{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T21:15:47Z","timestamp":1792012547026,"version":"4.4.0"},"reference-count":30,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"14","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:sec xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in\u2010hospital mortality.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG\u2010MI; n=410] and acute\u2010on\u2010chronic heart failure [CSWG\u2010HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG\u2010MI using the consensus k means algorithm and subsequently validated in CSWG\u2010HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: \"non\u2010congested (I)\", \"cardiorenal (II),\" and \"cardiometabolic (III)\" shock. Among the 3 cohorts (CSWG\u2010MI versus DDR versus CSWG\u2010HF), in\u2010hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The \"cardiometabolic shock\" cluster had the highest risk of developing stage D or E shock as well as in\u2010hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.120.020085","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T10:40:40Z","timestamp":1625568040000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":230,"title":["Phenotyping Cardiogenic Shock"],"prefix":"10.1161","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6739-476X","authenticated-orcid":false,"given":"Elric","family":"Zweck","sequence":"first","affiliation":[{"name":"The CardioVascular Center Tufts Medical Center Boston MA"},{"name":"Medical Faculty Heinrich Heine University D\u00fcsseldorf Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0848-5853","authenticated-orcid":false,"given":"Katherine L.","family":"Thayer","sequence":"additional","affiliation":[{"name":"The CardioVascular Center Tufts Medical Center Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3847-8758","authenticated-orcid":false,"given":"Ole K. 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