23 research outputs found
Quantifying the Learning Curve in the Use of a Novel Vascular Closure Device An Analysis of the NCDR (National Cardiovascular Data Registry) CathPCI Registry
ObjectivesThis 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.BackgroundThe impact of learning on the clinical outcomes complicates the assessment of the safety and efficacy during the early experience with newly introduced medical devices.MethodsWe 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.ResultsA 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.ConclusionsAn 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
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Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing
<p>Abstract</p> <p>Background</p> <p>Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery.</p> <p>Methods</p> <p>A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year.</p> <p>Results</p> <p>We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80).</p> <p>Conclusions</p> <p>The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.</p