{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T09:16:06Z","timestamp":1793438166250,"version":"4.5.0"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Interventions"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:sec>\n            <jats:title>Background:<\/jats:title>\n            <jats:p>Percutaneous coronary intervention is performed by operators with differing experience, technique, and case mix. It is unknown if operator practice patterns impact patient outcomes. We sought to determine if a cluster algorithm can identify distinct profiles of percutaneous coronary intervention operators and if these profiles are associated with patient outcomes.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods:<\/jats:title>\n            <jats:p>Operators performing at least 25 annual procedures between 2014 and 2018 were clustered using an agglomerative hierarchical clustering algorithm. Risk-adjusted in-hospital mortality was compared between clusters.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results:<\/jats:title>\n            <jats:p>We identified 4 practice profiles among 7706 operators performing 2\u2009937\u2009419 procedures. Cluster 1 (n=3345) demonstrated case mix and practice patterns similar to the national median. Cluster 2 (n=1993) treated patients with lower clinical acuity and were less likely to use intracoronary diagnostics, atherectomy, and radial access. Cluster 3 (n=1513) had the lowest case volume, were more likely to work at rural hospitals, and cared for a higher proportion of patients with ST-segment\u2013elevation myocardial infarction and cardiogenic shock. Cluster 4 (n=855) had the highest case volume, were most likely to treat patients with high anatomic complexity and use atherectomy, intracoronary diagnostics, and mechanical support. Compared with cluster 1, adjusted in-hospital mortality was similar for cluster 2 (estimated difference, \u22120.03 [95% CI, \u22120.10 to 0.04]), higher for cluster 3 (0.14 [0.07\u20130.22]), and lower for cluster 4 (\u22120.15 [\u22120.24 to \u22120.06]).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions:<\/jats:title>\n            <jats:p>Distinct percutaneous coronary intervention operator profiles are differentially associated with patient outcomes. A phenotypic approach to physician assessment may provide actionable feedback for quality improvement.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/circinterventions.121.010909","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T10:01:17Z","timestamp":1638352877000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":10,"title":["Percutaneous Coronary Intervention Operator Profiles and Associations With In-Hospital Mortality"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1459-0262","authenticated-orcid":true,"given":"Jacob A.","family":"Doll","sequence":"first","affiliation":[{"name":"Division of Cardiology, Department of Medicine, University of Washington (J.A.D.)."},{"name":"Section of Cardiology, VA Puget Sound Health Care System, Seattle, WA 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