OBJECTIVES: This study sought to develop a percutaneous coronary intervention (PCI) risk score for in-hospital/30-day mortality. BACKGROUND: Risk scores are simplified linear scores that provide clinicians with quick estimates of patients' short-term mortality rates for informed consent and to determine the appropriate intervention. Earlier PCI risk scores were based on in-hospital mortality. However, for PCI, a substantial percentage of patients die within 30 days of the procedure after discharge. METHODS: New York's Percutaneous Coronary Interventions Reporting System was used to develop an in-hospital/30-day logistic regression model for patients undergoing PCI in 2010, and this model was converted into a simple linear risk score that estimates mortality rates. The score was validated by applying it to 2009 New York PCI data. Subsequent analyses evaluated the ability of the score to predict complications and length of stay. RESULTS: A total of 54,223 patients were used to develop the risk score. There are 11 risk factors that make up the score, with risk factor scores ranging from 1 to 9, and the highest total score is 34. The score was validated based on patients undergoing PCI in the previous year, and accurately predicted mortality for all patients as well as patients who recently suffered a myocardial infarction (MI). CONCLUSIONS: The PCI risk score developed here enables clinicians to estimate in-hospital/30-day mortality very quickly and quite accurately. It accurately predicts mortality for patients undergoing PCI in the previous year and for MI patients, and is also moderately related to perioperative complications and length of stay.
OBJECTIVES: This study sought to develop a percutaneous coronary intervention (PCI) risk score for in-hospital/30-day mortality. BACKGROUND: Risk scores are simplified linear scores that provide clinicians with quick estimates of patients' short-term mortality rates for informed consent and to determine the appropriate intervention. Earlier PCI risk scores were based on in-hospital mortality. However, for PCI, a substantial percentage of patients die within 30 days of the procedure after discharge. METHODS: New York's Percutaneous Coronary Interventions Reporting System was used to develop an in-hospital/30-day logistic regression model for patients undergoing PCI in 2010, and this model was converted into a simple linear risk score that estimates mortality rates. The score was validated by applying it to 2009 New York PCI data. Subsequent analyses evaluated the ability of the score to predict complications and length of stay. RESULTS: A total of 54,223 patients were used to develop the risk score. There are 11 risk factors that make up the score, with risk factor scores ranging from 1 to 9, and the highest total score is 34. The score was validated based on patients undergoing PCI in the previous year, and accurately predicted mortality for all patients as well as patients who recently suffered a myocardial infarction (MI). CONCLUSIONS: The PCI risk score developed here enables clinicians to estimate in-hospital/30-day mortality very quickly and quite accurately. It accurately predicts mortality for patients undergoing PCI in the previous year and for MI patients, and is also moderately related to perioperative complications and length of stay.
Authors: Sameed Ahmed M Khatana; Paul N Fiorilli; Ashwin S Nathan; Daniel M Kolansky; Nandita Mitra; Peter W Groeneveld; Jay Giri Journal: Circ Cardiovasc Interv Date: 2018-09 Impact factor: 6.546
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Authors: Andriany Qanitha; Cuno S P M Uiterwaal; Jose P S Henriques; Abdul Hakim Alkatiri; Idar Mappangara; Ali Aspar Mappahya; Ilhamjaya Patellongi; Bastianus A J M de Mol Journal: BMJ Open Date: 2018-06-27 Impact factor: 2.692
Authors: Subhi J Al'Aref; Gurpreet Singh; Alexander R van Rosendael; Kranthi K Kolli; Xiaoyue Ma; Gabriel Maliakal; Mohit Pandey; Bejamin C Lee; Jing Wang; Zhuoran Xu; Yiye Zhang; James K Min; S Chiu Wong; Robert M Minutello Journal: J Am Heart Assoc Date: 2019-03-05 Impact factor: 5.501
Authors: Harmony R Reynolds; Leslee J Shaw; James K Min; John A Spertus; Bernard R Chaitman; Daniel S Berman; Michael H Picard; Raymond Y Kwong; C Noel Bairey-Merz; Derek D Cyr; Renato D Lopes; Jose Luis Lopez-Sendon; Claes Held; Hanna Szwed; Roxy Senior; Gilbert Gosselin; Rajesh Gopalan Nair; Ahmed Elghamaz; Olga Bockeria; Jiyan Chen; Alexander M Chernyavskiy; Balram Bhargava; Jonathan D Newman; Sasa B Hinic; Joanna Jaroch; Angela Hoye; Jeffrey Berger; William E Boden; Sean M O'Brien; David J Maron; Judith S Hochman Journal: JAMA Cardiol Date: 2020-07-01 Impact factor: 14.676