OBJECTIVES: This study sought to quantify the learning curve for the safety and effectiveness of a newly introduced vascular closure device through evaluation of the NCDR (National Cardiovascular Data Registry) CathPCI clinical outcomes registry. BACKGROUND: The impact of learning on the clinical outcomes complicates the assessment of the safety and efficacy during the early experience with newly introduced medical devices. METHODS: We performed a retrospective analysis of the relationship between cumulative institutional experience and clinical device success, defined as device deployment success and freedom from any vascular complications, for the StarClose vascular closure device (Abbott Vascular, Redwood City, California). Generalized estimating equation modeling was used to develop risk-adjusted clinical success predictions that were analyzed to quantify learning curve rates. RESULTS: A total of 107,710 procedures used at least 1 StarClose deployment, between January 1, 2006, and December 31, 2007, with overall clinical success increasing from 93% to 97% during the study period. The learning curve was triphasic, with an initial rapid learning phase, followed by a period of declining rates of success, followed finally by a recovery to a steady-state rate of improved device success. The rates of learning were influenced positively by diagnostic (vs. percutaneous coronary intervention) procedure use and teaching status and were affected inversely by annual institutional volume. CONCLUSIONS: An institutional-level learning curve for the initial national experience of StarClose was triphasic, likely indicating changes in patient selection and expansion of number of operators during the initial phases of device adoption. The rate of learning was influenced by several institutional factors, including overall procedural volume, utilization for percutaneous coronary intervention procedures, and teaching status.
OBJECTIVES: This study sought to quantify the learning curve for the safety and effectiveness of a newly introduced vascular closure device through evaluation of the NCDR (National Cardiovascular Data Registry) CathPCI clinical outcomes registry. BACKGROUND: The impact of learning on the clinical outcomes complicates the assessment of the safety and efficacy during the early experience with newly introduced medical devices. METHODS: We performed a retrospective analysis of the relationship between cumulative institutional experience and clinical device success, defined as device deployment success and freedom from any vascular complications, for the StarClose vascular closure device (Abbott Vascular, Redwood City, California). Generalized estimating equation modeling was used to develop risk-adjusted clinical success predictions that were analyzed to quantify learning curve rates. RESULTS: A total of 107,710 procedures used at least 1 StarClose deployment, between January 1, 2006, and December 31, 2007, with overall clinical success increasing from 93% to 97% during the study period. The learning curve was triphasic, with an initial rapid learning phase, followed by a period of declining rates of success, followed finally by a recovery to a steady-state rate of improved device success. The rates of learning were influenced positively by diagnostic (vs. percutaneous coronary intervention) procedure use and teaching status and were affected inversely by annual institutional volume. CONCLUSIONS: An institutional-level learning curve for the initial national experience of StarClose was triphasic, likely indicating changes in patient selection and expansion of number of operators during the initial phases of device adoption. The rate of learning was influenced by several institutional factors, including overall procedural volume, utilization for percutaneous coronary intervention procedures, and teaching status.
Authors: Neil J Wimmer; Eric A Secemsky; Laura Mauri; Matthew T Roe; Paramita Saha-Chaudhuri; David Dai; James M McCabe; Frederic S Resnic; Hitinder S Gurm; Robert W Yeh Journal: Circ Cardiovasc Interv Date: 2016-04 Impact factor: 6.546
Authors: Usha S Govindarajulu; Marco Stillo; David Goldfarb; Michael E Matheny; Frederic S Resnic Journal: Stat Med Date: 2017-05-03 Impact factor: 2.373
Authors: Frederic S Resnic; Arjun Majithia; Danica Marinac-Dabic; Susan Robbins; Henry Ssemaganda; Kathleen Hewitt; Angelo Ponirakis; Nilsa Loyo-Berrios; Issam Moussa; Joseph Drozda; Sharon-Lise Normand; Michael E Matheny Journal: N Engl J Med Date: 2017-01-25 Impact factor: 91.245
Authors: Amit Kumar; Michael E Matheny; Kalon K L Ho; Robert W Yeh; Thomas C Piemonte; Howard Waldman; Pinak B Shah; Richard Cope; Sharon-Lise T Normand; Sharon Donnelly; Susan Robbins; Frederic S Resnic Journal: Circ Cardiovasc Qual Outcomes Date: 2014-12-09