BACKGROUND: Existing models for outcome after percutaneous coronary interventions (PCIs) lack assessment of long-term prognosis. Our goal was to derive 1- and 5-year mortality and mortality/myocardial infarction (MI) risk models for PCI outcomes from simple, easily obtainable clinical and laboratory variables. METHODS AND RESULTS: Using the Mayo Clinic registry, we analyzed long-term mortality and mortality/MI following PCIs on 9165 unique patients from January 1, 2001, through December 31, 2007. Cox proportional hazards regression was used to model the calculated risk score and major procedural complications. A total of 1243 patients died, and 696 had MI. Separate risk models derived from clinical, procedural, and laboratory characteristics were made for mortality and mortality/MI. Older age, comorbid conditions, low ejection fraction, acute MI, history of smoking, heart failure, hyperlipidemia, 3-vessel disease, procedural failure, ventricular arrhythmia during PCI, and low medication score were predictors of long-term mortality and mortality/MI. Simple integer scores stratified patients into low, moderate, high, and very high risk for subsequent events. Models had adequate goodness of fit, and areas under the receiver operating characteristic curve were 0.786 and 0.728 for mortality and mortality/MI, respectively, indicating good overall discrimination. Bootstrap analysis indicated that the model was not overfit to the available data set. CONCLUSIONS: Easily obtainable variables can be combined into a convenient risk scoring system at the time of patient dismissal following PCI to accurately predict long-term mortality and mortality/MI. This model may be useful for providing patients with individualized, evidence-based estimates of long-term risk.
BACKGROUND: Existing models for outcome after percutaneous coronary interventions (PCIs) lack assessment of long-term prognosis. Our goal was to derive 1- and 5-year mortality and mortality/myocardial infarction (MI) risk models for PCI outcomes from simple, easily obtainable clinical and laboratory variables. METHODS AND RESULTS: Using the Mayo Clinic registry, we analyzed long-term mortality and mortality/MI following PCIs on 9165 unique patients from January 1, 2001, through December 31, 2007. Cox proportional hazards regression was used to model the calculated risk score and major procedural complications. A total of 1243 patients died, and 696 had MI. Separate risk models derived from clinical, procedural, and laboratory characteristics were made for mortality and mortality/MI. Older age, comorbid conditions, low ejection fraction, acute MI, history of smoking, heart failure, hyperlipidemia, 3-vessel disease, procedural failure, ventricular arrhythmia during PCI, and low medication score were predictors of long-term mortality and mortality/MI. Simple integer scores stratified patients into low, moderate, high, and very high risk for subsequent events. Models had adequate goodness of fit, and areas under the receiver operating characteristic curve were 0.786 and 0.728 for mortality and mortality/MI, respectively, indicating good overall discrimination. Bootstrap analysis indicated that the model was not overfit to the available data set. CONCLUSIONS: Easily obtainable variables can be combined into a convenient risk scoring system at the time of patient dismissal following PCI to accurately predict long-term mortality and mortality/MI. This model may be useful for providing patients with individualized, evidence-based estimates of long-term risk.
Authors: Lloyd W Klein; Kishore J Harjai; Fred Resnic; William S Weintraub; H Vernon Anderson; Robert W Yeh; Dmitriy N Feldman; Osvaldo S Gigliotti; Kenneth Rosenfeld; Peter Duffy Journal: Catheter Cardiovasc Interv Date: 2016-11-10 Impact factor: 2.692
Authors: Marta Wleklik; Quin Denfeld; Magdalena Lisiak; Michał Czapla; Marta Kałużna-Oleksy; Izabella Uchmanowicz Journal: Int J Environ Res Public Health Date: 2022-02-16 Impact factor: 3.390