptor blocker, statin, β-blocker and aldosterone blocker) and device based (consideration for automatic implantable cardioverter-defibrillator, biventricular pacer and biventricular implantable cardioverter-defibrillator). The influence of therapeutic change was presented in a focused clinical session with the primary care physician.
  
  
  RESULTS
  The mean age of the population examined was 73 ± 10 years with New York Heart Association class 2.2 ± 0.5 symptoms. In the 50 patients examined, the authors altered device or medical therapy in 82%. This included advancement of medical therapy in 50%, consideration for device referral in 10% or both (medical therapy and device referral) in 22%. This augmentation of therapy resulted in an increase in estimated mean life expectancy from 8.8 to 10.9 years (P < 0.001).
  
  
  CONCLUSIONS
  Use of the SHFM significantly impacted intensification of HF therapy in this ambulatory HF population.
authors.affiliation: Department of Internal Medicine, Guthrie Clinic, Ltd, Sayre, Pennsylvania 18840, USA.
authors.forename: Hari
authors.initials: H
authors.lastname: Prasad
authors.forename: Jaspinder
authors.initials: J
authors.lastname: Sra
authors.forename: Wayne C
authors.initials: WC
authors.lastname: Levy
authors.forename: Dwight D
authors.initials: DD
authors.lastname: Stapleton
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care / Q000379:methods*; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000627:therapeutic use; D058409:Cardiac Resynchronization Therapy Devices; D002317:Cardiovascular Agents / Q000627:therapeutic use*; D017147:Defibrillators, Implantable; D005260:Female; D006333:Heart Failure / Q000188:drug therapy / Q000401:mortality* / Q000503:physiopathology / Q000628:therapy*; D006353:Heart-Assist Devices; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D000451:Mineralocorticoid Receptor Antagonists / Q000627:therapeutic use; D015233:Models, Statistical; D010818:Practice Patterns, Physicians' / Q000592:standards* / Q000639:trends; D011237:Predictive Value of Tests; D011320:Primary Health Care; D012017:Referral and Consultation; D012720:Severity of Illness Index; D013318:Stroke Volume; D016019:Survival Analysis; D013599:Systole
chemical_list: D000319:Adrenergic beta-Antagonists; D000806:Angiotensin-Converting Enzyme Inhibitors; D002317:Cardiovascular Agents; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D000451:Mineralocorticoid Receptor Antagonists