Nihar R Desai1, Kevin F Kennedy2, David J Cohen2, Traci Connolly3, Deborah B Diercks4, Mauro Moscucci5, Stephen Ramee6, John Spertus2, Tracy Y Wang7, Robert L McNamara8. 1. Section of Cardiovascular Medicine, Yale University School of Medicine, Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, CT. 2. Saint-Luke's Mid America Heart Institute and University of Missouri-Kansas City School of Medicine, Kansas City, MO. 3. American College of Cardiology, Washington, DC. 4. University of Texas Southwestern Medical Center, Dallas, TX. 5. Sinai Hospital of Baltimore, Baltimore, MD; University of Michigan Health System, Ann Arbor, MI. 6. Ochsner Medical Center, New Orleans, LA. 7. Duke University Medical Center and Duke Clinical Research Institute, Durham, NC. 8. Section of Cardiovascular Medicine, Yale University School of Medicine, Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, CT. Electronic address: robert.mcnamara@yale.edu.
Abstract
BACKGROUND: Major bleeding is a frequent complication for patients with acute myocardial infarction (AMI) and is associated with significant morbidity and mortality. OBJECTIVE: To develop a contemporary model for inhospital major bleeding that can both support clinical decision-making and serve as a foundation for assessing hospital quality. METHODS: An inhospital major bleeding model was developed using the Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines (ACTION Registry-GWTG) database. Patients hospitalized with AMI between January 1, 2012 and December 31, 2013 across 657 hospitals were used to create a derivation cohort (n=144,800) and a validation cohort (n=96,684). Multivariable hierarchal logistic regression was used to identify significant predictors of major bleeding. A simplified risk score was created to enable prospective risk stratification for clinical care. RESULTS: The rate of major bleeding in the overall population was 7.53%. There were 8 significant, independent factors associated with major bleeding: presentation after cardiac arrest (OR 2.99 [2.77-3.22]); presentation in cardiogenic shock (OR 2.22 [2.05-2.40]); STEMI (OR 1.72 [1.65-1.80]); presentation in heart failure (OR 1.55 [1.47-1.63]); baseline hemoglobin less than 12 g/dL (1.55 [1.48-1.63]); heart rate (per 10 beat per minute increase) (OR 1.13 [1.12-1.14]); weight (per 10 kilogram decrease) (OR 1.12 [1.11-1.14]); creatinine clearance (per 5-mL decrease) (OR 1.07 [1.07-1.08]). The model discriminated well in the derivation (C-statistic = 0.74) and validation (C-statistic = 0.74) cohorts. In the validation cohort, a risk score for major bleeding corresponded well with observed bleeding: very low risk (2.2%), low risk (5.1%), moderate risk (10.1%), high risk (16.3%), and very high risk (25.2%). CONCLUSION: The new ACTION Registry-GWTG inhospital major bleeding risk model and risk score offer a robust, parsimonious, and contemporary risk-adjustment method to support clinical decision-making and enable hospital quality assessment. Strategies to mitigate risk should be developed and tested as a means to lower costs and improve outcomes in an era of alternative payment models.
BACKGROUND: Major bleeding is a frequent complication for patients with acute myocardial infarction (AMI) and is associated with significant morbidity and mortality. OBJECTIVE: To develop a contemporary model for inhospital major bleeding that can both support clinical decision-making and serve as a foundation for assessing hospital quality. METHODS: An inhospital major bleeding model was developed using the Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines (ACTION Registry-GWTG) database. Patients hospitalized with AMI between January 1, 2012 and December 31, 2013 across 657 hospitals were used to create a derivation cohort (n=144,800) and a validation cohort (n=96,684). Multivariable hierarchal logistic regression was used to identify significant predictors of major bleeding. A simplified risk score was created to enable prospective risk stratification for clinical care. RESULTS: The rate of major bleeding in the overall population was 7.53%. There were 8 significant, independent factors associated with major bleeding: presentation after cardiac arrest (OR 2.99 [2.77-3.22]); presentation in cardiogenic shock (OR 2.22 [2.05-2.40]); STEMI (OR 1.72 [1.65-1.80]); presentation in heart failure (OR 1.55 [1.47-1.63]); baseline hemoglobin less than 12 g/dL (1.55 [1.48-1.63]); heart rate (per 10 beat per minute increase) (OR 1.13 [1.12-1.14]); weight (per 10 kilogram decrease) (OR 1.12 [1.11-1.14]); creatinine clearance (per 5-mL decrease) (OR 1.07 [1.07-1.08]). The model discriminated well in the derivation (C-statistic = 0.74) and validation (C-statistic = 0.74) cohorts. In the validation cohort, a risk score for major bleeding corresponded well with observed bleeding: very low risk (2.2%), low risk (5.1%), moderate risk (10.1%), high risk (16.3%), and very high risk (25.2%). CONCLUSION: The new ACTION Registry-GWTG inhospital major bleeding risk model and risk score offer a robust, parsimonious, and contemporary risk-adjustment method to support clinical decision-making and enable hospital quality assessment. Strategies to mitigate risk should be developed and tested as a means to lower costs and improve outcomes in an era of alternative payment models.
Authors: Gregory B Auffenberg; Khurshid R Ghani; Shreyas Ramani; Etiowo Usoro; Brian Denton; Craig Rogers; Benjamin Stockton; David C Miller; Karandeep Singh Journal: Eur Urol Date: 2018-10-11 Impact factor: 20.096
Authors: John A Dodson; Judith S Hochman; Matthew T Roe; Anita Y Chen; Sarwat I Chaudhry; Stuart Katz; Hua Zhong; Martha J Radford; Jacob A Udell; Akshay Bagai; Gregg C Fonarow; Martha Gulati; Jonathan R Enriquez; Kirk N Garratt; Karen P Alexander Journal: JACC Cardiovasc Interv Date: 2018-11-26 Impact factor: 11.195
Authors: Jennifer A Rymer; Deepak L Bhatt; Dominick J Angiolillo; Miguel Diaz; Kirk N Garratt; Ron Waksman; Laura Edwards; Gudaye Tasissa; Khalid Salahuddin; Hijrah El-Sabae; Carmen Dell'Anna; Linda Davidson-Ray; Jeffrey B Washam; E Magnus Ohman; Tracy Y Wang Journal: J Am Heart Assoc Date: 2022-05-27 Impact factor: 6.106